Bayesian-based anomaly detection in the industrial processes

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 数字工厂研究室

	Bayesian-based anomaly detection in the industrial processes
	Pan YJ(潘怡君)1,2,3; Zheng ZY(郑泽宇)1,2,3; Fu DZ(付殿峥)1,2,3
	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
内容类型	会议论文
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.Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, 110016, Liaoning, China 2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, Liaoning, China 3.Institutes for Robotics and Intelligent Manufacturing, 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.