Sensors (Nov 2021)

Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN

  • Jiajun He,
  • Ping Wu,
  • Yizhi Tong,
  • Xujie Zhang,
  • Meizhen Lei,
  • Jinfeng Gao

DOI
https://doi.org/10.3390/s21217319
Journal volume & issue
Vol. 21, no. 21
p. 7319

Abstract

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Bearings are the key and important components of rotating machinery. Effective bearing fault diagnosis can ensure operation safety and reduce maintenance costs. This paper aims to develop a novel bearing fault diagnosis method via an improved multi-scale convolutional neural network (IMSCNN). In traditional convolutional neural network (CNN), a fixed convolutional kernel is often employed in the convolutional layer. Thus, informative features can not be fully extracted for fault diagnosis. In the proposed IMSCNN, a 1D dimensional convolutional layer is used to mitigate the effect of noise contained in vibration signals. Then, four dilated convolutional kernels with different dilation rates are integrated to extract multi-scale features through the inception structure. Experimental results from the popular CWRU and PU datasets show the superiority of the proposed method by comparison with other related methods.

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