Real-time fault diagnosis using knowledge-based expert system

Abnormal operating conditions (faults) cost process industry billons of dollars per year and can be prevented if they are predicted and controlled in advance. Advanced software applications, based on the expert system, has the potential to assist engineers in monitoring, detecting, and diagnosing abnormal conditions and thus providing safe guards against these unexpected process conditions. Abnormal operating conditions (faults) could be modeled and predicted with high confidence using software applications. A wide range of fault diagnosis methods exist which may be used to design safety systems.Due to the increased process complexity and possible instability in the operating conditions, the existing control systems have limited ability to provide practical assistance to both operators and engineers. This paper proposes a knowledge-based fault diagnosis method, which uses the valuable knowledge from the experts and operators, as well as real-time data from a variety of sensors. Fuzzy logic is also used to make inferences based on the acquired information (real-time data) and the knowledge. A computer-aided tool based on proposed methodology is developed on the platform of G2 expert shell using GDA (G2 Diagnostic Assistant) components. Performance of the methodology is verified using both industrial and simulated data.