Probabilistic Modeling of Failure Dependencies Using Markov Logic Networks

We present a methodology for the probabilistic modeling of failure dependencies in large, complex systems using Markov Logic Networks (MLNs), a state-of-the-art probabilistic relational modeling technique in machine learning. We illustrate this modeling methodology on example system architectures, and show how the the Probabilistic Consistency Engine (PCE) tool can create and analyze failure-dependency models. We compare MLN-based analysis with analytical symbolic analysis to validate our approach. The latter method yields bounds on the expected system behaviors for different component-failure probabilities, but it requires closed-form representations and is therefore often an impractical approach for complex system analysis. The MLN-based method facilitates techniques of early design analysis for reliability (e.g., probabilistic sensitivity analysis). We analyze two examples - a portion of the Time-Triggered Ethernet (TTEthernet) communication platform used in space, and an architecture based on Honeywell's Cabin Air Compressor(CAC) - that highlight the value of the MLN-based approach for analyzing failure dependencies in complex cyber-physical systems.