| 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 Publication | 2018 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-CHONGQING 2018) |
| 2018 | |
| Pages | 1301-1306 |
| DOI | 10.1109/PHM-Chongqing.2018.00228 |
| Language | 英语 |
| ISSN | 2166-5656 |
| ISBN | 978-1-5386-5380-7 |
| Abstract | In 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. |
| Keyword | deep learning PHM AE CNN |
| Conference Name | Prognostics and System Health Management Conference (PHM-Chongqing) |
| Conference Date | OCT 26-28, 2018 |
| Conference Place | Chongqing, PEOPLES R CHINA |
| Indexed By | CPCI ; EI |
| Citation statistics | |
| Document Type | 会议论文 |
| Identifier | http://ir.nssc.ac.cn/handle/122/6967 |
| Collection | 空间技术部 |
| Affiliation | 1.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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 201808603536.pdf（413KB） | 会议论文 | 开放获取 | CC BY-NC-SA | Application Full Text | ||
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