Feature selection for fault level diagnosis of planetary gearboxes

Zhiliang Liu (), Xiaomin Zhao, Ming Zuo and Hongbing Xu

Advances in Data Analysis and Classification, 2014, vol. 8, issue 4, 377-401

Abstract: Feature selection is critical to maintain high performance of classification-based fault diagnosis with a large feature size. In this paper, we propose a criterion to evaluate features effectiveness by class separability that is defined on cosine similarity in the kernel space of the Gaussian radial basis function. We develop a feature selection algorithm accordingly using the proposed criterion together with sequential backward selection and a feature re-ranking mechanism. We then employ the proposed feature selection algorithm to determine fault-sensitive features and select them for fault level diagnosis of planetary gearboxes. The experimental results demonstrate that the proposed algorithm can effectively reduce the feature size and improve accuracy of fault level diagnosis simultaneously. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Fault diagnosis; Feature selection; Class separability; Cosine similarity; Planetary gearbox; 93C85; 68T10; 62H30 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11634-014-0168-4

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