Bayesian fault diagnosis in large-scale measurement systems

Abstract

Because of both increasing complexity and increasing geographic distribution, faults in measurement systems are becoming more troublesome to diagnose in all life-cycle phases: manufacturing, deployment, and operation. This paper considers which features are necessary in an automatic diagnosis system for large-scale measurement systems. The paper describes MonteJade, a diagnosis system designed with these essential features in mind. Examples are given of MonteJade diagnosing faults and determining the next debugging actions to perform, in a measurement system constructed from more than one hundred components.