Journal
SENSORS
Volume 21, Issue 21, Pages -Publisher
MDPI
DOI: 10.3390/s21217319
Keywords
multi-scale; CNN; dilated convolutional; fault diagnosis
Funding
- Social Development Project of Zhejiang Provincial Public Technology Research [LGF19F030004, LGG21F030015]
- Fundamental Research Funds of Zhejiang Sci-Tech University [2021Q024]
- State Key Laboratory of Industrial Control Technology, Zhejiang University, China [ICT2021B45]
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This paper proposes a novel bearing fault diagnosis method using an improved multi-scale convolutional neural network (IMSCNN) to extract multi-scale features and mitigate the effect of noise in vibration signals. Experimental results show the superiority of the proposed method compared to other related methods.
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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