Interpretable logic tree analysis: A data-driven fault tree methodology for causality analysis

Abstract

This paper proposes an effective hybrid-based methodology, called interpretable logic tree analysis (ILTA), which characterizes and quantifies event causality occurring in engineering systems with the minimum involvement of human experts. It integrates two concepts: knowledge discovery in database (KDD) and fault tree analysis (FTA). The KDD extracts the root-causes in the form of a set of interpretable (meaningful) patterns and then is exploited to automatically construct a logic tree. Only the feasible solutions consisting of non-redundant patterns that cover the maximum number of observations in the dataset are selected using a burn-and-build algorithm. These solutions are employed first to visualize the discovered knowledge under the interpretable logic tree and second, to estimate the probability of an event given the occurrence of its root-causes. An actuator system dataset is used to illustrate and validate the proposed methodology. Moreover, the ILTA methodology allows the tuning of the system states based on Bayesian control rules that characterize the nature of the discovered root-causes.