Fault Diagnosis Based on Multi-scale LSTM-FCNs for Industrial Process
文献类型：会议论文
| 作者 | Yang SJ(阳少杰)1,3,4,5; Li P(里鹏)1,3,4 ; Li S(李帅)1,3,4,5; Zhou XF(周晓锋)1,3,4; Jiang, Shanghong2
|
| 出版日期 | 2021 |
| 会议日期 | December 17-18, 2021 |
| 会议地点 | Chengdu, China |
| 关键词 | industrial process fault diagnosis multi-scale feature extraction deep neural networks variational mode decomposition |
| 页码 | 1-6 |
| 英文摘要 | Aiming at the characteristics of multi-scale, time series and nonlinear of the complex industrial process data, in order to extract process information more efficiently for improving the diagnosibility, a novel fault diagnosis method based on multi-scale long short term memory- fully convolutional networks (LSTM-FCNs) for industrial process is proposed. Firstly, variational mode decomposition (VMD) is used to obtain the multi-scale components of the process data. Then a parallel LSTM and FCNs optimized by batch normalization and random inactivation is built to extract features for each component. Finally, based on the fusion of multi-scale features, a fully connected layer is used for diagnosis. The LSTM-FCNs method is evaluated by the Tennessee Eastman (TE) benchmark industrial process, and compared with other methods, its effectiveness and superiority are verified. |
| 产权排序 | 1 |
| 会议录 | 2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-6654-0115-9 |
| 源URL | [http://ir.sia.cn/handle/173321/30749]  |
| 专题 | 沈阳自动化研究所_数字工厂研究室 |
| 通讯作者 | Li S(李帅) |
| 作者单位 | 1.Institutes for Robotics and IntelligentManufacturing, Chinese Academy OfSciences, Shenyang 110169, China 2.Information Center of Ansteel Mining Limited Company, Anshan 114031, China 3.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China 4.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 5.University of Chinese Academy OfSciences, Beijing 100049, China |
| 推荐引用方式 GB/T 7714 | Yang SJ,Li P,Li S,et al. Fault Diagnosis Based on Multi-scale LSTM-FCNs for Industrial Process[C]. 见:. Chengdu, China. December 17-18, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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