Kernel orthogonal local fisher discrimination for rotor fault diagnosis

Guangbin Wang; Liangpei Huang

doi:10.1117/12.867052

20 August 2010 Kernel orthogonal local fisher discrimination for rotor fault diagnosis

Guangbin Wang, Liangpei Huang

Proceedings Volume 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering; 78202Q (2010) https://doi.org/10.1117/12.867052
Event: International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 2010, Xi'an, China

Abstract

In order to better identify the fault of rotor system, one new method based on kernel orthogonal local fisher discriminant (KOLFD) is proposed.Considering kernel mapping and iteration-orthgonal idea,training data with supervision information was mapped to kernel space, computed local with-class scatter and between-class scatter, constructed kernel fisher discriminant function. To ensure the minimum reconstruction error during deimensionality reduction, algorithm joined the orthonormal constraints condition,found optimal basic projection vector by iterative orthogonal approach.Then testing data was mapped by this vector and got new data's class information by neighbor classifier,and eventually realize fault diagnosis.The experiment of rotor fault diagnosis shows, KOLFD algorithm has better effect to other manifold learning algorithm.

Citation Download Citation

Guangbin Wang and Liangpei Huang "Kernel orthogonal local fisher discrimination for rotor fault diagnosis", Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78202Q (20 August 2010); https://doi.org/10.1117/12.867052

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