Bearing condition monitroing: A review of feature extraction in temporal and spectral, and joint temporal-spectraldomain
1
Mechanical Engineering Department, Wasit University, Iraq.
Submission date: 2025-06-22
Final revision date: 2026-01-10
Acceptance date: 2026-01-14
Online publication date: 2026-01-20
Publication date: 2026-01-20
Corresponding author
KEYWORDS
TOPICS
ABSTRACT
Rolling element bearings are critical components in rotating machinery. Their failure can lead to catastrophic consequences. Therefore, effective condition monitoring is very necessary to avoid the occurrence of such unexpected breakdowns and ensure safety. This review focuses on the recent advances in vibration-based feature extraction techniques for bearing fault diagnosis. More than 70 peer-reviewed journal articles published since 2019 are analysed. The analysis covers feature extraction techniques in the temporal domain, spectral domain, and joint temporal–spectral domain. Then, the reviewed features are critically assessed in terms of their diagnostic sensitivity, robustness to noise, and applicability under different operating conditions. The review aims to adopt a feature-centric and decision-oriented perspective and provides guidance for selecting suitable health indicators. It can serve as a useful reference for researchers and practitioners working in rolling element bearing fault diagnosis.
FUNDING
This research received no external funding.
REFERENCES (104)
1.
Lee CY, Le TA, Lin YT. A feature selection approach hybrid grey wolf and heap-based optimizer applied in bearing fault diagnosis. IEEE Access. 2022;10: 56691–705. https://doi.org/10.1109/ACCESS....
2.
Patil AA, Desai SS, Patil LN, Patil SA. Adopting artificial neural network for wear investigation of ball bearing materials under pure sliding condition. Appl Eng Lett. 2022;7:81–8. https://doi.org/10.18485/aelet....
3.
Neupane D, Seok J. Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review. IEEE Access. 2020;8:93155–78. https://doi.org/10.1109/ACCESS....
4.
Liang H, Cao J, Zhao X. Average descent rate singular value decomposition and two-dimensional residual neural network for fault diagnosis of rotating machinery. IEEE Trans Instrum Meas. 2022;71:1–16. https://doi.org/10.1109/TIM.20....
5.
Zhu Z, Lei Y, Qi G, Chai Y, Mazur N, An Y, et al. A review of the application of deep learning in intelligent fault diagnosis of rotating machinery. Measurement. 2023;206:112346. https://doi.org/10.1016/j.meas....
6.
Kuncan M. An intelligent approach for bearing fault diagnosis: Combination of 1D-LBP and GRA. IEEE Access. 2020;8:137517–29. https://doi.org/10.1109/ACCESS....
7.
Nayana BR, Geethanjali P. Improved identification of various conditions of induction motor bearing faults. IEEE Trans Instrum Meas. 2019;69:1908–19. https://doi.org/10.1109/TIM.20....
8.
Zhou J, Xiao M, Niu Y, Ji G. Rolling bearing fault diagnosis based on WGWOA-VMD-SVM. Sensors. 2022;22. https://doi.org/10.3390/s22166....
9.
Narendiranath Babu T, Manvel Raj T, Lakshmanan T. A review on application of dynamic parameters of journal bearing for vibration and condition monitoring. Journal of Mechanics. 2015;31:391–416. https://doi.org/10.1017/jmech.....
10.
Hameed Z, Hong YS, Cho YM, Ahn SH, Song CK. Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable and Sustainable Energy Reviews. 2009;13:1–39. https://doi.org/10.1016/j.rser....
11.
Praveenkumar T, Sabhrish B, Saimurugan M, Ramachandran KI. Pattern recognition based on-line vibration monitoring system for fault diagnosis of automobile gearbox. Measurement. 2018;114:233–42. https://doi.org/10.1016/j.meas....
12.
Siano D, Panza MA. Diagnostic method by using vibration analysis for pump fault detection. Energy Procedia. 2018;148:10–17. https://doi.org/10.1016/j.egyp....
13.
Araújo VG de, Bissiriou AOS, Villanueva JMM, Villarreal ERL, Salazar AO, Teixeira R de A, et al. Monitoring and diagnosing faults in induction motors’ three-phase systems using NARX neural network. Energies. 2024;17. https://doi.org/10.3390/en1718....
14.
Pinedo-Sánchez LA, Mercado-Ravell DA, Carballo-Monsivais CA. Vibration analysis in bearings for failure prevention using CNN. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2020;42. https://doi.org/10.1007/s40430....
15.
Lessmeier C, Kimotho JK, Zimmer D, Sextro W. Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification. PHM society European conference. 2016;3. https://doi.org/10.36001/phme.....
16.
Glowacz A. Recognition of Acoustic Signals of Loaded Synchronous Motor Using FFT, MSAF-5 and LSVM. Archives of Acoustics 2015;40:197–203. https://doi.org/10.1515/aoa-20.....
17.
Jia Z, Liu Z, Vong CM, Pecht M. A rotating machinery fault diagnosis method based on feature learning of thermal images. IEEE Access. 2019;7:12348–59. https://doi.org/10.1109/ACCESS....
18.
Malla C, Panigrahi I. Review of condition monitoring of rolling element bearing using vibration analysis and other techniques. Journal of Vibration Engineering & Technologies. 2019;7:407–14. https://doi.org/10.1007/s42417....
19.
Duan Z, Wu T, Guo S, Shao T, Malekian R, Li Z. Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review. International Journal of Advanced Manufacturing Technology. 2018;96:803–19. https://doi.org/10.1007/s00170....
20.
Jardine AKS, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 2006;20:1483–510. https://doi.org/10.1016/j.ymss....
21.
Lei Y, Yang B, Jiang X, Jia F, Li N, Nandi AK. Applications of machine learning to machine fault diagnosis: A review and roadmap. Mech Syst Signal Process. 2020;138. https://doi.org/10.1016/j.ymss....
22.
Leukel J, González J, Riekert M. Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review. J Manuf Syst 2021;61:87–96. https://doi.org/10.1016/j.jmsy....
23.
Süpürtülü M, Hatipoğlu A, Yılmaz E. An analytical benchmark of feature selection techniques for industrial fault classification leveraging time-domain features. Applied Sciences.2025;15. https://doi.org/10.3390/app150....
24.
Alonso-Gonzalez M, Diaz VG, Lopez Perez B, Cristina Pelayo G-Bustelo B, Anzola JP. Bearing fault diagnosis with envelope analysis and machine learning approaches using CWRU dataset. IEEE Access. 2023;11:57796–805. https://doi.org/10.1109/ACCESS....
25.
Verstraete D, Ferrada A, Droguett EL, Meruane V, Modarres M. Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings. Shock and Vibration. 2017;2017. https://doi.org/10.1155/2017/5....
26.
Kannan V, Zhang T, Li H. A review of the intelligent condition monitoring of rolling element bearings. Machines 2024;12. https://doi.org/10.3390/machin....
27.
H. Jain P, P. Bhosle S. A review on vibration signal analysis techniques used for detection of rolling element bearing defects. International Journal of Mechanical Engineering. 2021;8:14–29. https://doi.org/10.14445/23488....
28.
Kumar S, Raj KK, Cirrincione M, Cirrincione G, Franzitta V, Kumar RR. A comprehensive review of remaining useful life estimation approaches for rotating machinery. Energies. 2024;17. https://doi.org/10.3390/en1722....
29.
Kumar RR, Andriollo M, Cirrincione G, Cirrincione M, Tortella A. A comprehensive review of conventional and intelligence-based approaches for the fault diagnosis and condition monitoring of induction motors. Energies. 2022;15. https://doi.org/10.3390/en1523....
30.
Gawde S, Patil S, Kumar S, Kamat P, Kotecha K, Abraham A. Multi-fault diagnosis of industrial rotating machines using data-driven approach: A review of two decades of research. Eng Appl Artif Intell. 2023;123:106139. https://doi.org/10.1016/j.enga....
31.
Lee CY, Le TA. An enhanced binary particle swarm optimization for optimal feature selection in bearing fault diagnosis of electrical machines. IEEE Access. 2021;9:102671–86. https://doi.org/10.1109/ACCESS....
32.
Tayyab SM, Chatterton S, Pennacchi P. Intelligent defect diagnosis of rolling element bearings under variable operating conditions using convolutional neural network and order maps. Sensors 2022;22. https://doi.org/10.3390/s22052....
33.
Hlawatsch F, Boudreaux-Bartels GF. Linear and quadratic time-frequency signal representations. IEEE Signal Process Mag. 1992;9:21–67. https://doi.org/10.1109/79.127....
34.
Climente-Alarcon V, Antonino-Daviu JA, Riera-Guasp M, Puche-Panadero R, Escobar L. Application of the Wigner-Ville distribution for the detection of rotor asymmetries and eccentricity through high-order harmonics. Electric Power Systems Research. 2012; 91:28–36. https://doi.org/10.1016/j.epsr....
35.
Kumbhar SG, Desavale RG, Dharwadkar N V. Fault size diagnosis of rolling element bearing using artificial neural network and dimension theory. Neural Comput Appl. 2021;33:16079–93. https://doi.org/10.1007/s00521....
36.
Shen W, Xiao M, Wang Z, Song X. Rolling bearing fault diagnosis based on support vector machine optimized by improved grey wolf algorithm. Sensors. 2023;23. https://doi.org/10.3390/s23146....
37.
Wu S, Zhou J, Liu T. Compound fault feature extraction of rolling bearing acoustic signals based on AVMD-IMVO-MCKD. Sensors. 2022;22.https://doi.org/10.3390/s22186....
38.
Espinoza-Sepulveda NF, Sinha JK. Theoretical validation of earlier developed experimental rotor faults diagnosis model. International Journal of Hydromechatronics. 2021;4:295–308. https://doi.org/10.1504/IJHM.2....
39.
Ma R, Li B. Fault diagnosis of motor bearing equipment based on vibration signals, SPIE-Intl Soc Optical Eng. 2024:65. ttps://doi.org/10.1117/12.3026463.
40.
Kaya Y, Kuncan M, Kaplan K, Minaz MR, Ertunç HM. A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification. Journal of Experimental and Theoretical Artificial Intelligence. 2021;33:161–78. https://doi.org/10.1080/095281....
41.
Stepanić P, Dučić N, Vidaković J, Baralić J, Popović M. Implementation and evaluation of machine learning algorithms in ball bearing fault detection. Measurement Science Review. 2025;25:22–9. https://doi.org/10.2478/msr-20....
42.
Wang B, Qiu W, Hu X, Wang W. A rolling bearing fault diagnosis technique based on recurrence quantification analysis and Bayesian optimization SVM. Appl Soft Comput. 2024;156:111506. https://doi.org/10.1016/j.asoc....
43.
Huang X, Xie T, Wu J, Zhou Q, Hu J. Deep continuous convolutional networks for fault diagnosis. Knowl Based Syst. 2024;292:111623. https://doi.org/10.1016/j.knos....
44.
Qiu S, Cui X, Ping Z, Shan N, Li Z, Bao X, et al. Deep learning techniques in intelligent fault diagnosis and prognosis for industrial systems: A review. Sensors 2023;23. https://doi.org/10.3390/s23031....
45.
Zhang J, Sun Y, Guo L, Gao H, Hong X, Song H. A new bearing fault diagnosis method based on modified convolutional neural networks. Chinese Journal of Aeronautics. 2020;33:439–47. https://doi.org/10.1016/j.cja.....
46.
Han T, Zhang L, Yin Z, Tan ACC. Rolling bearing fault diagnosis with combined convolutional neural networks and support vector machine. Measurement. 2021;177. https://doi.org/10.1016/j.meas....
47.
Gong W, Chen H, Zhang Z, Zhang M, Wang R, Guan C, et al. A novel deep learning method for intelligent fault diagnosis of rotating machinery based on improved CNN-SVM and multichannel data fusion. Sensors. 2019;19. https://doi.org/10.3390/s19071....
48.
Hoang DT, Kang HJ. Rolling element bearing fault diagnosis using convolutional neural network and vibration image. Cogn Syst Res 2019;53:42–50. https://doi.org/10.1016/j.cogs....
49.
Kolar D, Lisjak D, Pajak˛ M, Pavković D. Fault diagnosis of rotary machines using deep convolutional neural network with wide three axis vibration signal input. Sensors. 2020;20:1–13. https://doi.org/10.3390/s20144....
50.
Kolar D, Lisjak D, Pajak M, Gudlin M. Intelligent fault diagnosis of rotary machinery by convolutional neural network with automatic hyper-parameters tuning using bayesian optimization. Sensors. 2021;21. https://doi.org/10.3390/s21072....
51.
Jin G, Zhu T, Akram MW, Jin Y, Zhu C. An adaptive anti-noise neural network for bearing fault diagnosis under noise and varying load conditions. IEEE Access. 2020;8:74793–807. https://doi.org/10.1109/ACCESS....
52.
Suhail Najim H. automatic feature extraction based on envelope analysis and its application in rolling-element bearing fault detection. Basrah Journal for Engineering Science. 2023;23:34–44. https://doi.org/10.33971/bjes.....
53.
Khalil AF, Rostam S. Machine learning-based predictive maintenance for fault detection in rotating machinery: a case study. Engineering. Technology and Applied Science Research. 2024;14:13181–9. https://doi.org/10.48084/etasr....
54.
Skowron M, Frankiewicz O, Jarosz JJ, Wolkiewicz M, Dybkowski M, Weisse S, et al. Detection and classification of rolling bearing defects using direct signal processing with deep convolutional neural network. Electronics. 2024;13. https://doi.org/10.3390/electr....
55.
Xu M, Yu Q, Chen S, Lin J. rolling bearing fault diagnosis based on CNN-LSTM with FFT and SVD. Information. 2024;15. ttps://doi.org/10.3390/info15070399.
56.
Walther S, Fuerst A. Reduced data volumes through hybrid machine learning compared to conventional machine learning demonstrated on bearing fault classification. Applied Sciences. 2022;12. https://doi.org/10.3390/app120....
57.
Wu J, Wu C, Cao S, Or SW, Deng C, Shao X. Degradation data-driven time-to-failure prognostics approach for rolling element bearings in electrical machines. IEEE Transactions on Industrial Electronics. 2019;66:529–39. https://doi.org/10.1109/TIE.20....
58.
Kannan V, Li H, Dao DV. Demodulation band optimization in envelope analysis for fault diagnosis of rolling element bearings using a real-coded genetic algorithm. IEEE Access 2019;7:168828–38. https://doi.org/10.1109/ACCESS....
59.
Chen Y, Chen Q, Wang R. bearing fault diagnosis based on vibration envelope spectral characteristics. Applied Sciences. 2025;15. https://doi.org/10.3390/app150....
60.
Chen B, Zhang W, Gu JX, Song D, Cheng Y, Zhou Z, et al. Product envelope spectrum optimization-gram: An enhanced envelope analysis for rolling bearing fault diagnosis. Mech Syst Signal Process. 2023;193:110270. https://doi.org/10.1016/j.ymss....
61.
Wang J, Mo Z, Zhang H, Miao Q. A deep learning method for bearing fault diagnosis based on time-frequency image. IEEE Access. 2019;7:42373–83. https://doi.org/10.1109/ACCESS....
62.
Liu Q, Huang C. A fault diagnosis method based on transfer convolutional neural networks. IEEE Access. 2019;7:171423–30. https://doi.org/10.1109/ACCESS....
63.
Yuan Z, Zhou T, Liu J, Zhang C, Liu Y. Fault diagnosis approach for rotating machinery based on feature importance ranking and selection. Shock and Vibration. 2021;2021. https://doi.org/10.1155/2021/8....
64.
Zhu H, He Z, Wei J, Wang J, Zhou H. Bearing fault feature extraction and fault diagnosis method based on feature fusion. Sensors. 2021;21. https://doi.org/10.3390/s21072....
65.
Dahmane S, Berrabah F, Defdaf M, Salah S. Diagnosis and monitoring method for detecting and localizing bearing faults. Indonesian Journal of Electrical Engineering and Informatics. 2024;12:1–14. https://doi.org/10.52549/ijeei....
66.
Peixi Y, Zheng X, He J. Rolling bearing fault diagnosis based on DWT-BPNN. WSEAS Transactions on Systems. 2023;22:264–71. https://doi.org/10.37394/23202....
67.
Mohiuddin M, Islam MS, Uddin J. Feature optimization for machine learning based bearing fault classification. Indonesian Journal of Electrical Engineering and Informatics. 2024;12:610–24. https://doi.org/10.52549/ijeei....
68.
Siddique MF, Saleem F, Umar M, Kim CH, Kim JM. A Hybrid Deep Learning approach for bearing fault diagnosis using continuous wavelet transform and attention-enhanced spatiotemporal feature extraction. Sensors. 2025;25. https://doi.org/10.3390/s25092....
69.
Lee J, Park B, Lee C. Fault diagnosis based on the quantification of the fault features in a rotary machine. Appl Soft Comput. 2020;97. https://doi.org/10.1016/j.asoc....
70.
Yang J, Huang D, Zhou D, Liu H. Optimal IMF selection and unknown fault feature extraction for rolling bearings with different defect modes. Measurement. 2020;157. https://doi.org/10.1016/j.meas....
71.
Wang J, Du G, Zhu Z, Shen C, He Q. Fault diagnosis of rotating machines based on the EMD manifold. Mech Syst Signal Process. 2020;135. https://doi.org/10.1016/j.ymss....
72.
Gai J, Shen J, Hu Y, Wang H. An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing. Measurement. 2020;162. https://doi.org/10.1016/j.meas....
73.
Minhas AS, Singh S. A new bearing fault diagnosis approach combining sensitive statistical features with improved multiscale permutation entropy method. Knowl Based Syst. 2021;218. https://doi.org/10.1016/j.knos....
74.
Imane M, Rahmoune C, Benazzouz D. Rolling bearing fault feature selection based on standard deviation and random forest classifier using vibration signals. Advances in Mechanical Engineering. 2023;15. https://doi.org/10.1177/168781....
75.
Shi L, Liu W, You D, Yang S. Rolling bearing fault diagnosis based on CEEMDAN and CNN-SVM. Applied Sciences. 2024;14. https://doi.org/10.3390/app141....
76.
Lu Y, Xie R, Liang SY. CEEMD-assisted kernel support vector machines for bearing diagnosis. International Journal of Advanced Manufacturing Technology. 2020;106:3063–70. https://doi.org/10.1007/s00170....
77.
Jiang Q, Chang F, Sheng B. Bearing fault classification based on convolutional neural network in noise environment. IEEE Access. 2019;7:69795–807. https://doi.org/10.1109/ACCESS....
78.
Chen Z, Cen J, Xiong J. Rolling bearing fault diagnosis using time-frequency analysis and deep transfer convolutional neural network. IEEE Access. 2020;8:150248–61. https://doi.org/10.1109/ACCESS....
79.
Yuan L, Lian D, Kang X, Chen Y, Zhai K. Rolling bearing fault diagnosis based on convolutional neural network and support vector machine. IEEE Access. 2020;8:137395–406. https://doi.org/10.1109/ACCESS....
80.
Mohiuddin M, Islam MS. rolling element bearing faults detection and classification technique using vibration signals. engineering proceedings. 2022;27. https://doi.org/10.3390/ecsa-9....
81.
Wang H, Sun W, He L, Zhou J. Rolling bearing fault diagnosis using multi-sensor data fusion based on 1D-CNN model. Entropy. 2022;24. https://doi.org/10.3390/e24050....
82.
Attaran B, Ghanbarzadeh A, Moradi S. A novel intelligent fault diagnosis approach for critical rotating machinery in the time-frequency domain. International Journal of Engineering, Transactions A: Basics. 2020;33:668–75. https://doi.org/10.5829/IJE.20....
83.
Li G, Deng C, Wu J, Chen Z, Xu X. Rolling bearing fault diagnosis based on wavelet packet transform and convolutional neural network. Applied Sciences. 2020;10. https://doi.org/10.3390/app100....
84.
Saad A, Liwicki M, Usman A, Almqvist A, Arif S. Bearing fault detection scheme using machine learning for condition monitoring applications. international conference on mechanical. Automotive and Mechatronics Engineering (ICMAME 2023). 29-30 April 2023, Dubai, UAE, ICMAME; 2023. https://doi.org/10.53375/icmam....
85.
Mohiuddin M, Islam MS, Islam S, Miah MS, Niu MB. Intelligent fault diagnosis of rolling element bearings based on modified AlexNet. Sensors. 2023;23. https://doi.org/10.3390/s23187....
86.
Yu P, Zhang J, Zhang B, Cao J, Peng Y. Research on small sample rolling bearing fault diagnosis method based on mixed signal processing technology. Symmetry. 2024;16:1178. https://doi.org/10.3390/sym160....
87.
Sawaqed LS, Alrayes AM. Bearing fault diagnostic using machine learning algorithms. Progress in Artificial Intelligence. 2020;9:341–50. https://doi.org/10.1007/s13748....
88.
Sui T, Feng Y, Sui S, Xie X, Li H, Liu X. A Bearing fault diagnosis method combining multi-source information and multi-domain information fusion. Machines. 2025;13. https://doi.org/10.3390/machin....
89.
Mishra RK, Choudhary A, Mohanty AR, Fatima S. Multi-domain bearing fault diagnosis using support vector machine. 2021 IEEE 4th international conference on computing, power and communication technologies (GUCON), IEEE. 2021, p. 1–6. https://doi.org/10.1109/GUCON5....
90.
Sharma A, Mathew L, Chatterji S, Goyal D. Artificial intelligence-based fault diagnosis for condition monitoring of electric motors. Intern J Pattern Recognit Artif. Intell. 2020;34. https://doi.org/10.1142/S02180....
91.
You K, Qiu G, Gu Y. Rolling bearing fault diagnosis using hybrid neural network with principal component analysis. Sensors. 2022;22. https://doi.org/10.3390/s22228....
92.
Goyal D, Dhami SS, Pabla BS. Vibration response-based intelligent non-contact fault diagnosis of bearings. J Nondestruct Eval Diagn Progn Eng Syst. 2021;4. https://doi.org/10.1115/1.4049....
93.
Saha DK, Hoque ME, Badihi H. Development of Intelligent Fault Diagnosis Technique of Rotary Machine Element Bearing: A Machine Learning Approach. Sensors. 2022;22. https://doi.org/10.3390/s22031....
94.
Altaf M, Akram T, Khan MA, Iqbal M, Ch MMI, Hsu CH. A new statistical features based approach for bearing fault diagnosis using vibration signals. Sensors. 2022;22. https://doi.org/10.3390/s22052....
95.
Sahraoui MA, Rahmoune C, Meddour I, Bettahar T, Zair M. New criteria for wrapper feature selection to enhance bearing fault classification. Advances in Mechanical Engineering. 2023;15. https://doi.org/10.1177/168781....
96.
Song X, Wei W, Zhou J, Ji G, Hussain G, Xiao M, et al. Bayesian-optimized Hybrid Kernel SVM for rolling bearing fault diagnosis. Sensors. 2023;23. https://doi.org/10.3390/s23115....
97.
Xue Y, Dou D, Yang J. Multi-fault diagnosis of rotating machinery based on deep convolution neural network and support vector machine. Measurement. 2020;156. https://doi.org/10.1016/j.meas....
98.
Said D, Kamel M, Khaled K, Mohsein T, Lakhdar S. Detection and diagnosis of fault bearing using wavelet packet transform and neural network. Frattura Ed Integrita Strutturale. 2019;13:291–301. https://doi.org/10.3221/IGF-ES....
99.
Metwally M, Moustafa HM, Hassaan G. Diagnosis of rotating machines faults using artificial intelligence based on preprocessing for input data. Conference of Open Innovations Association, FRUCT, FRUCT Oy. 2020, p. 572–82.
100.
Prabhakar S, Mohanty AR, Sekhar AS. Application of discrete wavelet transform for detection of ball bearing race faults. Tribol Int. 2002;35:793–800. https://doi.org/10.1016/S0301-....
101.
Cui B, Weng Y, Zhang N. A feature extraction and machine learning framework for bearing fault diagnosis. Renew Energy. 2022;191:987–97. https://doi.org/10.1016/j.rene....
102.
Abburi H, Chaudhary T, Ilyas H, Manne L, Mittal D, Williams D, et al. A closer look at bearing fault classification approaches. ArXiv Preprint ArXiv. 230917001 2023. https://doi.org/10.48550/arXiv....
103.
Chen X, Zhang B, Gao D. Bearing fault diagnosis base on multi-scale CNN and LSTM model. J Intell Manuf. 2021;32:971–87. https://doi.org/10.1007/s10845....
104.
Su Y, Shi L, Zhou K, Bai G, Wang Z. Knowledge-informed deep networks for robust fault diagnosis of rolling bearings. Reliab Eng Syst Saf. 2024;244:109863. https://doi.org/10.1016/j.ress....
Share
RELATED ARTICLE
| eISSN: | 2449-5220 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
