Process Fault Detection and Diagnosis: Past, Present and Future

Abstract

One of the most important challenges facing control system engineers is the design and implementation of intelligent control systems that can assist operators to make supervisory control decisions such as abnormal situations management (ASM), start up and shut down, controller performance assessment and so on. Operator failure to exercise the appropriate supervisory control decisions often have an adverse effect on product quality, process safety, occupational health and environmental impact. The economic impact of such abnormal situations is enormous; about $20 billion/year in losses in the petrochemical industries alone in the US. Furthermore, process safety, occupational health and environmental hazards are ever increasing in importance in response to heightening public concern and the resultant tightening of regulations. Thus, there exists considerable incentive in developing intelligent control systems that can provide automated operator assistance for supervisory control situations for complex process plants. People in the process industries view this as the next major challenge in control systems research and application. The automation of process fault detection and diagnosis forms the first step in automating supervisory control and ASM. In this paper, I present an overview of the various approaches to fault diagnosis, challenges we face and encouraging emerging trends. The recent progress has promising implications on the use of intelligent systems for inherently safer design, operator training, abnormal situation management, process hazards analysis and optimal process operations.