Journal
MEASUREMENT
Volume 46, Issue 3, Pages 1178-1189Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2012.11.025
Keywords
Ensemble empirical mode decomposition; Kernel principal component analysis; Support vector machine; Feature extraction; Fault diagnosis
Funding
- National Natural Science Foundation of China
- Civil Aviation Administration of China [60939003, 61179058]
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Effective fault location classification and especially performance degradation assessment of a roller bearing have been the subject extensive research, which can reduce costs and the nonscheduled down time. In this paper, a new fault diagnosis method based on multiple features, kernel principal component analysis (KPCA) and particle swarm optimization-support vector machine (PSO-SVM) is put forward. First, traditional features of the vibration signals in time-domain and frequency-domain are calculated, and then two types of features referred to as singular values and AR model parameters based on ensemble empirical mode decomposition (EEMD) are introduced. After that, the original feature vectors are mapped into higher dimensional space and the kernel principal components are extracted as new feature vectors, which are used as inputs to PSO-SVM. The experimental results show that the new diagnosis approach proposed in this paper can identify not only the fault locations but also the performance degradation of the roller bearing. (c) 2012 Elsevier Ltd. All rights reserved.
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