An empirical evaluation of Bayesian networks derived from fault trees

Abstract—Fault Isolation Manuals (FIMs) are derived from a type of decision tree and play an important role in maintenance troubleshooting of large systems. However, there are some drawbacks to using decision trees for maintenance, such as requiring a static order of tests to reach a conclusion. One method to overcome these limitations is by converting FIMs to Bayesian networks. However, it has been shown that Bayesian networks derived from FIMs will not contain the entire set of fault and alarm relationships present in the system from which the FIM was developed. In this paper we analyze Bayesian networks that have been derived from FIMs and report on several measurements, such as accuracy, relative probability of target diagnoses, diagnosis rank, and KL-divergence. Based on our results, we found that even with incomplete information, the Bayesian networks derived from the FIMs were still able t