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Issues and Tips:A Set of Integrated Experiments of Applying Auto-Encoder and Convolutional Neural Network in Feature Extraction and Fault Diagnosis
Li, Xudong1; Li, Mingtao1; Zheng, Jianhua1; Hu, Yang2
Department空间技术部
Source Publication2018 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-CHONGQING 2018)
2018
Pages1301-1306
DOI10.1109/PHM-Chongqing.2018.00228
Language英语
ISSN2166-5656
ISBN978-1-5386-5380-7
AbstractIn recent years, deep learning technology has made a breakthrough and rapid development, and it provides a new direction for the research of Prognostics Health and Management (PHM). In this paper, we propose two deep learning models to solve feature extraction problem and faults diagnosis problem. First model is based on Auto-Encoder (AE) and Support Vector Machines (SVM). AE is used to reduce the dimensions of original signal and efficiently extract features. Then the extracted features are classified as the input of SVM. Second model is based on Convolution Neural Network (CNN), we propose a 1D-CNN model to process the original bearing vibration signal and directly output the type of fault. These models have yielded good results on the milling datasets and CWRU bearing dataset respectively. This paper verified the feasibility of these methods, summarized the application experiences and obtained their performance indicators as a benchmark for research.
Keyworddeep learning PHM AE CNN
Conference NamePrognostics and System Health Management Conference (PHM-Chongqing)
Conference DateOCT 26-28, 2018
Conference PlaceChongqing, PEOPLES R CHINA
Indexed ByCPCI ; EI
Citation statistics
Document Type会议论文
Identifierhttp://ir.nssc.ac.cn/handle/122/6967
Collection空间技术部
Affiliation1.National Space Science Center, CAS, University of Chinese Academy of Sciences, Beijing, China;
2.Science and Technology on Complex Aviation System Simulation Laboratory, Beijing; 9236, China
Recommended Citation
GB/T 7714
Li, Xudong,Li, Mingtao,Zheng, Jianhua,et al. Issues and Tips:A Set of Integrated Experiments of Applying Auto-Encoder and Convolutional Neural Network in Feature Extraction and Fault Diagnosis[C],2018:1301-1306.
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