A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era

Xinqing Gao; Fan Yang; Chao Shang; Dexian Huang

doi:10.1016/j.cjche.2016.05.039

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A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era

Review | Updated：2026-01-06

- A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era
- Chinese Journal of Chemical Engineering Vol. 24, Issue 8, Pages: 952-962(2016)
- Affiliations：
  
  1. Department of Automation, Tsinghua University,Beijing,China,100084
  2. Tsinghua National Laboratory for Information Science and Technology,Beijing,China,100084
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2016.05.039
  CLC：
- Published：2016
- Accepted：
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Xinqing Gao, Fan Yang, Chao Shang, Dexian Huang. A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era[J]. , 2016, 24(8): 952-962.
DOI：

Xinqing Gao, Fan Yang, Chao Shang, Dexian Huang. A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era[J]. , 2016, 24(8): 952-962. DOI： 10.1016/j.cjche.2016.05.039.

摘要

Abstract

Owing to wide applications of automatic control systems in the process industries

the impacts of controller performance on industrial processes are becoming increasingly significant. Consequently

controller maintenance is critical to guarantee routine operations of industrial processes. The workflow of controller maintenance generally involves the following steps:monitor operating controller performance and detect performance degradation

diagnose probable root causes of control system malfunctions

and take specific actions to resolve associated problems. In this article

a comprehensive overview of the mainstream of control loop monitoring and diagnosis is provided

and some existing problems are also analyzed and discussed. From the viewpoint of synthesizing abundant information in the context of big data

some prospective ideas and promising methods are outlined to potentially solve problems in industrial applications.

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