A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion.

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作者： Wenfeng,Gong ^[1] ; Hui,Chen ^[2] ; Zehui,Zhang ^[3] ; Meiling,Zhang ^[4] ; Ruihan,Wang ^[5] ; Cong,Guan ^[6] ; Qin,Wang ^[7]

作者单位： Key Laboratory of High-Performance Ship Technology of Ministry of Education in China, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China. wfgongcn@163.com. ^[1] Beihai campus, Guilin University of Electronic and Technology, Beihai 536000, China. wfgongcn@163.com. ^[2] Key Laboratory of High-Performance Ship Technology of Ministry of Education in China, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China. hchen@whut.edu.cn. ^[3] Key Laboratory of High-Performance Ship Technology of Ministry of Education in China, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China. zhangtianxia918@163.com. ^[4] Beihai campus, Guilin University of Electronic and Technology, Beihai 536000, China. zhangmeilingcn@163.com. ^[5] Key Laboratory of High-Performance Ship Technology of Ministry of Education in China, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China. rhan_wang@163.com. ^[6] Key Laboratory of High-Performance Ship Technology of Ministry of Education in China, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China. guancong2008@gmail.com. ^[7] Beihai campus, Guilin University of Electronic and Technology, Beihai 536000, China. guetwqin@163.com. ^[8]

关键词 convolutional neural network data fusion deep learning global average pooling intelligent fault diagnosis multichannel rotating machinery support vector machine

DOI 10.3390/s19071693

PMID 30970672

发布时间 2020-02-25

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