| An online outlier detection method based on wavelet technique and robust RBF network | |
Su WX(苏卫星) ; Zhu YL(朱云龙); Liu F(刘芳); Hu KY(胡琨元)
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| Department | 信息服务与智能控制技术研究室 |
| Source Publication | Transactions of the Institute of Measurement and Control
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| ISSN | 0142-3312 |
| 2013 | |
| Volume | 35Issue:8Pages:1046-1057 |
| Indexed By | SCI ; EI |
| EI Accession number | 20134616963515 |
| WOS ID | WOS:000326371500008 |
| Contribution Rank | 1 |
| Keyword | Computer Simulation Control Systems Hidden Markov Models Process Control Radial Basis Function Networks Time Series |
| Abstract | We focus on the issue of outlier detection for time-series data in a process control system (PCS), since outlier detection is a critical step before performing data-based system analysis. Several published articles have proved that a wavelet transform (WT) technique can be used to detect outliers in time-series data, but the standard WT detection method, as well as any other univariate outlier detection technique, does not distinguish between the sudden change caused by the changes of inputs and the fluctuations caused by outliers in PCS. In order to improve this shortcoming of the conventional WT method for the data in a PCS, a new algorithm combining the wavelet technique with a robust radial basis function (RBF) network is proposed here. In this method, a robust RBF network (RBFN) training algorithm is proposed, which can train the RBFN online using the original data as a training set without the need of clean data and thus fits the application of online detection. Furthermore, a hidden Markov model is adopted as an analysis tool to accomplish online automatic detection without pre-selecting the threshold. We compare the performance of our proposed method with the conventional wavelet method and the AR model method to demonstrate its validity through simulation and experimental applications to the data pretreatment process in an electric arc furnace electrode regulator system. © The Author(s) 2013. |
| Language | 英语 |
| WOS Subject | Automation & Control Systems ; Instruments & Instrumentation |
| WOS Research Area | Automation & Control Systems ; Instruments & Instrumentation |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.sia.cn/handle/173321/14012 |
| Collection | 信息服务与智能控制技术研究室 |
| Corresponding Author | Su WX(苏卫星) |
| Affiliation | 1.Institute of Automation, Chinese Academy of Sciences, Laboratory of Information Service and Intelligent Control, Shenyang, China 2.Northeastern University, School of Information Science and Engineering, Shenyang, China |
| Recommended Citation GB/T 7714 | Su WX,Zhu YL,Liu F,et al. An online outlier detection method based on wavelet technique and robust RBF network[J]. Transactions of the Institute of Measurement and Control,2013,35(8):1046-1057. |
| APA | Su WX,Zhu YL,Liu F,&Hu KY.(2013).An online outlier detection method based on wavelet technique and robust RBF network.Transactions of the Institute of Measurement and Control,35(8),1046-1057. |
| MLA | Su WX,et al."An online outlier detection method based on wavelet technique and robust RBF network".Transactions of the Institute of Measurement and Control 35.8(2013):1046-1057. |
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| File Name/Size | DocType | Version | Access | License | ||
| An online outlier de（713KB） | 期刊论文 | 出版稿 | 开放获取 | ODC PDDL | View Application Full Text | |
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