Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 72-73, Issue -, Pages 92-104Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2015.11.014
Keywords
Deep belief networks (DBNs); Wavelet packet transform (WPT); Hierarchical diagnosis network (HDN); Fault diagnosis; Rolling element bearing
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Funding
- National Key Basic Research Program of China (973 Program) [2014CB049500]
- Key Technologies R&D Program of Anhui Province [1301021005]
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A novel hierarchical diagnosis network (HDN) is proposed by collecting deep belief networks (DBNs) by layer for the hierarchical identification of mechanical system. The deeper layer in HDN presents a more detailed classification of the result generated from the last layer to provide representative features for different tasks. A two-layer HDN is designed for a two stage diagnosis with the wavelet packet energy feature. The first layer is intended to identify fault types, while the second layer is developed to further recognize fault severity ranking from the result of the first layer. To confirm the effectiveness of HDN, two similar networks constructed by support vector machine and back propagation neuron networks (BPNN) are employed to present a comprehensive comparison. The experimental results show that HDN is highly reliable for precise multi-stage diagnosis and can overcome the overlapping problem caused by noise and other disturbances. (C) 2015 Elsevier Ltd. All rights reserved.
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