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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)
Department信息服务与智能控制技术研究室
Source PublicationTransactions of the Institute of Measurement and Control
ISSN0142-3312
2013
Volume35Issue:8Pages:1046-1057
Indexed BySCI ; EI
EI Accession number20134616963515
WOS IDWOS:000326371500008
Contribution Rank1
KeywordComputer Simulation Control Systems Hidden Markov Models Process Control Radial Basis Function Networks Time Series
AbstractWe 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 SubjectAutomation & Control Systems ; Instruments & Instrumentation
WOS Research AreaAutomation & Control Systems ; Instruments & Instrumentation
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/14012
Collection信息服务与智能控制技术研究室
Corresponding AuthorSu WX(苏卫星)
Affiliation1.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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