A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

• Published in: IEEE transactions on industrial electronics (1982) Vol. 61; no. 11; pp. 6418 - 6428
• Main Authors: Shen Yin, Ding, Steven X., Xiaochen Xie, Hao Luo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Correlation; data-driven; Fault diagnosis; industrial operating conditions; Mathematical model; Matrix decomposition; Monitoring; Principal component analysis; process monitoring; fault diagnosis; Data-driven; process monitoring; industrial operating conditions
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2014.2301773

Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.