The DOI matches its Crossref record; 460 of the 473 references it cites were found in a registry or an index; 3 of the 65 sources its claims cite were checked for support.
Applications of machine learning to machine fault diagnosis: A review and roadmap
Open accessAbstract
This paper presents a review and roadmap to systematically cover the development of intelligent fault diagnosis following the progress of machine learning theories and offer a future perspective.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review traces intelligent fault diagnosis through three eras—traditional machine learning, deep learning, and transfer learning—and proposes a roadmap toward machine health management. It argues that transfer learning is the key future direction for applying diagnosis models in engineering settings where labeled fault data are scarce.
Key findings
- The authors divide intelligent fault diagnosis into three periods: traditional machine learning before the 2010s, which relied on handcrafted features; deep learning since the 2010s, which enables end-to-end feature learning; and transfer learning as the future direction for cross-task and cross-domain diagnosis.
- Publication counts from Web of Science show rapid growth since the 2010s, with output from 2017 to 2019 nearly matching the total from before 2017.
- Among the transfer-learning studies reviewed, most address transfer within the same machine (TIM), while only four publications target transfer across different machines (TDM).
- The roadmap highlights future challenges including data quality and cleaning, imbalanced health states, streaming data analytics, negative transfer and source-domain selection, and interpretability of deep diagnosis models.
Methods
- Narrative literature review and roadmap, not an experimental study. It organizes intelligent fault diagnosis by three periods and by method families: expert systems, ANN, SVM, kNN, PGM, and decision trees; stacked AE, DBN, CNN, and ResNet; and transfer-learning categories.
- Uses Web of Science search results to count publications and motivate the periodization, then reviews representative applications across bearings, gears, motors, engines, and other machines.
- For transfer learning, it defines domains and tasks, distinguishes TIM from TDM scenarios, and categorizes approaches as feature-based, GAN-based, instance-based, and parameter-based.
Limitations
- The paper states no limitations of its own review; the following are evident from the text.
- Evident: no formal systematic-review protocol, search strings, inclusion/exclusion criteria, or quality appraisal are reported, and the publication-counting procedure is described only briefly.
- Evident: the roadmap and future directions are conceptual and perspective-based rather than empirically tested in the paper.
- Evident: coverage is selective and organized around the authors’ three-period framework, so it may not exhaustively capture all intelligent fault diagnosis literature.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
3 of 65 cited sources checked · 3 supported · 0 flagged
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“First, the signal processing methods, such as the wavelet packet [369][370][371], the continue wavelet transform [372,373], the dual-tree complex wavelet transform [374,375], and the synchrosqueezing transform [376], are employed to preprocess the input 1D data, which are expected to convert the signals from the time domain to the time-frequency domain.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (2) Applications of CNN to machine fault diagnosis
What the sources say
- Not matchedcites [369][370][371]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Supportedcites [372] 372. S. Guo, T. Yang, W. Gao, C. Zhang, A novel fault diagnosis method for rotating machinery based on a convolutional neural network, Sensors 18 (2018) 1429. source
The Introduction describes transforming vibration signals with a continuous wavelet transform into a two-dimensional time-frequency scalogram and using its coefficients as CNN input. Section 3.2 confirms that the scalograms are processed as pixel images. This supports the claim as applied to the continuous wavelet transform, the method for which this source is cited; the supplied passages have no page numbers. (LLM, conf=0.93)
- Supportedcites [373] 373. S. Guo, T. Yang, W. Gao, C. Zhang, Y. Zhang, An intelligent fault diagnosis method for bearings with variable rotating speed based on pythagorean spatial pyramid pooling CNN, Sensors 18 (2018) 3857. source
The Introduction says the method uses continuous wavelet transform to turn vibration signals into two-dimensional scalograms that retain time-frequency information, which the CNN then processes. This supports the claim for the continuous-wavelet-transform citation; the paper need not establish the other listed methods. (LLM, conf=0.92)
- Supportedcites [374] 374. W. Sun, B. Yao, N. Zeng, B. Chen, Y. He, X. Cao, W. He, An intelligent gear fault diagnosis methodology using a complex wavelet enhanced convolutional neural network, Materials 10 (2017) 790. source
The citation supports the dual-tree complex wavelet transform example in the citing sentence, not the other listed methods. Section 6.2 describes applying DTCWT to a 1D time-domain signal and arranging frequency sub-bands over time as 2D data; Section 3.1 describes those sub-bands as input to the CNN’s convolutional layer. No page numbers are available in our copy. (LLM, conf=0.91)
- Not checked — no full text of the sourcecites [375] 375. X. Cao, B. Chen, B. Yao, W. He, Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification, Comput. Ind. 106 (2019) 71-84. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in Fig. 1, the number of publications from 2017 to 2019 almost approaches to the total of that before 2017.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, majority of studies focus on TIM scenarios, and only four 8 publications aim at TDM scenarios.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 6. Conclusions
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Among them, the studies about feature-based approaches account for the 7 largest proportion.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 6. Conclusions
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, majority of studies focus on TIM scenarios, and only four 8 publications aim at TDM scenarios.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 6. Conclusions
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“The previous reviews just concerned IFD in a certain period like using traditional machine learning or using deep learning.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These reviews have not reported a roadmap on IFD to predict future trends yet.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“They just cover the publications of IFD before 2017.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the past, traditional machine learning theories were popularly conducted in IFD from the debut to the 2010s.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > Abstract
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a result, a review to systematically cover the development of IFD from the past to the future is still left blank.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- The quote only partly states this claim. 1
- It may state two separate claims as one. 47
- Probably not a substantial claim. 52
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“According to the results on the topic of machine fault diagnosis by using machine learning, the number of publications has rapidly increased since the 2010s.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > Abstract
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“With regard to fault diagnosis, Tong et al. [420,421] employed JDA to complete the diagnosis tasks of bearings respectively used in the motor and the belt conveyor idler, and achieve better diagnosis accuracy than TCA-based diagnosis models.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 9
What the sources say
- Not checked — no full text of the sourcecites [420] 420. Z. Tong, W. Li, B. Zhang, F. Jiang, G. Zhou, Bearing fault diagnosis under variable working conditions based on domain adaptation using feature transfer learning, IEEE Access 6 (2018) 76187-76197. source
- Not checked — no full text of the sourcecites [421] 421. Z. Tong, W. Li, B. Zhang, M. Zhang, Bearing fault diagnosis based on domain adaptation using transferable features under different working conditions, Shock Vibrat. (2018) Doi: 10.1155/2018/6714520. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“The results showed that the trained diagnosis model could correctly recognize the unlabeled samples in the target domain although there were a small number of labeled samples in the target domain.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 4.3.3. Instance-based approaches
What the sources say
- Not checked — no full text of the sourcecites [36] 36. F. Shen, C. Chen, R. Yan, R.X. Gao, Bearing fault diagnosis based on svd feature extraction and transfer learning classification, in: Prognostics and System Health Management Conference, Beijing, China, 2015, pp. 1-6. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Compared with the diagnosis model just trained with a small number of target-domain samples, the fine-tuned diagnosis model presented the faster convergence rate and higher diagnosis accuracy.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 6. Conclusions
What the sources say
- Not checked — no full text of the sourcecites [438] 438. R. Zhang, H. Tao, L. Wu, Y. Guan, Transfer learning with neural networks for bearing fault diagnosis in changing working conditions, IEEE Access 5 (2017) 14347-14357. source
- Not checked — no full text of the sourcecites [37] 37. M.J. Hasan, J.M. Kim, Bearing fault diagnosis under variable rotational speeds using stockwell transform- based vibration imaging and transfer learning, Appl. Sci.-Basel 8 (2018) 2357. source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Over the recent years, the advent of deep learning theories has reformed IFD in further releasing the artificial assistance since the 2010s, which encourages to construct an end-to-end diagnosis process.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > Abstract
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, it needs a new review to summary the current research progress of IFD.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With the help of traditional machine learning, the diagnosis models began to establish the relationship between the selected features and the health states of machines, which weakened the contribution of human labor in machine fault diagnosis and pushed it into the era of artificial intelligence.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > Abstract
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the future, transfer learning theories will promote the research of IFD in engineering scenarios.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > Abstract
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In order to improve the diagnosis accuracy of SVM-based models, researchers mainly focused on two branches, i.e., the modified SVM and the algorithm optimization.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 2) Applications of SVM to machine fault diagnosis
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Third, the SVM algorithm is originally used to solve binary classification tasks.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 2) Applications of SVM to machine fault diagnosis
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is easy for PGM-based diagnosis models to achieve fault diagnosis with multiple health states of machines.They can be used to analyze the among-class discrepancy in convenience.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (1) kNN
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, if the probabilistic relationship among the variables is not clear, it would be difficult to construct the diagnosis models.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (1) kNN
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, transfer-learning theories are expected to overcome the problems of lacking labeled samples and finally enlarge the applications of IFD in engineering scenarios.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > Abstract
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The kNN-based diagnosis models are easily-implemented.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (1) kNN
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, the expert system lacks the self-learning capability so that the diagnosis knowledge base is difficult to be expanded and corrected.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (2) Applications of expert system to machine fault diagnosis
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Second, the performance of SVM-based diagnosis models is sensitive to the kernel parameters.The inappropriate kernel parameter even cannot induce the reliable diagnosis result.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 2) Applications of SVM to machine fault diagnosis
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Among the proposed methods, the algorithm of C4.5 is widely used to induce a decision tree for classification and obtain satisfactory accuracy and easily-understood classification rules [300].”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3) Decision tree
What the sources say
- Not checked — no full text of the sourcecites [300] 300. J.R. Quinlan, C4.5: Programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, 1993.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, it takes much computation cost to handle the large-volume dataset.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (1) kNN
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The optimization objective solution of SVM refers to the convex quadratic optimization so that the diagnosis models could easily obtain the global optimal solution and further get the high diagnosis accuracy.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 2) Applications of SVM to machine fault diagnosis
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the performance of the diagnosis models greatly relies on the expert knowledge, which is difficult to be acquired and expressed.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (2) Applications of expert system to machine fault diagnosis
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Fortunately, transfer learning is concerned to overcome such weaknesses by applying the knowledge learned from one or multiple tasks to other related but novel ones [27].”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > Abstract
What the sources say
- Not checked — no full text of the sourcecites [27] 27. S.J. Pan, Q. Yang, A survey on transfer learning, IEEE Trans. Knowl. Data Eng. 22 (2010) 1345-1359. source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“The non-naive Bayesian classifier [288] is further developed to release the assumption, and it has also been used for fault diagnosis of bearings [289] and gears [290].”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (1) kNN
What the sources say
- Not checked — no full text of the sourcecites [288] 288. Y. He, R. Wang, S. Kwong, X. Wang, Bayesian classifiers based on probability density estimation and their applications to simultaneous fault diagnosis, Inf. Sci. 259 (2014) 252-268. source
- Not checked — no full text of the sourcecites [289] 289. J. Yu, B. Ding, Y. He, Rolling bearing fault diagnosis based on mean multigranulation decision-theoretic rough set and non-naive Bayesian classifier, J. Mech. Sci. Technol. 32 (2018) 5201-5211. source
- Not checked — no full text of the sourcecites [290] 290. M.Y. Asr, M.M. Ettefagh, R. Hassannejad, S.N. Razavi, Diagnosis of combined faults in rotary machinery by non-naive Bayesian approach, Mech. Syst. Signal Process. 85 (2017) 56-70. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In terms of multi-class classification tasks in IFD, it always needs to use complicated 2 architectures, such as OAA and OAO, to integrate results from multiple SVM-based models.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 1
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For these diagnosis objects, SVM-based diagnosis models are expected to recognize multiple states but not just the binary states of health and faults.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 2) Applications of SVM to machine fault diagnosis
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is noted that the applications of naive Bayesian classifier are subject to an assumption of independence among features.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (1) kNN
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Different from the ANN, SVM-based diagnosis models are trained by minimizing the structural risk, which is beneficial to improving the interpretability of the models due to the rigorous theories.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 2) Applications of SVM to machine fault diagnosis
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“In order to improve the generalization performance of decision tree, the random forest [306] is further investigated by integrating decisions from multiply tree-based classifiers.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3) Decision tree
What the sources say
- Not checked — no full text of the sourcecites [306] 306. L. Breiman, Random forests, Mach. Learn. 45 (2001) 5-32. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Thus, the one-against-all strategy (OAA) and one-against-one strategy (OAO) are mainly concerned [17].”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 2) Applications of SVM to machine fault diagnosis
What the sources say
- Not checked — no full text of the sourcecites [17] 17. A. Widodo, B.S. Yang, Support vector machine in machine condition monitoring and fault diagnosis, Mech. Syst. Signal Process. 21 (2007) 2560-2574. source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, the parameter 𝑘𝑘 is difficult to be determined, which greatly affects the performance of the diagnosis models.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (1) kNN
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The kNN is a commonly-used supervised learning model to complete the classification tasks [13].”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (1) kNN
What the sources say
- Not checked — no full text of the sourcecites [13] 13. T.M. Cover, P.E. Hart, Nearest neighbor pattern classification, IEEE Trans. Inf. Theory 13 (1967) 21-27. source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the performance of kNN is subject to some problems, such as the indistinguishable neighborhood boundary and the difficulty in selecting the optimal neighborhood parameter.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (1) kNN
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“The diagnosis process includes three steps [1], i.e., data collection, artificial feature extraction, and health state recognition, as shown in Fig. 2.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 2.1. Overview
What the sources say
- Not matchedcites [1] 1. Y. Lei, Intelligent fault diagnosis and remaining useful life prediction of rotating machinery, Butterworth- Heinemann Elsevier Ltd, Oxford, 2016.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In particular, the imbalanced distribution of the collected data would reduce the diagnosis accuracy of this kind of diagnosis models.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (1) kNN
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“4) The prosperous development of sensor technologies and data transmission, especially with the advent of IoT and high-speed internet, promotes to collect a large number of data that contain the real-time information.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3.2. Step 1: Big data collection
What the sources say
- Not matchedcites [314] 314. G.I. Platforms, The rise of industrial big data, 2012, Available: http://www.geautomation.com/download/rise-industrial-big-data.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [315] 315. S. Yin, X. Li, H. Gao, O. Kaynak, Data-based techniques focused on modern industry: An overview, IEEE Trans. Ind. Electron. 62 (2015) 657-667. source
- Not matchedcites [316] 316. J. Lee, Industrial big data: The revolutionary transformation and value creation in industry 4.0 era, Mechanical Industry Press, Beijing, China, 2015.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Multiple AE can be stacked to represent the information contained in the input data deeply, and further obtain the features in deep layers, as shown in Fig. 11.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3.3.1. Stacked AE-based approaches (1) A brief introduction to AE and Stacked AE
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In contrast, monitoring big data of machines holds these characteristics, and further extends to the much-specialized ones.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3.2. Step 1: Big data collection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Stacked AE-based diagnosis models are able to automatically represent the health information from the input monitoring data, which does not rely on much expert knowledge in feature extraction.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 2) Applications of AE to machine fault diagnosis
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“First, the step of artificial feature extraction depends on the human labor, in which the engineers need to design powerful algorithms to extract sensitive features to health states of machines.However, it is still unrealistic for engineers to extract specialized features from the large-volume monitoring data by expert experience because of the huge labor cost.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 2.5. Epilog
What the sources say
- Not checked — no full text of the sourcecites [310] 310. F. Jia, Y. Lei, J. Lin, X. Zhou, N. Lu, Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mech. Syst. Signal Process. 72-73 (2016) 303-315. source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Generally, big data includes four characteristics, i.e., volume, velocity, variety, and veracity [312].”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3.2. Step 1: Big data collection
What the sources say
- Not matchedcites [312] 312. V. Mayer-Schnberger, K. Cukier, Big data: A revolution that will transform how we live, work, and think, Hodder & Stoughton Press, Carmelite House, London, 2013.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“First, the healthy condition accounts for the majority of the longterm operation for machines, while faults seldom happen.Consequently, it is more easily to collect healthy data than faulty data.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3.2. Step 1: Big data collection
What the sources say
- Not matchedcites [314] 314. G.I. Platforms, The rise of industrial big data, 2012, Available: http://www.geautomation.com/download/rise-industrial-big-data.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [315] 315. S. Yin, X. Li, H. Gao, O. Kaynak, Data-based techniques focused on modern industry: An overview, IEEE Trans. Ind. Electron. 62 (2015) 657-667. source
- Not matchedcites [316] 316. J. Lee, Industrial big data: The revolutionary transformation and value creation in industry 4.0 era, Mechanical Industry Press, Beijing, China, 2015.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Similar to other deep learning-based models, the diagnosis performance of CNN-based diagnosis models is subject to training with sufficient labeled samples.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (2) Applications of CNN to machine fault diagnosis
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The decision tree-based diagnosis models are naturally interpreted, which may not relay on the explanation of experts and can be easily converted into diagnosis rules.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3) Decision tree
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As an unsupervised learning method, the stacked AE cannot be directly used to recognize the health states of machines.Therefore, a classification layer is usually added at the top of the architecture of the model, and the constructed diagnosis models need to be trained with sufficient labeled samples.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 2) Applications of AE to machine fault diagnosis
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, the ResNet-based diagnosis models inherit the advantages of the CNN-based diagnosis models, and possibly obtain high diagnosis accuracy, especially for the complicated operation conditions such as varying-speed or varying-load conditions.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3.3.4. ResNet-based approaches
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, they could achieve the diagnosis tasks with missing data.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3) Decision tree
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, the number of training parameters in diagnosis models is reduced by sharing weights, which could accelerate the convergence and restrain the over fitting.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (2) Applications of CNN to machine fault diagnosis
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, the output layer is placed after the last extraction layer for health state recognition, generally with an ANN-based classifier because of the high capability in multi-class classification.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3.3. Step 2: Deep learning-based diagnosis
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Different from the stacked AE, DBN-based diagnosis models could automatically learn features from the input data by pre-training a set of stacked RBMs, which solves the problem of vanishing gradient in using BP algorithm to fine-tune the deep-layer networks.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3.3.2. DBN-based approaches (1) A brief introduction to RBM and DBN
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is necessary to use sufficient labeled data to train the constructed diagnosis models so as to obtain the convinced diagnosis results.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3.3.2. DBN-based approaches (1) A brief introduction to RBM and DBN
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Deep learning-based diagnosis models automatically learn features from the input monitoring data and simultaneously recognize the health states of machines according to the learned features.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3.3. Step 2: Deep learning-based diagnosis
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“3) In order to collect sufficient health information of machines, there are many monitoring points on machines.Furthermore, multisource sensors are used to collect different kinds of data.The collected data are stored with various data structures.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3.2. Step 1: Big data collection
What the sources say
- Not matchedcites [314] 314. G.I. Platforms, The rise of industrial big data, 2012, Available: http://www.geautomation.com/download/rise-industrial-big-data.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [315] 315. S. Yin, X. Li, H. Gao, O. Kaynak, Data-based techniques focused on modern industry: An overview, IEEE Trans. Ind. Electron. 62 (2015) 657-667. source
- Not matchedcites [316] 316. J. Lee, Industrial big data: The revolutionary transformation and value creation in industry 4.0 era, Mechanical Industry Press, Beijing, China, 2015.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, this kind of diagnosis models is easily caught by the over fitting and the low generalization performance, which would reduce the diagnosis performance of the models on the diagnosis tasks.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3) Decision tree
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Furthermore, the advent of state-of-art technologies, such as the edge computing and the applications of GPU, helps cope with monitoring data stream effectively.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3.2. Step 1: Big data collection
What the sources say
- Not matchedcites [314] 314. G.I. Platforms, The rise of industrial big data, 2012, Available: http://www.geautomation.com/download/rise-industrial-big-data.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [315] 315. S. Yin, X. Li, H. Gao, O. Kaynak, Data-based techniques focused on modern industry: An overview, IEEE Trans. Ind. Electron. 62 (2015) 657-667. source
- Not matchedcites [316] 316. J. Lee, Industrial big data: The revolutionary transformation and value creation in industry 4.0 era, Mechanical Industry Press, Beijing, China, 2015.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In addition, the tree-type models are mostly constructed according to the expert knowledge.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3) Decision tree
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The deep learning theories construct end-to-end diagnosis models to automatically learn features from the collected data, and subsequently recognize the health states of machines.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 6. Conclusions
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“GAN-based approaches are able to generate fake labeled data that are similar to the actual data in the target domain.These fake data are able to help train reliable diagnosis models for the target domain.Therefore, such approaches are not affected by the condition whether labeled samples in the target domain are available, and they are expected to work for both the TIM scenarios and the TDM”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 4.3.2. GAN-based approaches
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear? Based: unclearIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Among them, feature-based approaches and GAN-based approaches are widely concerned because they can accomplish TDM scenarios subject to serious cross-domain discrepancy.In contrast, the instance-based approaches and parameter-based approaches are easily-implemented, and mostly focus on TIM scenarios subject to slight cross-domain discrepancy.Furthermore, the transfer performance of these”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 4.4. Epilog
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Instance-based approaches are easily-implemented for transfer scenarios.However, the transfer performance of them relates to the number of target-domain samples.Furthermore, they just are able to correct discrepancy in TIM scenarios, but incapable to implement transfer scenarios with serious discrepancy, such as TDM scenarios, because these approaches may lack the powerful capability of data”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 4.3.3. Instance-based approaches
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although there are state-of-the-art achievements of deep learning-based diagnosis models, an open issue of black box for deep hierarchical networks still confuses the academic researchers.It is difficult to acquire how these models learn the diagnosis knowledge from the monitoring data.As a result, the diagnosis models are constructed by experimental trials once and once again rather than the”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 5. Discussions: Future challenges and a roadmap in IFD
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Deep learning-based diagnosis models are beneficial to reducing the cross-domain discrepancy by extracting deep-layer features [33].However, they are unable to adapt serious cross-domain discrepancy in some diagnosis scenarios, such as TDM.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 2) Deep learning and AdaBN
What the sources say
- Not checked — no full text of the sourcecites [33] 33. M. Long, J. Wang, Y. Cao, J. Sun, P.S. Yu, Deep learning of transferable representation for scalable domain adaptation, IEEE Trans. Knowl. Data Eng. 28 (2016) 2027-2040. source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To select the optimal one and guarantee the transfer success, it must take much time by experimental trials.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 5. Discussions: Future challenges and a roadmap in IFD
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, the ANN-based diagnosis models are black-boxed due to the lack of rigorous 10 theoretical supports.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 6. Conclusions
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Imbalanced distribution in health states of machines is a common phenomenon in engineering scenarios.For example, the data collected from the healthy state are far more sufficient than those from the faults.If the imbalanced data are used to train the deep learning-based diagnosis models, the decision boundary of the models might be enforced to shift towards the health states with minority of”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 5. Discussions: Future challenges and a roadmap in IFD
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Transfer learning is promising to expand IFD from academic research to engineering scenarios.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 4.4. Epilog
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
In the paper
“The fact is that machines mostly work under the healthy state, while the faults seldom happen.Thus, it is easier to collect healthy data than faulty data.As a result, the collected data are seriously imbalanced.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3.4. Epilog
What the sources say
- Not checked — no full text of the sourcecites [26] 26. B. Yang, Y. Lei, F. Jia, S. Xing, An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings, Mech. Syst. Signal Process. 122 (2019) 692-706. source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The majority of the collected data are unlabeled.It is unrealistic to frequently stop the machines and inspect the health states due to the huge loss of wealth.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3.4. Epilog
What the sources say
- Not checked — no full text of the sourcecites [26] 26. B. Yang, Y. Lei, F. Jia, S. Xing, An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings, Mech. Syst. Signal Process. 122 (2019) 692-706. source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To bridge the gap, transfer learning theories are promising to construct diagnosis models, in which the diagnosis knowledge can be transferred across multiple diagnosis tasks.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 6. Conclusions
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The deep learning-based diagnosis models are trained by minimizing empirical risk, which really lacks rigorous theory.As a result, the physical meanings of them are difficult to be interpretable.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 5. Discussions: Future challenges and a roadmap in IFD
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is possible to collect data from multiple source domains to match a common target domain, but the health information contained in these source domains is not all related to the that in the target domain in engineering scenarios.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 5. Discussions: Future challenges and a roadmap in IFD
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Transfer learning is able to get the above purpose, in which the knowledge from one or more diagnosis tasks can be reused to other related but different ones [27].”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 4.1. Motivation of applying transfer learning to IFD in engineering scenarios
What the sources say
- Not checked — no full text of the sourcecites [27] 27. S.J. Pan, Q. Yang, A survey on transfer learning, IEEE Trans. Knowl. Data Eng. 22 (2010) 1345-1359. source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The process of learning features through deep learning is similar to the filtering process.Therefore, the adaptive filter theory is beneficial to analyzing the physical meanings of deep learning-based diagnosis models.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 5. Discussions: Future challenges and a roadmap in IFD
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The constructed transfer learning-based diagnosis models are incapable to extract the related health information.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 5. Discussions: Future challenges and a roadmap in IFD
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“According to Section 4.3, several approaches can be used in IFD, but the transfer performance of them is different for a given transfer scenario due to diverse performance ceilings.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 5. Discussions: Future challenges and a roadmap in IFD
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the past, IFD is implemented by the steps of data collection, artificial feature extraction, and health state recognition.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 6. Conclusions
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With the revolution of big data, two special issues come to bring negative effects on the IFD using deep learning theories, i.e., imbalanced health states and analytics for data stream.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 5. Discussions: Future challenges and a roadmap in IFD
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Diagnosis models based on TCA and JDA use the simple nonlinear mapping to extract features, which is difficult to fit the complicated distribution of data.As a result, the diagnosis models may get poor transfer results on the target domain because of the under-corrected discrepancy of the source and target domains.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 9
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such issue has been concerned by the researchers for years, but the unreliable data transmission and the bandwidth limitations prevent the monitoring data stream from arriving the destination in an unbroken sequence.Furthermore, the inefficient computation capability impedes the success of data stream analytics.As a result, the IFD using deep learning is just implemented based on the off-line”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 5. Discussions: Future challenges and a roadmap in IFD
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Parameter-based approaches fine-tune the pre-trained diagnosis models for fault diagnosis tasks in the target domain, which consumes less computation resources in handling massive data.However, the transfer performance of these approaches mostly depends on the number of labeled samples in the target domain.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 4.3.4. Parameter-based approaches
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By using traditional machine learning theories, the diagnosis models are able to automatically recognize the health states of machines.However, the literature review presents that the artificial feature extraction still relies on the expert knowledge.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 6. Conclusions
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“TCA just adapts the marginal probability distribution of the input cross-domain data, but 8 ignores the conditional probability distribution from the feature space to the target classes.Thus,”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 7
What the sources say
- Not checked — no full text of the sourcecites [30] 30. M. Long, J. Wang, G. Ding, J. Sun, P.S. Yu, Transfer feature learning with joint distribution adaptation, in: IEEE International Conference on Computer Vision, Sydney, Australia, 2013, pp. 2200-2207. source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The statistical learning theories, such as SVM and PGM, have rigorous theory grounds, which promote to constructed diagnosis models with easily-understood model parameters, features, and diagnosis results.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 5. Discussions: Future challenges and a roadmap in IFD
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, it takes much cost to recollect sufficient data and further label them, which is unpractical for machines in engineering scenarios.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 4.1. Motivation of applying transfer learning to IFD in engineering scenarios
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper relies on this without support.
In engineering scenarios, machine users would like an automatic method to shorten the maintenance cycle and improve the diagnosis accuracy.
“In engineering scenarios, however, the machine users would like an automatic method to shorten the maintenance cycle and improve the diagnosis accuracy.”
The paper relies on this without support.
Traditionally, the process of fault diagnosis is mostly developed by manually inspecting the health states of machines, which increases the labor intensity and reduces the diagnosis accuracy.
“Traditionally, the process of fault diagnosis is mostly developed by manually inspecting the health states of machines, which increases the labor intensity and reduces the diagnosis accuracy.”
The paper relies on this without support.
Advanced signal processing methods greatly rely on specialized knowledge that maintainers mostly lack in engineering scenarios.
“However, these methods greatly rely on the specialized knowledge that maintainers mostly lack in engineering scenarios.”
The paper relies on this without support.
The diagnosis results by signal processing methods are too specialized to be understood by the machine users.
“Furthermore, the diagnosis results by signal processing methods are too specialized to be understood by the machine users.”
The paper relies on this without support.
The volume of collected data is dramatically increasing due to rapid development of internet technologies and IoT.
“With the rapid development of internet technologies and internet of things (IoT), the volume of collected data is dramatically gathered than ever before.”
The paper relies on this without support.
Increasingly-grown data bring more sufficient information to machine fault diagnosis, making it more possible to provide accurate diagnosis results.
“The increasingly-grown data bring more sufficient information to machine fault diagnosis so that it is more possible to provide accurate diagnosis results.”
The authors state this as an assumption.
Labeled data are sufficient and contain complete information about the health states of machines.
“the labeled data are sufficient and contain completed information about the health states of machines [35]”
The authors state this as an assumption.
The source-domain data are labeled, while there are minority of labeled data or even none of labeled data in the target domain.
“These two categories are both subject to the common assumption, in which the source-domain data are labeled, while there are minority of labeled data or even none of labeled data in the target domain.”
The authors state this as an assumption.
In instance-based approaches, minority of labeled samples are labeled in the target domain, which is insufficient to train reliable diagnosis models.
“In instance-based approaches, it is assumed that minority of labeled samples are labeled in the target domain, which is insufficient to train reliable diagnosis models.”
The authors state this as an assumption.
In parameter-based approaches, there are minority of labeled samples in the target domain.
“Similar to the instance-based approaches, parameter-based approaches also assume that there are minority of labeled samples in the target domain [27].”
The authors state this as an assumption.
The successes of transfer learning are subject to the assumption of related health information across multiple diagnosis tasks.
“The successes of transfer learning are subject to the assumption of related health information across multiple diagnosis tasks.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Polynomial-kernel study is assigned the GAN reference number
MinorThe discussion attributes a polynomial-kernel MMD study to reference [443], but [443] is a paper on generative adversarial nets. The polynomial-kernel paper appears instead as reference [444].
“Yang et al [443] further proposed the polynomial kernel induced MMD to improve the transfer performance of the deep transfer learning-based diagnosis models”
“443. I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, in: Conference on Neural Information Processing Systems, Montreal, Canada, 2014, pp. 2672-2680.”
Motors and generators are both allowed and ruled out as a transfer pair
MinorThe paper gives transfer between motors and generators as an example of a TDM scenario, but elsewhere says using knowledge from motors to diagnose generators is unrealistic.
“In TDM scenarios, the source and target domain data are collected from different but related machines like motors and generators.”
“Such constraint can be explained by the example that it is unrealistic to use the diagnosis knowledge from motors for fault diagnosis of generators.”
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
(1992) · Cited by 23,431
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Flagged first, then by citations
13 of the 473 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 601–700 of 2,939
Zero-Shot Attribute Consistent Model for Bearing Fault Diagnosis Under Unknown Domain
Qin, Y., Wang, L., Qian, Q., & Mao, Y. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3378256 · Cited by 34
“Research on the technique for diagnosing bearing faults holds significant methods are the most widely studied at present among these approaches due to the rapid development of industrial big data and deep neural networks [5], [6], [7], [8].”
BackgroundMeta-learning-based approach for tool condition monitoring in multi-condition small sample scenarios
Zhang, B., Liu, X., Yue, C., et al. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111444 · Cited by 34
Liao, W., Wu, L., Xu, S., et al. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3427866 · Cited by 34
“For example, the support vector machine (SVM) [19] and its derivatives, which are tailored for handling small datasets, can enhance fault detection precision even with a scarce number of defective data points.”
BackgroundIn-process monitoring strategies and methods in metal forming: A selective review
He, X., Welo, T., & Ma, J. (2025) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2025.02.011 · Cited by 36
Guidotti, D., Pandolfo, L., & Pulina, L. (2025) · IEEE Access · doi:10.1109/access.2025.3578686 · Cited by 43
“Meanwhile, Lei et al. [27] provided a comprehensive roadmap for intelligent fault diagnosis, highlighting the integration of multiple ML models to enhance predictive accuracy.”
Lee, K., Han, S., Pham, V. H., Cho, S., Choi, H., Lee, J.-W., Noh, I., & Lee, S. W. (2021) · Applied Sciences · doi:10.3390/app11052370 · Cited by 34
“Intelligent fault diagnosis refers to the use of machine learning to detect and diagnose faults, and to manage health information in mechanical equipment [1].”
BackgroundMethodZhao, S., Ren, X., Deng, W., et al. (2021) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2021.116321 · Cited by 34
Tayyab, S. M., Chatterton, S., & Pennacchi, P. (2022) · Sensors · doi:10.3390/s22052026 · Cited by 37
“Gener lly hidden layers in convolutional neural networks consist of convolutional layers, Rectified Linear Linear Unit (Re U) layers, pooling or subsampli layers and fully connected layers [22].”
BackgroundMethodInfluentialMachine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting
Cordeiro-Costas, M., Villanueva, D., Eguía, P., & Granada, E. (2022) · Applied Sciences · doi:10.3390/app12178769 · Cited by 37
“Furthermore, these techniques can be important in preventive maintenance, since through forecasting it is possible to detect faults in the system [17].”
Multi-Sensor Fusion by CWT-PARAFAC-IPSO-SVM for Intelligent Mechanical Fault Diagnosis
Chen, H., & Li, S. (2022) · Sensors · doi:10.3390/s22103647 · Cited by 33
Transformer-Enabled Cross-Domain Diagnostics for Complex Rotating Machinery With Multiple Sensors
Zhang, Y., Ren, Z., Feng, K., Yu, K., Ma, H., & Liu, Z. (2023) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2023.3237233 · Cited by 35
“Therefore,itiscriticaltoaccuratelymonitorthehealth conditionsofthecomplexrotatingmachineryinreal-timeto enhancetheoperationalperformanceandreliabilityofindustrial systems[3],[4],[5].”
BackgroundXie, Q., Tao, G., Lo, S., et al. (2023) · Wear · doi:10.1016/j.wear.2023.204770 · Cited by 33
Li, Y., Dong, Y., Xu, M., Liu, P., & Wang, R. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3276027 · Cited by 33
“…measurement, and computing technologies in industry and the emerging exploration of artificial intelligence in advanced manufacture and novel devices, deep learning has been a promising approach for health state estimating [1], fault diagnosis [2], and remaining life prediction [3], [4], etc.”
BackgroundSi, L., Li, J., Wang, Z., et al. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3316250 · Cited by 33
“…learning technology, the vibration signal and its featured images are widely used as the input samples of deep learning-based pattern recognition [24], [25], [26], and the learning model is established through end-to-end sample training to then realize intelligent identification of the data to…”
BackgroundAutonomous selection of the fault classification models for diagnosing microservice applications
Song, Y., Xin, R., Chen, P., Zhang, R., Chen, J., & Zhao, Z. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.12.005 · Cited by 31
“In recent years, various approaches have been proposed for fault diagnosis and root cause localization [7–9].”
Zhang, W., Yang, J., Bo, X., et al. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad1811 · Cited by 33
“…of intelligent technology, deep neural networks (DNNs), as a recent research hotspot, have been widely used in mechanical fault diagnosis [15], such as in the popular convolutional neural network (CNN) [16, 17], auto-encoder [18], visual geometry group (VGG) [19], and residual neural…”
Zhuang, J., Jia, M., Huang, C., et al. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111186 · Cited by 33
Zhang, S., Liang, W., Zhao, W., Luan, Z., Wang, C., & Xu, K. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111644 · Cited by 38
DEViT: Deformable Convolution-Based Vision Transformer for Bearing Fault Diagnosis
Ji, M., & Zhao, G. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3440383 · Cited by 39
“On the other hand, machine learning methods such as support vector machine (SVM), artificial neural network (ANN), and K -nearest neighbor (KNN) algorithm rely on artificial feature extraction and classifiers to classify the bearing state [6], [7], [8].”
MethodWang, C., Jiang, W., Shi, L., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-93165-4 · Cited by 33
Few-Shot Bearing Anomaly Detection via Model-Agnostic Meta-Learning
Zhang, S., Ye, F., Wang, B., et al. (2020) · International Conference on Electrical Machines and Systems (ICEMS) · doi:10.23919/icems50442.2020.9291099 · Cited by 33
“Many data-driven and AI-based technologies have been applied to enhance the accuracy and reliability of bearing anomaly detection [1]–[3], but most of them require a large amount of training data such as vibration [4]–[6], acoustic [7], [8], and motor current [9], [10] signals.”
BackgroundCompound fault diagnosis of rolling bearings based on improved tunable Q-factor wavelet transform
Hu, Y., Zhou, Q., Gao, J., et al. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/abf25e · Cited by 33
A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery
Jia, S., Wang, J., Zhang, X., & Han, B. (2021) · Entropy · doi:10.3390/e23040424 · Cited by 33
Si, J., Shi, H., Han, T., Chen, J., & Zheng, C. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3126864 · Cited by 33
Jiang, Y., Xia, T., Wang, D., Zhang, K., & Xi, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.005 · Cited by 34
Ruan, D., Chen, Y., Gühmann, C., Yan, J., & Li, Z. (2022) · Electronics · doi:10.3390/electronics11040622 · Cited by 37
Ye, Z., & Yu, J. (2022) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2022.3191051 · Cited by 32
“They have been adopted to perform machinery fault diagnosis in the last decade [10].”
MethodRotor Fault Diagnosis Using Domain-Adversarial Neural Network with Time-Frequency Analysis
Xu, Y., Liu, J., Wan, Z., Zhang, D., & Jiang, D. L. (2022) · Machines · doi:10.3390/machines10080610 · Cited by 35
A Transfer Learning Method Using High-Quality Pseudo Labels for Bearing Fault Diagnosis
Zhu, W., Shi, B., & Feng, Z. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3223146 · Cited by 32
“Moreover, labeling data is laborious [6], [7].”
Correa-Jullian, C., & Groth, K. M. (2022) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2022.04.048 · Cited by 32
Intelligent fault diagnosis for triboelectric nanogenerators via a novel deep learning framework
Wu, H., Xu, X., Xin, C., Liu, Y., Rao, R., Li, Z., Zhang, D., Wu, Y., & Han, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120244 · Cited by 32
Ma, Y., Yang, J. H., & Li, L. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109516 · Cited by 34
A Rotating Machinery Fault Diagnosis Method Based on Multi-Sensor Fusion and ECA-CNN
Wang, H., Zhu, H., & Li, H. (2023) · IEEE Access · doi:10.1109/access.2023.3320065 · Cited by 34
“X is the signal of the j -th sensor under state i , Insufficient training samples in data-driven intelligent fault diagnosis likely cause model overfitting [5].”
BackgroundLi, W., Fu, H., Zhuo, Y., Liu, C., & Jin, H. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.09.011 · Cited by 32
Wang, H., & Li, Y. (2023) · International Conference on System Reliability and Safety Engineering (SRSE) · doi:10.1109/srse59585.2023.10336112 · Cited by 41
“This AI-based approach enables automated data analysis and accurate predictions based on learning and induction from historical data [7, 8].”
MethodWang, R., Yan, F., Yu, L., et al. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111121 · Cited by 32
Advanced Bearing-Fault Diagnosis and Classification Using Mel-Scalograms and FOX-Optimized ANN
Siddique, M., Zaman, W., Ullah, S., Umar, M. I., Saleem, F., Shon, D., Yoon, T. H., Yoo, D.-S., & Kim, J.-M. (2024) · Sensors · doi:10.3390/s24227303 · Cited by 35
Adversarial domain adaptation based on contrastive learning for bearings fault diagnosis
Pan, X., Chen, H., Wang, W., & Su, X. (2024) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2024.103058 · Cited by 44
Zeng, T., Jiang, H., Liu, Y., & Bai, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126815 · Cited by 33
Men, Z., Gong, D., Zhou, K., Chen, Y., & Zhou, J. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.113359 · Cited by 34
Cross-domain fault diagnosis of rolling bearing using similar features-based transfer approach
Qin, A., Mao, H., & Hu, Q. (2020) · Measurement · doi:10.1016/j.measurement.2020.108900 · Cited by 34
Lee, C., & Le, T. (2021) · IEEE Access · doi:10.1109/access.2021.3083646 · Cited by 31
“Consequently, the number of research publications on this issue has increased rapidly in recent years [1].”
BackgroundAn adaptive order-band energy ratio method for the fault diagnosis of planetary gearboxes
Zhang, M., Li, D., Wang, K., Li, Q. '., Ma, Y., Liu, Z., & Kang, T. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108336 · Cited by 31
Stator Winding Fault Detection and Classification in Three-Phase Induction Motor
Hussain, M. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2021.017790 · Cited by 32
Kuang, J., Xu, G., Tao, T., Wu, Q., Han, C., & Wei, F. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3196948 · Cited by 31
“Digital Object Identifier 10.1109/TIM.2022.3196948 academia [1], [2], [3], [4].”
InfluentialWei, J., Wang, J., Huang, H., Jiao, W., Yuan, Y., Chen, H., Wu, R., & Yi, J. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121799 · Cited by 33
Huo, J., Qi, C., Li, C., & Wang, N. C. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3311062 · Cited by 34
“the feature extraction of large-scale nonlinear data [8], [9].”
BackgroundLu, F., Tong, Q., Jiang, X., et al. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102787 · Cited by 31
Self-citationDeng, M., Deng, A., Zhu, J., Shi, Y., & Liu, Y. (2020) · Measurement · doi:10.1016/j.measurement.2020.108601 · Cited by 31
Garcia-Calva, T. A., Moríñigo-Sotelo, D., Fernandez-Cavero, V., et al. (2021) · Energies · doi:10.3390/en14051469 · Cited by 31
Review of AI-based methods for chatter detection in machining based on bibliometric analysis
Kounta, C. A. K. A., Arnaud, L., Kamsu-Foguem, B., & Tangara, F. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-10059-9 · Cited by 35
“2) with vibration analysis is a promising way to auto-mate the procedures currently used by the document [28].”
MethodLiu, C., Ma, X., Han, T., Shi, X., Qin, C., & Hu, S. (2022) · Measurement · doi:10.1016/j.measurement.2022.112041 · Cited by 32
A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management
Khalid, S., Song, J., Azad, M. M., Elahi, M. U., Lee, J. H., Jo, S.-H., & Kim, H. S. (2023) · Mathematics · doi:10.3390/math11183837 · Cited by 35
“In the time–frequency domain, features such as energy entropy are often derived through techniques like wavelet transform, wavelet package transform, or empirical mode decomposition applied to sensor data [70].”
MethodLaubichler, C., Kiesling, C., da Silva, M. M., Wimmer, A., & Hager, G. (2022) · Lubricants · doi:10.3390/lubricants10050103 · Cited by 30
Predictive maintenance for offshore oil wells by means of deep learning features extraction
Gatta, F., Giampaolo, F., Chiaro, D., & Piccialli, F. (2022) · Expert Systems · doi:10.1111/exsy.13128 · Cited by 31
“Some examples are Zhang et al. (2019); Jimenez et al. (2020); Lei et al. (2020); Zhao et al. (2019).”
BackgroundWang, L., Liu, J., Zhang, H., & Zuo, F. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3296919 · Cited by 30
“Recently, with the development of computer science, the deep learning (DL)-based methods have aroused attention in industrial processes due to their potential to extract features [5].”
BackgroundShi, M., Ding, C., Wang, R., et al. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111397 · Cited by 30
Abraham, O. A., Ochiai, H., Hossain, M. D., Taenaka, Y., & Kadobayashi, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3366493 · Cited by 38
“Although synthetic data generation is often considered a form of supervised learning owing to the creation of labels through a synthetic approach [20], [21], it applies to unsupervised methods when dealing with unlabeled data.”
BackgroundDictionary domain adaptation transformer for cross-machine fault diagnosis of rolling bearings
Cui, L., Wang, G., Liu, D., & Pan, X. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109261 · Cited by 32
Yang, H., Song, Y., Wang, D., Xing, J., & Li, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110312 · Cited by 39
Duan, Y., Yang, T., Wang, C., et al. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127718 · Cited by 30
A Hybrid Deep Learning Framework for Fault Diagnosis in Milling Machines
Siddique, M., Zaman, W., Umar, M. I., et al. (2025) · Sensors · doi:10.3390/s25185866 · Cited by 30
“Consequently, there is an urgent need for innovative solutions that can overcome data scarcity, remain resilient in noisy environments, and deliver scalable performance under practical industrial constraints [15,16].”
Miao, M., Liu, C., & Yu, J. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3039612 · Cited by 30
“FAULT diagnosis has been paid increasing attentions in recent years so as to ensure the safety of the machinery and reduce the economic loss [1], [2].”
BackgroundSamanta, A. K., Routray, A., Khare, S., & Naha, A. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3047433 · Cited by 30
“However, the last five years have seen phenomenal growth of deep learning (DL) for fault diagnosis and prognosis [31].”
BackgroundSmart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework
Sundaram, S., & Zeid, A. (2021) · Sensors · doi:10.3390/s21185994 · Cited by 37
Chang, H., Wang, Y.-C., Shih, Y.-Y., & Kuo, C. (2022) · Applied Sciences · doi:10.3390/app12084080 · Cited by 30
“The authors in [19], statistics on the evolution and classification of fault diagnosis methods can be divided into traditional machine learning, deep learning, and transfer learning methods, as shown in Table 1.”
BackgroundYe, Z., & Yu, J. (2022) · IEEE Systems Journal · doi:10.1109/jsyst.2022.3183134 · Cited by 28
“Deep neural networks (DNNs) have powerful feature learning performance due to their hierarchical structures, which has been widely used for machinery prognostics [10].”
BackgroundSynthesizing Rolling Bearing Fault Samples in New Conditions: A Framework Based on a Modified CGAN
Ahang, M., Jalayer, M., Shojaeinasab, A., Ogunfowora, O., Charter, T., & Najjaran, H. (2022) · Sensors · doi:10.3390/s22145413 · Cited by 30
“Although the adoption of deep learning methods has led to many successes, these approaches assume that labeled data is sufficient for training diagnosis models [18].”
BackgroundWang, B., Feng, G., Huo, D., & Kang, Y. (2022) · Processes · doi:10.3390/pr10071426 · Cited by 31
Aliyu, R., Mokhtar, A. A., & Hussin, H. (2022) · Applied Sciences · doi:10.3390/app122211691 · Cited by 33
A Multichannel MN-GCN for Wheelset-Bearing System Fault Diagnosis
Yuan, Z., Ma, Z., Li, X., & Li, J. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3227035 · Cited by 27
“Also, many machine learning methods have been developed for intelligent fault diagnosis [16].”
Zhao, N., Zhang, J., Mao, Z., et al. (2023) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-023-00859-9 · Cited by 29
“The development of fault monitoring, diagnosis, and health management technology for key equipment has always attracted the attention of researchers [1, 2].”
BackgroundDu, W., Zhu, P., Pu, Z., et al. (2023) · Measurement · doi:10.1016/j.measurement.2023.113985 · Cited by 29
Sun, J., Sun, C., Yan, Z., et al. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-15007-x · Cited by 29
“As a branch of machine learning, deep learning offers significant advantages in feature self-learning and end-to-end training [35, 36].”
An outline of Prognostics and health management Large Model: Concepts, Paradigms, and challenges
Tao, L., Li, S., Liu, H., Huang, Q., Ma, L., Ning, G., Chen, Y., Wu, Y., Li, B., Zhang, W., et al. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112683 · Cited by 37
Review on tool condition classification in milling: A machine learning approach
Patange, A. D., & Jegadeeshwaran, R. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.01.523 · Cited by 29
Zhou, X., Zhou, H., Wen, G., Huang, X., Lei, Z., Zhang, Z., & Chen, X. (2021) · Measurement · doi:10.1016/j.measurement.2021.110633 · Cited by 29
Dong, W. G., Zhang, S., Hu, M., et al. (2022) · Nonlinear Dynamics · doi:10.1007/s11071-021-07032-8 · Cited by 29
“The next key step is to design a multi-classifier for intelligent fault identification in WT gearboxes [36].”
BackgroundChoi, Y., Kim, K., Kim, S., & Kim, D. (2022) · Atmospheric Environment X · doi:10.1016/j.aeaoa.2022.100156 · Cited by 28
Zhang, Z., Shao, M., Ma, C., Lv, Z., & Zhou, J. (2022) · Nonlinear Dynamics · doi:10.1007/s11071-022-07341-6 · Cited by 28
“…in machine learning based on characterization learning of data, which can automatically, rapidly and efficiently extract the representative data feature from the massive collected signals [7–9], it has been a promising tool in fault diagnosis under the background of mechanical big data [10–12].”
MethodLu, F., Tong, Q., Feng, Z., Wan, Q., An, G., Li, Y., Wang, M., Cao, J., & Guo, T. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac78c5 · Cited by 30
Review of Artificial Intelligent Algorithms for Engine Performance, Control, and Diagnosis
Havugimana, L. F. I., Liu, B., Liu, F., Zhang, J., Li, B., & Wan, P. (2023) · Energies · doi:10.3390/en16031206 · Cited by 29
An IoT-Based Monitoring System for Induction Motor Faults Utilizing Deep Learning Models
Irgat, E., Çinar, E. M., Ünsal, A., & Yazıcı, A. T. (2022) · Journal of Vibration Engineering & Technologies · doi:10.1007/s42417-022-00769-5 · Cited by 28
“An ML model, once trained, works in three stages [14]: It takes the data, learns patterns in the data, and predicts the patterns for the unseen data.”
BackgroundWan, H., Gu, X., Yang, S., et al. (2023) · Sensors · doi:10.3390/s23063130 · Cited by 28
“Deep learning (DL) algorithms have recently gained increasing interest and have proved their ability to diagnose faults more automatically and intelligently [11,12].”
BackgroundRobust Multiple-Fault Diagnosis of PMSM Drives Under Variant Operations and Noisy Conditions
Mahmoud, M. S., Khang, H. V., Senanayaka, J. S. L., & Robbersmyr, K. G. (2023) · IEEE Open Journal of the Industrial Electronics Society · doi:10.1109/ojies.2024.3350443 · Cited by 29
“It can learn the features from the row data automatically [8].”
BackgroundMethodKernel adapted extreme learning machine for cross-domain fault diagnosis of aero-engines
Li, B., Xue, S.-K., Fu, Y.-H., Tang, Y., & Zhao, Y.-P. (2024) · Aerospace Science and Technology · doi:10.1016/j.ast.2024.108970 · Cited by 29
Li, J., Tao, R., Chen, R., Chen, Y., Zhao, C., & Huang, X. (2024) · Computers & Industrial Engineering · doi:10.1016/j.cie.2024.110209 · Cited by 30
Chapelin, J., Voisin, A., Rose, B., Iung, B., Steck, L., Chaves, L., Lauer, M., & Jotz, O. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109552 · Cited by 32
Multi-label domain adversarial reinforcement learning for unsupervised compound fault recognition
Wang, Z., Xuan, J., Shi, T., & Li, Y. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110638 · Cited by 29
Wei, Z., Wang, H., Zhao, Z., Zhou, Z., & Yan, R. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109644 · Cited by 31
Dong, X., Ding, H., Gao, D., Zheng, G., Wang, J., & Lang, Q. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103092 · Cited by 27
Wu, M., Zhang, J. J., Xu, P., et al. (2025) · Electronics · doi:10.3390/electronics14030515 · Cited by 28
“Conventional machine-learning-based fault diagnosis typically involves three key stages: data collection, feature extraction, and fault classification [2,3].”
Dong, S., Meng, Y., Yin, S., & Liu, X. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112473 · Cited by 31
Chen, G., Xiang, D., Liu, T., Xu, F., & Fang, K. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127296 · Cited by 32
Maggio, L. G. D. (2025) · Applied Sciences · doi:10.3390/app152111515 · Cited by 35
Hu, Q., Zhang, Q., Si, X., Sun, G., & Qin, A. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2995817 · Cited by 28
“As a result, the deep learning models are constructed by experimental trials once and once again rather than the strictly theoretical background [1].”
BackgroundHow to use lean manufacturing for improving a Healthcare logistics performance
Dossou, P.-E., Rafael, P., Cristiane, S., & Joao, C. J. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.10.231 · Cited by 28
Ahmad, Z., Prosvirin, A. E., Kim, J., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3044195 · Cited by 28
“These methods consist of three key steps: signal fault indicators (features) extraction, feature preprocessing, and fault classification [21]–[25].”
MethodChoudhury, T., Kurvinen, E., Viitala, R., & Sopanen, J. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108117 · Cited by 28
Peng, B., Bi, Y., Xue, B., et al. (2021) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2021.3084495 · Cited by 28
“The reason for using KNN is that it is a simple classification algorithm, easy to implement, and treats each feature equally without any feature weighting or selection [22], [52].”
Method
Metadata
- Authors
- Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K.
- Affiliations
- Xi'an Jiaotong University (CN)
- Brunel University of London (GB)
- Corresponding author
- Lei, Yaguo
- Venue
- Mechanical Systems and Signal Processing
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 138
- Pages
- 106587
- ISSN
- 0888-3270 (ISSN-L)
- OpenAlex
- W2998506103
- Cited by
- 2,997
- References
- 473
- Citations by year
- 2026: 553 · 2025: 549 · 2024: 497 · 2023: 515 · 2022: 439 · 2021: 327 · 2020: 115
- FWCI
- 172.43
- Type
- journal article
- Language
- en
- Open access
- green
- bura.brunel.ac.uk
- Locations
- Mechanical Systems and Signal Processing (publishedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion)
- Funders
- National Natural Science Foundation of China
- Ministry of Science and Technology of the People's Republic of China
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Keywords
- mechanical fault diagnosis, intelligent fault diagnosis, deep learning, transfer learning, condition monitoring, research trends
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
