Data-Driven Modelling Of Repairable Fault Trees From Time Series Data With Missing Information

Fault tree analysis is one of the most popular techniques for dependability analysis of a wide range of systems. True fault-related behavior of a system would be more accurately reflected if the system's fault tree is derived from a combination of observational data and expert knowledge, rather than expert knowledge alone. The concept of learning fault trees from data becomes more significant when systems change their behaviors during their lifetimes. We present an algorithm for learning fault trees of systems with missing information on fault occurrences of basic events. This algorithm extracts repairable fault trees from incomplete multinomial time series data, and then uses simulation to estimate the system's reliability measures. Our algorithm is not limited to exponential distributions or binary events. Furthermore, we assess the sensitivity of our algorithm to different percentages of missingness and amounts of available data.