Adaptive Local Outlier Probability for Dynamic Process Monitoring

Yuxin Ma; Hongbo Shi; Mengling Wang

doi:10.1016/j.cjche.2014.05.015

Your Location：

Home >

Browse articles >

Adaptive Local Outlier Probability for Dynamic Process Monitoring

PROCESS MONITOR | Updated：2026-01-06

- Adaptive Local Outlier Probability for Dynamic Process Monitoring
- Adaptive Local Outlier Probability for Dynamic Process Monitoring
- 中国化学工程学报（英文版） 2014年22卷第7期页码：820-827
- 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
  中图分类号：
- 纸质出版：2014
- Accepted：
Scan QR Code
Yuxin Ma, Hongbo Shi, Mengling Wang. Adaptive Local Outlier Probability for Dynamic Process Monitoring[J]. 中国化学工程学报（英文版）, 2014,22(7):820-827.

Yuxin Ma, Hongbo Shi, Mengling Wang. Adaptive Local Outlier Probability for Dynamic Process Monitoring[J]. , 2014, 22(7): 820-827.
Yuxin Ma, Hongbo Shi, Mengling Wang. Adaptive Local Outlier Probability for Dynamic Process Monitoring[J]. 中国化学工程学报（英文版）, 2014,22(7):820-827. DOI： 10.1016/j.cjche.2014.05.015.

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.

关键词

Keywords

references

浏览量

110

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Randomized autoregressive dynamic slow feature analysis method for industrial process fault monitoring

Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

Research on fault detection method for heat pump air conditioning system under cold weather

Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring