Distributed Hierarchical Fault Diagnosis Based on Sparse Auto-Encoder and Random Forest
文献类型：会议论文
| 作者 | Li, Tong4; Song CH(宋纯贺)2,3 ; Liu, Yang4; Wang ZF(王忠锋)2,3; Yu SM(于诗矛)2,3; Su, Shanting1
|
| 出版日期 | 2019 |
| 会议日期 | August 24-25, 2019 |
| 会议地点 | Nanjing, China |
| 关键词 | Sparse auto-encoder Distributed fault diagnosis Fault classification Random forest |
| 页码 | 244-255 |
| 英文摘要 | For the diagnosis of large-scale local devices, the traditional centralized fault diagnosis systems are becoming incompetent to meet the requirement of real-time monitoring. This paper proposes the Distributed hierarchical Fault Diagnosis System (DFDS). Specifically, DFDS implements fault monitoring by an improved Sparse Auto-Encoder (SAE) on the monitor layer, classifies faults and identifies unknown faults by an improved random forest on the classification layer, learns new knowledge and updates the system on the decision layer. We apply DFDS in the laboratory data of Case Western Reserve University to verify the efficiency of the proposed system. The experimental results show that our method can accurately detect the fault and accurately identify the fault type. |
| 产权排序 | 2 |
| 会议录 | Machine Learning and Intelligent Communications - 4th International Conference, MLICOM 2019, Proceedings
 |
| 会议录出版者 | Springer |
| 会议录出版地 | Berlin |
| 语种 | 英语 |
| ISSN号 | 1867-8211 |
| ISBN号 | 978-3-030-32387-5 |
| 源URL | [http://ir.sia.cn/handle/173321/26024]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 通讯作者 | Song CH(宋纯贺) |
| 作者单位 | 1.Nanjing University of Aeronautics and Astronautics, Nanjing, China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 3.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.Liaoning Electric Power Research Institute, State Grid Liaoning Electric Power Co., Ltd., 110000, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Li, Tong,Song CH,Liu, Yang,et al. Distributed Hierarchical Fault Diagnosis Based on Sparse Auto-Encoder and Random Forest[C]. 见:. Nanjing, China. August 24-25, 2019. |
入库方式： OAI收割
来源：沈阳自动化研究所
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