Abstract: A diagnostic tool based on a hybrid knowledge representation of a system under test is disclosed. Data collected from the system during its operation is compared to an event based representation of the system which comprises a plurality of predefined events. An event is recognized when the collected data matches the event's critical parameter. The recognized event is analyzed and an associated set of ambiguity group effects, which specify components to be re-ranked in an ambiguity group according to an associated ranking effect. Additonally, a symptom-fault model and a failure model can be analyzed to determine symptom-fault relationships and failure modes which are applicable to the system operation. Each applicable symptom-fault relationship and failure mode is also associated with a set of ambiguity group effects which rerank the ambiguity group. A structural model is analyzed starting with the components in the ambiguity group having the greatest probability of failure.

Abstract: Method and apparatus for performing system monitoring and diagnostics is disclosed. In performing system monitoring, data is acquired from the system under test and compared to an event model. The event model comprises a database having event records which pre-define events which can occur. Each event record includes a state vector dependency which lists the events which must occur prior to the pre-defined event occurring and one or more critical parameters defining the data which must occur during the system's performance for the event to have occurred. Event recognition is performed by comparing each event record to acquired operational data and to events already recognized. Associated with each event record in the database is an intelligent data acquisition action which defines an action to be taken as a result of the event record being recognized. These actions can modify the performance of the system being monitored or the acquisition of data.