中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data

文献类型:期刊论文

作者Yang Liu(刘洋)4,5; Xunshi Yan1,2,3; Zhang CA(张陈安)5; Wen Liu (刘文)5; Liu Y(刘洋); Liu W(刘文)
刊名Sensors
出版日期2019-12-02
卷号19期号:23页码:5300
关键词Rotating Machinery Fault Diagnosis Multi-sensor Fusion Convolutional Neural Network Ensemble Model
DOI10.3390/s19235300
英文摘要

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.

分类号二类/q1
URL标识查看原文
语种英语
源URL[http://dspace.imech.ac.cn/handle/311007/80803]  
专题力学研究所_高温气体动力学国家重点实验室
空天飞行科技创新研究中心(筹)
通讯作者Yang Liu(刘洋); Zhang CA(张陈安)
作者单位1.Collaborative Innovation Center of Advanced Nuclear Energy Technology, Beijing 100084, China
2.The Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Beijing 100084, China
3.Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
4.School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China
5.State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
推荐引用方式
GB/T 7714
Yang Liu,Xunshi Yan,Zhang CA,et al. An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data[J]. Sensors,2019,19(23):5300.
APA Yang Liu,Xunshi Yan,Zhang CA,Wen Liu ,Liu Y,&Liu W.(2019).An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data.Sensors,19(23),5300.
MLA Yang Liu,et al."An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data".Sensors 19.23(2019):5300.

入库方式: OAI收割

来源:力学研究所

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