He J, Wu P, Tong Y, Zhang X, Lei M, Gao J. Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN.
SENSORS 2021;
21:s21217319. [PMID:
34770636 PMCID:
PMC8588188 DOI:
10.3390/s21217319]
[Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 10/26/2021] [Accepted: 10/30/2021] [Indexed: 11/24/2022]
Abstract
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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