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Proceedings Paper

Application of lifting wavelet and random forest in compound fault diagnosis of gearbox
Author(s): Tang Chen; Yulian Cui; Fuzhou Feng; Chunzhi Wu
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Paper Abstract

Aiming at the weakness of compound fault characteristic signals of a gearbox of an armored vehicle and difficult to identify fault types, a fault diagnosis method based on lifting wavelet and random forest is proposed. First of all, this method uses the lifting wavelet transform to decompose the original vibration signal in multi-layers, reconstructs the multi-layer low-frequency and high-frequency components obtained by the decomposition to get multiple component signals. Then the time-domain feature parameters are obtained for each component signal to form multiple feature vectors, which is input into the random forest pattern recognition classifier to determine the compound fault type. Finally, a variety of compound fault data of the gearbox fault analog test platform are verified, the results show that the recognition accuracy of the fault diagnosis method combined with the lifting wavelet and the random forest is up to 99.99%.

Paper Details

Date Published: 5 March 2018
PDF: 6 pages
Proc. SPIE 10710, Young Scientists Forum 2017, 107102A (5 March 2018); doi: 10.1117/12.2317655
Show Author Affiliations
Tang Chen, Army Academy of Armored Forces (China)
Yulian Cui, Army Academy of Armored Forces (China)
Fuzhou Feng, Army Academy of Armored Forces (China)
Chunzhi Wu, Army Academy of Armored Forces (China)

Published in SPIE Proceedings Vol. 10710:
Young Scientists Forum 2017
Songlin Zhuang; Junhao Chu; Jian-Wei Pan, Editor(s)

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