A Bayesian Approach to Fault Isolation with Application to Diesel Engine Diagnosis

This paper considers a Bayesian approach to fault isolation. Given a set of measurements from the system, and a set of possible faults, the task is to calculate the probability that the faults are present. This probability can then be used to rank the faults, or for decisions on fault sccomodation. The method requires the conditional probability distribution describing how the measurements react to the faults. In particular, the structure of dependencies between the tests is important. Knowing the structure facilitates efficient computation methods and makes it possible to reduce the memory capacity needed. In this paper, the structure is estimated from training data using Bayesian methods. The method is applied to diagnosis of the gas flow in a diesel engine.