A Data-Driven Fault Tree for a Time Causality Analysis in an Aging System

This paper develops a data-driven fault tree methodology that addresses the problem of prognosis an aging system based on interpretable time causality analysis model. The model merges concepts knowledge discovery in dataset and to interpret effect structure over time. At periodic intervals, captures cause–effect relations form logic trees, then represents them one reflects changes due aging. proposed provides probability for occurrence using set extracted rules combine discovered root causes bottom-up manner. well-known NASA turbofan engine is used as illustrative example methodology.