Cited by 2,997Machine Fault Diagnosis TechniquesFault Detection and Control SystemsReliability and Maintenance Optimization

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 access

Lei, Yaguo, Yang, Bin, Jiang, Xinwei, Jia, Feng, Li, Naipeng, Nandi, Asoke K.·2020·Mechanical Systems and Signal Processing

DOI 10.1016/j.ymssp.2019.106587

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Abstract

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.

AI snapshot

Written from the paper’s full text.

TL;DR

This review traces intelligent fault diagnosis through three eras—traditional machine learning, deep learning, and transfer learning—and proposes a roadmap toward machine health management. It argues that transfer learning is the key future direction for applying diagnosis models in engineering settings where labeled fault data are scarce.

Key findings

The authors divide intelligent fault diagnosis into three periods: traditional machine learning before the 2010s, which relied on handcrafted features; deep learning since the 2010s, which enables end-to-end feature learning; and transfer learning as the future direction for cross-task and cross-domain diagnosis.
Publication counts from Web of Science show rapid growth since the 2010s, with output from 2017 to 2019 nearly matching the total from before 2017.
Among the transfer-learning studies reviewed, most address transfer within the same machine (TIM), while only four publications target transfer across different machines (TDM).
The roadmap highlights future challenges including data quality and cleaning, imbalanced health states, streaming data analytics, negative transfer and source-domain selection, and interpretability of deep diagnosis models.

Methods

Narrative literature review and roadmap, not an experimental study. It organizes intelligent fault diagnosis by three periods and by method families: expert systems, ANN, SVM, kNN, PGM, and decision trees; stacked AE, DBN, CNN, and ResNet; and transfer-learning categories.
Uses Web of Science search results to count publications and motivate the periodization, then reviews representative applications across bearings, gears, motors, engines, and other machines.
For transfer learning, it defines domains and tasks, distinguishes TIM from TDM scenarios, and categorizes approaches as feature-based, GAN-based, instance-based, and parameter-based.

Limitations

The paper states no limitations of its own review; the following are evident from the text.
Evident: no formal systematic-review protocol, search strings, inclusion/exclusion criteria, or quality appraisal are reported, and the publication-counting procedure is described only briefly.
Evident: the roadmap and future directions are conceptual and perspective-based rather than empirically tested in the paper.
Evident: coverage is selective and organized around the authors’ three-period framework, so it may not exhaustively capture all intelligent fault diagnosis literature.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.ymssp.2019.106587

Cites

460 of 460 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

3 of 65 cited sources checked · 3 supported · 0 flagged

The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“First, the signal processing methods, such as the wavelet packet [369][370][371], the continue wavelet transform [372,373], the dual-tree complex wavelet transform [374,375], and the synchrosqueezing transform [376], are employed to preprocess the input 1D data, which are expected to convert the signals from the time domain to the time-frequency domain.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (2) Applications of CNN to machine fault diagnosis
What the sources say
- Not matched
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
  cites [369][370][371]
- Supported
  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)
  cites [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
- Supported
  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)
  cites [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
- Supported
  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)
  cites [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
- 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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Show all 45 claims

The quote only partly states this claim. 1
It may state two separate claims as one. 47
Probably not a substantial claim. 52

A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“With regard to fault diagnosis, Tong et al. [420,421] employed JDA to complete the diagnosis tasks of bearings respectively used in the motor and the belt conveyor idler, and achieve better diagnosis accuracy than TCA-based diagnosis models.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 9
What the sources say
- Not checked — no full text of the sourcecites [420] 420. Z. Tong, W. Li, B. Zhang, F. Jiang, G. Zhou, Bearing fault diagnosis under variable working conditions based on domain adaptation using feature transfer learning, IEEE Access 6 (2018) 76187-76197. source
- Not checked — no full text of the sourcecites [421] 421. Z. Tong, W. Li, B. Zhang, M. Zhang, Bearing fault diagnosis based on domain adaptation using transferable features under different working conditions, Shock Vibrat. (2018) Doi: 10.1155/2018/6714520. source
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
In the paper
“The results showed that the trained diagnosis model could correctly recognize the unlabeled samples in the target domain although there were a small number of labeled samples in the target domain.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 4.3.3. Instance-based approaches
What the sources say
- Not checked — no full text of the sourcecites [36] 36. F. Shen, C. Chen, R. Yan, R.X. Gao, Bearing fault diagnosis based on svd feature extraction and transfer learning classification, in: Prognostics and System Health Management Conference, Beijing, China, 2015, pp. 1-6. source
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Compared with the diagnosis model just trained with a small number of target-domain samples, the fine-tuned diagnosis model presented the faster convergence rate and higher diagnosis accuracy.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 6. Conclusions
What the sources say
- Not checked — no full text of the sourcecites [438] 438. R. Zhang, H. Tao, L. Wu, Y. Guan, Transfer learning with neural networks for bearing fault diagnosis in changing working conditions, IEEE Access 5 (2017) 14347-14357. source
- Not checked — no full text of the sourcecites [37] 37. M.J. Hasan, J.M. Kim, Bearing fault diagnosis under variable rotational speeds using stockwell transform- based vibration imaging and transfer learning, Appl. Sci.-Basel 8 (2018) 2357. source
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
In the paper
“Among the proposed methods, the algorithm of C4.5 is widely used to induce a decision tree for classification and obtain satisfactory accuracy and easily-understood classification rules [300].”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3) Decision tree
What the sources say
- Not checked — no full text of the sourcecites [300] 300. J.R. Quinlan, C4.5: Programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, 1993.
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
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
“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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“The non-naive Bayesian classifier [288] is further developed to release the assumption, and it has also been used for fault diagnosis of bearings [289] and gears [290].”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (1) kNN
What the sources say
- Not checked — no full text of the sourcecites [288] 288. Y. He, R. Wang, S. Kwong, X. Wang, Bayesian classifiers based on probability density estimation and their applications to simultaneous fault diagnosis, Inf. Sci. 259 (2014) 252-268. source
- Not checked — no full text of the sourcecites [289] 289. J. Yu, B. Ding, Y. He, Rolling bearing fault diagnosis based on mean multigranulation decision-theoretic rough set and non-naive Bayesian classifier, J. Mech. Sci. Technol. 32 (2018) 5201-5211. source
- Not checked — no full text of the sourcecites [290] 290. M.Y. Asr, M.M. Ettefagh, R. Hassannejad, S.N. Razavi, Diagnosis of combined faults in rotary machinery by non-naive Bayesian approach, Mech. Syst. Signal Process. 85 (2017) 56-70. source
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
In the paper
“In order to improve the generalization performance of decision tree, the random forest [306] is further investigated by integrating decisions from multiply tree-based classifiers.”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 3) Decision tree
What the sources say
- Not checked — no full text of the sourcecites [306] 306. L. Breiman, Random forests, Mach. Learn. 45 (2001) 5-32. source
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
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
Strength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The kNN is a commonly-used supervised learning model to complete the classification tasks [13].”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > (1) kNN
What the sources say
- Not checked — no full text of the sourcecites [13] 13. T.M. Cover, P.E. Hart, Nearest neighbor pattern classification, IEEE Trans. Inf. Theory 13 (1967) 21-27. source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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 matched
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
  cites [1] 1. Y. Lei, Intelligent fault diagnosis and remaining useful life prediction of rotating machinery, Butterworth- Heinemann Elsevier Ltd, Oxford, 2016.
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Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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 matched
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
  cites [314] 314. G.I. Platforms, The rise of industrial big data, 2012, Available: http://www.geautomation.com/download/rise-industrial-big-data.
- 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 matched
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
  cites [316] 316. J. Lee, Industrial big data: The revolutionary transformation and value creation in industry 4.0 era, Mechanical Industry Press, Beijing, China, 2015.
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
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 matched
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
  cites [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.
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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 matched
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
  cites [314] 314. G.I. Platforms, The rise of industrial big data, 2012, Available: http://www.geautomation.com/download/rise-industrial-big-data.
- 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 matched
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
  cites [316] 316. J. Lee, Industrial big data: The revolutionary transformation and value creation in industry 4.0 era, Mechanical Industry Press, Beijing, China, 2015.
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
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 matched
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
  cites [314] 314. G.I. Platforms, The rise of industrial big data, 2012, Available: http://www.geautomation.com/download/rise-industrial-big-data.
- 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 matched
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
  cites [316] 316. J. Lee, Industrial big data: The revolutionary transformation and value creation in industry 4.0 era, Mechanical Industry Press, Beijing, China, 2015.
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
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 matched
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
  cites [314] 314. G.I. Platforms, The rise of industrial big data, 2012, Available: http://www.geautomation.com/download/rise-industrial-big-data.
- 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 matched
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
  cites [316] 316. J. Lee, Industrial big data: The revolutionary transformation and value creation in industry 4.0 era, Mechanical Industry Press, Beijing, China, 2015.
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim.
In the paper
“TCA just adapts the marginal probability distribution of the input cross-domain data, but 8 ignores the conditional probability distribution from the feature space to the target classes.Thus,”
§ Applications of machine learning to machine fault diagnosis: A review and roadmap > 7
What the sources say
- Not checked — no full text of the sourcecites [30] 30. M. Long, J. Wang, G. Ding, J. Sun, P.S. Yu, Transfer feature learning with joint distribution adaptation, in: IEEE International Conference on Computer Vision, Sydney, Australia, 2013, pp. 2200-2207. source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.

82 of 82

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.”

11 of 11

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

Polynomial-kernel study is assigned the GAN reference number
Minor
The discussion attributes a polynomial-kernel MMD study to reference [443], but [443] is a paper on generative adversarial nets. The polynomial-kernel paper appears instead as reference [444].
Motors and generators are both allowed and ruled out as a transfer pair
Minor
The paper gives transfer between motors and generators as an example of a TDM scenario, but elsewhere says using knowledge from motors to diagnose generators is unrealistic.

2 of 2

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Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928

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Dual-driven optimization of collaborative multi-agent via case learning and curiosity
Chen, R., Fei, R., Li, J., Li, A., Miao, Y., Wu, L., & Chen, Z. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108083 · Cited by 3
Safety assessment for dynamic systems: A survey
Liu, C., He, X., Zhou, D., & Huang, B. (2026) · Cybernetics and Intelligence · doi:10.26599/cai.2024.9390001 · Cited by 4
Physics-Informed Digital Twins for Real-Time Health Monitoring Performance Prediction and Remaining Useful Life Assessment of Gas Turbines Foundations and Structural Support Systems
Addy, R. Z., Atoo, A. A., & Nwafor, E. O. (2022) · International Journal of Scientific Research in Civil Engineering · doi:10.32628/ijsrce229674 · Cited by 2
Adversarial multi-domain adaptation for machine fault diagnosis with variable working conditions
Li, Q., Liu, S., Yang, B., Xu, Y., Chen, L., & Shen, C. (2020) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin45582.2020.9442084 · Cited by 2
“GAN is an efficient structure to learn data representation; therefore, GAN-based domain adaptation is utilized to reduce the discrepancy of the source domain and target domain[14], which has a wide prospect in equipment monitoring [15].”
MethodSelf-citation
Fault Diagnosis of Gearbox Based on Deep Residual Shrinkage Network in Noise Environment
Wang, Y., Luo, S., Luo, R., et al. (2022) · Global Reliability and Prognostics and Health Management (PHM) · doi:10.1109/phm-yantai55411.2022.9942196 · Cited by 2
Convolutional Neural Network-based Fault Diagnosis for Spacecraft Attitude Multiple Components
Yan, X., Sheng, T., Xie, X. G., et al. (2022) · Chinese Control Conference (CCC) · doi:10.23919/ccc55666.2022.9902600 · Cited by 2
An Improved Anti-Noise Network for Bearing Fault Diagnosis under Noisy Environment
Weng, C., Lu, B., & Sun, W. (2022) · International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) · doi:10.1109/sdpc55702.2022.9915839 · Cited by 2
Deep learning based optimum fault diagnosis of electrical and mechanical faults in induction motor
Singh, V., Gangsar, P., Atulkar, A., & Porwal, R. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1136/1/012059 · Cited by 2
“Overpast 30 years several Artificial Intelligence techniques have been evolved and put in the monitoring of Induction motor like Support Vector Machines (SVM), Artificial Neural Network (ANN), Fuzzy Logic (FL), Neuro-Fuzzy etc [6, 7, 8, 9].”
Background
A Structured Approach to Machine Learning Condition Monitoring
Capelli, L., Massaccesi, G., Molano, J. C. C., et al. (2021) · Applied condition monitoring · doi:10.1007/978-3-030-79519-1_3 · Cited by 2
The Era of Advanced Machine Learning and Deep Learning Algorithms for Malware Detection
Chui, K. T., de Pablos, P. O., Lytras, M. D., Liu, R. W., & Shen, C.-W. (2021) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-7998-7789-9.ch004 · Cited by 2
On Training Data Selection in Condition Monitoring Applications—Case Azimuth Thrusters
Nikula, R.-P., Ruusunen, M., & Böhme, S. A. (2022) · Applied Sciences · doi:10.3390/app12084024 · Cited by 2
A Novel Nonlinear Mapping based Compatibility Method for Zero-Shot Classification in Intelligent Fault Diagnosis
Lin, J., Ye, R., Su, X., et al. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9727822 · Cited by 2
“However, due to the low generalization of machine learning algorithms, as the rapid increase in the number of sensing data, the accuracy of machine learning based fault diagnosis methods decreases [3].”
Background
Transient running stability assessment of industrial rotating machinery based on bag of correlated vibration feature representation
Gui, W., Chen, X., Zhou, H., et al. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac8b28 · Cited by 2
“Actually, as discussed in [22], the difficulty of health condition monitoring and diagnosis involves highly-noised condition signals, a time-varying environment, etc, making the methods of health index extraction still progressive with a continuous manner until the present day.”
Fault Detection and Diagnosis using AutoEncoders and Interpretable AI - Case Study on an Industrial Chiller
Gupta, S., Venugopal, A., & Mohan, M. J. (2022) · International Symposium on Advanced Control of Industrial Processes · doi:10.1109/adconip55568.2022.9894262 · Cited by 5
“For the detection of the Faults, Deep learning techniques [2], have been used in several studies.”
Method
Few-shot Multi-domain Fault Diagnosis for Planetary Gearbox in Nuclear Circulating Water Pump
Wang, S., Cheng, W., Liu, Y., et al. (2022) · IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) · doi:10.1109/sdpc55702.2022.9915800 · Cited by 2
Multi-Task Data Imputation for Time-Series Forecasting in Turbomachinery Health Prognostics
Chen, X., Ding, X., Wang, X., Zhao, Y., Liu, C., Liu, H., & Chen, K. (2022) · Machines · doi:10.3390/machines11010018 · Cited by 2
“Data-driven models have experienced Machines 2023 , 11 , 18 2 of 18 two stages [7].”
Method
Islamic Religious Education Learning Model Based on Living Values Educations in Higher Education
Solehuddin, M. S., Tarihoran, D., Nurteti, L., Philipp, C., & Henkin, C. (2023) · al-fikrah Jurnal Manajemen Pendidikan · doi:10.31958/jaf.v11i1.8612 · Cited by 6
“…learning process so that students actively develop their potential to have religious spiritual strength, self-control, personality, intelligence, noble character, and skills needed by themselves, society, nation and country (Barredo Arrieta et al., 2020; Guidotti et al., 2019; Lei et al., 2020).”
Background
MCAN: Interpretable Multi-scale Component Analysis Network for Mechanical Fault Diagnosis
Qin, F., Wang, S., Wang, S., et al. (2023) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc53148.2023.10175935 · Cited by 2
“In 2020, Lei et al. [7] reviewed the process of mechanical equipment intelligent diagnosis methods from traditional machine learning to deep learning.”
Background
Multi-channel gearbox compound fault diagnosis based on RF and D-S evidence theory
Qi, Y., Wei, S., Jia, S., et al. (2023) · CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) · doi:10.1109/safeprocess58597.2023.10295584 · Cited by 2
DSE-VAE: an Interpretable Fault Data Generation Method for the Traction Motors
Peng, T., Peng, X., Yang, C., et al. (2023) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon51785.2023.10312410 · Cited by 2
“To address the issue of incomplete data, data augmentation techniques have emerged as a promising solution[6][7].”
Background
Large-Scale Language Models for PHM in Railway Systems - Potential Applications, Limitations, and Solutions
Wang, H., & Li, Y. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-9311-6_59 · Cited by 4
Fault Diagnosis of Gearbox Based on Improved Transformer
Guan, K., Du, B., Wu, Z., et al. (2023) · doi:10.1145/3603273.3636498 · Cited by 2
An Improved U-Net Image Segmentation Network for Crankshaft Surface Defect Detection
Moosavian, A., Bagheri, E., Yazdanijoo, A., & Barshooi, A. H. (2024) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/mvip62238.2024.10491179 · Cited by 2
“With the advancement of the level of automation in modern industries, machine vision has played a significant role in quality control and replaced visual inspection by the operator [1-3].”
Background
Intelligent Diagnosis of Planetary Gearboxes Based on DAE-CNN
Kang, T., Zhang, M., Xie, R., Chen, J., Xiang, H., & Wang, Y. (2023) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-hangzhou58797.2023.10482753 · Cited by 2
“In general, CNNs consist of convolutional layers, pooling layers, and fully connected layers [17].”
Background
Machine Learning for Credit Risk and Default Prediction in Retail and SME Lending : A Systematic Review
Ashore-Onisemo, F., Okwesa, I. J., & Unamma, U. M.-L. (2020) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2066446 · Cited by 5
Intelligent Fault Diagnosis Based on Multi-Resolution and One-Dimension Convolutional Neural Networks
Liu, P.-Y., Chen, C., Liong, S., Tsai, M., Hsieh, P.-C., & Wang, K.-C. (2021) · International Conference on System Science and Engineering · doi:10.1109/icsse52999.2021.9538454 · Cited by 2
Modified Stacked Convolutional Auto-Encoder for Hydraulic Pump Fault Diagnosis with Unbalanced Data
Wang, C., Zhang, Y., Ma, J., et al. (2021) · Global Reliability and Prognostics and Health Management (PHM) · doi:10.1109/phm-nanjing52125.2021.9612951 · Cited by 2
An Adaptive Fusion Convolutional Denoising Network and Its Application to the Fault Diagnosis of Shore Bridge Lift Gearbox
Zhao, R., & Hu, X. (2022) · Machines · doi:10.3390/machines10060424 · Cited by 2
Nuclear Power Fault Diagnostics and Preventative Maintenance Optimization
Walker, C. M., USDOE Office of Nuclear Energy (NE), Lybeck, N., et al. (2021) · doi:10.2172/1768612 · Cited by 2
“Model-based techniques require a priori knowledge about the system and its potential failures [3].”
Background
A Brief Study on a Novel Approach for the Gearbox Fault DiagnosisPreprint
Mukherjee, S., Kumar, V., & Sarangi, S. (2021) · Preprints.org · doi:10.20944/preprints202106.0633.v1 · Cited by 2
“Several features need to be determined from the vibration signal to consider the fault characteristics’ behaviour [7]–[9].”
Background
Explainable Fault Diagnosis of Oil-Gas Treatment Station Based on Transfer LearningPreprint
Liu, J., Hou, L., Zhang, R., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3996107 · Cited by 2
Troubleshooting: a Dynamic Solution for Achieving Reliable Fault Detection by Combining Augmented Reality and Machine LearningPreprint
Scheffer, S., Limmen, N., Damgrave, R., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3945964 · Cited by 2
“Inference control engines can be classified into four categories: (1) rule-based reasoning, (2) fuzzy-logicbased reasoning, (3) ML/DL-based reasoning, and (4) case-based reasoning [6].”
BackgroundMethodInfluential
Comparative Evaluation of Machine Learning Methods for the Detection of the Eccentricity Faults of Induction Motors by Using Vibration SignalsPreprint
Irgat, E., & Ünsal, A. (2022) · Research Square · doi:10.21203/rs.3.rs-1967988/v1 · Cited by 2
Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive ReviewPreprint
Ramezani, S. B., Cummins, L., Killen, B., Carley, R., Amirlatifi, A., Rahimi, S., Seale, M. A., & Bian, L. (2023) · doi:10.36227/techrxiv.21903768 · Cited by 2
Artificial intelligence to enable smart prognostics and health management of manufacturing systems for industry 4.0
Sundaram, S. (2023) · doi:10.17760/d20592692 · Cited by 3
A Comprehensive Gear Eccentricity Dataset with Multiple Fault Severity Levels: Description, Characteristics Analysis, and Fault Diagnosis ApplicationsPreprint
Li, J., Chen, H., Wang, X., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4822609 · Cited by 2
Bear fault diagnosis based on multiscale feature learning
Tian, S., Chen, Z., & He, J. (2021) · International Symposium on Artificial Intelligence and Robotics (ISAIR) · doi:10.1117/12.2602060 · Cited by 1
“For those reasons, the shared learning idea has been developed in machine learning research.”
Background
Filtering Misleading Repair Log Labels to Improve Predictive Maintenance Models
del Moral, P., Nowaczyk, S., & Pashami, S. (2022) · PHM Society European Conference · doi:10.36001/phme.2022.v7i1.3360 · Cited by 1
“A quick overview of recent surveys (Carvalho et al., 2019), (Lei et al., 2020), shows how most data-driven methods for predictive maintenance use supervised methods, i.e., reliable information about the historical faults are needed to train models for predictive maintenance.”
Method
Closed-Loop Fault Diagnosis of SDR Using Gap Metric, PCA, and Kalman’s Principle
Wang, H., Zeng, Q., Wang, A., et al. (2023) · International Journal of Aerospace Engineering · doi:10.1155/2023/9994093 · Cited by 1
Iterative-AMC: a novel model compression and structure optimization method in mechanical system safety monitoring
Ji, M., Peng, G., Li, S., Huang, W., Li, W., & Li, Z. (2023) · Structural Health Monitoring · doi:10.1177/14759217231155486 · Cited by 1
Technology and application of industrial big data-driven intelligent design manufacturing and service for nuclear power equipment
YiXiong, F. E. N. G., Zhao, Z., BingTao, H. U., et al. (2023) · Scientia Sinica Technologica · doi:10.1360/sst-2022-0472 · Cited by 1
Framework Construction of an Adversarial Federated Transfer Learning Classifier
Hang Yi, T. B. (2023) · The Frontiers of Society Science and Technology · doi:10.25236/fsst.2023.050312 · Cited by 1
A General-Purpose Multi-stage Multi-group Machine Learning Framework for Knowledge Discovery and Decision Support
Lee, E. K., Fan, Y., Man, B. J., & Egan, B. M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-43471-6_4 · Cited by 1
A review of bearing fault diagnosis based on weakly supervised learning
Zhao, B., Cen, J., Si, W., Huang, H., Yang, Z., & Chen, H. (2023) · CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) · doi:10.1109/safeprocess58597.2023.10295892 · Cited by 1
“Aiming at the problem of small samples and zero samples bearing faults, weakly supervised learning including few-shot learning and zero-shot learning with less data dependence has become a hot research direction for researchers[4], [5], [6].”
Background
KI-basiertes akustisches Monitoring: Herausforderungen und Lösungsansätze für datengetriebene Innovationen auf Basis audiovisueller Analyse
Aichroth, P., & Liebetrau, J. (2023) · Springer eBooks · doi:10.1007/978-3-658-42060-4_4 · Cited by 1
A predictive model for oil well maintenance: a case study in Kazakhstan
Aktaukenov, D., Alshaalan, M., Omirbekova, Z. Z., & Pinsky, E. (2024) · Proceedings of OilGasScientificResearchProjects Institute SOCAR · doi:10.5510/ogp20240100939 · Cited by 1
“Second, the quality of the collected data is not always satisfactory because some of them may suffer from emergencies, such as transmission interruption and the anomaly of measurement devices [43].”
A Sparse Convolutional Autoencoding Fault Diagnosis Method Integrating Multiple Regularizers
Niu, M., Jiang, H., & Wang, X. (2024) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm61352.2024.10627135 · Cited by 1
“In view of the existing problems, researchers have made many optimization improvements on the basis of the classical model of deep learning, which has promoted a series of important achievements in area of intelligent failed diagnosis [5].”
BackgroundMethod
Dual-Path Fault Diagnosis of Small Sample for Mechanical Systems Based on Multiple Attention Mechanisms
Li, X. Y., Zhang, M., & Guo, H. (2024) · IEEE Access · doi:10.1109/access.2024.3444828 · Cited by 2
“The real-time diagnosis of bearing faults in mechanical systems based on vibration signals is imperative for ensuring safety and high production in industry [1], [2].”
Background
Varying Speed Diagnosis of High Speed Train Bogie Rolling Bearing: Real Train Experiment, Comparison and Prediction of Transfer Learning Performance
Yang, B., Wang, T., & Xie, J. (2024) · Civil-comp conferences · doi:10.4203/ccc.7.28.1 · Cited by 1
Self-citation
Network architecture search methods for constructing efficient fault diagnosis models in rotating machinery
Li, J., Cao, X., Chen, R., et al. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad8f4c · Cited by 1
A two-step optimization strategy for the inductance control of TSV-based 3-D inductor based on the SAE model
Wang, X., Yang, Y., Chen, D., et al. (2024) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-024-03925-5 · Cited by 1
“The autoencoder algorithm is an unsupervised algorithm, which can automatically learn features from unlabeled data using sparsity (Lv et al. 2015; Lei et al. 2020).”
Enhanced Control of Nonlinear Systems Under Control Input Constraints and Faults: A Neural Network-Based Integral Fuzzy Sliding Mode Approach
Yang, G., Bekiros, S. D., Yao, Q., Mou, J., Aly, A. A., & Sayed, O. R. (2024) · Entropy · doi:10.3390/e26121078 · Cited by 3
Quality and capacity optimization in production: Preventive maintenance and fault analysis insights
Ojha, V. K., Goyal, S., & Chand, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26474-0.00017-8 · Cited by 2
Assessment of Machine Learning Techniques for Identifying Demagnetization Stress in PMSG Using Flux and Current Signals
Shahbaz, N., Chen, Y., Liang, F., et al. (2024) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-beijing63284.2024.10874811 · Cited by 1
Motor Loosened Screws Diagnosing System Based on Vibration Detection and Neural Network
Dantong, F., Ying, Z., & Yijie, A. (2024) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip64812.2024.10873661 · Cited by 1
Refined frequency monitoring based on characteristic excitation with application to early fault diagnosis of thin plate damage
Wang, Z., Shi, H., Dong, Z., et al. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112432 · Cited by 1
Subdomain Uncertainty Optimization for Cross-Speed Fault Diagnosis
Zheng, J., Huang, L., Zhang, T., et al. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889230 · Cited by 1
Application of Bispectral Technology and Deep Learning for Feature Extraction and Classification
Liu, C., Yang, J. M., Zhan, X., et al. (2025) · Shock and Vibration · doi:10.1155/vib/8119232 · Cited by 1
“However, the difculty of feature extraction and description in images restricts the practical application of bispectral analysis and signal graphs in feature extraction and classi-fcation recognition [13, 14].”
TFFT-Net: A Hybrid CNN-Transformer Approach for Time-Frequency Feature Fusion in Bearing Fault Diagnosis
Wu, M., Li, D., Gao, D. Q., & Zhou, J. (2024) · International Symposium on Computer Science and Intelligent Control (ISCSIC) · doi:10.1109/iscsic64297.2024.00042 · Cited by 1
“In industrial manufacturing, the reliability of equipment operation is of paramount importance, as prolonged equipment failures not only lead to machine damage but also pose significant safety risks [1].”
Evaluation of Machine Learning Algorithms for Diagnosing Demagnetization Stress in PMSG Using Flux and Current Signals
Shahbaz, N., Chen, Y., Liang, F., et al. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-4675-3_64 · Cited by 1
HMC Method for Defect Classification and Location of Postbond TSVs
Zhi, C., Dong, G., Yang, D., Liu, D., & Zhu, Z. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3569929 · Cited by 1
“Its performance is compared against several high-performance neural networks, including CNN [46], SE-CNN [47], and LSTM-CNN [48], as well as their variants based on hierarchical local models, denoted as HMC-LCNN, HMC-LSNN, and HMC-LLNN.”
Deep hierarchical classification network for end-to-end anomaly detection, fault classification, and severity assessment in diesel engine
Deng, L., Zhu, X., Cai, K., et al. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/addddc · Cited by 1
“In this context, deep learning architecture realizes end-to-end adaptive feature extraction and fault mode mapping through multi-layer nonlinear network construction [7].”
Fault Diagnosis of Mining Machine Gearboxes Based on Continuous Wavelet Transform and Machine Learning
Muratbakeev, E., Kozhubaev, Y. K., & Haodong, C. (2025) · International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) · doi:10.1109/icieam65163.2025.11028450 · Cited by 1
“However, as one of the common industrial equipment, the gearbox is more likely to fail due to various reasons during long-term operation compared to the motor [4-6].”
Dual-branch sample augmentation with mutual-supervised learning for rolling bearing fault diagnosis
Liu, D., Liu, C., Xu, J., Hu, X., Chen, W., & Cheng, G. (2025) · Engineering Research Express · doi:10.1088/2631-8695/adf2f6 · Cited by 1
A class confusion-aware spherical network approach for closed-set domain adaptation in rotating machinery fault diagnosis
Xu, M., Bian, W., Shi, Y., Deng, M., Shen, Y., & Deng, A. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adf98f · Cited by 1
Halbach-enhanced variable reluctance energy harvesting for self-powered condition monitoring
Zhang, Y., Li, H., Lei, Y., et al. (2025) · Energy · doi:10.1016/j.energy.2025.137915 · Cited by 1
Self-citation
IoT-Based Secure Framework for Smart Grids Using Machine Learning and Blockchain Technologies
Al-Odat, Z. A. (2025) · Journal of Advances in Information Technology · doi:10.12720/jait.16.8.1061-1071 · Cited by 1
An uncertainty decomposition-based automated out-of-distribution detection framework- for trustworthy machinery fault diagnosis
Zeng, T., Jiang, H., Wang, X., Bai, Y., & Yi, Z. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adfcf8 · Cited by 1
“This greatly reduces reliance on expert knowledge and significantly improves the efficiency of industrial fault diagnosis [16–18].”
Open-set domain adaptation for fault diagnosis using Gaussian auxiliary classifier and separation loss
Bi, W., Cai, X., Liu, P., et al. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae02b9 · Cited by 1
“With the advent of deep learning, models such as convolutional neural networks (CNNs) and recurrent neural networks have demonstrated the ability to automatically learn discriminative features from raw vibration signals [3].”
A lightweighted planetary gearbox fault diagnosis method based on multisensor fusion
Chen, R., Zeng, C., Puzhou, W., et al. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae035a · Cited by 1
“These can adaptively learn potential features without knowledge of the fault mechanism [4].”
Adaptive Ensemble Learning for Predictive Maintenance: Neural-Gated Mixture of Experts Architecture
Kataria, V., Surasani, V. R., & Jeswani, S. (2025) · International Conference on Big Data Analytics and Practices (IBDAP) · doi:10.1109/ibdap65587.2025.11145853 · Cited by 1
“Additionally, the need for real-time inference capabilities and interpretable predictions constrains the complexity of deployable solutions [19].”
From platforms to price: the impact of condition prediction using computer vision on real estate pricing models
Koch, D., Despotović, M., Thaler, S., & Zhang, Z. (2025) · Journal of European real estate research · doi:10.1108/jerer-03-2024-0013 · Cited by 1
Enhanced approach for gears-bearings defect diagnosis based on optimized artificial neural network model and features selection graphical technique
BENABBOUD, M. S., Bouacha, K., Soula, M., et al. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.110404 · Cited by 1
Prognosis of multi-stage degradation of elastomeric rubber bearing through entropy-assisted genetic optimization of Gaussian process regression
Ojha, S., & Shelke, A. (2025) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-025-01016-7 · Cited by 1
Ensemble Learning for Predictive Maintenance: A Comparative Analysis of Bagging, Boosting, and Stacking Strategies
Khattar, S., Verma, T., KrishanKant, et al. (2025) · doi:10.1109/ic2e365635.2025.11167068 · Cited by 1
Early Fault Detection in a Real Scenario of Hybrid Fiber–Coaxial Networks Using Machine Learning: An Approach Based on Decision Trees and Random Forests
Szcerba, C., Dávalos, E., Leiva, A., et al. (2025) · Applied Sciences · doi:10.3390/app151910442 · Cited by 1
Enhanced Fault Diagnosis Using Large Language Models and Probabilistic Label Fusion
Chen, Q., Yao, S., Wang, P., Shi, J., Qin, W., Li, J., Wang, J., & He, W. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.09.401 · Cited by 1
MLDA-GLFD: a multi-layer domain-adaptive rolling bearing fault diagnosis method fusing global and local features
Jiang, Y., Song, Q., Yang, D., et al. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae0e8a · Cited by 1
“Among them, convolutional neural networks demonstrate outstanding performance in fault diagnosis tasks due to their inherent properties such as local connectivity, weight sharing, and translation invariance [12, 13].”
Research progress on bearing fault diagnosis with deep learning methods for rolling element bearings
Patel, S., & Patel, S. (2025) · Noise & Vibration Worldwide · doi:10.1177/09574565251382130 · Cited by 2
“12 To overcome these constraints, deep learning techniques like CNNs and RNNs create hierarchical feature representations straight from raw input data, scale effectively with big datasets, and show resilience to noise, imbalance, and unpredictability in a variety of operating situations.”
FuseCT: a rotating machinery fault diagnosis method based on generalized linear chirplet transform and multimodal machine learning
Xu, J., He, Q., Gao, X., Xu, K., Huang, F., & Wang, H. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ae1278 · Cited by 1
Framework for Single Misfire Identification in a Marine Diesel Engine using Machine Learning
Guerra, V. N., Castro, B. M., Martins, D. H. C. D. S. S., Gutiérrez, R. H. R., & Monteiro, U. A. B. V. (2025) · Journal of Marine Science and Application · doi:10.1007/s11804-025-00752-y · Cited by 2
“The majority label among these K samples is then assigned to the unlabeled sample as its predicted result (Lei et al., 2020; Abu Alfeilat et al., 2019), as depicted in Figure 1.”
Single-cell multi-omics uncovers CPS1 as a breast cancer immune evasion therapeutic target
Yue, J., Wen, F., He, D., Chen, S., & Huang, G. (2025) · Scientific Reports · doi:10.1038/s41598-025-19890-y · Cited by 2
Domain Experience and Expertise in Explainable AI Applications: A Bearing Fault Diagnosis Case Study
Zhao, Z., Castelle, M., & Turkay, Ç. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757583 · Cited by 1
“Traditionally, this relies heavily on expert intuition—for example, identifying bearing faults through abnormal engine noises [76].”
Fault diagnosis of rolling bearings using hybrid-domain features and dung beetle optimization-based support vector machine
Ji, C., Wang, B., & Hu, X. (2025) · Journal of Vibration and Control · doi:10.1177/10775463251387920 · Cited by 1
“Rolling bearings are critical components in mechanical systems, and their operational status directly affects overall equipment performance and safety (Lei et al., 2020).”
Deep clustering of rotating machinery faults based on cross-domain information guidance
Chen, C., Huang, X., Liu, Z., et al. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.113514 · Cited by 1
Adversarial Neighborhood Contrastive Domain Adaptation for Open-Set Bearing Fault Diagnosis
Lu, N., Xu, Z., Meng, X., et al. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3626900 · Cited by 1
“However, the model can work with the condition that test data should have the identical distribution with the training set, while the real application is not the case [3].”
Decoupling Machine and Operational Variances: A Spectral Attention Framework for Robust Few‐Shot Cross‐Machine Fault Diagnosis
Wei, H., He, C., Liu, S., Song, Z., & Lu, F. (2025) · Structural Control and Health Monitoring · doi:10.1155/stc/6359435 · Cited by 1
An Integrated Domain Adaptation Fault Diagnosis Method Based on Transferable Batch Normalization and Class Confusion Constraint
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“Therefore, high-precision equipment health monitoring and fault diagnosis approaches are urgently demand in actual industry [3-4].”
Non-contact Sensor-Based Gear Fault Diagnosis with Bark Domain Feature Analysis
Su, H., Huang, J., Gu, J., Xu, C., Xu, J. Q., & Hu, H. (2025) · Journal of Failure Analysis and Prevention · doi:10.1007/s11668-025-02322-4 · Cited by 1
“The application of machine learning theory to gear fault diagnosis is expected to enable the automatic detection and recognition of the health condition of gears [16].”
An uneven maximum classifier discrepancy rolling bearing transfer fault diagnosis method combining GCN and KAN
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Research on Bearing Fault Diagnosis Technology Based on Multi-Source Data Fusion
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“Currently, bearing fault diagnosis mainly divides into three categories[12][13]: signal processing methods[14], model-based methods, and data-driven intelligent fault diagnosis methods[15].”
Comparison of Neural Network Architectures for Modeling Complex System Behavior in Digital Twins: Analysis Based on Bearing Data
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Causality-Inspired Multiscale Deconfounder Network for Cross-Domain Fault Diagnosis
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A Deep Learning Framework for Severity-Aware Fault Diagnosis in Industrial Suction Fans Using Multi-Modal Sensing
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“As demonstrated by [27], such approaches are valid when indices correlate with physical degradation metrics, as discussed in [20].”
Fault prototype weighted guided multi-level dynamic alignment network for partial set domain adaptation fault diagnosis
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Source-free cross-machine fault diagnosis method in a two-stage pseudo-supervised framework
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Integral-cage-based triboelectric assembly for accurate skidding and instability monitoring and fluid‒drag torque prediction of ball bearings
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Fault Diagnosis of Rolling Bearings Based on ISSA-XGBOOST with FLDA Feature Dimensionality Reduction
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“2) To overcome the problem of uneven sparrow population initialization and the tendency of the traditional Sparrow Search Algorithm (SSA) to get trapped in local optima, a Sobol sequence initialization and Lévy flight-based improvement strategy is proposed for optimizing the XGBoost model's…”

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
ORCID: Lei, Yaguo
Yang, Bin
Jiang, Xinwei
Jia, Feng
Li, Naipeng
Nandi, Asoke K.
Venue: Mechanical Systems and Signal Processing
Publisher: Elsevier BV
Published: 2020-04
Volume: 138
Pages: 106587
ISSN: 0888-3270 (ISSN-L)
DOI: 10.1016/j.ymssp.2019.106587
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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

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Applications of machine learning to machine fault diagnosis: A review and roadmap

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