Issues and Tips:A Set of Integrated Experiments of Applying Auto-Encoder and Convolutional Neural Network in Feature Extraction and Fault Diagnosis
文献类型：会议论文
| 作者 | Li, Xudong1; Li, Mingtao1; Zheng, Jianhua1; Hu, Yang2 |
| 出版日期 | 2018 |
| 会议日期 | OCT 26-28, 2018 |
| 会议地点 | Chongqing, PEOPLES R CHINA |
| 关键词 | deep learning PHM AE CNN |
| DOI | 10.1109/PHM-Chongqing.2018.00228 |
| 页码 | 1301-1306 |
| 英文摘要 | 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. |
| 会议录 | 2018 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-CHONGQING 2018)
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| 语种 | 英语 |
| ISSN号 | 2166-5656 |
| ISBN号 | 978-1-5386-5380-7 |
| 源URL | [http://ir.nssc.ac.cn/handle/122/6967]  |
| 专题 | 国家空间科学中心_空间技术部 |
| 作者单位 | 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 |
| 推荐引用方式 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]. 见:. Chongqing, PEOPLES R CHINA. OCT 26-28, 2018. |
入库方式： OAI收割
来源：国家空间科学中心
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