Sparsity-based algorithm for detecting faults in rotating machines

Mechanical Systems and Signal Processing

Volumn 72-73, Issue , 2016, Pages 46-64

  He, Wangpeng ^a,b   Ding, Yin ^b   Zi, Yanyang ^a   Selesnick, Ivan W ^b  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b POLYTECHNIC UNIVERSITY   (United States)

Author keywords

Compound fault diagnosis; Feature extraction; Group sparsity denoising; Non convex optimization; Rotating machinery; Sparse optimization

Indexed keywords

ALGORITHMS; CONVEX OPTIMIZATION; EXTRACTION; FAILURE ANALYSIS; FEATURE EXTRACTION; ITERATIVE METHODS; MACHINERY; OPTIMIZATION; ROTATING MACHINERY; SIGNAL DETECTION;

COMPARATIVE METHODS; COMPOUND FAULT DIAGNOSIS; CONVEX OPTIMIZATION PROBLEMS; DE-NOISING; DETECTION PERFORMANCE; NONCONVEX OPTIMIZATION; RECEIVER OPERATING CHARACTERISTIC CURVES; SPARSE OPTIMIZATIONS;

FAULT DETECTION;

EID: 84955701960     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2015.11.027     Document Type: Article

References (26)

1. A. Heng, S. Zhang, A.C. Tan, and J. Mathew Rotating machinery prognostics: state of the art, challenges and opportunities Mech. Syst. Signal Process. 23 3 2009 724 739
2. R. Yan, R.X. Gao, and X. Chen Wavelets for fault diagnosis of rotary machines: a review with applications Signal Process. 96 2014 1 15
3. R.B. Randall, and J. Antoni Rolling element bearing diagnostics - a tutorial Mech. Syst. Signal Process. 25 2 2011 485 520
4. H. Jiang, J. Chen, G. Dong, T. Liu, and G. Chen Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis Mech. Syst. Signal Process. 52 2015 338 359
5. Y. Lei, J. Lin, Z. He, and M.J. Zuo A review on empirical mode decomposition in fault diagnosis of rotating machinery Mech. Syst. Signal Process. 35 1 2013 108 126
6. C. Zhang, B. Li, B. Chen, H. Cao, Y. Zi, and Z. He Periodic impulsive fault feature extraction of rotating machinery using dual-tree rational dilation complex wavelet transform ASME J. Manuf. Sci. Eng. 136 5 2014 051011
7. R. Yan, and R.X. Gao Harmonic wavelet-based data filtering for enhanced machine defect identification J. Sound Vib. 329 15 2010 3203 3217
8. W. He, Y. Zi, B. Chen, F. Wu, and Z. He Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform Mech. Syst. Signal Process. 54-55 2015 457 480
9. W. Fan, G. Cai, W. Huang, L. Shang, and Z. Zhu Sparse representation of transients based on wavelet basis and majorization-minimization algorithm for machinery fault diagnosis Math. Probl. Eng. 2014
10. P.-Y. Chen, and I.W. Selesnick Translation-invariant shrinkage/thresholding of group sparse signals Signal Process. 94 2014 476 489
11. P.-Y. Chen, and I.W. Selesnick Group-sparse signal denoising: non-convex regularization, convex optimization IEEE Trans. Signal Process. 62 13 2014 3464 3478
12. Q. He, and X. Wang Time-frequency manifold correlation matching for periodic fault identification in rotating machines J. Sound Vib. 332 10 2013 2611 2626
13. W. He, Y. Zi, B. Chen, S. Wang, and Z. He Tunable Q-factor wavelet transform denoising with neighboring coefficients and its application to rotating machinery fault diagnosis Sci. China Technol. Sci. 56 8 2013 1956 1965
14. Z. Li, Z. He, Y. Zi, and Y. Wang Customized wavelet denoising using intra-and inter-scale dependency for bearing fault detection J. Sound Vib. 313 1 2008 342 359
15. B. Chen, Z. Zhang, C. Sun, B. Li, Y. Zi, and Z. He Fault feature extraction of gearbox by using overcomplete rational dilation discrete wavelet transform on signals measured from vibration sensors Mech. Syst. Signal Process. 33 2012 275 298
16. I. Selesnick, and I. Bayram Sparse signal estimation by maximally sparse convex optimization IEEE Trans. Signal Process. 62 5 2014 1078 1092
17. K. Lange Optimization 2004 Springer New York
18. S. Boyd, and L. Vandenberghe Convex Optimization 2009 Cambridge University Press New York
19. I.W. Selesnick, A. Parekh, and I. Bayram Convex 1-D total variation denoising with non-convex regularization IEEE Signal Process. Lett. 22 2 2015 141 144
20. M. Figueiredo, J. Bioucas-Dias, and R. Nowak Majorization-minimization algorithms for wavelet-based image restoration IEEE Trans. Image Process. 16 12 2007 2980 2991
21. R.R. Coifman, and D.L. Donoho Translation-invariant de-noising Wavelet and Statistics 1995 Springer-Verlag New York 125 150
22. J. Antoni, and R. Randall The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines Mech. Syst. Signal Process. 20 2 2006 308 331
23. S. Lee, and P. White Higher-order time-frequency analysis and its application to fault detection in rotating machinery Mech. Syst. Signal Process. 11 4 1997 637 650
24. J. Nichols, S. Trickey, M. Seaver, and S. Motley Using ROC curves to assess the efficacy of several detectors of damage-induced nonlinearities in a bolted composite structure Mech. Syst. Signal Process. 22 7 2008 1610 1622
25. D. Donoho, I. Johnstone, and I.M. Johnstone Ideal spatial adaptation by wavelet shrinkage Biometrika 81 1993 425 455
26. H. Endo, and R. Randall Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter Mech. Syst. Signal Process. 21 2 2007 906 919