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
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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.
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Claims the paper makes
43 Own interpretation · 43 No basis given · 26 No full text of the sources · 7 Not checked yet · 4 Own result · 3 Sources not found · 1 Supported so far
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
plain- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
hedgedconditional- Asserted: passes — The authors put it forward as true.
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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
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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
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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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.
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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
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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
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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
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
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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
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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
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably 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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
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It 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.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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, 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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a 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, they could achieve the diagnosis tasks with missing data.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3) Decision tree
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says 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
“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
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a 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 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
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3
“Yang et al [443] further proposed the polynomial kernel induced MMD to improve the transfer performance of the deep transfer learning-based diagnosis models”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > References
“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.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 4.2.2. Transfer scenarios in IFD
“In TDM scenarios, the source and target domain data are collected from different but related machines like motors and generators.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 4.2.1. Transfer problems in IFD
“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
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“Lei et al. [5] during a thorough review of intelligent fault diagnosis, showed the flexibility of the ML methods to obtain the fault diagnostic knowledge based on the collected data to improve the reliability of the fault detection systems.”
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“These studies have achieved success in scenarios with well-defined physical models and controllable sampling [45, 46].”
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“Therefore, accurate measurement and monitoring of equipment operating conditions and diagnosis of the fault states from the measurement sensory signals have become critical [1], [2].”
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“45 Only frequency domain features are considered in this study since the track geometry irregularities can alter the amplitude and distribution of time-domain signals, potentially interfering with responses associated with track component defects at required sensor locations, as discussed in…”
Ding, F., Feng, J., & Hou, P. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae4ac5 · Cited by 1
“However, when dealing with complex working conditions and multiple fault types, these approaches exhibited certain limitations, particularly in terms of feature completeness and diagnostic accuracy [5].”
Fault Detection and Monitoring in Induction Machines Using Data-Driven Model Drift Detection
Ricaldi-Morales, A. J., Ramírez, C. A. P., Silva, J. F., et al. (2026) · Sensors · doi:10.3390/s26051595 · Cited by 1
“When transferred to dynamic industrial environments, the discrepancy in data distribution—a challenge technically known as domain shift—often leads to performance degradation [5,18].”
Zhang, D., Hao, Y., Li, H., et al. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae4f0d · Cited by 1
“To address this problem, two main categories of approaches are commonly adopted for fault diagnosis: traditional methods based on signal processing and machine learning, and end-to-end intelligent diagnosis methods based on deep learning [6].”
Xiao, K., Zhou, J., Cao, Z., Fei, X., & Zhou, Y. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae4f12 · Cited by 1
“As a matter of fact, in real industrial scenarios, wind turbine equipment almost always works under normal conditions and usually does not fail, so it is difficult to obtain real failure sample data [14, 15].”
Li, J., Liu, D., & Chen, G. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132028 · Cited by 1
A diffusion-based data augmentation framework for hydraulic pump fault diagnosis
An, H.-J., Dong, Z.-K., Xiang, X., et al. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114419 · Cited by 1
Faraz, M., Shubina, V., Mäkilä, T., & Heikkonen, J. (2025) · International Conference on System Reliability and Safety (ICSRS) · doi:10.1109/icsrs68021.2025.11422078 · Cited by 1
“…time-series data streams contain subtle patterns of impending failures, but extracting actionable insights presents challenges, including distinguishing normal variations from anomalies, addressing class imbalance, detecting temporal precursors, and identifying predictive features [5], [6], [7].”
Aldrini, J., & Chihi, I. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-17769-4 · Cited by 2
Li, H., Wu, W., & Zou, C. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae5287 · Cited by 1
“Rolling bearings constitute the cornerstone of rotating machinery in modern industry, serving as the critical ‘joints’ for aero-engines, high-speed trains, and wind turbines [1, 2].”
Predictive maintenance of electric drives based on IIoT and Random Forest
Safiullin, R. N., & Isavnin, A. G. (2026) · Transportation and Information Technologies in Russia · doi:10.12731/3033-5965-2026-16-1-417 · Cited by 1
Sahib, M. M., Plänitz, P., Hackert-Oschätzchen, M., & Lerez, C. (2026) · Machines · doi:10.3390/machines14030348 · Cited by 1
NeuraEngineDx: An IoT condition monitoring device for Small-Scale fishing vessel engines
Priharanto, Y. E., Jaya, I., Rahmat, A., et al. (2026) · HardwareX · doi:10.1016/j.ohx.2026.e00763 · Cited by 1
“…therefore, the integration of vibration, temperature, and acoustic sensors in a Condition Monitoring System (CMS) enables a multidimensional assessment of these mechanical health indicators, ensuring that subtle deviations are detected before they lead to catastrophic engine failure [18,22,23].”
Xu, B. (2026) · doi:10.1109/icpege67691.2026.11451566 · Cited by 1
“Nevertheless, most existing schemes exploit single modalities—commonly accelerometers [4] or motor current [5]—and neglect contextual information such as cutting parameters or human-generated maintenance logs.”
Laib, A., Ouagueni, F., Herizi, A., Djeriou, S., Ahmed, H., Chedjara, Z., & Dahmane, S. (2026) · PLoS ONE · doi:10.1371/journal.pone.0345870 · Cited by 1
Horváth, K. (2026) · Data · doi:10.3390/data11040070 · Cited by 1
“Numerous studies show that vibration signals contain rich information about gear defects and degradation mechanisms, and both classical signal-processing methods and modern machine learning approaches have demonstrated high diagnostic accuracy [1].”
Rony, M. H., Ramiah, H., Kanesan, J., & Madhuranga, H. P. D. S. (2026) · Scientific Reports · doi:10.1038/s41598-026-46198-2 · Cited by 1
“Lei et al. 32 described the progressive evolution of fault diagnosis techniques from traditional machine learning to deep learning and further to transfer learning, identifying context-aware diagnosis as a promising future trend.”
Wu, Z., Zheng, S., Chen, M., Gong, X., & Shao, H. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2026.3677480 · Cited by 1
“R OTATING machinery constitutes the backbone of modern industrial infrastructure, where its health condition directly dictates operational safety, production efficiency, and maintenance costs [1].”
Jiao, Z., Zhang, J., Bi, Z., et al. (2026) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2026.114251 · Cited by 1
Huang, J., & Tong, J. (2026) · Electronics · doi:10.3390/electronics15081631 · Cited by 1
Karkadakattil, A. (2026) · Journal of Intelligent Manufacturing and Special Equipment · doi:10.1108/jimse-12-2025-0035 · Cited by 1
Ma, H., Zuo, D., Zhang, G., et al. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae6523 · Cited by 1
“The experimental results are shown in figures 10 and 11.”
Zhang, H., Gong, W., AL-Bukhaiti, K., et al. (2026) · International Journal of Energy Research · doi:10.1155/er/3844653 · Cited by 1
Lv, Y., Wang, X., Liu, Y., & Lu, M. (2026) · Measurement · doi:10.1016/j.measurement.2026.121637 · Cited by 1
Open-set tool condition monitoring of end mills
Yue, C., Zhang, B., Liu, X., et al. (2026) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2026.114335 · Cited by 1
Sun, H. H., Chen, K., & Chen, H. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae693e · Cited by 1
Predictive Analytics and Simulation in Enterprise Environments for Industry 4.0 and Lean Management
Roy, R., Parihar, A. S., & Tiwari, M. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-9998-0.ch005 · Cited by 1
Zhou, Z., Liu, C., & Chen, M. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae6a03 · Cited by 1
“Therefore, there is a strong need for methods that can simultaneously handle domain discrepancies, data imbalance, and predictive uncertainty to improve the robustness and trustworthiness of fault diagnosis under variable operating conditions [14–17].”
Reliability-aware dynamic graph fusion and LLM-based diagnostic assistant for bearing faults
Lin, T., Ren, Z., Huang, K., Zhang, X., Karimi, H. R., Zhu, Y., & Hong, J. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104720 · Cited by 2
Liao, J.-X., He, C., Li, J., Zhong, X.-C., Sun, J., Cheung, Y., Fan, F., Zhang, S., & Zhang, X. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109143 · Cited by 2
Wang, H., Luo, X., Liu, J., et al. (2026) · IEEE Transactions on Reliability · doi:10.1109/tr.2026.3696311 · Cited by 1
“IFD methods have made significant progress, propelled by deep learning (DL) techniques, and are widely applied to the prediction and health management of mechanical equipment [6].”
Temporal-physics aware dynamic network for remaining useful life prediction of rolling bearings
Fu, L., Mao, T., Zhou, H., et al. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae78ee · Cited by 1
Kibrete, F., Woldemichael, D. E., Gebremedhen, H. S., Çakar, O., & Çelik, E. (2026) · Journal of Engineering · doi:10.1155/je/6113740 · Cited by 1
Source-free domain adaptation for machinery fault diagnosis under a non-ideal initial model
He, B., Wang, H., & Fan, S. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115375 · Cited by 1
Li, X., Zu, S., Liu, X., et al. (2026) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2026.114587 · Cited by 1
Devarapelly, A. R., Singh, A., Najeh, T., Kankar, P. K., Mandal, T. K., Karim, R., Kumar, U., & Varde, P. V. (2026) · MAPAN · doi:10.1007/s12647-026-00941-2 · Cited by 1
“Compared with classical methods of signal processing, ML based approaches typically provide better accuracy in predicting the RUL of bearings [4], however these models are still limited to the extraction of features that are predetermined and they also struggle to account for the long-term temporal…”
Sui, Z., Wang, P., Malik, H., & Akter, K. (2026) · IET Generation Transmission & Distribution · doi:10.1049/gtd2.70361 · Cited by 2
Feng, Z., Tong, Q., He, Y., et al. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae80f1 · Cited by 1
Self-citationZhen, Y., Si, Y., Liu, S., Li, W., Wang, S., Song, M., & Li, Z. (2026) · Processes · doi:10.3390/pr14132171 · Cited by 1
Interpretable Fault Diagnosis for Industrial Processes: A Unified Perspective
Huo, M., & Yin, S. (2026) · Artificial Intelligence Science and Engineering · doi:10.23919/aise.2026.000007 · Cited by 1
Ogaili, A. A. F., Jweri, A.-R. K., Flayyih, H. A., Jaber, A. A., & Ali, K. M. (2026) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2026.2710390 · Cited by 2
Intelligent risk-oriented predictive diagnostics system based on vibration signal analysis
Doshanova, M., Talipov, M., Shaazizova, M. E., Alijonovna, M. N., Karaxanova, S., & Khodzamuratova, I. (2026) · Mathematical Models in Engineering · doi:10.21595/mme.2026.26531 · Cited by 2
“The training procedure uses the Adam optimizer [29]; the deployment requirements are consistent with those discussed for intelligent fault diagnosis systems in [16].”
Tang, S., Ren, Z., Zheng, L., & Wang, J. (2026) · Sensors · doi:10.3390/s26175379 · Cited by 2
“In addition to core engineering bottlenecks including the scarcity of fault samples, poor adaptability to wide-range variable operating conditions, and insufficient generalization ability under strong noise [11–13].”
Zhang, Y., Qian, X., Tang, R., Xi, C., Cui, T., & Tang, Z. (2026) · Sensors · doi:10.3390/s26175679 · Cited by 1
Fault diagnosis of rotating machinery based on FA-DSAE
Zhou, H., Yan, P., Pei, J., et al. (2020) · doi:10.1109/cyber50695.2020.9279113 · Cited by 1
“Intelligent fault diagnosis technology such as SVM and ANN has been widely used [2].”
MethodA novel intelligent fault diagnosis method based on variant sparse filtering and back-propagation
Chan, L., & Cheng, C. (2021) · Noise & Vibration Worldwide · doi:10.1177/09574565211055786 · Cited by 1
“With the coming of the big data era, data-driven methods using machine learning models are attracting the attention of scholars.(4,5) The main diagnostic steps of the data-driven method are (1) obtaining features using a feature learning method and (2) recognizing health conditions using a feature classifier.”
MethodTrade-offs in Metric Learning for Bearing Fault Diagnosis
Cody, T., Adams, S., & Beling, P. A. (2021) · International Conference on Machine Learning and Applications · doi:10.1109/icmla52953.2021.00180 · Cited by 1
Wang, C., Xu, Z., Wang, J., & Song, Y. (2022) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm53196.2022.9815704 · Cited by 1
Expert System Development to Predict Canned Motor Pump Status
Thuensuwan, K., & Chutima, P. (2022) · doi:10.1145/3535782.3535831 · Cited by 1
“The article [17] describe research result that uses failure history combined with machine learning by skilled engineers to identify the failure.”
BackgroundA Hybrid Fault Diagnosis Approach Using FEM Optimized Sensor Positioning and Machine Learning
Jung, S. J., Shifat, T. A., & Hur, J.-W. (2022) · Processes · doi:10.3390/pr10101919 · Cited by 1
DIAGNOSTICS OF AN ELECTRIC MOTOR BASED ON A STATE-SPACE MODEL
Božek, P., & Zelník, R. (2022) · MM Science Journal · doi:10.17973/mmsj.2022_10_2022085 · Cited by 1
Adv-IFD: Adversarial Attack Datasets for An Intelligent Fault Diagnosis
Tripathi, A. M., Behera, S. R., & Paul, K. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891963 · Cited by 1
“Li et al. [19] evaluated performance of several deep models that use transfer learning technique for providing monitoring of rotatory machinery.”
BackgroundAdaptive Shapelets Preservation for Time Series Augmentation
Pin, L., Guo, X., Chen, P., Shi, B., Wo, T., & Liu, X. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892768 · Cited by 2
“…classification (TSC) is a task that learns to assign labels to a given set of time series from different classes, which plays a pivotal role in a vast number of research fields, such as fault diagnosis [1], speech analysis [2], action recognition [3], auxiliary medical and diagnosis [4], etc.”
BackgroundIntelligent diagnosics of vehicles
Migal, V., Arhun, S., Hnatov, A., et al. (2022) · Vehicle and Electronics Innovative Technologies · doi:10.30977/veit.2022.22.0.5 · Cited by 1
Mersha, B. W., & Ma, H. (2022) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc55256.2022.10033981 · Cited by 1
“Model-based [12, 13, 14, 15] and data-driven [16, 17, 18] SFDIA are discussed in the literature.”
Method夏, 鹏., 黄, 亦., 王, 誉., et al. (2023) · Scientia Sinica Technologica · doi:10.1360/sst-2023-0136 · Cited by 1
Application of Support Vector Machine for Near Real-Time Health Structural Diagnosis for Drones
Lai, W. H., Liang, Y.-R., Cardona, C. R. C., & Cheng, D.-L. (2023) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim46323.2023.10196114 · Cited by 1
“The study of UAV health diagnosis can be divided into two research directions, real-time health diagnosis and long-term health diagnosis [9].”
BackgroundLi, X., Lei, Y., Li, X., & Yang, B. (2023) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim46323.2023.10196265 · Cited by 3
“Currently, deep neural networks have been widely studied in the field of machine fault diagnosis since they are able to extract health information of the equipment from the monitoring signals directly [1, 2].”
BackgroundSelf-citationField Winding Short Circuit Fault Signature Analysis in Stray Flux of large Hydrogenerator
Bechara, H., Merkhouf, A., Zemouri, R., et al. (2023) · doi:10.1109/redec58286.2023.10208184 · Cited by 1
“dataset, including both normal and faulty conditions [12].”
BackgroundSingle-source UDA for privacy-preserving intelligent fault diagnosis based on domain augmentation
Zhao, X., Ma, M., Xue, X., et al. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/acf3ff · Cited by 1
Das, P. P., Tiwari, R., & Bordoloi, D. J. (2023) · Mechanisms and machine science · doi:10.1007/978-3-031-40455-9_38 · Cited by 1
Bearing Fault Diagnosis Using Transfer Learning with ICCEMDAN
Pan, C., Xue, S., Zhang, Y., Chen, L., Lian, P., Wang, C., Xu, Q., & Zhao, W. (2023) · Mechanisms and machine science · doi:10.1007/978-3-031-40455-9_10 · Cited by 1
Fault diagnosis of automobile drive based on a novel deep neural network
Guo, C. (2023) · Energy Harvesting and Systems · doi:10.1515/ehs-2023-0049 · Cited by 1
Machine Learning Comparison in Application of Power System Instability Detection
Rahmani, R., Hariyanto, N., & Anggoro, B. (2023) · International Conference on High Voltage Engineering and Power Systems (ICHVEPS) · doi:10.1109/ichveps58902.2023.10257314 · Cited by 1
A Deep Anomaly Detection With Same Probability Distribution and Its Application in Rolling Bearing
Kang, Y., Guo, C., Wenping, P., et al. (2023) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.4063608 · Cited by 1
Wu, W., Liu, Y., Li, Z., et al. (2023) · Manufacturing Letters · doi:10.1016/j.mfglet.2023.08.075 · Cited by 1
Labelling of Annotated Condition Monitoring Data Through Technical Language Processing
Löwenmark, K., Taal, C., Vurgaft, A. V., Nivre, J., Liwicki, M., & Sandin, F. (2023) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2023.v15i1.3507 · Cited by 2
“…shift in features and noise levels between lab and industry environments, which has motivated research into transfer learning approaches (Lei et al., 2020; W. Li et al., 2022; T. Zhang et al., 2022), with the fundamental goal being to facilitate optimisation using unlabelled or weakly…”
BackgroundTjen, J., & Pratama, V. (2023) · Journal of Applied Computer Science and Technology · doi:10.52158/jacost.v4i2.556 · Cited by 2
Low-cost Fault Diagnosis of Pneumatic Systems with Exergy and Machine Learning:
Wang, Z., Hongwei, Z. H. U., & Xiong, W. (2023) · JFPS International Journal of Fluid Power System · doi:10.5739/jfpsij.16.24 · Cited by 1
Bearing Fault Detection Based on Multiresolution Permutation Entropy
Ma, C., Cai, Z., Li, Y., et al. (2023) · International Conference on System Reliability and Safety Engineering (SRSE) · doi:10.1109/srse59585.2023.10336100 · Cited by 1
“Generally, the fault detection procedure includes data collection, feature extraction, and fault recognition [2].”
MethodApplications of Machine Learning and Artificial Intelligence in the COVID-19 Pandemic
Różyło‐Kalinowska, I., & Orhan, K. (2023) · Springer eBooks · doi:10.1007/978-3-031-43827-1_15 · Cited by 1
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.
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