Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings

Mechanical Systems and Signal Processing

Volumn 92, Issue , 2017, Pages 173-195

  Miao, Yonghao ^a   Zhao, Ming ^a,b   Lin, Jing ^c   Lei, Yaguo ^c  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b UNIVERSITY OF CINCINNATI   (United States)

^c XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Autocorrelation; Compound fault; Fault diagnosis; Improved MCKD; Maximum correlated kurtosis deconvolution (MCKD); Rolling element bearing

Indexed keywords

AUTOCORRELATION; BEARINGS (MACHINE PARTS); FAILURE ANALYSIS; HIGHER ORDER STATISTICS; ITERATIVE METHODS; PLASMA DIAGNOSTICS; ROLLER BEARINGS; SIGNAL PROCESSING; SPECTRUM ANALYSIS;

APPLICATION RANGE; COMPOUND FAULTS; DECONVOLUTION METHOD; FREQUENCY SPECTRUM ANALYSIS; IMPROVED MCKD; MAXIMUM CORRELATED KURTOSIS DECONVOLUTION (MCKD); MINIMUM ENTROPY DECONVOLUTION; ROLLING ELEMENT BEARING;

FAULT DETECTION;

EID: 85013647609     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2017.01.033     Document Type: Article

References (44)

1. [1] Randall, R.B., Vibration-Based Condition Monitoring: Industrial, Aerospace and Automotive Applications. 2011, John Wiley & Sons.
2. [2] Randall, R.B., Antoni, J., Rolling element bearing diagnostics—A tutorial. Mech. Syst. Signal Process. 25 (2011), 485–520.
3. [3] Lei, Y., Lin, J., He, Z., Zi, Y., Application of an improved kurtogram method for fault diagnosis of rolling element bearings. Mech. Syst. Signal Process. 25 (2011), 1738–1749.
4. [4] Ou, L., Yu, D., Yang, H., A new rolling bearing fault diagnosis method based on GFT impulse component extraction. Mech. Syst. Signal Process. 81 (2016), 162–182.
5. [5] Li, Y., Xu, M., Wang, R., Huang, W., A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy. J. Sound Vib. 360 (2016), 277–299.
6. [6] Janjarasjitt, S., Ocak, H., Loparo, K., Bearing condition diagnosis and prognosis using applied nonlinear dynamical analysis of machine vibration signal. J. Sound Vib. 317 (2008), 112–126.
7. [7] Wang, D., Peter, W.T., Tsui, K.L., An enhanced Kurtogram method for fault diagnosis of rolling element bearings. Mech. Syst. Signal Process. 35 (2013), 176–199.
8. [8] Wang, Y., He, Z., Zi, Y., Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform. Mech. Syst. Signal Process. 24 (2010), 119–137.
9. [9] Zhang, H., Chen, X., Du, Z., Yan, R., Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis. Mech. Syst. Signal Process. 80 (2016), 349–376.
10. [10] Braun, S., Mechanical Signature Analysis: Theory and Applications. 1986, Academic Pr.
11. [11] Barszcz, T., JabŁoński, A., A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram. Mech. Syst. Signal Process. 25 (2011), 431–451.
12. [12] Dyer, D., Stewart, R., Detection of rolling element bearing damage by statistical vibration analysis. J. Mech. Des. 100 (1978), 229–235.
13. [13] Antoni, J., The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech. Syst. Signal Process. 20 (2006), 282–307.
14. [14] Antoni, J., Randall, R., The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech. Syst. Signal Process. 20 (2006), 308–331.
15. [15] Antoni, J., Fast computation of the kurtogram for the detection of transient faults. Mech. Syst. Signal Process. 21 (2007), 108–124.
16. [16] Wang, Y., Liang, M., An adaptive SK technique and its application for fault detection of rolling element bearings. Mech. Syst. Signal Process. 25 (2011), 1750–1764.
17. [17] Xu, X., Zhao, M., Lin, J., Lei, Y., Periodicity-based kurtogram for random impulse resistance. Meas. Sci. Technol., 26, 2015, 085011.
18. [18] Combet, F., Gelman, L., Optimal filtering of gear signals for early damage detection based on the spectral kurtosis. Mech. Syst. Signal Process. 23 (2009), 652–668.
19. [19] Zhang, Y., Randall, R., Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram. Mech. Syst. Signal Process. 23 (2009), 1509–1517.
20. [20] Barszcz, T., Randall, R.B., Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine. Mech. Syst. Signal Process. 23 (2009), 1352–1365.
21. [21] Sawalhi, N., Randall, R.B., Spectral kurtosis optimization for rolling element bearings, Signal Processing and Its Applications, 2005. Proceedings of the Eighth International Symposium on, 2005, IEEE, 839–842.
22. [22] Endo, H., Randall, R.B., Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter. Mech. Syst. Signal Process. 21 (2007), 906–919.
23. [23] Sawalhi, N., Randall, R.B., Endo, H., The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mech. Syst. Signal Process. 21 (2007), 2616–2633.
24. [24] McDonald, G.L., Zhao, Q., Zuo, M.J., Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection. Mech. Syst. Signal Process. 33 (2012), 237–255.
25. [25] Hong, L., Dhupia, J.S., A time domain approach to diagnose gearbox fault based on measured vibration signals. J. Sound Vib. 333 (2014), 2164–2180.
26. [26] Zhou, H., Chen, J., Dong, G., Application of Maximum Correlated Kurtosis Deconvolution on Rolling Element Bearing Fault Diagnosis. Engineering Asset Management-Systems, Professional Practices and Certification, 2015, Springer, 159–172.
27. [27] Jia, F., Lei, Y., Shan, H., Lin, J., Early fault diagnosis of bearings using an improved spectral kurtosis by maximum correlated kurtosis deconvolution. Sensors 15 (2015), 29363–29377.
28. [28] Wang, Y., Liang, M., Identification of multiple transient faults based on the adaptive spectral kurtosis method. J. Sound Vib. 331 (2012), 470–486.
29. [29] He, S., Chen, J., Zhou, Z., Zi, Y., Wang, Y., Wang, X., Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis. Mech. Syst. Signal Process. 76 (2016), 742–758.
30. [30] Chen, J., Zi, Y., He, Z., Yuan, J., Compound faults detection of rotating machinery using improved adaptive redundant lifting multiwavelet. Mech. Syst. Signal Process. 38 (2013), 36–54.
31. [31] Zhao, M., Lin, J., Miao, Y., Xu, X., Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings. Measurement 91 (2016), 421–439.
32. [32] Tang, G., Wang, X., He, Y., Diagnosis of compound faults of rolling bearings through adaptive maximum correlated kurtosis deconvolution. J. Mech. Sci. Technol. 30 (2016), 43–54.
33. [33] Barszcz, T., Sawalhi, N., Fault detection enhancement in rolling element bearings using the minimum entropy deconvolution. Arch. Acoust., 37, 2012.
34. [34] Jiang, R., Chen, J., Dong, G., Liu, T., Xiao, W., The weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelope spectrum. Proc. Inst. Mech. Eng., Part C: J. Mech. Eng. Sci., 2012 0954406212457892.
35. [35] Ricci, R., Borghesani, P., Chatterton, S., Pennacchi, P., The Combination of Empirical Mode Decomposition and Minimum Entropy Deconvolution for Roller Bearing Diagnostics in Non-Stationary Operation. ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2012, American Society of Mechanical Engineers, 723–730.
36. [36] Wang, H., Fault diagnosis method for rolling bearing's weak fault based on minimum entropy deconvolution and sparse decomposition. J. Mech. Eng., 49, 2013, 88.
37. [37] Endo, H., Randall, R.B., Gosselin, C., Differential diagnosis of spall vs. cracks in the gear tooth fillet region: experimental validation. Mech. Syst. Signal Process. 23 (2009), 636–651.
38. [38] He, D., Wang, X., Li, S., Lin, J., Zhao, M., Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis. Mech. Syst. Signal Process. 81 (2016), 235–249.
39. [39] Wiggins, R.A., Minimum entropy deconvolution. Geoexploration 16 (1978), 21–35.
40. [40] Zhang, X., Kang, J., Xiao, L., Zhao, J., Teng, H., A new improved kurtogram and its application to bearing fault diagnosis. Shock Vib. 2015 (2015), 1–22.
41. [41] Miao, Y., Zhao, M., Lin, J., Xu, X., Sparse maximum harmonics-to-noise-ratio deconvolution for weak fault signature detection in bearings. Meas. Sci. Technol., 27, 2016, 105004.
42. [42] Xu, X., Zhao, M., Lin, J., Lei, Y., Envelope harmonic-to-noise ratio for periodic impulses detection and its application to bearing diagnosis. Measurement 91 (2016), 385–397.
43. [43] McDonald, G.L., Zhao, Q., Multipoint optimal minimum entropy deconvolution and convolution fix: application to vibration fault detection. Mech. Syst. Signal Process. 82 (2017), 461–477.
44. [44] He, W., Zi, Y., Chen, B., Wu, F., He, Z., Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform. Mech. Syst. Signal Process. 54 (2015), 457–480.