Abstract
Most industrial processes are equipped with a large number of alarms. In a failure state it is quite usual that many of the alarms will trigger. Some of them will be directly connected to the primary sources of error, but others may be secondary, i.e., not connected to any failed equipment, but due only to consequential effects of the primary failures. In a failure state it is vital for the operator to separate the primary from the secondary alarms. This paper describes a new method for automatically recognizing the primary failures. It is fairly general and built upon model-based reasoning. The modeling technique used is multilevel flow models (MFM), as described by Lind (1990b). First, the basics of MFM are described, and then an example of how such a model can be used in alarm analysis is given.
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