IMECH-IR  > 高温气体动力学国家重点实验室
An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data
Liu Y(刘洋)1,2; Yan XS3,4,5; Zhang CA(张陈安)1; Liu W(刘文)1
Source PublicationSensors
2019-12-02
Volume19Issue:23Pages:5300
Abstract

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.

KeywordRotating Machinery Fault Diagnosis Multi-sensor Fusion Convolutional Neural Network Ensemble Model
DOI10.3390/s19235300
URL查看原文
Indexed BySCI ; EI
Language英语
WOS IDWOS:000507606200243
DepartmentLHD高超声速空气动力学
Classification二类/Q1
Ranking1
Contributor张陈安,Yan XS
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://dspace.imech.ac.cn/handle/311007/80803
Collection高温气体动力学国家重点实验室
空天飞行科技中心(筹)
Corresponding AuthorYan XS; Zhang CA(张陈安)
Affiliation1.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
Recommended Citation
GB/T 7714
Liu Y,Yan XS,Zhang CA,et al. An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data[J]. Sensors,2019,19(23):5300.
APA Liu Y,Yan XS,Zhang CA,&Liu W.(2019).An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data.Sensors,19(23),5300.
MLA Liu Y,et al."An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data".Sensors 19.23(2019):5300.
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