Data-Driven Fault Tree Modeling for Reliability Assessment of Cyber-Physical Systems

Reliability of a system is a measure of the likelihood that the system will perform as expected for a predefined time period. Fault tree analysis, as a popular method for analyzing reliability of systems, has gained a widespread use in many areas, such as the automotive and aviation industry. Fault trees of systems are usually designed by domain experts, based on their knowledge. Since systems change their behaviors during their lifetimes, knowledge-driven fault trees might not accurately reflect true behaviors of systems. Thus, deriving fault trees from data would be a better alternative, especially for non-safety-critical systems, where the amounts of data on faults can be significant. We present an approach and a tool for Data-Driven Fault Tree Analysis (DDFTA) that extract fault trees from time series data of a system, and uses simulation to analyze the extracted fault trees to estimate reliability measures of systems.