Gross Error Detection and Identification Based on Parameter Estimation for Dynamic Systems

›› 2009, Vol. 17 ›› Issue (3): 460-467.

Gross Error Detection and Identification Based on Parameter Estimation for Dynamic Systems

姜春阳, 邱彤, 赵劲松, 陈丙珍

Department of Chemical Engineering, Tsinghua University, Beijing 100084, China

收稿日期:2008-09-23 修回日期:2009-03-11 出版日期:2009-06-28 发布日期:2009-06-28
通讯作者: QIU Tong,E-mail:qiutong@tsinghua.edu.cn
基金资助:
Supported by the National High Technology Research and Development Program of China (2006AA04Z176)

Gross Error Detection and Identification Based on Parameter Estimation for Dynamic Systems

JIANG Chunyang, QIU Tong, ZHAO Jinsong, CHEN Bingzhen

Department of Chemical Engineering, Tsinghua University, Beijing 100084, China

Received:2008-09-23 Revised:2009-03-11 Online:2009-06-28 Published:2009-06-28
Supported by:
Supported by the National High Technology Research and Development Program of China (2006AA04Z176)

摘要/Abstract

摘要： The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a powerful tool for bias identification, without a reliable and efficient bias detection strategy, the method is limited in efficiency and cannot be applied widely. In this paper, a new bias detection strategy is constructed to detect the presence of measurement bias and its occurrence time. With the help of this strategy, the number of parameters to be estimated is greatly reduced, and sequential detections and iterations are also avoided. In addition, the number of decision variables of the optimization model is reduced, through which the influence of the parameters estimated is reduced. By incorporating the strategy into the parameter estimation model, a new methodology named IPEBD (Improved Parameter Estimation method with Bias Detection strategy) is constructed. Simulation studies on a continuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) problem show that IPEBD is efficient for eliminating random errors, measurement biases and outliers contained in dynamic process data.

关键词: gross error detection, data reconciliation, parameter estimation

Abstract: The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a powerful tool for bias identification, without a reliable and efficient bias detection strategy, the method is limited in efficiency and cannot be applied widely. In this paper, a new bias detection strategy is constructed to detect the presence of measurement bias and its occurrence time. With the help of this strategy, the number of parameters to be estimated is greatly reduced, and sequential detections and iterations are also avoided. In addition, the number of decision variables of the optimization model is reduced, through which the influence of the parameters estimated is reduced. By incorporating the strategy into the parameter estimation model, a new methodology named IPEBD (Improved Parameter Estimation method with Bias Detection strategy) is constructed. Simulation studies on a continuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) problem show that IPEBD is efficient for eliminating random errors, measurement biases and outliers contained in dynamic process data.

Key words: gross error detection, data reconciliation, parameter estimation

姜春阳, 邱彤, 赵劲松, 陈丙珍. Gross Error Detection and Identification Based on Parameter Estimation for Dynamic Systems[J]. , 2009, 17(3): 460-467.

JIANG Chunyang, QIU Tong, ZHAO Jinsong, CHEN Bingzhen. Gross Error Detection and Identification Based on Parameter Estimation for Dynamic Systems[J]. , 2009, 17(3): 460-467.

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Gross Error Detection and Identification Based on Parameter Estimation for Dynamic Systems

Gross Error Detection and Identification Based on Parameter Estimation for Dynamic Systems

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