Adaptive Local Outlier Probability for Dynamic Process Monitoring

doi:10.1016/j.cjche.2014.05.015

›› 2014, Vol. 22 ›› Issue (7): 820-827.DOI: 10.1016/j.cjche.2014.05.015

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Adaptive Local Outlier Probability for Dynamic Process Monitoring

Yuxin Ma, Hongbo Shi, Mengling Wang

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2013-12-24 Revised:2014-02-07 Online:2014-08-23 Published:2014-07-28
Supported by:
Supported by theNational Natural Science Foundation of China (61374140), Shanghai Postdoctoral Sustentation Fund (12R21412600), the Fundamental Research Funds for the Central Universities (WH1214039), and Shanghai Pujiang Program (12PJ1402200).

Adaptive Local Outlier Probability for Dynamic Process Monitoring

Yuxin Ma, Hongbo Shi, Mengling Wang

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

通讯作者: Hongbo Shi
基金资助:
Supported by theNational Natural Science Foundation of China (61374140), Shanghai Postdoctoral Sustentation Fund (12R21412600), the Fundamental Research Funds for the Central Universities (WH1214039), and Shanghai Pujiang Program (12PJ1402200).

Abstract

Abstract: Complex industrial processes often havemultiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient moving windowlocal outlier probability algorithmis proposed. Its key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probabilitymodels, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.

Key words: Time-varying, Complex data distribution, Local outlier probability, Multi-mode, Fault detection

摘要： Complex industrial processes often havemultiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient moving windowlocal outlier probability algorithmis proposed. Its key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probabilitymodels, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.

关键词: Time-varying, Complex data distribution, Local outlier probability, Multi-mode, Fault detection

Yuxin Ma, Hongbo Shi, Mengling Wang. Adaptive Local Outlier Probability for Dynamic Process Monitoring[J]. , 2014, 22(7): 820-827.

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Adaptive Local Outlier Probability for Dynamic Process Monitoring

Adaptive Local Outlier Probability for Dynamic Process Monitoring

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