Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features

Mechanical Systems and Signal Processing

Volumn 99, Issue , 2018, Pages 459-477

  Ahmed, H O A ^a   Wong, M L D ^b   Nandi, A K ^a,c  

^a BRUNEL UNIVERSITY   (United Kingdom)

^b HERIOT WATT UNIVERSITY MALAYSIA   (Malaysia)

^c Ministry of Education   (China)

Author keywords

Bearing fault classification; Compressed sensing; Deep neural network; Machine condition monitoring; Sparse autoencoder; Sparse over complete representations

Indexed keywords

BACKPROPAGATION ALGORITHMS; BEARINGS (MACHINE PARTS); COMPRESSED SENSING; DEEP LEARNING; DEEP NEURAL NETWORKS; FAULT DETECTION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MACHINERY; ROLLER BEARINGS; SIGNAL RECONSTRUCTION;

AUTO ENCODERS; BEARING FAULT; CLASSIFICATION PERFORMANCE; FAULT DETECTION AND CLASSIFICATION; INTELLIGENT CONDITION MONITORING; MACHINE CONDITION MONITORING; OVER-COMPLETE REPRESENTATIONS; UNSUPERVISED FEATURE LEARNING;

CONDITION MONITORING;

EID: 85026870301     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2017.06.027     Document Type: Article

References (71)

1. Randall, R.B., Antoni, J., Rolling element bearing diagnostics – a tutorial. Mech. Syst. Signal Process. 25 (2011), 485–520.
2. Loutas, T.H., Roulias, D., Pauly, E., Kostopoulos, V., The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery. Mech. Syst. Signal Process. 25:4 (2011), 1339–1352.
3. A.K. Nandi, C. Liu, M.L.D. Wong, Intelligent vibration signal processing for condition monitoring, Surveill. 7 Int. Conf. – Oct. 29-30, 2013 Inst. Technol. Chartres, Fr. Plenary Session, 2013.
4. Zhang, L., Nandi, A.K., Fault classification using genetic programming. Mech. Syst. Signal Process. 21:3 (2007), 1273–1284.
5. Wong, M.L.D., Jack, L.B., Nandi, A.K., Modified self-organising map for automated novelty detection applied to vibration signal monitoring. Mech. Syst. Signal Process. 20:3 (2006), 593–610.
6. Jardine, A.K.S., Lin, D., Banjevic, D., a review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20 (2006), 1483–1510.
7. Norton, Michael Peter, Karczub, Denis G., Fundamentals of noise and vibration analysis for engineers. 2003, Cambridge University Press.
8. Nikolaou, N.G., Antoiadis, I.A., Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted morlet wavelets. Mech. Syst. Signal Process. 16 (2002), 677–694.
9. Pandya, D.H., Upadhyay, S.H., Harsha, S.P., Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform. Soft. Comput. 18 (2014), 255–266.
10. Pandya, D.H., Upadhyay, S.H., Harsha, S.P., Ann based fault diagnosis of rolling element bearing using time-frequency domain feature. J. Eng. Sci. Technol. 4 (2012), 2878–2886.
11. Lei, Y.G., Lin, J., He, Z.J., et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech. Syst. Signal Process. 35:1–2 (2013), 108–126.
12. Paya, B.A., Esat, I.I., Badi, M.N.M., Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a pre-processor. Mech. Syst. Signal Process. 11 (1997), 751–765.
13. Wang, C., Gan, M., Zhu, C., Non-negative EMD manifold for feature extraction in machinery fault diagnosis. Meas. J. Int. Meas. Confed. 70 (2015), 188–202.
14. Feng, Z., Liang, M., Chu, F., Recent advances in time-frequency analysis methods for machinery fault diagnosis: a review with application examples. Mech. Syst. Signal Process. 38 (2013), 165–205.
15. Yan, R., Gao, R.X., Chen, X., Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process. 96 (2014), 1–15.
16. Antoni, Jérôme, Randall, R.B., The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech. Syst. Signal Process. 20:2 (2006), 308–331.
17. Eftekharnejad, B., Carrasco, M.R., Charnley, B., Mba, D., The application of spectral kurtosis on acoustic emission and vibrations from a defective bearing. Mech. Syst. Signal Process. 25:1 (2011), 266–284.
18. Antoniadis, I., Glossiotis, G., Cyclostationary analysis of rolling-element bearing vibration signals. J. Sound Vib. 248:5 (2001), 829–845.
19. McCormick, A.C., Nandi, A.K., Cyclostationarity in rotating machine vibrations. Mech. Syst. Signal Process. 12:2 (1998), 225–242.
20. Donoho, D.L., Compressed sensing. IEEE Trans. Inf. Theory 52 (2006), 1289–1306.
21. Candes, E.J., Wakin, M.B., An Introduction to Compressive Sampling. IEEE Signal Process. Mag., 25, 2008.
22. Rossi, M., Haimovich, A.M., Eldar, Y.C., Spatial compressive sensing for MIMO radar. IEEE Trans. Signal Process. 62 (2014), 419–430.
23. Li, S., Da Xu, L., Wang, X., Compressed sensing signal and data acquisition in wireless sensor networks and internet of things. IEEE Trans. Ind. Infor. 9 (2013), 2177–2186.
24. Holland, D.J., Malioutov, D.M., Blake, A., Sederman, A.J., Gladden, L.F., reducing data acquisition times in phase-encoded velocity imaging using compressed sensing. J. Magn. Reson. 203 (2010), 236–246.
25. Merlet, S.L., Deriche, R., Continuous diffusion signal, EAP and ODF estimation via compressive sensing in diffusion MRI. Med. Image Anal. 17 (2013), 556–572.
26. Qaisar, S., Bilal, R.M., Iqbal, W., Naureen, M., Lee, S., Compressive sensing: from theory to applications, a survey. J. Commun. Networks. 15 (2013), 443–456.
27. Berger, C.R., Wang, Z., Huang, J., Zhou, S., Application of compressive sensing to sparse channel estimation. IEEE Commun. Mag. 48 (2010), 164–174.
28. Renna, F., Calderbank, R., Carin, L., Rodrigues, M.R.D., Reconstruction of signals drawn from a Gaussian mixture via noisy compressive measurements. IEEE Trans. Signal Process. 62 (2014), 2265–2277.
29. Dai, W., Milenkovic, O., Subspace pursuit for compressive sensing signal reconstruction. IEEE Trans. Inf. Theory 55 (2009), 2230–2249.
30. Candès, E.J., Eldar, Y.C., Needell, D., Randall, P., Compressed sensing with coherent and redundant dictionaries. Appl. Comput. Harmon. Anal. 31 (2011), 59–73.
31. Stanković, S., Orović, I., Stanković, L., an automated signal reconstruction method based on analysis of compressive sensed signals in noisy environment. Signal Process. 104 (2014), 43–50.
32. Wong, M.L.D., Zhang, M., Nandi, A.K., Effects of compressed sensing on classification of bearing faults with entropic features. EUSIPCO Proceeding, 2015, IEEE, 2296–2300.
33. Li, X.F., Fan, X.C., Jia, L.M., Compressed sensing technology applied to fault diagnosis of train rolling bearing. Vib. Struct. Eng. Meas. Ii, Pts 1-3 226–228 (2012), 2056–2061.
34. Davenport, M.a., Wakin, M.B., Baraniuk, R.G., Detection and estimation with compressive measurements. Comput. Eng, 2007, 1–16.
35. Tang, G., Yang, Q., Wang, H.Q., Luo, G. gang, Ma, J. wei, Sparse classification of rotating machinery faults based on compressive sensing strategy. Mechatronics 31 (2014), 60–67.
36. Zhang, X., Hu, N., Hu, L., Chen, L., Cheng, Z., A bearing fault diagnosis method based on the low-dimensional compressed vibration signal. Adv. Mech. Eng. 7 (2015), 1–12.
37. Chen, X., Du, Z., Li, J., Li, X., Zhang, H., Compressed sensing based on dictionary learning for extracting impulse components. Signal Process. 96 (2014), 94–109.
38. Tang, G., Hou, W., Wang, H., Luo, G., Ma, J., Compressive sensing of roller bearing faults via harmonic detection from under-sampled vibration signals. Sensors (Switzerland) 15 (2015), 25648–25662.
39. R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, X. Gao, Deep Learning and its applications to machine health monitoring: a survey, 2016.
40. Ren, L., Lv, W., Jiang, S., Xiao, Y., Fault diagnosis using a joint model based on sparse representation and SVM. IEEE Trans. Instrum. Meas., 2016, 1–8.
41. Zhu, H., Wang, X., Zhao, Y., Li, Y., Wang, W., Li, L., Sparse representation based on adaptive multiscale features for robust machinery fault diagnosis. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 229 (2015), 2303–2313.
42. Tang, H., Chen, J., Dong, G., Sparse representation based latent components analysis for machinery weak fault detection. Mech. Syst. Signal Process. 46 (2014), 373–388.
43. Liu, H., Liu, C., Huang, Y., Adaptive feature extraction using sparse coding for machinery fault diagnosis. Mech. Syst. Signal Process. 25 (2011), 558–574.
44. Fan, W., Cai, G., Zhu, Z.K., Shen, C., Huang, W., Shang, L., Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction. Mech. Syst. Signal Process. 56 (2015), 230–245.
45. Lewicki, M.S., Sejnowski, T.J., Learning overcomplete representations. Neural Comput. 12 (2000), 337–365, 10.1162/089976600300015826.
46. Olshausen, B.A., Field, D.J., Sparse coding with an overcomplete basis set: A strategy employed by V1?. Vision. Res. 37 (1997), 3311–3325.
47. Doi, E., Balcan, D., A theoretical analysis of robust coding over noisy overcomplete channels. Adv. Neural. Inf., 2006 http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.69.9617&rep=rep1&type=pdf.
48. Ng, A., CS294A lecture notes sparse autoencoder. Cs294a, 2011, 1–19.
49. Lee, H., Ekanadham, C., Ng, A., Sparse deep belief net model for visual area V2. Nips, 2007, 1–8.
50. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., Greedy layer-wise training of deep networks. Adv. Neural Inf. Process. Syst., 19, 2007, 153.
51. S. Tao, T. Zhang, J. Yang, X. Wang, W. Lu, Bearing fault diagnosis method based on stacked autoencoder and softmax regression, in: Chinese Control Conf. CCC. 2015-September; 2015, pp. 6331–6335.
52. Jia, F., Lei, Y., Lin, J., Zhou, X., Lu, N., 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.
53. Sun, W., Shao, S., Zhao, R., Yan, R., Zhang, X., Chen, X., A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 89 (2016), 171–178.
54. Candes, E., Tao, T., Near-optimal signal recovery from random projections: universal encoding strategies?. IEEE Trans. Inf. Theory 52:12 (2006), 5406–5425.
55. Baraniuk, Richard, et al. A simple proof of the restricted isometry property for random matrices. Constr. Approx. 28:3 (2008), 253–263.
56. Schmidhuber, J., Deep Learning in neural networks: an overview. Neural Networks 61 (2015), 85–117.
57. Erhan, D., Courville, A., Vincent, P., Why does unsupervised pre-training help deep learning. J. Mach. Learn. Res. 11 (2010), 625–660.
58. S. Lange, M. Riedmiller, Deep auto-encoder neural networks in reinforcement learning. In: The 2010 International Joint Conference on Neural Networks (IJCNN), IEEE; 2010 Jul 18, pp. 1–8.
59. Tim Salimans, Diederik P. Kingma, Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks; 2016.
60. Hosmer Jr, David, W., Lemeshow, Stanley, Sturdivant, Rodney X., Applied Logistic Regression. 2013, John Wiley & Sons.
61. Møller, Martin Fodslette, A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6:4 (1993), 525–533.
62. Bao, Yuequan, Beck, James L., Li, Hui, Compressive sampling for accelerometer signals in structural health monitoring. Struct. Health Monit., 2010.
63. Bao, Yuequan, Li, Hui, Jinping, Ou., Emerging data technology in structural health monitoring: compressive sensing technology. J. Civil Struct. Health Mon. 4:2 (2014), 77–90.
64. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/wavelet/ch06_a47.html.
65. B. Roman, A. Bastounis, B. Adcock, A.C. Hansen, On Fundamentals of Models and Sampling in Compressed Sensing, Available: < http://www.damtp.cam.ac.uk/research/afha/bogdan/ Fundamentals_17.pdf>.
66. B. Adcock, A.C. Hansen, C. Poon, B. Roman, Breaking the Coherence Barrier: A New Theory for Compressed Sensing, 2014.
67. B. Roman, B. Adcock, A. Hansen, On Asymptotic Structure in Compressed Sensing, 2014.
68. Needell, D., Tropp, J.A., CoSaMP: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmon. Anal. 26:3 (2009), 301–321.
69. Case Western Reserve University Bearing Data Center < http://csegroup.case.edu/bearingdatacenter/home>.
70. Smith, Wade A., Randall, Robert B., Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study. Mech. Syst. Signal Process. 64 (2015), 100–131.
71. de Almeida, L.F., Bizarria, J.W., Bizarria, F.C., Mathias, M.H., Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron. J. Vib. Control 21:16 (2015), 3456–3464.