Fault diagnosis approach based on fractal dimension LLE and Fisher discriminant

Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument

Volumn 31, Issue 2, 2010, Pages 325-333

  Zhang, Wei ^a,b   Zhou, Weijia ^a,c   Li, Bin ^a  

^a SHENYANG INSTITUTE OF AUTOMATION   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c Wisconsin Center for Space Automation and Robotics   (United States)

Author keywords

Fault diagnosis; Fisher discriminant; Intrinsic dimension; Local linear embedding (LLE); Nonlinear dimensionality reduction

Indexed keywords

FAULT DIAGNOSIS; FISHER DISCRIMINANT; INTRINSIC DIMENSIONS; LOCAL LINEAR EMBEDDING; NONLINEAR DIMENSIONALITY REDUCTION;

FAILURE ANALYSIS; FRACTAL DIMENSION; NONLINEAR ANALYSIS; NONLINEAR SYSTEMS; PARTIAL DISCHARGES; SENSORS;

FAULT DETECTION;

EID: 77950976748     PISSN: 02543087     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. Xu B G, Song Ai G. EEG signal recognition method based on wave packet transform and clustering analysis[J]. Chinese Journal of Scientific Instrument, 2009, 30(1): 24-28.
2. Yang J, Wen Y M, Li P. Features extraction and identification of leak acoustic signal in water distribution pipelines using correlation analysis and approximate entropy[J]. Chinese Journal of Scientific Instrument, 2009, 30(2): 272-279.
3. Samuel L. Some studies in machine learning using the game of checkers[J]. Part II. IBM Journal Research and Development, 1967, 11(4):601-608.
4. Sang H F, Yuan W Q. Palmprint recognition based on two-dimensional principle component analysis[J]. Chinese Journal of Scientific Instrument, 2008, 29(9): 1929-1933
5. Dunia R. Use of principal component analysis for sensor fault identification[J]. Computers chem. Eng., 1996,(1.20):713-718.
6. Jackson J E. Principal component and factor analysis: Part I - principal component[J]. Journal of Quality Technology, 1980,(12):201-211.
7. Hastie T. Principle curves and surfaces[R]. Technical Report, Laboratory for Computational Statistics, Stanford University, 1984.
8. Roweis S, Saul L. Nonlinear Dimensionality Reduction by Locally Linear Embedding[J]. Science, 2000, 290(22):2323-2326.
9. Donoho D L, Grimes C. Hessian eigenmaps: locally linear embedding, techniques for high-dimensional data[C]. Proceedings of the National Academy of Sciences, 2003, 100(10):5591-5596.
10. De Ridder D, Kouropteva O, Okun O. Supervised locally linear embedding[R]. Lecture Notes in Artificial Intelligence Germany: Springer, 2003:333-341.
11. Kegl B. Intrinsic dimension estimation using packing numbers[C]. Neural Information Processing Systems 15 (NIPS'2002), 2003.
12. Grassbergel J, Procacria L. Measuring the strangeness of strange attractor[J]. Physical D, 1983, (9):189-208.
13. Downs J J, Vogel E F. Plant-wide industrial process control problem[J]. Computers & Chemical Engineering, 1993, 17(3):245-255.
14. Bengio Y, Paiement J F, Vincen T P. Out-of- sample extensions for LlE, isomap, MDS, Eigenmaps, and spectral clustering[R]. Technical Report 1238, Univ. de Montreal, 2003. R
15. Taylor J S, Cristianini N. Kernel Methods for Pattern Analysis[M]. Cambridge University Press, 2004.