An online outlier detection method for process control time series
The ability to detect outlier online in process control filed is essential in many real-world system analysis applications. Previous algorithms require some 攃lean?data to construct the statistical model at beginning, which was used to detect outlier. But actually, these clean data can not obtain at all. In this paper, we investigate a machine learning, descriptor-based approach that dose not require clean data to model, based on least square support vector outlier detection. A online window-based learn algorithm is introduced. Theoretical consideration as well as simulations on real process data demonstrate its practical efficiency.
Outlier detection least square support vector process control online detection
Liu Fang Mao Zhi-zhong
School of Information Science and Engineering; Northeastern University, Shenyang , China School of Information Science and Engineering; Key Laboratory of Integrated Automation of Process In
国际会议
2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)
四川绵阳
英文
3272-3276
2011-05-23（万方平台首次上网日期，不代表论文的发表时间）