In model-based diagnostic algorithms, it is assumed that the model is correct. If the model is incorrect, the diagnostic algorithm may diagnose the wrong fault, which can be costly and time consuming. Using past maintenance events, one should be able to make corrections to the model in order for diagnostic algorithm to correctly diagnosis faults. In this paper, a maturation approach is proposed which uses the graph-theoretic representations of Timed Failure Propagation Graph (TFPG) models and diagnostic sessions based on recently standardized diagnostic ontologies to determine statistical discrepancies between that which is expected by the models and that which has been encountered in practice. These discrepancies are then analyzed to generate recommendations for maturing the diagnostic models. Maturation recommendations include identifying new dependencies and erroneous or tenuous dependencies. 1 2 TABLE OF CONTENTS 1. INTRODUCTION .
Abstract-Diagnostic model development presents a significant engineering challenge to ensure subsequent diagnostic processes using such models will yield accurate results. One approach to developing system-level diagnostic models that has been receiving attention is the Timed Failure Propagation Graph (TFPG), developed at Vanderbilt University. Unfortunately, developing TFPG models is also difficult and error-prone. In this paper, we extend previous work in using historical maintenance and diagnostic information to identify potential errors in the TFPGbased diagnostic models and recommend ways of maturing these models. This is done by extending the maturation process to incorporate historical alarm sequences and to model these sequences using a probabilistic transition matrix (similar to a Markov chain). The resulting sequence model is compared to the causal relationships identified in the original TFPG to discover discrepancies between the two. Potential sequence modeling errors with recommendations are given back to an engineer or analyst. We report on the maturation process and algorithms and also provide preliminary experimental results.
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