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Open AccessArticle

An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data

by Yang Liu 1,2, Xunshi Yan 3,4,5,*, Chen-an Zhang 1,* and Wen Liu 1
1
State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
2
School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China
3
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
4
The Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Beijing 100084, China
5
Collaborative Innovation Center of Advanced Nuclear Energy Technology, Beijing 100084, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(23), 5300; https://doi.org/10.3390/s19235300
Received: 1 November 2019 / Revised: 23 November 2019 / Accepted: 26 November 2019 / Published: 2 December 2019
(This article belongs to the Section Intelligent Sensors)
Multi-sensor data fusion is a feasible technique to achieve accurate and robust results in fault diagnosis of rotating machinery under complex conditions. However, the problem of information losses is always ignored during the fusion process. To solve above problem, an ensemble convolutional neural network model is proposed for bearing fault diagnosis. The framework of the proposed model contains three convolutional neural network branches: one multi-channel fusion convolutional neural network branch and two 1-D convolutional neural network branches. The former branch extracts the coupling features based on multi-sensor data and the latter two branches extract the inherent features based on single-sensor data, which can collect comprehensive fault information and reduce information losses. Furthermore, the support vector machine ensemble strategy is employed to fuse the results of multiple branches, which can improve the generalization and robustness of the proposed model. The experiments show that the proposed can obtain more effective and robust results than other methods. View Full-Text
Keywords: rotating machinery; fault diagnosis; multi-sensor fusion; convolutional neural network; ensemble model rotating machinery; fault diagnosis; multi-sensor fusion; convolutional neural network; ensemble model
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Liu, Y.; Yan, X.; Zhang, C.-A.; Liu, W. An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data. Sensors 2019, 19, 5300.

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