Abstract
The objective of this paper is to present a comprehensive review of the contemporary techniques for fault detection, diagnosis, and prognosis of rolling element bearings (REBs). Data-driven approaches, as opposed to model-based approaches, are gaining in popularity due to the availability of low-cost sensors and big data. This paper first reviews the fundamentals of prognostics and health management (PHM) techniques for REBs. A brief description of the different bearing-failure modes is given, then, the paper presents a comprehensive representation of the different health features (indexes, criteria) used for REB fault diagnostics and prognostics. Thus, the paper provides an overall platform for researchers, system engineers, and experts to select and adopt the best fit for their applications. Second, the paper provides overviews of contemporary REB PHM techniques with a specific focus on modern artificial intelligence (AI) techniques (i.e., shallow learning algorithms). Finally, deep-learning approaches for fault detection, diagnosis, and prognosis for REB are comprehensively reviewed.
Similar content being viewed by others
References
V. Mnih, K. Kavukcuoglu, D. Silver, A.A. Rusu, J. Veness, M.G. Bellemare, A. Graves, M. Riedmiller, A.K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, D. Hassabis, Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)
V. Venkatasubramanian, R. Rengaswamy, S.N. Kavuri, K. Yin, A review of process fault detection and diagnosis Part III: process history based methods. Comput. Chem. Eng. 27(3), 327–346 (2003)
S.X. Ding, P. Zhang, T. Jeinsch, E.L. Ding, P. Engel, W. Gui, A survey of the application of basic data-driven and model-based methods in process monitoring and fault diagnosis. Preprints of the 18th IFAC World Congress Milano (Italy) (2011), pp. 12380–12388
S. Yin, S.X. Ding, X. Xie, H. Luo, A review on basic data-driven approaches for industrial process monitoring. IEEE Trans. on Industrial Electronics 61(11), 6418–6428 (2014)
C. Hu, B.D. Youn, P. Wang, Time-dependent reliability analysis in operation: prognostics and health management. in Engineering Design Under Uncertainty and Health Prognostics. Springer Series in Reliability Engineering (Springer, Cham, 2019). pp. 233–301
C. Hu, B.D. Youn, P. Wang, Case studies: prognostics and health management (PHM). in Engineering Design under Uncertainty and Health Prognostics. Springer Series in Reliability Engineering (Springer, Cham, pp. 303–342), 2019
I. Shin, J. Lee, J.Y. Lee, K. Jung, D. Kwon, B.D. Youn, A framework for prognostics and health management applications toward smart manufacturing systems. International Journal of Precision Engineering and Manufacturing-Green Technology 5, 519–538 (2018)
C. Hu, B.D. Youn, P. Wang, J.T. Yoon, Ensemble of Data-Driven Prognostic Algorithms for Robust Prediction of Remaining Useful Life. Reliability Engineering and System Safety 103, 120–135 (2012)
G. Niu, J. Jiang, B.D. Youn, M. Pecht, Autonomous health management for PMSM rail vehicles through demagnetization monitoring and prognosis control. ISA Trans. 72, 245–255 (2018)
C. Hu, B.D. Youn, P. Wang, Engineering Design Under Uncertainty and Health Prognostics (Springer, Cham, 2018). ISBN 978-3-319-92572-1
A. Rai, S.H. Upadhyay, A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribol. Int. 96, 289–306 (2016)
S. Choi, B. Akin, M. Rahimian, H. Toliyat, Performance-oriented electric motors diagnostics in modern energy conversion systems. IEEE Trans. Ind. Elect. 59(2), 1266–1277 (2012)
C. Lanham, Understanding the tests that are recommended for electric motor predictive maintenance. Baker Instrument Company (2002)
S. Nandi, H. Toliyat, X. Li, Condition monitoring and fault diagnosis of electrical motors- Areview. IEEE Trans. Energy Convers. 20(4), 719–729 (2005)
IEEE Motor Reliability Working Group, Report of large motor reliability survey of industrial and commercial installations. IEEE Trans. Industrial Appl. 21(4), 853–872 (1985)
W. Zhou, T.G. Habetler, R.G. Harley, Bearing condition monitoring methods for electrical machines: a general review. Proc. IEEE SPEEDAM, 6–8 (2007)
M. Hamadache, D. Lee, K.C. Veluvolu, Rotor speed-based bearing fault diagnosis (RSB-BFD) under variable speed and constant load. IEEE Trans. Ind. Electro. 62(10), 6486–6495 (2015)
S.X. Ding, Model-based fault diagnosis techniques: design schemes, algorithms and tools (Springer, Germany, 2008)
S. Schuet, D. Timuçin, K. Wheeler, Physics-based precurs or wiring diagnostics for shielded-twisted-pair cable. IEEE Trans. on Instrum. and Measurement 64(2), 378–391 (2015)
J. Liu, W. Luo, X. Yang, L. Wu, Robust model-based fault diagnosis for PEM fuel cell air-feed system. IEEE Trans. on Industrial Electronics 63(5), 3261–3270 (2016)
R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, R.X. Gao, Deep learning and its applications to machine health monitoring: a survey. Journal of Latex Class Files 14(8), 1–13 (2015)
G. Zurita, V. Sánchez, D. Cabrera, A review of vibration machine diagnostics by using artificial intelligence methods. Investigación & Desarrollo 1(16), 102–114 (2016)
J. Wang, Y. Ma, L. Zhang, R.X. Gao, D. Wu, Deep learning for smart manufacturing: methods and applications. J. Manuf. Syst. 13. Available online Jan. 2018 (In Press)
B. Sung, J. Lee, Reliability improvement of machine tool changing servo motor. Journal of International Council on Electrical Engineering 1(1), 28–32 (2011)
J. Slavic, A. Brkovic, M. Boltezar, Typical bearing-fault rating using force measurements-application to real data. J. Vib. Control 17(14), 2164–2174 (2011)
Emerson, Bearing failure analysis, ebook. http://www.emerson-bearing.com/bearing-failure-modes (2017)
ISO 15243 Rolling bearings: damage and failures—terms, characteristics and causes (2004)
P.P. Kharche, S.V. Kshirsagar, Review of fault detection in rolling element bearing. Int. J. Innov Res Adv Eng (IJIRAE) 1(5), 169–174 (2014)
S. Devendiran, K. Manivannan, S.C. Kamani, R. Refai, An early bearing fault diagnosis using effective feature selection methods and data mining techniques. Int. J. Eng. Technol. (IJET) 7(2), 583–598 (2015)
L.S. Dhamande, M.B. Chaudhari, Compound gear-bearing fault feature extraction using statistical features based on time-frequency method. Measurement 125, 63–77 (2018)
L. Gelman, T.H. Patel, B. Murray, A. Thomson, Rolling bearing diagnosis based on the higher order spectra. Int. J. Prog. Health Manag. 022 (2013) (ISSN 2153-2648)
M. Hamadache, D. Lee, Principal component analysis based signal-to-noise ratio improvement for inchoate faulty signals: application to ball bearing fault detection. Int. J. Control Autom. Syst. 15(2), 506–517 (2017)
J. Lin, Q. Chen, Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion. Mech. Syst. Signal Proc. 38(2), 515–533 (2013)
P.H. Nguyen, J.M. Kim, Multifault diagnosis of rolling element bearings using a wavelet kurtogram and vector median-based feature analysis. Shock Vib. 215, 14 (2015). Article ID 320508
W. Li, M. Qiu, Z. Zhu, B. Wu, G. Zhou, Bearing fault diagnosis based on spectrum images of vibration signals. Meas. Sci. Technol. 27(035005), 10 (2016)
B. Attaran, A. Ghanbarzadeh, Bearing Fault Detection Based on Maximum Likelihood Estimation and Optimized ANN Using the Bees Algorithm. Journal of Applied and Computational Mechanics 1(1), 35–43 (2015)
M. Hamadache, Rotor speed based bearing fault diagnosis using absolute value PCA, PhD Thesis, School of electronics Engineering, Kyungpook National University, (2015), pp. 50–54
G. Georgoulas, G. Nikolakopoulos, Bearing fault detection and diagnosis by fusing vibration data. in IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, IECON 2016—42nd Annual Conference of the IEEE, (2016), pp. 6955–6960
J. Harmouche, C. Delpha, D. Diallo, Improved fault diagnosis of ball bearings based on the global spectrum of vibration signals. IEEE Trans. Energy Convers. 30(1), 376–383 (2015)
J. Park, M. Hamadache, J.M. Ha, Y. Kim, K. Na, B.D. Youn, A positive energy residual (per) based planetary gear fault detection method under variable speed conditions. Mechanical Systems and Signal Processing 117, 347–360 (2019)
J.M. Ha, J. Park, K. Na, Y. Kim, B.D. Youn, Toothwise fault identification for a planetary gearbox based on a health data map. IEEE Trans. on Ind. Electronics 65(7), 5903–5912 (2018)
J.H. Jung, B.C. Jeon, B.D. Youn, M. Kim, D. Kim, Y. Kim, Omnidirectional regeneration (ODR) of proximity sensor signals for robust diagnosis of journal bearing systems. Mechanical Systems and Signal Processing 90, 189–207 (2017)
C. Hu, P. Wang, B.D. Youn, W. Lee, J.T. Yoon, Copula-based statistical health grade system against mechanical faults of power transformers. IEEE Trans. Power Deliv. 27(4), 1809–1819 (2012)
B.D. Youn, B.C. Jeon, J.H. Jung, Apparatus and method for diagnosing rotor shaft. US Patent App. 15/239,987, June 2017
J.M. Ha, H. Oh, J. Park, B.D. Youn, Classification of operating conditions of wind turbines for a class-wise condition monitoring strategy. Renewable Energy 103, 594–605 (2017)
W. Zhou, T.G. Habetler, R.G. Harley, Bearing fault detection via stator current noise cancellation and statistical control. IEEE Trans. Ind. Electr. 55(12), 4260–4269 (2008)
H. Zoubek, S. Villwock, M. Pacas, Frequency response analysis for rolling-bearing damage diagnosis. IEEE Trans. on Ind. Electr. 55(12), 4270–4276 (2008)
M. Kang, J. Kim, L.M. Wills, J.M. Kim, Time-varying and multiresolution envelope analysis and discriminative feature analysis for bearing fault diagnosis. IEEE Trans. on Ind. Electr. 62(12), 7749–7761 (2015)
F. Zhang, T. Zhang, H. Yu, A Novel rolling bearing fault diagnosis method. in 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (2016) pp. 1148–1152
D. Rossetti, Y. Zhang, S. Squartini, S. Collura, Classification of bearing faults through time-frequency analysis and image processing. in 2016 17th International Conference on Mechatronics-Mechatronika (ME)
Z. Huo, Y. Zhang, P. Francq, L. Shu, J. Huang, Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures. IEEE Access 5, 19442–19456 (2017)
A.A. Krishnamurthy, M.N. Belur, D. Chakraborty, Comparison of various linear discriminant analysis techniques for fault diagnosis of Re-usable Launch Vehicle. in 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), Orlando, FL, USA, pp. 3050–3055 (2011)
J. Harmouche, C. Delpha, D. Diallo, Linear discriminant analysis for the discrimination of faults in bearing balls by using spectral features. in 2014 First International Conference on Green Energy ICGE 2014, (2014), pp. 182–187
T. Liu, J. Chen, X.N. Zhou, W.B. Xiao, Bearing performance degradation assessment using linear discriminant analysis and coupled HMM. J. Phys: Conf. Ser. 364(012028), 12 (2012)
M. Zhao, X. Jin, Z. Zhang, B. Li, Fault diagnosis of rolling element bearings via discriminative subspace learning: visualization and classification. Expert Syst. Appl. 41, 3391–3401 (2014)
X. Jin, M. Zhao, T.W.S. Chow, M. Pecht, Motor bearing fault diagnosis using trace ratio linear discriminant analysis. IEEE Tran. on Ind. Electr. 61(5), 2441–2451 (2014)
L. Ciabattoni, G. Cimini, F. Ferracuti, A. Freddi, G. Ippoliti, A. Monteri`u, A novel LDA-based approach for motor bearing fault detection. in 2015 IEEE 13th International Conference on Industrial Informatics (INDIN) (IEEE, 2015), pp. 771–776
C.P. Mbo’o, K. Hameyer, Fault diagnosis of bearing damage by means of the linear discriminant analysis of stator current features from the frequency selection. IEEE Trans. Ind. Appl. 52(5), 3861–3868 (2016)
W. Yan, H. Shao. Application of support vector machine nonlinear classifier to fault diagnoses. in Proceedings of the 4th World Congress on Intelligent Control and Automation, 2002, vol. 4, (IEEE, 2002), pp. 2697–2700
V. Sugumaran, K.I. Ramachandran, Effect of number of features on classification of roller bearing faults using SVM and PSVM. Expert Syst. Appl. 38(4), 4088–4096 (2011)
K.C. Gryllias, I. Ioannis, A. Antoniadis, A support vector machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Eng. Appl. Artif. Intell. 25(2), 326–344 (2012)
D. Fernández-Francos, D. Martínez-Rego, O. Fontenla-Romero, A. Alonso-Betanzos, Automatic bearing fault diagnosis based on one-class ν-SVM. Comput. Ind. Eng. 64(1), 357–365 (2013)
G. Wang, Y. He, K. He, Multi-layer kernel learning method faced on roller bearing fault diagnosis. J. Softw. 7(7), 1531–1538 (2012)
X.M. Liu, J.W. Yin, Z.L. Feng, J. Dong, Incremental manifold learning via tangent space alignment. in Artificial Neural Networks in Pattern Recognition, (Ulm, Germany, 2006), pp. 107–121
X. Li, A. Zheng, X. Zhang, C. Li, L. Zhang, Rolling element bearing fault detection using support vector machine with improved ant colony optimization. Measurement 46(8), 2726–2734 (2013)
D. Hwang, Y. Youn, J. Sun, K. Choi, J. Lee, Y. Kim, Support vector machine based bearing fault diagnosis for induction motors using vibration signals. J. Electr. Eng. Technol. 10, 30–40 (2015)
R. Liua, B. Yang, X. Zhang, S. Wang, X. Chen, Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis. Mech. Syst. Signal Proc. 75, 345–370 (2016)
Y. Li, M. Xu, Y. Wei, W. Huang, A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree. Measurement 77, 80–94 (2016)
M.M. Manjurul Islam, J. Kim, S.A. Khan, J. Kima, Reliable bearing fault diagnosis using Bayesian inference-based multi-class support vector machines. J. Acoust. Soc. Am. 141(2), 7 (2017)
N. Zhang, L. Wu, J. Yang, Y. Guan, Naive bayes bearing fault diagnosis based on enhanced independence of data. Sensors 18(463), 17 (2018)
M. Tabaszewski, Optimization of a nearest neighbors classifier for diagnosis of condition of rolling bearings. Diagnostyka 15(1), 37–42 (2014)
M. Tabaszewski, Fault detection and diagnosis in low speed rolling element bearings Part II The use of nearest neighbour classification. Mech. Syst. Signal Proc. 6(4), 309–316 (1992)
Y. Lei, Z. He, Y. Zi, A combination of WKNN to fault diagnosis of rolling element bearings. J. Vib. Acoust. 131, 6 (2009)
A.B. Andre, E. Beltrame, J. Wainer, A combination of support vector machine and k-nearest neighbors for machine fault detection. Applied Artificial Intelligence: An Int. J. 27(1), 36–49 (2013)
Q. Wang, Y. Liu, X. He, S. Liu, J. Liu, Fault diagnosis of bearing based on KPCA and KNN method. Advanced Materials Research 986–987, 1491–1496 (2014)
S. Dong, X. Xu, R. Chen, Application of fuzzy C-means method and classification model of optimized K-nearest neighbor for fault diagnosis of bearing. J. Braz. Soc. Mech. Sci. Eng. 38(8), 2255–2263 (2016)
P. Baraldi, F. Cannarile, F.D. Maio, E. Zio, Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions. Eng. App. of Artificial Intell. 56, 1–13 (2016)
R.K. Sharma, V. Sugumaran, H. Kumar, M. Amarnath, Condition monitoring of roller bearing by k-star classifier and k-nearest neighborhood classifier using sound signal. SDHM Structural Durability and Health Monitoring 12(1), 1–16 (2017)
S. Dong, T. Luo, L. Zhong, L. Chen, X. Xu, Fault diagnosis of bearing based on the kernel principal component analysis and optimized k-nearest neighbour model. J. Low Freq. Noise Vib. Active Control 36(4), 354–365 (2017)
G. Huang, Q. Zhu, C. Siew, Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)
R. Razavi-Far, M. Saif, Ensemble of extreme learning machines for diagnosing bearing defects in non-stationary environments under class imbalance condition. in 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (2016)
G. Ditzler, R. Polikar, N. Chawla, An incremental learning algorithm for non-stationary environments and dass imbalance. in International Conference on Pattern Recognition (ICPR) (2010), pp. 2997–3000
G. Ditzler, R. Polikar, Incremental learning of concept drift from streaming imbalanced data. IEEE Trans. Knowl. Data Eng. 25(10), 2283–2301 (2013)
W. Mao, L. He, Y. Yan, J. Wang, Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine. Mech. Syst. Signal Proc. 83, 450–473 (2017)
V. Sugumaran, K.I. Ramachandran, Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing. Mech. Syst. Signal Proc. 21(5), 2237–2247 (2007)
V. Sugumarana, K.I. Ramachandran, Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learning. Expert Syst. Appl. 38(5), 4901–4907 (2011)
J. Yu, Local and nonlocal preserving projection for bearing defect classification and performance assessment. IEEE Trans. Ind. Electr. 59(5), 2363–2376 (2012)
P.K. Kankar, S.C. Sharma, S.P. Harsha, Rolling element bearing fault diagnosis using wavelet transform. Neurocomputing 74(5), 1638–1645 (2011)
S. Cao, X. Ma, Y. Zhang, L. Luo, F. Yi, A fault diagnosis method based on semisupervised fuzzy c-means cluster analysis. Inter. J. on Cyber. & Informatics (IJCI) 4(2), 281–289 (2015)
T. Han, D. Jiang, Rolling bearing fault diagnostic method based on VMD-AR model and random forest classifier. Shock Vib 216, 11 (2016). Article ID 5132046
Y. Mohsenzadeh, H. Sheikhzadeh, A.M. Reza, N. Bathaee, M.M. Kalayeh, The relevance sample-feature machine: a sparse bayesian learning approach to joint feature-sample selection. IEEE Trans. Cybern. 43(6), 2241–2254 (2013)
P.K. Wong, J. Zhong, Z. Yang, C.M. Vong, A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine. Arch. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 1989-1996 203–210, 16 (2016)
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 (PHM) (IEEE, 2015), pp. 1–6
S.J. Pan, Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
B. Lei, L.Y. Soon, E.L. Tan, Robust SVD-based audio watermarking scheme with differential evolution optimization. IEEE Trans. Audio Speech Lang. Process. 21(1), 2368–2378 (2013)
H.S. Seung, D.L. Daniel, The manifold ways of perception. Science 290, 2268–2269 (2000)
S. Kadoury, M.D. Levine, Face detection in gray scale images using locally linear embeddings. Comput. Vis. Image Underst. 105, 1–20 (2007)
X. Liu, D. Tosun, M.W. Weiner, N. Schuff, Locally linear embedding (LLE) for MRI based Alzheimer’s disease classification. Neuroimage 83, 148–157 (2013)
K. Kima, J. Lee, Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction. Pattern Recognit. 47, 758–768 (2014)
J.H. Yang, J.W. Xu, D.B. Yang, Noise reduction method for nonlinear time series based on principal manifold learning and its application to fault diagnosis. Chin. J. Mech. Eng. 42, 154–158 (2006)
X. Wang, Y. Zheng, Z. Zhao, J. Wang, Bearing fault diagnosis based on statistical locally linear embedding. Sensors 15, 16225–16247 (2015)
S.T. Roweis, L.K. Saul, Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)
Y. Wang, G. Xu, L. Liang, K. Jiang, Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis. Mech. Syst. Signal Process. 54–55, 259–276 (2015)
A. Hertzmann, Introduction to Bayesian Learning, Course Notes (University of Toronto, Ontario, 2004)
M.R.G. Meireles, P.E.M. Almeida, M.G. Simões, A comprehensive review for industrial applicability of artificial neural networks. IEEE Trans. Ind. Electr. 50(3), 585–601 (2003)
N. Qian, On the momentum term in gradient descent learning algorithms. Neural Netw. 12(1), 145–151 (1999)
K.F. Al-Raheem, W. Abdul-Karem, Rolling bearing fault diagnostics using artificial neural networks based on Laplace wavelet analysis. Int. J. Eng. Sci. Technol. 2(6), 278–290 (2010)
M. Nielsen, Chapter 6, Neural Networks and Deep Learning (2015)
A.T. Vemuri, M.M. Polycarpou, Neural-network-based robust fault diagnosis in robotic systems. IEEE Trans. Neural Netw. 8(6), 1410–1420 (1997)
V.N. Ghate, S.V. Dudul, Cascade neural-network-based fault classifier for three-phase induction motor. IEEE Trans. Ind. Electr. 58(5), 1555–1563 (2011)
S.S. Moosavi, A. Djerdir, Y. Ait-Amirat, D.A. Khaburi, A. N’Diaye, Artificial neural network-based fault diagnosis in the AC–DC converter of the power supply of series hybrid electric vehicle. IET Electr. Syst. Transp. 6(2), 96–106 (2016)
B. Li, M. Chow, Y. Tipsuwan, J.C. Hung, Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Ind. Electr. 47(5), 1060–1069 (2000)
B. Samanta, K.R. Al-Balushi, Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech. Sys. and Sig. Proc. 17(2), 317–328 (2003)
D.H. Pandya, S.H. Upadhyay, S.P. Harsha, “ANN based fault diagnosis of rolling element bearing using time-frequency domain feature,” Int. J. Eng. Science and Technology (IJEST) 4(06), 2878–2886 (2012)
B. Samanta, K.R. Al-Balushi, S.A. Al-Araimi, Bearing fault detection using artificial neural networks and genetic algorithm. J. on Applied Sig. Processing 2004(3), 366–377 (2004)
H. Yang, J. Mathew, L. Ma, V. Kosse, Matching pursuit feature based neural network pattern recognition of ball bearing faults. in International Conference of Maintenance Societies (Australia, 2004), pp. 25–28
N. Gebraeel, M. Lawley, R. Liu, V. Parmeshwaran, Residual life predictions from vibration-based degradation signals: a neural network approach. IEEE Trans. Ind. Electr. 51(3), 694–700 (2004)
V. Hariharan, P.S.S. Srinivasan, New approach of classification of rolling element bearing fault using artificial neural network. J. Mech. Eng. 40(2), 119–130 (2009)
M. Delgado, G. Cirrincione, A.G. Espinosa, J.A. Ortega, H. Henao, Bearing faults detection by a novel condition monitoring scheme based on statistical-time features and neural networks. IEEE Trans. Ind. Electr. 60(8), 3398–3407 (2013)
M. Unal, M. DEmetgul, M. Onat, H. Kucuk, Fault diagnosis of rolling bearing based on feature extration and neural network algorithm. Recent Adv. Telecom Signal Syst 179–185 (2013)
S.S. Refaat, H. Abu-Rub, M.S. Saad, E.M. Aboul-Zahab, A. Iqbal, ANN-based for detection, diagnosis the bearing fault for three phase induction motors using current signal. in 2013 IEEE International Conference on Industrial Technology (ICIT), (2013), pp. 253–258
J.P. Patela, S.H. Upadhyayb, Comparison between artificial neural network and support vector method for a fault diagnostics in rolling element bearings. Proc. Eng. 12th Int. Conf. Vib. Probl. ICOVP2015 144, 390–397 (2016)
D.K. Gaud, P. Jayaswal, Effects of artificial neural network parameters on rolling element bearing fault diagnosis. Int. J. Curr Eng. Sci. Res. 3(1), 55–60 (2016)
N. Zhao, H. Zheng, L. Yang, Z. Wang, A fault diagnosis approach for rolling element bearing based on S-transform and artificial neural network. in Proceedings of ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition GT2017, USA, (2017)
R.G. Stockwell, L. Mansinha, R.P. Lowe, Localization of the complex spectrum: the S-transform. IEEE Trans. Signal Process. 44(4), 998–1001 (1996)
J.B. Ali, L. Saidi, A. Mouelhi, B. Chebel-Morello, F. Fnaiech, Linear feature selection and classification using PNN and SFAM neural networks for an early online diagnosis of bearing naturally progressing degradations. Eng. Appl. Artif. Intell. 42, 67–81 (2015)
A.A. Jaber, R. Bicker, Fault diagnosis of industrial robot bearings based on discrete wavelet transform and artificial neural network. Int. J. Progn. Health Manag. 017, 13 (2016). ISSN 2153-2648
J. Zheng, H. Pan, J. Cheng, Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mech. Syst. Signal Process. 85, 746–759 (2017)
D. Yao, J. Yang, Y. Bai, X. Cheng, Railway rolling bearing fault diagnosis based on multi-scale intrinsic mode function permutation entropy and extreme learning machine classifier. Adv. Mech. Eng. 8(10), 1–9 (2016)
Q. Tong, J. Cao, B. Han, X. Zhang, Z. Nie, J. Wang, Y. Lin, W. Zhang, A fault diagnosis approach for rolling element bearings based on RSGWPT-LCD bilayer screening and extreme learning machine. IEEE Access 5, 5515–5530 (2017)
M. Liang, D. Su, D. Hu, M. Ge, A novel faults diagnosis method for rolling element bearings based on ELCD and extreme learning machine. Shock Vib. 218, 10 (2018). Article ID 1891453
L.B. Jack, A.K. Nandi, Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms. Mech. Syst. Signal Process. 16(2–3), 373–390 (2002)
P. Jayaswal, S.N. Verma, A.K. Wadhwani, Development of EBP-Artificial neural network expert system for rolling element bearing fault diagnosis. J. Vib. Control 17(8), 1131–1148 (2011)
H.M. Ertunc, H. Ocak, C. Aliustaoglu, ANN- and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faults. Neural Comput. Appl. 22(1), S435–S446 (2013)
B.A. Paya, I.I. Esat, Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor. Mech. Syst. Signal Process. 11(5), 751–765 (1997)
Y. Yu, Y. Dejie, C. Junsheng, A roller bearing fault diagnosis method based on EMD energy entropy and ANN. J. Sound Vib. 294, 269–277 (2006)
K.F. Al-Raheem, A. Roy, K.P. Ramachandran, D.K. Harrison, S. Grainger, Application of the laplace-wavelet combined with ANN for rolling bearing fault diagnosis. J. Vib. Acoust. 130, 9 (2008)
Y. Hwang, K. Jen, Y. Shen, Application of cepstrum and neural network to bearing fault detection. J. Mech. Sci. Technol. 23, 2730–2737 (2009)
K. Al-Raheem, Wavelet analysis and neural networks for bearing fault diagnosis. Advances in Wavelet Theory and Their Applications in Eng., Physics and Technology, (2012), pp. 313–352
J.B. Ali, B. Chebel-Morello, L. Saidi, S. Malinowski, F. Fnaiech, Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mech. Syst. Signal. Process. 56–57, 150–172 (2015)
J.B. Ali, N. Fnaiech, L. Saidi, B. Chebel-Morello, F. Fnaiech, Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl. Acoust. 89, 16–27 (2015)
R. Dubey, D. Agrawal, Bearing fault classification using ANN-based Hilbert footprint analysis. IET Sci. Meas. Technol. 9(8), 1016–1022 (2015)
Q. Hu, Z. He, Z. Zhang, Y. Zi, Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mech. Syst. Signal Proc ess. 21, 688–705 (2007)
J. Yang, Y. Zhang, Y. Zhu, Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension. Mech. Syst. Signal Process. 21, 2012–2024 (2007)
L. Guo, J. Chen, X. Li, Rolling bearing fault classification based on envelope spectrum and support vector machine. J. Vib. Control 15(9), 1349–1363 (2009)
P. Konar, P. Chattopadhyay, Bearing fault detection of induction motor using wavelet and support vector machines (SVMs). Appl. Soft Comput. 11, 4203–4211 (2011)
S. Wu, P. Wu, C. Wu, J. Ding, C. Wang, Bearing fault diagnosis based on multiscale permutation entropy and support vector machine. Entropy 14, 1343–1356 (2012)
Z. Liu, H. Cao, X. Chen, Z. He, Z. Shen, Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings. Neurocomputing 99, 399–410 (2013)
X. Zhang, Y. Liang, J. Zhou, Y. Zang, A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement 69, 164–179 (2015)
L. Saidi, J.B. Ali, F. Fnaiech, Application of higher order spectral features and support vector machines for bearing faults classification. ISA Trans. 54, 193–206 (2015)
Y. Li, M. Xu, H. Zhao, W. Huang, Hierarchical fuzzy entropy and improved support vector machine based binary tree approach for rolling bearing fault diagnosis. Mech. Mach. Theory 98, 114–132 (2016)
J. Tian, C. Morillo, M.H. Azarian, M. Pecht, Motor bearing fault detection using spectral kurtosis-based feature extraction coupled with k-nearest neighbor distance analysis. IEEE Trans. Ind. Electr. 63, 3 (2016)
Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015)
I. Goodfellow, Y. Bengio, A. Courville, Deep learning. MIT Press, ww.deeplearningbook.org (2016)
W. Yan, L. Yu, On accurate and reliable anomaly detection for gas turbine combustors: A deep learning approach. in Annual Conference of The Prognostics and Health Management Society 2015, vol. 6, 2015
H. Dong, L. Yang, H. Li, Small fault diagnosis of front-end speed controlled wind generator based on deep learning. WSEAS Trans. Circ. Syst. 15, 64–72 (2016)
F. Lv, C. Wen, Z. Bao, M. Liu, Fault diagnosis based on deep learning. 2016 American Control Conference (ACC). Boston Marriott Copley Place, Boston, MA, USA, July 6–8, (2016)
H. Liu, C. Liu, Y. Huang, Adaptive feature extraction using sparse coding for machinery fault diagnosis. Mech. Syst. Signal Process. 25(2), 558–574 (2011)
N.K. Verma, V.K. Gupta, M. Sharma, R.K. Sevakula, Intelligent condition based monitoring of rotating machines using sparse autoencoders. in Proceedings of IEEE Conference on Prognostics and Health Management, (Gaithersburg, 2013) pp. 1–7, June 24–27
S. Min, B. Lee, S. Yoo, Deep learning in bioinformatics. Briefings Bioinf 18, 851–869 (2017)
D. Lee, V. Siu, R. Cruz, C. Yetman, Convolutional neural net and bearing fault analysis. in Proceedings of the International Conference on Data Mining (DMIN’16), (2016), pp. 194–200
X. Guo, L. Chen, C. Shen, Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement 93, 490–502 (2016)
X. Ding, Q. He, Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis. IEEE Trans. Inst. Meas 66(8), 1926–1935 (2017)
O. Janssens, V. Slavkovikj, B. Vervisch, K. Stockman, M. Loccufier, S. Verstockt, R. Van de Walle, S. Van Hoecke, Convolutional neural network based fault detection for rotating machinery. J. Sound Vib. 377, 331–345 (2016)
W. You, C. Shen, X. Guo, Z. Zhu, Bearing fault diagnosis using convolution neural network and support vector regression. in 2017 International Conference on Mech. Engineering and Cont. Automation, (2017), pp. 6–11
W. You, C. Shen, X. Guo, X. Jiang, J. Shi, Z. Zhu, A hybrid technique based on convolutional neural network and support vector regression for intelligent diagnosis of rotating machinery. Adv. Mech. Eng. 9(6), 1–17 (2017)
W. Zhang, G. Peng, C. Li, Y. Chen, Z. Zhang, A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors 17(425), 1–21 (2017)
W. Fuan, J. Hongkai, S. Haidong, D. Wenjing, W. Shuaipeng, An adaptive deep convolutional neural network for rolling bearing fault diagnosis. Meas. Sci. Technol. 28(9), 1–25 (2017)
S. Li, G. Liu, X. Tang, J. Lu, J. Hu, An ensemble deep convolutional neural network model with improved D-S evidence fusion for bearing fault diagnosis. Sensors 17(1729), 1–19 (2017)
Y. Xie, T. Zhang, Fault diagnosis for rotating machinery based on convolutional neural network and empirical mode decomposition. Shock Vib. 2017, 12 (2017)
C. Lu, Z. Wang, B. Zhou, Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification. Adv. Eng. Inf. 32, 139–151 (2017)
R. Socher, C.C. Lin, A.Y. Ng, C.D. Manning, Parsing natural scenes and natural language with recursive neural networks. The 28th International Conference on Machine Learning (ICML 2011), (2011)
K. Cho, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, Learning phrase representations using RNN encoder–decoder for statistical machine translation. in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), (Doha, Qatar, October 25-29, 2014), pp. 1724–1734
R. Dey, F.M. Salem, Gate-variants of gated recurrent unit (GRU) neural networks. in IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), 2017 (2017), pp. 5
W. Abed, S. Sharma, R. Sutton, A. Motwani, A robust bearing fault detection and diagnosis technique for brushless DC motors under non-stationary operating conditions. J. Control Autom. Electr. Syst. 26, 14 (2015)
A. Malhi, R. Yan, R.X. Gao, Prognosis of defect propagation based on recurrent neural networks. in IEEE Transaction on Instrumentation and Measurement, (vol. 60, no. 3, March 2011)
S. Sharma, W. Abed, R. Sutton, B. Subudhi, Corrosion fault diagnosis of rolling element bearing under constant and variable load and speed conditions. IFAC-PapersOnLine 48–30, 049–054 (2015)
Y. Xie, T. Zhang, The application of echo state network and recurrent multilayer perceptron in rotating machinery fault prognosis. in Proceedings of 2016 IEEE Chinese Guidance, Navigation and Control Conference, (China, 2016), pp. 2286–2291
L. Guo, N. Li, F. Jia, Y. Lei, J. Lin, A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing 240, 98–109 (2017)
Q. Cui, Z. Li, J. Yang, B. Liang, Rolling bearing fault prognosis using recurrent neural network. in 29th Chinese Control And Decision Conference (CCDC), (2017), pp. 1196–1201
G.E. Hinton, S. Osindero, Y. Teh, A fast learning algorithm for deep belief nets. Neural Comput. 18, 16 (2006)
G. Hinton, Deep belief nets. Encycl. Mach. Learn. 4, 5947 (2010)
R. Salakhutdinov, G. Hinton, Deep boltzmann machines. in Proceedings of the 12th International Conference on Artificial Intelligence and Statistics 2009, (Florida, USA. vol. 5 of JMLR: W&CP 2009), p. 5
T. Jie, L. Yi-Lun, Y. Da-Lian, T. Fang, L. Chi, Fault diagnosis of rolling bearing using deep belief networks. in International Symposium on Material, Energy and Environment Engineering, (2015), pp. 566–569
H. Shao, H. Jiang, X. Zhang, M. Niu, Rolling bearing fault diagnosis using an optimization deep belief network. Meas. Sci. Technol. 26(115002), 17 (2015)
X. Wang, Y. Li, T. Rui, H. Zhu, J. Fei, Bearing fault diagnosis method based on Hilbert envelope spectrum and deep belief network. J. Vibroeng. 17(3), 1295–1308 (2015)
R. Zhang, L. Wu, X. Fu, B. Yao, Classification of bearing data based on deep belief networks. in Prognostics and System Health Management Conference (PHM-Chengdu), (2016), pp. 1–6
M. Ma, X. Chen, S. Wang, Y. Liu, W. Li, Bearing degradation assessment based on weibull distribution and deep belief network. in 2016 Internatinal Symposium on Flexible Automat., (Ohio, U.S.A., 2016), pp. 1–4
Y. Liu, D. Yang, Bearing fault diagnosis based on deep belief network and multisensor information fusion. Shock Vib. 216, 9 (2016). (Article ID 9306205)
A. Yin, J. Lu, Z. Dai, J. Li, Q. Ouyang, Isomap and deep belief network-based machine health combined assessment model. Strojniški vestnik J. Mech. Eng. 62(12), 740–750 (2016)
M. Gan, C. Wang, C. Zhu, Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mech. Syst. Signal Process. 72–73, 92–104 (2016)
J. Deutsch, M. He, D. He, Remaining useful life prediction of hybrid ceramic bearings using an integrated deep learning and particle filter approach. Appl. Sci. 7(649), 17 (2017)
S. Devendiran, K. Manivannan, S.C. Kamani, R. Refai, An early bearing fault diagnosis using effective feature selection methods and data mining techniques. Int. J. Eng. Technol. (IJET) 7(2), 583–598 (2015)
R. Zhang, Z. Peng, L. Wu, B. Yao, Y. Guan, Fault diagnosis from raw sensor data using deep neural networks considering temporal coherence. Sensors 17(549), 17 (2017)
J. Deutsch, D. He, Using deep learning-based approach to predict remaining useful life of rotating components. IEEE Trans. Syst. Man Cybern. Syst. 48(1), 11–20 (2018)
H. Shao, H. Jiang, H. Zhang, T. Liang, Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network. IEEE Trans. Ind. Electr. 65(3), 2727–2736 (2018)
H. Oh, J.H. Jung, B.C. Jeon, B.D. Youn, Scalable and unsupervised feature engineering using vibration-imaging and deep learning for rotor system diagnosis. IEEE Trans. Ind. Electr. 65(4), 3539–3549 (2018)
S. Deng, Z. Cheng, C. Li, X. Yao, Z. Chen, R.V. Sanchez, Rolling bearing fault diagnosis based on deep boltzmann machines. in 2016 Prognostics and System Health Management Conference (PHM-Chengdu), (2016), pp. 19–21
L. Liao, W. Jin, R. Pavel, Enhanced restricted boltzmann machine with prognosability regularization for prognostics and health assessment. IEEE Trans. Ind. Electr. 63(11), 7076–7083 (2016)
X. He, D. Wang, Y. Li, C. Zhou, A novel bearing fault diagnosis method based on gaussian restricted boltzmann machine. Math. Probl. Eng. 216, 8 (2016). Article ID 2957083
K.H. Cho, A. Ilin, T. Raiko, Improved learning of gaussian-bernoulli restricted boltzmann machines. in Artificial Neural Networks and Machine Learning—ICANN 2011, (Springer Berlin Heidelberg: Berlin, Germany, vol. 6791), pp. 10–17
C. Li, R. Sánchez, G. Zurita, M. Cerrada, D. Cabrera, Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning. Sensors 16(895), 19 (2016)
J. Deutsch, D. He, Using deep learning based approaches for bearing remaining useful life prediction. in Annual Conference of the Prognostics and Health Management Society, (2016)
G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
W. Lu, X. Wang, C. Yang, T. Zhang, A novel feature extraction method using deep neural network for rolling bearing fault diagnosis. in The 27th Chinese Control and Decision Conference (2015 CCDC). (IEEE, 2015) pp. 2427–2431
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, 303–315 (2016)
T. Junbo, L. Weining, A. Juneng, W. Xueqian, Fault diagnosis method study in roller bearing based on wavelet transform and stacked autoencoder. in The 27th Chinese Control and Decision Conference (2015 CCDC) (IEEE, 2015), pp. 4608–4613
W. Zhao, C. Lu, J. Ma, Z. Wang, A deep learning method using SDA combined with dropout for bearing fault diagnosis. Vibroeng. Proc. 5(151), 156 (2015)
H. O. A. Ahmed, M. L. Dennis Wong, and A. K. Nandi, Effects of deep neural network parameters on classification of bearing faults. in IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, (2016), pp. 6329–6334
L. Guo, H. Gao, H. Huang, X. He, S. Li, Multifeatures fusion and nonlinear dimension reduction for intelligent bearing condition monitoring. Shock Vib 216, 10 (2016). (Article ID 4632562)
S. Tao, T. Zhang, J. Yang, X. Wang, W. Lu, Bearing fault diagnosis method based on stacked autoencoder and softmax regression. in Control Conference (CCC), 2015 34th Chinese. (IEEE, 2015), pp. 6331–6335
H. Liu, L. Li, J. Ma, Rolling bearing fault diagnosis based on STFT-deep learning and sound signals. Shock Vib. 2016, 12 (2016). (Article ID 6127479)
W. Mao, J. He, Y. Li, Y. Yan, Bearing fault diagnosis with autoencoder extreme learning machine: a comparative study. Proc. Mech. E Part C J. Mech. Eng. Sci. 231, 1560–1578 (2016)
R. Thirukovalluru, S. Dixit, R. K. Sevakula, N. K. Verma, and A. Salour, Generating feature sets for fault diagnosis using denoising stacked autoencoder. in 2016 IEEE International Conference on Prognostics and Health Management (ICPHM) (IEEE, 2016), pp. 1–7, 2016
X. Guo, C. Shen, L. Chen, Deep fault recognizer: an integrated model to denoise and extract features for fault diagnosis in rotating machinery. Appl. Sci. 7(41), 1–17 (2017)
Z. Chen, W. Li, Multi-sensor feature fusion for bearing fault diagnosis using sparse auto encoder and deep belief network. IEEE Trans. Instr. Meas. 66(7), 1693–1702 (2017)
C. Lu, Z. Wang, W. Qin, J. Ma, Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Process. 130, 377–388 (2017)
R. M. Hasani, G. Wang, R. Grosu, An automated autoencoder correlation-based health-monitoring and prognostic method for machine bearings. arXiv:1703.06272v1 [cs.LG], 2017
M. Sohaib, C. Kim, J. Kim, A hybrid feature model and deep-learning-based bearing fault diagnosis. Sensors 17(2876), 1–16 (2017)
H. Shao, H. Jiang, F. Wang, H. Zhao, An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowl Based Syst 119, 200–220 (2017)
A. Shaheryar, X. Yin, W.Y. Ramay, Deep-learning framework: an application for fault identification in rotary machines. Int. J. Comput. Appl. (0975–8887) 167(4), 37–45 (2017)
J. Sun, C. Yan, J. Wen, Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning. IEEE Trans. Instr. Meas. 67(1), 185–195 (2018)
H.O.A. Ahmed, M.L.D. Wong, A.K. Nandi, Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features. Mech. Syst. Signal Process. 99, 459–477 (2018)
D. Ravı, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo, G. Yang, Deep learning for health informatics. IEEE J. Biomed. Health Inform. 21(1), 4–21 (2017)
D.L. Donoho, Compressed sensing. IEEE Trans. Inf. Theory 52, 1289–1306 (2006)
Acknowledgements
This research was supported by Korea Electric Power Corporation (R17TH02), the Basic Research Lab Program through the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) (No. 2018R1A4A1059976), and a grant from the Institute of Advanced Machinery and Design at Seoul National University (SNU-IAMD).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hamadache, M., Jung, J.H., Park, J. et al. A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning. JMST Adv. 1, 125–151 (2019). https://doi.org/10.1007/s42791-019-0016-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42791-019-0016-y