Process fault diagnosis with model- and knowledge-based approaches: Advances and opportunities

Abstract

Fault diagnosis plays a vital role in ensuring safe and efficient operation of modern process plants. Despite the encouraging progress in its research, developing a reliable and interpretable diagnostic system remains a challenge. There is a consensus among many researchers that an appropriate modelling, representation and use of fundamental process knowledge might be the key to addressing this problem. Over the past four decades, different techniques have been proposed for this purpose. They use process knowledge from different sources, in different forms and on different details, and are also named model-based methods in some literature. This paper first briefly introduces the problem of fault detection and diagnosis, its research status and challenges. It then gives a review of widely used model- and knowledge-based diagnostic methods, including their general ideas, properties, and important developments. Afterwards, it summarises studies that evaluate their performance in real processes in process industry, including the process types, scales, considered faults, and performance. Finally, perspectives on challenges and potential opportunities are highlighted for future work.