Abstract
Systems such as chemical plants and nuclear plants have many sensors to detect abnormal events. When some event becomes abnormal, sensors monitoring it make alarms. But too many sensor alarms may prevent the operator from identifying the cause. In design of diagnosis systems, how to choose necessary channels for locating the cause of system failure becomes important.
This paper considers the fault distinguishability of diagnosis systems which have many observation items to identify causes of system failure. The diagnosis system must distinguish an abnormal event from the others based on the observation data. A simple method is developed for obtaining minimal combinations of observation items which guarantee the fault distinguishability. Two types of diagnoses are considered: one directly identifies the abnormal event based on all observation data, and the other identifies it step by step from the system level to the component level. Illustrative examples show the detail of the method and its application to the diagnosis of a marine-engine cooling system.
