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题名: Outlier detection for process control data based on a non-linear Auto-Regression Hidden Markov Model method
作者: Liu, Fang ; Mao, Zhizhong ; Su WX(苏卫星)
作者部门: 信息服务与智能控制技术研究室
关键词: Auto-Regression Hidden Markov Model ; industrial process control ; outlier detection ; Radial Basis Function Network ; time series
刊名: TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
ISSN号: 0142-3312
出版日期: 2012
卷号: 34, 期号:5, 页码:527-538
收录类别: SCI ; EI
产权排序: 2
摘要: This paper focuses on the issue of outlier detection for time series in the process industry. Considering the characteristics of time series in process control systems, such as high non-linearity, strong noise and the special relationship between the input and output of the controlled object, a new outlier detection algorithm is proposed. The algorithm adopts an improved Radial Basis Function Network to construct the model of the controlled object and an Auto-Regression Hidden Markov Model to detect outliers. Unlike many conventional outlier detection methods, this algorithm does not need any prior data and can detect outliers accurately without preselecting the threshold. The proposed detection algorithm is validated by the application to the electrode regulator system of an arc furnace and comparison with Takeuchi's auto-regressive model detection approach.
WOS记录号: WOS:000304884400003
WOS标题词: Science & Technology ; Technology
类目[WOS]: Automation & Control Systems ; Instruments & Instrumentation
关键词[WOS]: NOVELTY DETECTION ; RBF NETWORKS ; TIME-SERIES ; SELECTION
研究领域[WOS]: Automation & Control Systems ; Instruments & Instrumentation
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/9970
Appears in Collections:信息服务与智能控制技术研究室_期刊论文

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