Research on Fault Diagnosis Method of Rod Pumping Wells Based on CNN_APRCSO_SVM
文献类型：会议论文
| 作者 | Wang MX(王明新)1,2,3,5; Zang CZ(臧传治)1,2,5 ; Ji ZP(纪振平)3; He YP(贺云鹏)1,2,4,5; Zeng P(曾鹏)1,2,5
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| 出版日期 | 2021 |
| 会议日期 | May 26-28, 2021 |
| 会议地点 | Chicago |
| 关键词 | Rod pumping well Fault diagnosis Indicator diagrams Convolutional neural network (CNN) Support vector machine (SVM) Chicken swarm optimization (CSO) Algorithm optimization |
| 页码 | 339-350 |
| 英文摘要 | The traditional work status recognition methods based on indicator diagrams require manual selection of indicator diagram features, and the recognition accuracy is low. In response to this problem, this paper proposes an intelligent fault diagnosis method combined convolutional neural network (CNN) with support vector machine (SVM). The CNN is used to automatically extract the features of the indicator diagrams, SVM is used to make diagnosis, and the improved chicken swarm optimization is used to solve the problem of difficult determination of the SVM parameters. The improved chicken swarm optimization avoids the problem that chicken swarm optimization (CSO) is easy to fall into local optimum, and it is better than particle swarm optimization (PSO) and the traditional CSO in accuracy. Compared with the traditional CNN model fault diagnosis method, the fault diagnosis method proposed in this paper has better recognition performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| 产权排序 | 1 |
| 会议录 | 2021 International Conference on Intelligent Automation and Soft Computing (IASC 2021)
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| 会议录出版者 | Springer Science and Business Media Deutschland GmbH |
| 会议录出版地 | Berlin |
| 语种 | 英语 |
| ISSN号 | 2367-4512 |
| 源URL | [http://ir.sia.cn/handle/173321/29408]  |
| 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 | Zang CZ(臧传治) |
| 作者单位 | 1.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China 2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China 4.University of Chinese Academy of Sciences, Beijing 100049, China 5.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China |
| 推荐引用方式 GB/T 7714 | Wang MX,Zang CZ,Ji ZP,et al. Research on Fault Diagnosis Method of Rod Pumping Wells Based on CNN_APRCSO_SVM[C]. 见:. Chicago. May 26-28, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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