A recognition and novelty detection approach based on Curvelet transform, nonlinear PCA and SVM with application to indicator diagram diagnosis

Authors:
Kun Feng;Zhinong Jiang;Wei He;Bo Ma
Affiliations:
Diagnosis and Self-recovery Engineering Research Center, Beijing University of Chemical Technology, Beijing 100029, China;Diagnosis and Self-recovery Engineering Research Center, Beijing University of Chemical Technology, Beijing 100029, China;Diagnosis and Self-recovery Engineering Research Center, Beijing University of Chemical Technology, Beijing 100029, China;Diagnosis and Self-recovery Engineering Research Center, Beijing University of Chemical Technology, Beijing 100029, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Indicator diagram plays an important role in the health monitoring and fault diagnosis of reciprocating compressors. Different shapes of indicator diagram indicate different faults of reciprocating compressor. A proper feature extraction and pattern recognition method for indicator diagram is significant for practical uses. In this paper, a novel approach is presented to handle the multi-class indicator diagrams recognition and novelty detection problems. When multi-class faults samples are available, this approach implements multi-class fault recognition; otherwise, the novelty detection is implemented. In this approach, the discrete 2D-Curvelet transform is adopted to extract the representative features of indicator diagram, nonlinear PCA is employed for multi-class recognition to reduce dimensionality, and PCA is used for novelty detection. Finally, multi-class and one-class support vector machines (SVMs) are used as the classifier and novelty detector respectively. Experimental results showed that the performance of the proposed approach is better than the traditional wavelet-based approach.