Bearing damage assessment using Jensen-Rényi Divergence based on EEMD

Mechanical Systems and Signal Processing

Volumn 87, Issue , 2017, Pages 307-339

  Singh, Jaskaran ^a   Darpe, A K ^a   Singh, S P ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

Author keywords

Bearing damage; Confidence value; Ensemble empirical mode decomposition; Jensen R nyi divergence; R nyi entropy

Indexed keywords

BEARINGS (MACHINE PARTS); DEFECTS; PROBABILITY DISTRIBUTIONS; ROLLER BEARINGS; SIGNAL PROCESSING; TESTING;

BEARING DAMAGE; CONFIDENCE VALUES; ENSEMBLE EMPIRICAL MODE DECOMPOSITION; ENSEMBLE EMPIRICAL MODE DECOMPOSITIONS (EEMD); INTRINSIC MODE FUNCTIONS; ROLLING ELEMENT BEARING; SYSTEMATIC METHODOLOGY; VARIABLE OPERATING CONDITION;

DAMAGE DETECTION;

EID: 84996671126     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2016.10.028     Document Type: Article

References (55)

1. [1] Randall, R.B., Antoni, J., Rolling element bearing diagnostics—a tutorial. Mech. Syst. Signal Process. 25 (2011), 485–520.
2. [2] Pan, Y.N., Chen, J., Li, X.L., Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means. Mech. Syst. Signal Process. 24 (2010), 559–566.
3. [3] Heng, R.B.W., Nor, M.J.M., Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition. Appl. Acoust. 53 (1998), 211–226.
4. [4] Shao, Y., Nezu, K., Prognosis of remaining bearing life using neural networks. Proc. Inst. Mech. Eng. I: J. Syst. Control Eng., 214(3), 2000, 217.
5. [5] Qiu, H., Lee, J., Lin, J., Yu, G., Robust performance degradation assessment methods for enhanced rolling element bearing prognostics. Adv. Eng. Inform. 17 (2003), 127–140.
6. [6] Tse, P.W., Peng, Y.H., Yam, R., Wavelet analysis and envelope detection for rolling bearing fault diagnosis—their effectiveness and flexibilities. J. Vib. Acoust. 123 (2001), 303–310.
7. [7] Gebraeel, N., Lawley, M., Liu, R., Parmeshwaran, V., Residual life predictions from vibration-based degradation signals: a neural network approach. IEEE Trans. Ind. Electron. 51 (2004), 694–700.
8. [8] Bin, G.F., Gao, J.J., Li, X.J., et al. Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network. Mech. Syst. Signal Process. 27 (2012), 696–711.
9. [9] Huang, R., Xi, L., Li, X., Richard Liu, C., Qiu, H., Lee, J., Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mech. Syst. Signal Process. 21 (2007), 193–207.
10. [10] Pan, Y.N., Chen, J., Guo, L., Robust bearing performance degradation assessment method based on improved wavelet packet-support vector data description. Mech. Syst. Signal Process. 23 (2009), 669–681.
11. [11] Pan, Y.N., Chen, J., Li, X.L., Spectral entropy: a complementary index for rolling element bearing performance degradation assessment. Proc. Inst. Mech. Eng. C: J. Mech. Eng. Sci. 223 (2009), 1223–1231.
12. [12] Yu, J., Bearing performance degradation assessment using locality preserving projections. Expert Syst. Appl. 38 (2011), 7440–7450.
13. [13] Yu, J., Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models. Mech. Syst. Signal Process. 25 (2011), 2573–2588.
14. [14] Yu, J., A hybrid feature selection scheme and self-organizing map model for machine health assessment. Appl. Soft Comput. 11:5 (2011), 4041–4054.
15. [15] Mi, L., Tan, W., Chen, R., Multi-steps degradation process prediction for bearing based on improved back propagation neural network. Proc. Inst. Mech. Eng. C.-J. Mech. Eng. Sci. 227 (2012), 1544–1553.
16. [16] Wang, Z., Lu, C., Wang, Z., Liu, H., Fan, H., Fault diagnosis and health assessment for bearings using the Mahalanobis-Taguchi system based on EMD-SVD. Trans. Inst. Meas. Control 35 (2013), 798–807.
17. [17] Li, R., Sopon, P., He, D., Fault features extraction for bearing prognostics. J. Intell. Manuf. 23 (2012), 313–321 (ISSN 0956-5515).
18. [18] Hong, S., Zhou, Z., Zio, E., Hong, K., Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method. Digit. Signal Process. 27 (2014), 159–166.
19. [19] Shakya, P., Kulkarni, M.S., Darpe, A.K., A novel methodology for online detection of bearing health status for naturally progressing defect. J. Sound Vib. 333 (2014), 5614–5629.
20. [20] Ben Ali, J., Fnaiech, N., Saidi, L., Chebel-Morello, B., Fnaiech, F., Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl. Acoust. 89 (2015), 16–27.
21. [21] Hu, J., Zhang, L., Liang, W., Dynamic degradation observer for bearing fault by MTS-SOM system. Mech. Syst. Signal Process. 36:2 (2013), 385–400.
22. [22] Yang, Y., Yu, D.J., Cheng, J.S., A roller bearing fault diagnosis method based on EMD energy entropy and ANN. J. Sound Vib. 294 (2006), 269–277.
23. [23] Jiang, L., Xuan, J., Shi, T., Feature extraction based on semi-supervised kernel Marginal Fisher analysis and its application in bearing fault diagnosis. Mech. Syst. Signal Process. 41 (2013), 113–126.
24. [24] Lei, Y.G., He, Z.J., Zi, Y.Y., A new approach to intelligent fault diagnosis of rotating machinery. Expert Syst. Appl., 35, 2008, 1593 (160).
25. [25] Boškoski, P., Juričić, Đ., Fault detection of mechanical drives under variable operating conditions based on wavelet packet Rényi entropy signatures. Mech. Syst. Signal Process 31 (2012), 369–381.
26. [26] Boškoski, P., Gašperin, M., Petelin, D., Juričić, Đ., Bearing fault prognostics using Rényi entropy based features and Gaussian process models. Mech. Syst. Signal Process 52–53 (2015), 327–337.
27. [27] Lee, J., Qiu, H., Yu, G., Lin, J., RT Services Bearing data set. Cincinnati, U.O., (eds.) NASA Ames Prognostics Data Repository, 2007, IMS, University of Cincinnati.
28. [28] Huang, N.E., Shen, Z., Long, S.R., et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A 454 (1998), 903–995.
29. [29] Wu, Z.H., Huang, N.E., Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1 (2009), 1–41.
30. [30] Lei, Y.G., Zuo, M.J., Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs. Meas. Sci. Technol. 20 (2009), 2280–2294.
31. [31] Wu, Z.H., Huang, N.E., A study of the characteristics of white noise using the empirical mode decomposition method. Proc. R. Soc. Lond. 460 A (2004), 1597–1611.
32. [32] Rai, V., Mohanty, A., Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform. Mech. Syst. Signal Process. 21 (2007), 2607–2615.
33. [33] Shakya, P., Darpe, A.K., Kulkarni, M.S., Vibration-based fault diagnosis in rolling element bearings: ranking of various time, frequency and time-frequency domain data-based damage identification parameters. Int. J. Cond. Monit. 3 (2013), 53–62.
34. [34] Baraniuk, R., Flandrin, P., Janssen, A., Michel, O., Measuring time-frequency information content using the Renyi entropies. IEEE Trans. Inform. Theory 47 (2001), 1391–1409.
35. [35] Tao, B., Zhu, L., Ding, H., Xiong, Y., An alternative time-domain index for condition monitoring of rolling element bearings—A comparison study. Reliab. Eng. Syst. Saf. 92 (2007), 660–670.
36. [36] Neyman, J., Fourth Berkeley Symposium on Mathematical Statistics and Probability Announced. Science 131 (1960), 1595–1596.
37. [37] Hero, A.O., Ma, B., Michel, O., Gorman, J., Alpha-Divergence for Classification, Indexing and Retrieval. (Technical Report CSPL-328), 2002, Communications and Signal Processing Laboratory, The University of Michigan.
38. [38] Markel, D., Zaidi, H., El Naqa, I., Novel multimodality segmentation using level sets and Jensen-Renyi divergence. Med. Phys., 40, 2013, 121908.
39. [39] A. Ben-Hamza, H. Krim, Jensen–Rényi divergence measure: theoretical and computational perspectives, in: Proceedings of the IEEE International Symp. on Inform. Theory., 2003, 257.
40. [40] Príncipe, J., Information Theoretic Learning. 2010, Springer, New York.
41. [41] He, Y., Hamza, A.B., Krim, H., A generalized divergence measure for robust image registration. IEEE Trans. Signal Process. 51 (2003), 1211–1220.
42. [42] B. Dolenc, P. Boškoski, Đ. Juričić, Robust information indices for diagnosing mechanical drives under non-stationary operating conditions, In: Proceedings of CMMNO, 2014, pp. 139–149.
43. [43] Hayter, A., Probability and Statistics for Engineers and Scientists. 1996, PWS Publishing Co., Boston, MA.
44. [44] McFadden, P.D., Smith, J.D., Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review. Tribol. Int. 17:1 (1984), 3–10.
45. [45] Rubini, R., Meneghetti, U., Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings. Mech. Syst. Signal Process. 15 (2001), 287–302.
46. [46] Yang, Y., Yu, D., Cheng, J., A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurement 40 (2007), 943–950.
47. [47] Zhang, Y., Ai, S., EMD based envelope analysis for bearing faults detection. (in:) Intell. Control Autom., 2008, 4257–4260.
48. [48] Du, Q.H., Yang, S.N., Application of the EMD method in the vibration analysis of ball bearings. Mech. Syst. Signal Process. 21 (2007), 2634–2644.
49. [49] Tsao, W.C., Li, Y.F., D.L, An insight concept to select appropriate IMFs for envelope analysis of bearing fault diagnosis. Measurement 45 (2012), 1489–1498.
50. [50] Pan, M.C., Tsao, W.C., Using appropriate IMFs for envelope analysis in multiple fault diagnosis of ball bearings. Int. J. Mech. Sci. 69 (2013), 114–124.
51. [51] Diego, F.F., David, M.R., Oscar, F.R., et al. Automatic bearing fault diagnosis based on one-class ν-SVM. Comput. Ind. Eng. 64 (2013), 357–365.
52. [52] Goode, K.B., Moore, J., Roylance, B.J., Plant machinery working life prediction method utilizing reliability and condition-monitoring data. Proc. Inst. Mech. Eng. E: J. Process Mech. Eng. 214 (2000), 109–122.
53. [53] Gebraeel, N., Lawley, M., Li, R., Ryan, J., Residual-life distributions from component degradation signals: a Bayesian approach. IIE Trans. 37 (2005), 543–557.
54. [54] Shakya, P., Darpe, A.K., Kulkarni, M.S., Bearing damage classification using instantaneous energy density. J. Vib. Control., 2015.
55. [55] Qiu, H., Lee, J., Lin, J., Yu, G., Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. J. Sound. Vib. 289:4–5 (2006), 1066–1090.