Intelligent fault detection using raw vibration signals via dilated convolutional neural networks


Fault detection and diagnosis is critical to improve the reliability and availability in induction motors (IMs). Machine learning and deep learning techniques have been widely used in induction motor fault detection and diagnosis. In this paper, we propose a new deep learning model based on a dilated convolutional neural network (D-CNN) for detecting bearing faults in IMs. The proposed model works directly on raw vibration signals without any hand-crafted feature extraction process. Our model can incorporate global context without losing important local information by stacking dilated convolutions with an increasing width. Numerical results show that the proposed D-CNN is not only capable of classifying normal signals perfectly but also can achieve higher accuracy than conventional techniques under noisy environments.

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This research was in part supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2017R1C1B1012259) and in part supported by Korea Electric Power Corporation (Grant No. R17XA05-22).

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Correspondence to Yong-Hwa Kim.

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This work is an extended version of [14].

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Khan, M.A., Kim, YH. & Choo, J. Intelligent fault detection using raw vibration signals via dilated convolutional neural networks. J Supercomput 76, 8086–8100 (2020).

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  • Dilated convolution
  • Intelligent fault detection
  • Vibration signals
  • Deep neural networks
  • Convolutional neural networks