Usi G the G2 Diag Ostic Assista T for Real-time Fault

This paper presents an overview of real-time fault diagnosis and describes essential features of a software environment for developing real-time fault diagnosis systems. The G2 Diagnostic Assistant is an environment for real-time fault diagnosis, created using the G2 Real-Time Expert System. The principle component of the Diagnostic Assistant is a graphical language for representing diagnostic knowledge called GDL. GDL contains tools for common diagnostic problems such as malfunction detection, alarm filtering, intelligent information display, and fault recovery. Some of the features that make GDL well suited for diagnosis of dynamic systems are described and applications areas are discussed. Real-Time Fault Diagnosis The objective of fault diagnosis is to pinpoint and correct problems that occur in dynamic systems. The objective of real-time fault diagnosis is to perform these tasks within a time frame that allows continued, safe operation of the system. Economic incentives for real-time fault diagnosis include product quality, equipment protection, and environmental protection. The time-frame for real-time diagnosis (TFD) can vary dramatically depending on the system being diagnosed. For many electronic or power systems, TFD may be one second or less. For some chemical or biological systems, TFD may be several minutes or hours. Automated computer systems have many advantages for real-time fault diagnosis [1]. Automated systems are capable of continuously monitoring more variables with greater accuracy and faster response time than human beings. To fully realize the advantages, however, an automated diagnosis system must be able to: 1. access real-time data, 2. intelligently interpret data, 3. communicate with human decisionmakers, and 4. initiate corrective actions. Items 1 and 4 can usually be accomplished by integrating the diagnosis system with existing automation systems. Data is usually available from a real-time database that serves as a repository for data collected from remote sensors. Corrective actions can usually be initiated by accessing the same supervisory control systems used by human operators. This integration can be accomplished by