Bayesian belief networks for system fault diagnostics
Version of Record online: 4 NOV 2008
Copyright © 2008 John Wiley & Sons, Ltd.
Quality and Reliability Engineering International
Volume 25, Issue 4, pages 409–426, June 2009
How to Cite
Lampis, M. and Andrews, J. D. (2009), Bayesian belief networks for system fault diagnostics. Qual. Reliab. Engng. Int., 25: 409–426. doi: 10.1002/qre.978
- Issue online: 8 MAY 2009
- Version of Record online: 4 NOV 2008
- fault diagnostics;
- fault tree analysis;
- Bayesian belief networks
Fault diagnostic methods aim to recognize when faults exist on a system and to identify the failures that have caused the fault. The symptoms of the fault are obtained from readings from sensors located on the system. When the observed readings do not match those expected then a fault can exist. Using the detailed information provided by the sensors, a list of the failures (singly or in combinations) that could cause the symptoms can be deduced. In the last two decades, fault diagnosis has received growing attention due to the complexity of modern systems and the consequent need for more sophisticated techniques to identify the failures when they occur. Detecting the causes of a fault quickly and efficiently means reducing the costs associated with the system unavailability and, in certain cases, avoiding the risks of unsafe operating conditions. Bayesian belief networks (BBNs) are probabilistic models that were developed in artificial intelligence applications but are now applied in many fields. They are ideal for modelling the causal relations between faults and symptoms used in the detection process. The probabilities of events within the BBN can be updated following observations (evidence) about the system state. In this paper we investigate how BBNs can be applied to diagnose faults on a system. Initially Fault trees (FTs) are constructed to indicate how the component failures can combine to cause unexpected deviations in the variables monitored by the sensors. Converting FTs into BNs enables the creation of a model that represents the system with a single network, which is constituted by sub-networks. The posterior probabilities of the components' failures give a measure of those components that have caused the symptoms observed. The method gives a procedure that can be generalized for any system where the causality structure can be developed relating the system component states to the sensor readings. The technique is demonstrated with a simple example system. Copyright © 2008 John Wiley & Sons, Ltd.