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 |
DOI | 10.3390/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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