Fault diagnosis method based on incremental enhanced supervised locally linear embedding and adaptive nearest neighbor classifier

Measurement: Journal of the International Measurement Confederation

Volumn 48, Issue 1, 2014, Pages 136-148

  Su, Zuqiang ^a   Tang, Baoping ^a   Ma, Jinghua ^a   Deng, Lei ^a  

^a CHONGQING UNIVERSITY   (China)

Author keywords

Adaptive nearest neighbor classifier; Fault diagnosis; Incremental enhanced locally linear embedding; Manifold learning; Vibration signal

Indexed keywords

FACE RECOGNITION; FAILURE ANALYSIS; MACHINERY; SAMPLING;

FAULT TYPE IDENTIFICATION; LOCALLY LINEAR EMBEDDING; MACHINERY FAULT DIAGNOSIS; MANIFOLD LEARNING; NEAREST NEIGHBOR CLASSIFIER; NONLINEAR DIMENSIONALITY REDUCTION; SUPERVISED LOCALLY LINEAR EMBEDDING; VIBRATION SIGNAL;

ITERATIVE METHODS;

EID: 84888260875     PISSN: 02632241     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.measurement.2013.10.041     Document Type: Article

References (28)

1. C. Shen, D. Wang, and F.R. Kong Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and generic support vector regressive classifier Measurement 46 2013 1551 1564
2. W.Y. Liu, Z.F. Wang, and J.G. Han Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM Renew. Energy 50 2013 1 6
3. B.W. Li, and Y. Zhang Supervised locally linear embedding projection for machinery fault diagnosis Mech. Syst. Signal Process. 25 2011 3125 3134
4. A.H. Zamanian, and A. Ohadi Gear fault diagnosis based on Gaussian correlation of vibrations signals and wavelet coefficients Appl. Soft Comput. 11 2011 4807 4819
5. D.P. Jena, and S.N. Panigrahi Bearing and gear fault diagnosis using adaptive wavelet transform of vibration signals Proc. Eng. 50 2012 265 274
6. Y.G. Lei, Z.J. He, and Y.Y. Zi A new approach to intelligent fault diagnosis of rotating machinery Exp. Syst. Appl. 35 2008 1593 1600
7. J.B. Yu Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models Mech. Syst. Signal Process. 25 2011 2573 2588
8. I.T. Jolliffe Principle Component Anal. 1986 Springer New York
9. T. Cox, and M. Cox Multidimensional Scaling 1994 Chapman & Hall London
10. A. Hyvarinen, and E. Oja Independent component analysis: algorithms and applications Neural Networks 13 425 2000 411 430 (Pubitemid 30447427)
11. Y.X. Huang, X.F. Zha, and J. Lee Discriminant diffusion maps analysis: a robust manifold learner for dimensionality reduction and its application in machine condition monitoring and fault diagnosis Mech. Syst. Signal Process. 34 2013 277 297
12. S.T. Rweis, and L.K. Saul Nonlinear dimensionality reduction by locally linear embedding Since 290 2000 2323 2326 (Pubitemid 32041578)
13. Z.Y. Zhang, and H.Y. Zha Principal manifolds and nonlinear dimensionality reduction via tangent space alignment Soc. Ind. Appl. Math. J. Sci. Comput. 26 2004 313 338
14. Y.J. Cheon, and D.J. Kim Natural facial expression recognition using differential-AAM and manifold learning Pattern Recogn. 42 2009 1340 1350
15. B. Li, C.H. Zheng, and D.S. Huang Gene expression data classification using locally linear discriminant embedding Comput. Biol. Med. 40 2010 802 810
16. D.D. Ridder, O. Kouropteva, O. Okun, Supervised locally linear embedding, in: Processing of the Joint International Conference, ICANN/ICONIP 2003. Lecture Notes in Computer Science, Springer, Heidelbeg, 2003, pp. 333-341.
17. X. Geng, D.C. Zhan, and Z.H. Zhou Supervised nonlinear dimensionality reduction for visualization and classification IEEE Trans. Syst. Man Cyber.-Part B: Cyber. 35 2005 1098 1107 (Pubitemid 41800411)
18. Q.S. Jiang, M.P. Jia, and J.Z. Hu Machinery fault diagnosis using supervised manifold learning Mech. Syst. Signal Process. 23 2009 2301 2311
19. S.Q. Zhang Enhanced supervised locally linear embedding Pattern Recogn. Lett. 30 2009 1208 1218
20. S.M. Xiang, F.P. Nie, and Y.Q. Song Embedding new date for manifold learning via coordinate propagation Knowledge Inform. Syst. J. 19 2009 159 184
21. D.H. Pandya, S.H. Upadhyay, and S.P. Harsha Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN Expert Syst. Appl. 40 2013 4137 4145
22. Z.B. Zhu, and Z.H. Song A novel fault diagnosis system using pattern classification on kernel FDA subspace Expert Syst. Appl. 38 2011 6895 6905
23. Y. Xu, Q. Zhu, and Z. Fan Coarse to fine K nearest neighbor classifier Pattern Recogn. Lett. 2013 http://dx.doi.org/10.1016/j.patrec.2013.01.028
24. Z.J. Shen, X.F. Chen, and X.L. Zhang A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM Measurement 45 2012 30 40
25. N.E. Huang, Z. Shen, S.R. Long, M.L.C. Wu, H.H. Shih, Q.N. Zheng, N.C. Yen, C.C. Tung, and H.H. Liu The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis Proc. Roy. Soc. Lond. Ser. A - Math. Phys. Eng. Sci. 454 1971 1998 903 995 (Pubitemid 128733183)
26. Y.G. Lei, J. Lin, and Z.J. He A review on empirical mode decomposition in fault diagnosis of rotating machinery Mech. Syst. Signal Process. 35 2013 108 126
27. X. He, and P. Niyogi Locality Preserving Projections, Advances in Neural Information Processing Systems 16 2003 MIT Press Cambridge, Mass
28. M. Sugiyama Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis J. Mach. Learn. Res. 8 2007 1027 1061 (Pubitemid 46798409)