Adaptive Local Outlier Probability for Dynamic Process Monitoring

Yuxin Ma; Hongbo Shi; Mengling Wang

doi:10.1016/j.cjche.2014.05.015

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Adaptive Local Outlier Probability for Dynamic Process Monitoring

PROCESS MONITOR | Updated：2026-01-06

- Adaptive Local Outlier Probability for Dynamic Process Monitoring
- Chinese Journal of Chemical Engineering Vol. 22, Issue 7, Pages: 820-827(2014)
- Affiliations：
  
  Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology,Shanghai,China,200237
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2014.05.015
  CLC：
- Published：2014
- Accepted：
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Yuxin Ma, Hongbo Shi, Mengling Wang. Adaptive Local Outlier Probability for Dynamic Process Monitoring[J]. , 2014, 22(7): 820-827.
DOI：

Yuxin Ma, Hongbo Shi, Mengling Wang. Adaptive Local Outlier Probability for Dynamic Process Monitoring[J]. , 2014, 22(7): 820-827. DOI： 10.1016/j.cjche.2014.05.015.

摘要

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.

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