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Proceedings Paper

A method of gear defect intelligent detection based on transmission noise
Author(s): Hong-fang Chen; Yun Zhao; Jia-chun Lin; Mian Guo
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Paper Abstract

A new approach was proposed by combing Ensemble Empirical Mode Decomposition (EEMD) algorithm and Back Propagation (BP) neural network for detection of gear through transmission noise analysis. Then feature values of the feature signals are calculated. The feature values which have a great difference for different defect types are chosen to build an eigenvector. BP neural network is used to train and learn on the eigenvector for recognition of gear defects intelligently. In this study, a comparative experiment has been performed among normal gears, cracked gears and eccentric gears with fifteen sets of different gears. Experimental results indicate that the proposed method can detect gear defect features carried by the transmission noise effectively.

Paper Details

Date Published: 6 March 2015
PDF: 10 pages
Proc. SPIE 9446, Ninth International Symposium on Precision Engineering Measurement and Instrumentation, 944649 (6 March 2015); doi: 10.1117/12.2181856
Show Author Affiliations
Hong-fang Chen, Beijing Univ. of Technology (China)
Yun Zhao, Beijing Univ. of Technology (China)
Jia-chun Lin, Beijing Univ. of Technology (China)
Mian Guo, Beijing Univ. of Technology (China)

Published in SPIE Proceedings Vol. 9446:
Ninth International Symposium on Precision Engineering Measurement and Instrumentation
Junning Cui; Jiubin Tan; Xianfang Wen, Editor(s)

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