中文摘要：

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.

英文摘要：

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. (C) 2016 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. All rights reserved.