Machinery fault diagnosis using supervised manifold learning

Mechanical Systems and Signal Processing

Volumn 23, Issue 7, 2009, Pages 2301-2311

  Jiang, Quansheng ^a,b   Jia, Minping ^a   Hu, Jianzhong ^a   Xu, Feiyun ^a  

^a SOUTHEAST UNIVERSITY   (China)

^b CHAOHU UNIVERSITY   (China)

Author keywords

Fault diagnosis; Laplacian eigenmaps; Pattern classification; Supervised manifold learning

Indexed keywords

BENCHMARK DATA; CLASS LABELS; CLASSIFICATION PERFORMANCE; COMPLEX MODES; FAULT DATA; FAULT DIAGNOSIS; FEATURE EXTRACTION METHODS; FEATURE SPACE; GEOMETRIC STRUCTURE; HIGH-DIMENSIONAL; LAPLACIAN EIGENMAPS; MACHINERY FAULTS; MANIFOLD LEARNING; MANIFOLD LEARNING ALGORITHM; NEW APPROACHES; OBSERVATION DATA; PATTERN CLASSIFICATION; RECOGNITION PERFORMANCE; SUPERVISED MANIFOLD LEARNING;

EDUCATION; ELECTRIC FAULT LOCATION; FAILURE ANALYSIS; LAPLACE TRANSFORMS; LEARNING ALGORITHMS; MACHINERY; QUALITY ASSURANCE;

FEATURE EXTRACTION;

EID: 67349249907     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2009.02.006     Document Type: Article

References (26)

1. Kohonen T. Self-Organization and Associative Memory (1988), Springer, Berlin
2. Yuan S.F., and Chu F.L. Fault diagnostics based on particle swarm optimisation and support vector machines. Mechanical Systems and Signal Processing 21 4 (2007) 1787-1798
3. Jolliffe I.T. Principal Component Analysis (1986), Springer, New York
4. Cox T.F., and Cox M.A. Multi-Dimensional Scaling (1994), Chapman & Hall, London
5. Hyvarinen A., and Oja E. Independent component analysis: algorithms and applications. Neural Networks 13 425 (2000) 411-430
6. Sammon J.W. A nonlinear mapping for data structure analysis. IEEE Transactions on Computers C 18 5 (1969) 401-409
7. Lowe D., and Tipping M.E. Neuroscale: novel topographic feature extraction using RBF networks. In: Mozer M.C., Jordan M.I., and Petsche T. (Eds). Advances in Neural Information Processing Systems (1997), MIT Press, Cambridge, MA, London, UK 543-549
8. Tenenbaum J., Silva D.D., and Langford J. A global geometric framework for nonlinear dimensionality reduction. Science 290 5500 (2000) 2319-2323
9. Roweis S., and Saul L. Nonlinear dimensionality reduction by locally linear embedding. Science 290 5500 (2000) 2323-2326
10. Belkin M., and Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15 6 (2003) 1373-1396
11. Belkin M., and Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. Neural Information Processing Systems 14 (2001) 585-591
12. Zhang Z.Y., and Zha H.Y. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM Journal of Scientific Computing 26 1 (2005) 313-318
13. Samuel K., and Martin D.L. Face detection in gray scale images using locally linear embeddings. Computer Vision and Image Understanding (2006) 1-20
14. Vlachos M., Domeniconi C., and Gunopulos D. Non-linear dimensionality reduction techniques for classification and visualization. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2002), ACM Press, Edmonton, NY 645-651
15. Zhang J.P., Li. S.Z., and Wang J. Manifold learning and applications in recognition. In: Peng T.Y., Hui Y.K., and Lipo W. (Eds). Intelligent Multimedia Processing with Soft Computing (2005), Springer, Heidelberg 281-300
16. Yang J.H., Xu J.W., and Yang D.B. Noise reduction method for nonlinear time series based on principal manifold learning and its application to fault diagnosis. Chinese Journal of Mechanical Engineering 42 8 (2006) 154-158 (In Chinese)
17. Ridder D.D., Kouropteva O., and Okun O. Supervised locally linear embedding. In: Kaynak O., Alpaydin E., Oja E., and Xu L. (Eds). ICANN/ICONIP 2003. Lecture Notes in Computer Science (2003), Springer, Heidelberg 333-341
18. M. Vlachos, C. Domeniconi, D. Gunopulos, Non-linear dimensionality reduction techniques for classification and visualization, in: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, AB, Canada, 2002, pp. 645-651.
19. Zhang J.P., Shen H.X., and Zhou Z.H. Unified locally linear embedding and linear discriminant analysis algorithm (ULLELDA) for face recognition. In: Li S.Z., Lai J., Tan T., Feng G., and Wang Y. (Eds). Advances in Biometric Personal Authentication. Lecture Notes in Computer Science vol. 3338 (2004), Springer, Berlin, Heidelberg, New York 209-307
20. Zhao H.T., Sun S.Y., and Jing Z.L. Local structure based supervised feature extraction. Pattern Recognition 39 8 (2006) 1546-1550
21. Cheng J., Liu Q.S., and Lu H.Q. Supervised kernel locality preserving projections for face recognition. Neurocomputing 67 (2005) 443-449
22. Silva V.D., and Tenenbaum J.B. Global versus local methods in nonlinear dimensionality reduction. Advances in Neural Information Proceeding Systems (2002), MIT Press, Cambridge, MA 705-712
23. Yan S.C., Xu D., and Zhang B. Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Transactions on PAMI 29 1 (2007) 40-51
24. Yin J.S., Hu D.W., and Zhou Z.T. Growing locally linear embedding for manifold learning. Journal of Pattern Recognition Research 2 1 (2007) 1-16
25. C.L. Blake, C.J. Merz, UCI repository of machine learning databases, 1998.
26. G.L. Liao, Research on technology of Mechanical condition monitoring and fault diagnosis based on unsupervised learning, Ph.D. Thesis, School of Mechanical Engineering, Huazhong University of Science and Technology, Wuhan, China, 2003.