Improved kernel fisher discriminant analysis for fault diagnosis

Authors:
Junhong Li;Peiling Cui
Affiliations:
Aigo Research Institute of Image Computing, Beijing 100089, China;School of Instrumentation Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

This paper improves kernel fisher discriminant analysis (KFDA) for fault diagnosis from three aspects. Firstly, a feature vector selection (FVS) scheme based on a geometrical consideration is given to reduce the computational complexity of KFDA when the number of samples becomes large. Secondly, a new kernel function, called the Cosine kernel, is proposed to increase the discriminating capability of the original polynomial kernel function. Thirdly, nearest feature line (NFL) classifier is employed to further enhance the fault diagnosis performance when the sample number is very small. Experimental results show the effectiveness of our methods.