Novel method for bearing performance degradation assessment - A kernel locality preserving projection-based approach

Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science

Volumn 228, Issue 3, 2014, Pages 548-560

  Sun, Chuang ^a   Zhang, Zhousuo ^a   He, Zhengjia ^a   Shen, Zhongjie ^a   Chen, Binqiang ^a   Xiao, Wenrong ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Bearing; degradation assessment; kernel locality preserving projection; non linear subspace

Indexed keywords

BEARING PERFORMANCE; DEGRADATION ASSESSMENT; DEGRADATION DEGREE; KERNEL LOCALITY PRESERVING PROJECTIONS; LOCALITY PRESERVING PROJECTIONS; LOCALITY-PRESERVING; MECHANICAL RELIABILITY; NON-LINEAR SUBSPACE;

BEARINGS (STRUCTURAL); ENGINEERING; INDUSTRIAL ENGINEERING;

BEARINGS (MACHINE PARTS);

EID: 84893680430     PISSN: 09544062     EISSN: 20412983     Source Type: Journal    
DOI: 10.1177/0954406213486735     Document Type: Article

References (36)

1. Wang SB, Huang W, Zhu ZK. Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis. Mech Syst Signal Process. 2011 ; 25: 1299-1320
2. Xiao WB, Chen J, Dong GM, et al. A multichannel fusion approach based on coupled hidden Markov models for rolling element bearing fault diagnosis. Proc IMechE, Part C: J Mechanical Engineering Science. 2012 ; 226: 202-216
3. Zhang L, Xiong G, Liu H, et al. Applying improved multi-scale entropy and support vector machines for bearing health condition identification. Proc IMechE, Part C: J Mechanical Engineering Science. 2010 ; 224: 1315-1325
4. Pan YN, Chen J, Li XL. Spectral entropy: a complementary index for rolling element bearing performance degradation assessment. Proc IMechE, Part C: J Mechanical Engineering Science. 2009 ; 223: 1223-1231
5. Renwick JT, Babson PE. Vibration analysis - a proven technique as a predictive maintenance tool. IEEE Trans Ind Appl. 1985 ; 21 (2). 324-332
6. Gebraeel N, Lawley M, Liu R, et al. Residual life predictions from vibration-based degradation signals: A neural network approach. IEEE Trans Ind Electron. 2004 ; 51: 694-700
7. Samanta B, Balushi K. Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech Syst Signal Process. 2003 ; 17: 317-328
8. Malhi A, Gao RX. PCA-based feature selection scheme for machine defect classification. IEEE Trans Instrum Meas. 2004 ; 53 (6). 1517-1525
9. He QB, Yan RQ, Kong FR, et al. Machine condition monitoring using principal component representations. Mech Syst Signal Process. 2009 ; 23: 446-466
10. Liao LX, Lee J. A novel method for machine performance degradation assessment based on ïxed cycle features test. J Sound Vib. 200 (326). 894-908
11. Widodo A, Yang BS. Application of relevance vector machine and survival probability to machine degradation assessment. Expert Syst Appl. 2011 ; 38 (3). 2592-2599
12. Pan YN, Chen J, Dong GM. A hybrid model for bearing performance degradation assessment based on support vector data description and fuzzy c-means. Proc IMechE, Part C: J Mechanical Engineering Science. 2009 ; 223: 2687-2695
13. Cong F, Chen J, Pan YN. Kolmogorov-Smirnov test for rolling bearing performance degradation assessment and prognosis. J Vib Control. 2011 ; 17 (9). 1337-1347
14. Huang RQ, Xi LF, Li XL., et al. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mech Syst Signal Process. 2007 ; 21: 193-207
15. Tenenbaum JB, Langford JC, Silva V. A global geometric framework for nonlinear dimensionality reduction. Science. 2000 ; 290 (5500). 2319-2323
16. Roweis S, Saul L. Nonlinear dimensionality reduction by locally linear embedding. Science. 2000 ; 290 (5500). 2323-2326
17. Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv Neural Inform Process Syst. 2002 ; 14: 585-591
18. He XF, Niyogi P. Locality preserving projections. Adv Neural Inform Process Syst. 2004 ; 16: 1-8
19. Lu G, Lin Zh, Jin Zh. Face recognition using discriminant locality preserving projections based on maximum margin criterion. Pattern Recog. 2010 ; 43: 3572-3579
20. Yu J, Liu M, Wu H. Local preserving projections-based feature selection and Gaussian mixture model for machine health assessment. Proc IMechE, Part C: J Mechanical Engineering Science. 2011 ; 225: 1223-1231
21. Yu J. Bearing performance degradation assessment using locality preserving projections. Expert Syst Appl. 2011 ; 38 (6). 7440-7450
22. He QB, Kong FR, Yan RQ. Subspace-based gearbox condition monitoring by kernel principal component analysis. Mech Syst Signal Process. 2007 ; 21: 1755-1772
23. Kwak N. Kernel discriminant analysis for regression problems. Pattern Recog. 2012 ; 45 (5). 2019-2031
24. Yan RQ, Gao RX. Approximate entropy as a diagnostic tool for machine health monitoring. Mech Syst Signal Process. 2007 ; 21: 824-839
25. Rolo-Naranjo A, Montesino-Otero ME. A method for the correlation dimension estimation for on-line condition monitoring of large rotating machinery. Mech Syst Signal Process. 2005 ; 19: 939-954
26. Loutridis S, Trochidis A. Classiïcation of gear faults using Hoelder exponents. Mech Syst Signal Process. 2004 ; 18: 1009-1030
27. Gao RX, Yan R. Non-stationary signal processing for bearing health monitoring. Int J Manuf Res. 2006 ; 1 (1). 18-39
28. Lei YG, He ZJ, Zi YY, et al. Fault diagnosis of rotating machinery based on multiple ANFIS combination with Gas. Mech Syst Signal Process. 2007 ; 21: 2280-2294
29. Chen BJ, Chen XF, Li B, et al. Reliability estimation for cutting tools based on logistic regression model using vibration signals. Mech Syst Signal Process. 2011 ; 25: 2526-2537
30. He ZJ, Zhang XL, Chen XF. A new method of reliability evaluation based on wavelet information entropy for equipment condition identification. J Phys: Conf Ser. 2012 ; 364 (1). 1-8
31. Li JB, Pan JS, Chu SC. Kernel class-wise locality preserving projection. Inform Sci. 2008 ; 178: 1825-1835
32. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov. 1998 ; 2 (2). 121-167
33. Guo G, Li S. Content-based audio classification and retrieval by support vector machines. IEEE Trans Neural Netw. 2003 ; 14 (1). 209-215
34. Lei Z, Zhang Z, Li SZ. Feature space locality constraint for kernel based nonlinear discriminant analysis. Pattern Recog. 2012 ; 45: 2733-2742
35. Jiang XD. Linear subspace learning-based dimensionality reduction. IEEE Signal Process Mag. 2011 ; 28 (2). 16-26
36. 2 -statistic PCA-based measures for damage assessment in structures. Struct Health Monit. 2011 ; 10 (5). 539-553