Diagnosing Root Causes and Generating Graphical Explanations by Integrating Temporal Causal Reasoning and CBR
Journal article, Peer reviewed
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Original versionCEUR Workshop Proceedings. 2017, 1815, 162-172.
This study proposes a methodology to diagnose the root causes of failures in the domain of oil well drilling. The idea is to combine a Bayesian network, which is generated based on an expert knowledge, with situation-specific knowledge of past failure cases. A causal chain is viewed as a temporal sequence. To test the model’s capability, six failure cases from the study’s application domain (oil well drilling) are considered and one of them has been picked up as the studying case. The model is applied to diagnose the root causes of the chosen failure case. A temporal reasoning approach has been employed to narrow down the determination of the effective concepts, given the observations. The preliminary results show some advantages of the new model in comparison with the model that integrated a multi relational knowledge model with case based reasoning.