A graphic modeling and analysis tool for human fault diagnosis tasks

Abstract

This study presents a graphic modeling and analysis tool for use in constructing an operator's mental model in fault diagnosis tasks. In most automatic and complicated process control systems, human fault diagnosis tasks have become increasingly complex and specialized. The system designer should consider the cognitive process of human operator to avert failure of implement action owing to a lack of compatibility between humans and aiding system interface. Here, an experiment is performed to investigate the nature of human fault diagnosis. A graphic modeling and analysis tool is then proposed to model the continuous process of human fault diagnosis. The approach proposed herein exploits both the line-chart and Petri nets to demonstrate the operator's thoughts and actions. Moreover, results in this study are integrated into an adaptive standard diagnosis model that can assess the operators' mental workload and accurately depict the interactions between human operator and aiding system. Relevance to industry Automatic intelligent diagnosis systems can not provide satisfactory operating performance. Human diagnosticians are more effective than computer ones. Results in this study offer further insight into an operator behavior in graphic form and also how to design a better aiding system.