Real-Time Health Monitoring for a Mobile Robot Using Fault Tree-Bayesian Network (FT-BN)

This paper presents a hybrid Fault Tree-Bayesian Network (FT-BN) framework for the systematic reliability assessment and real-time health monitoring of autonomous ground rovers. While conventional Fault Tree Analysis (FTA) is effective for identifying fault propagation paths, it is inherently static. To overcome this limitation, we integrate FTA with Bayesian Networks (BNs), which offer dynamic inference capabilities under uncertainty. Our methodology utilizes Mission Planner log data to extract anomalous events, which are then used to manually construct fault trees. These trees are subsequently converted into BNs to enable probabilistic reasoning. Experimental results demonstrate that magnetometer failure is the most significant contributor to the overall mission failure probability. The proposed FT-BN approach facilitates both structural fault analysis and dynamic risk evaluation, enhancing the safety and reliability of autonomous ground systems.