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Sensors 2017, 17(2), 425; doi:10.3390/s17020425

A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

State Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin 150001, China
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Author to whom correspondence should be addressed.
Academic Editor: Xue Wang
Received: 18 January 2017 / Revised: 16 February 2017 / Accepted: 20 February 2017 / Published: 22 February 2017
(This article belongs to the Section Physical Sensors)
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Abstract

Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions. View Full-Text
Keywords: intelligent fault diagnosis; convolutional neural networks; domain adaptation; anti-noise intelligent fault diagnosis; convolutional neural networks; domain adaptation; anti-noise
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, W.; Peng, G.; Li, C.; Chen, Y.; Zhang, Z. A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals. Sensors 2017, 17, 425.

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