Industrial gearbox fault diagnosis based on multi-scale convolutional neural networks and thermal imaging

Yongbo Li, Xiaoqiang Du, Xianzhi Wang, Shubin Si

科研成果: 期刊稿件文章同行评审

49 引用 (Scopus)

摘要

Infrared thermal technology plays a vital role in the health condition monitoring of gearbox. In the traditional infrared thermal technology-based methods, Gaussian pyramid is applied as the feature extraction approach, which has disadvantages of noise influence and information missing. Focus on such disadvantages, an improved multi-scale decomposition method combined with convolutional neural network is proposed to extract the fault features of the multi-scale infrared images in this paper. It can enlarge the data length at large scales, and thus reduce the fluctuations of feature values and reserve the fault information. The effectiveness of the proposed method is validated using the experiment infrared data of one industrial gearbox. Results demonstrate that our proposed method has the best performance comparing with five methods.

源语言英语
页(从-至)309-320
页数12
期刊ISA Transactions
129
DOI
出版状态已出版 - 10月 2022

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