Probabilistic fault diagnosis in communication systems through incremental hypothesis updating

This paper presents a probabilistic event-driven fault localization technique, which uses a probabilistic symptomfault map as a fault propagation model. The technique isolates the most probable set of faults through incremental updating of a symptom-explanation hypothesis. At any time, it provides a set of alternative hypotheses, each of which is a complete explanation of the set of symptoms observed thus far. The hypotheses are ranked according to a measure of their goodness. The technique allows multiple simultaneous independent faults to be identified and incorporates both negative and positive symptoms in the analysis. As shown in a simulation study, the technique offers close-to-optimal accuracy and is resilient both to noise in the symptom data and to inaccuracies of the probabilistic fault propagation model.

• Type: Journal
• Year: 2004
• Where: CN
• Authors: Malgorzata Steinder, Adarshpal S. Sethi
• Keywords: CN 2004, Fault Propagation Model, Probabilistic Event-driven Fault, Probabilistic Symptomfault Map