Bayesian-based anomaly detection in the industrial processes
文献类型：会议论文
| 作者 | Pan YJ(潘怡君)1,2,3 ; Zheng ZY(郑泽宇)1,2,3; Fu DZ(付殿峥)1,2,3
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| 出版日期 | 2020 |
| 会议日期 | July 12-17, 2020 |
| 会议地点 | Berlin, Germany |
| 关键词 | Anomaly detection Bathyscaphe Bayesian Change point detection Dempster-Shafer theory Leakage detection TE process |
| 页码 | 11729-11734 |
| 英文摘要 | In general, the industrial processes are semi-automatic, and are controlled by the operators. Since the operation principles of the industrial processes are complicated, it is difficult to label observations. The disturbances may be contained in the observations. Therefore, the unsupervised anomaly detection method is promising for research in the industrial processes. In the paper, a multivariate anomaly detection method is proposed, which is unsupervised and online. The priori probability of anomaly occurrence is necessary, and a hazard function selection method is defined at first. Secondly, Bayesian-based method is adopted for anomaly detection. In final, the Dempster-Shafer theory is introduced for fusing the univariate anomaly detection results. The numerical simulation is used for illustrating the anomaly detection power of the proposed method, and the TE process is implemented for testing the fault detection effectiveness. A real data set collected from a bathyscaphe is applied for demonstrating the power of leakage detection. |
| 产权排序 | 1 |
| 会议录 | 21st IFAC World Congress 2020
 |
| 会议录出版者 | IFAC |
| 会议录出版地 | Laxenburg, AUSTRIA |
| 语种 | 英语 |
| ISSN号 | 2405-8963 |
| WOS记录号 | WOS:000652593100471 |
| 源URL | [http://ir.sia.cn/handle/173321/28779]  |
| 专题 | 沈阳自动化研究所_数字工厂研究室 |
| 通讯作者 | Zheng ZY(郑泽宇) |
| 作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, Liaoning, China 2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, Liaoning, China 3.Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, 110016, Liaoning, China |
| 推荐引用方式 GB/T 7714 | Pan YJ,Zheng ZY,Fu DZ. Bayesian-based anomaly detection in the industrial processes[C]. 见:. Berlin, Germany. July 12-17, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
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