  Model-based fault-tolerant control (FTC) often consists of two distinct
steps: fault detection & isolation (FDI), and fault accommodation. In this work
we investigate posing fault-tolerant control as a single Bayesian inference
problem. Previous work showed that precision learning allows for stochastic FTC
without an explicit fault detection step. While this leads to implicit fault
recovery, information on sensor faults is not provided, which may be essential
for triggering other impact-mitigation actions. In this paper, we introduce a
precision-learning based Bayesian FTC approach and a novel beta residual for
fault detection. Simulation results are presented, supporting the use of beta
residual against competing approaches.
