Deep Stack Dictionary learning for Fault Diagnosis of Rotating Machinery

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Published Jul 14, 2017
Jinyang Jiao Ming Zhao Jing Lin

Abstract

Effective feature extraction of rotating machinery has been a hot topic in the prognosis and health management. However, it is a challenging problem to extract periodic impulses under heavy background noise and other interference. In the last decade, deep learning and dictionary learning have been promising methods to extract feature information, which have made great achievement in the field of image, video denoising, etc. In this paper, via fusing the deep learning with dictionary learning, an algorithm called deep stack dictionary learning is proposed. This algorithm is trained in a layer-wise greedy manner so as to suppress the noise and highlight periodic impulses.

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Keywords

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References
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Section
Regular Session Papers