Fault Diagnosis Based on RseNet-LSTM for Industrial Process
文献类型：会议论文
| 作者 | Yao, Peifu1; Yang SJ(阳少杰)2,3,4,5; Li P(里鹏)2,4,5
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| 出版日期 | 2021 |
| 会议日期 | March 12-14, 2021 |
| 会议地点 | Chongqing, China |
| 关键词 | Fault Diagnosis Residual Network Long Short-Term Memory Tennessee Eastman Process |
| 页码 | 728-732 |
| 英文摘要 | Aiming at the problems that conventional data-driven diagnosis methods are difficult to adaptively extract effective features from industrial process data, and do not make full use of the time series characteristics of process data, in this paper, a fault diagnosis method based on residual convolutional neural networks and long short-term memory networks (ResNet-LSTM) is developed. Firstly, the local spatial features of process data are captured by the deep residual convolution network. Then, the time series characteristics of process data are extracted by LSTM. Finally, the output of the fault category is performed through the softmax classifier. This method can extract features adaptively, more fully extract features in time series fault data, and effectively reduce the difficulty of deep neural network training. The benchmark Tennessee-Eastman (TE) process is used to validate performance of the proposed method. The ResNet-LSTM model is compared with the CNN, LSTM, ResNets, CNN-LSTM models, and the experiment results show that the ResNet-LSTM method achieves better performance. |
| 源文献作者 | Chengdu Union Institute of Science and Technology ; Chongqing Geeks Education Technology Co., Ltd ; Chongqing Global Union Academy of Science and Technology ; Global Union Academy of Science and Technology ; IEEE Beijing Section |
| 产权排序 | 2 |
| 会议录 | 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISSN号 | 2689-6621 |
| ISBN号 | 978-1-7281-8028-1 |
| 源URL | [http://ir.sia.cn/handle/173321/28744]  |
| 专题 | 沈阳自动化研究所_数字工厂研究室 |
| 通讯作者 | Yao, Peifu |
| 作者单位 | 1.China Copper Co.LTD, Kunming, China 2.Chinese Academy of Sciences, Key Laboratory of Networked Control Systems, Shenyang, China 3.University of Chinese Academy of Sciences, Beijing, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China 5.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Yao, Peifu,Yang SJ,Li P. Fault Diagnosis Based on RseNet-LSTM for Industrial Process[C]. 见:. Chongqing, China. March 12-14, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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