Bearing fault detection using multi-scale fractal dimensions based on morphological covers

Shock and Vibration

Volumn 19, Issue 6, 2012, Pages 1373-1383

  Zhang, Pei Lin ^a   Li, Bing ^a   Mi, Shuang Shan ^a   Zhang, Ying Tang ^a   Liu, Dong Sheng ^a  

^a MECHANICAL ENGINEERING COLLEGE   (China)

Author keywords

fault diagnosis; feature extraction; mathematical morphology; multi scale fractal dimensions (MFDs); Rolling element bearing

Indexed keywords

BEARINGS (MACHINE PARTS); FAILURE ANALYSIS; FEATURE EXTRACTION; GEOMECHANICS; MATHEMATICAL MORPHOLOGY;

BEARING FAULT; BEARING FAULT DETECTION; BEARING FAULT DIAGNOSIS; CLASSIFICATION PERFORMANCE; EFFECTIVE TOOL; FEATURE EXTRACTION METHODS; FRACTAL GEOMETRY; MULTISCALES; NONLINEAR CHARACTERISTICS; ROLLING ELEMENT BEARING; SELF-SIMILAR FRACTALS; VIBRATION SIGNAL;

FRACTAL DIMENSION;

EID: 84872467442     PISSN: 10709622     EISSN: None     Source Type: Journal    
DOI: 10.3233/SAV-2012-0679     Document Type: Conference Paper

References (41)

1. C.H. Chen, R.J. Shyu and C.K. Ma, A new fault diagnosis method of rotating machinery, Shock and Vibration 6 (2008), 585-598.
2. J. Cheng, D. Yu, J. Tang and Y. Yang, Application of SVM and SVD technique based on EMD to the fault diagnosis of the rotating machinery, Shock and Vibration 1 (2009), 89-98.
3. R.G.T. De Almeida, S.A. Da Silva Vicente and L.R. Padovese, New technique for evaluation of global vibration levels in rolling bearings, Shock and Vibration 4-5 SPEC (2002), 225-234.
4. C.T. Yiakopoulos and I.A. Antoniadis, Wavelet based demodulation of vibration signals generated by defects in rolling element bearings, Shock and Vibration 6 (2002), 293-306. (Pubitemid 35476738)
5. Y.C. Choi and Y.H. Kim, Fault detection in a ball bearing system using minimum variance cepstrum, Measurement Science and Technology 5 (2007), 1433-1440.
6. X. Wang, Y. Zi and Z. He, Multiwavelet construction via an adaptive symmetric lifting scheme and its applications for rotating machinery fault diagnosis, Measurement Science and Technology 4 (2009), 1-17.
7. Y.D. Chen, R. Du and L.S. Qu, Fault features of large rotating machinery and diagnosis using sensor fusion, Journal of Sound and Vibration 2 (1995), 227-242.
8. B. Liu, Selection of wavelet packet basis for rotating machinery fault diagnosis, Journal of Sound and Vibration 3-5 (2005), 567-582. (Pubitemid 40643826)
9. Y.T. Sheen, A complex filter for vibration signal demodulation in bearing defect diagnosis, Journal of Sound and Vibration 1-2 (2004), 105-119.
10. Y. Yu, Y. Dejie and C. Junsheng, A roller bearing fault diagnosis method based on EMD energy entropy and ANN, Journal of Sound and Vibration 1-2 (2006), 269-277.
11. C.L. Chen and H.T.Yau, Chaos in the Imbalance Response of a Flexible Rotor Supported byOil Film Bearings with Non-Linear Suspension, Nonlinear Dynamics 1 (1998), 71-90.
12. R.D. Brown, P. Addison and A.H.C. Chan, Chaos in the unbalance response of journal bearings, Nonlinear Dynamics 4 (1994), 421-432.
13. J. Yang, Y. Zhang and Y. Zhu, Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension, Mechanical Systems and Signal Processing 5 (2007), 2012-2024. (Pubitemid 46550771)
14. C. Craig, R.D. Neilson and J. Penman, The use of correlation dimension in condition monitoring of systems with clearance, Journal of Sound and Vibration 1 (2000), 1-17.
15. D. Logan and J. Mathew, Using the correlation dimension for vibration fault diagnosis of rolling element bearings - I. Basic concepts, Mechanical Systems and Signal Processing 3 (1996), 241-250. (Pubitemid 126359392)
16. J.D. Jiang, J. Chen and L.S. Qu, The application of correlation dimension in gearbox condition monitoring, Journal of Sound and Vibration 4 (1999), 529-541. (Pubitemid 129550292)
17. W.J.Wang, J. Chen, X.K.Wu and Z.T.Wu, The application of some non-linear methods in rotating machinery fault diagnosis, Mechanical Systems and Signal Processing 4 (2001), 697-705. (Pubitemid 32732398)
18. C.H. Chen, R.J. Shyu and C.K. Ma, Rotating machinery diagnosis using wavelet packets-fractal technology and neural networks, Journal of Mechanical Science and Technology 7 (2007), 1058-1065.
19. L.S. Liebovitch and T. Toth, A fast algorithm to determine fractal dimensions by box counting, Physics Letters A 8-9 (1989), 386-390.
20. F. Camastra and A. Vinciarelli, Estimating the intrinsic dimension of data with a fractal-based method, IEEE Transactions on Pattern Analysis and Machine Intelligence 10 (2002), 1404-1407. (Pubitemid 35327462)
21. T. Marwala, U. Mahola and F.V. Nelwamondo, Hidden markov models and gaussian mixture models for bearing fault detection using fractals, in: IEEE International Conference on Neural Networks - Conference Proceedings, 2006, 3237-3242. (Pubitemid 351369754)
22. P. Maragos and F.-K. Sun, Measuring the fractal dimension of signals: Morphological covers and iterative optimization, IEEE Transactions on Signal Processing 1 (1993), 108-121.
23. Y. Xia, D. Feng and R. Zhao, Morphology-based multifractal estimation for texture segmentation, IEEE Transactions on Image Processing 3 (2006), 614-623. (Pubitemid 43257560)
24. P. Maragos and A. Potamianos, Fractal dimensions of speech sounds: Computation and application to automatic speech recognition, Journal of the Acoustical Society of America 3 (1999), 1925-1932.
25. A.R. Backes and O.M. Bruno, Shape classification using complex network and Multi-scale Fractal Dimension, Pattern Recognition Letters 1 (2010), 44-51.
26. J. Serra, Introduction to mathematical morphology, Computer Vision, Graphics, and Image Processing 3 (1986), 283-305.
27. P. Maragos and R.W. Schafer, Morphological filters - Part i: Their set - Theoretic analysis and relations to linear shift - invariant filters, IEEE Transactions on Acoustics, Speech, and Signal Processing 8 (1987), 1153-1169. (Pubitemid 17651298)
28. P. Maragos and R.W. Schafer, Morphological filters - Part ii: Their relations tomedian, order - statistic, and stack filters, IEEE Transactions on Acoustics, Speech, and Signal Processing 8 (1987), 1170-1184. (Pubitemid 17651297)
29. K.A. Loparo, Case Western Reserve University, http://www.eecs.cwru.edu/ laboratory/bearing/download.htm (2000).
30. P.J. Grother, G.T. Candela and J.L. Blue, Fast implementations of nearest neighbor classifiers, Pattern Recognition 3 (1997), 459-465. (Pubitemid 127406431)
31. C.J. Veenman and M.J.T. Reinders, The nearest subclass classifier: A compromise between the nearest mean and nearest neighbor classifier, IEEE Transactions on Pattern Analysis and Machine Intelligence 9 (2005), 1417-1429. (Pubitemid 41387773)
32. J.A.K. Suykens and J. Vandewalle, Least squares support vector machine classifiers, Neural Processing Letters 3 (1999), 293-300.
33. P.J. R.P.W. Duin, P. Paclik, E. Pekalska, D. de Ridder, D.M.J. Tax and S. Verzakov, PRTools4.1, A Matlab Toolbox for Pattern Recognition, Delft University of Technology (2007).
34. LS-SVMlab1.5, http://www.esat.kuleuven.ac.be/sista/lssvmlab/.
35. B. Samanta and K.R. Al-Balushi, Artificial neural network based fault diagnostics of rolling element bearings using time-domain features, Mechanical Systems and Signal Processing 2 (2003), 317-328.
36. Q. Sun, P. Chen, D. Zhang and F. Xi, Pattern recognition for automatic machinery fault diagnosis, Journal of Vibration and Acoustics, Transactions of the ASME 2 (2004), 307-316.
37. Q. Hu, Z. He, Z. Zhang and Y. Zi, Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble, Mechanical Systems and Signal Processing 2 (2007), 688-705. (Pubitemid 44667414)
38. Y. Lei, Z. He and Y. Zi, A new approach to intelligent fault diagnosis of rotating machinery, Expert Systems with Applications 4 (2008), 1593-1600.
39. T. Karacay and N. Akturk, Experimental diagnostics of ball bearings using statistical and spectral methods, Tribology International 6 (2009), 836-843.
40. P. Stepanic, I.V. Latinovic and Z. Djurovic, A new approach to detection of defects in rolling element bearings based on statistical pattern recognition, International Journal of Advanced Manufacturing Technology (2009), 1-10.
41. Y. Lei, Z. He and Y. Zi, Application of an intelligent classification method to mechanical fault diagnosis, Expert Systems with Applications 6 (2009), 9941-9948.