Monitoring complex systems with causal networks

Complex industrial systems, such as utility turbine generators, are usually monitored by observing data recorded by sensors placed at various locations in the system. Typically, data are collected continuously and an expert, or a team of experts, monitors the readings. From the readings they assess the “health” of the system. Should readings at some sensors become unusual,the experts then use their diagnostic skills to determine the cause of the problem. It is better to detect problems early and correct them rather than waiting for more serious problems or a major failure. However, there are several problems associated with using human expertise to monitor complex systems which are outlined. There have been considerable efforts to develop expert computer systems that can perform the monitoring and diagnosis. These efforts include the use of ruled based artificial intelligence. At General Electric corporate R&D, one of the authors has been leading an effort to design monitoring systems that use a causal network. They have been shown to deliver much ofthe diagnostic ability needed in various GE applications. Indeed, the GE work has a wide range of applications, and can be used in complex systems such as power generators, transportation equipment (planes, trains, and automobiles), medical equipment, and production plants. Causal networks use a directed graph and probability theory to produce continuous probabilistic information on why a system has abnormal readings at some sensors