Bayesian network-based framework for the design of reconfigurable health management monitors

Modern small-size UAVs depend on highly complex architectures with many sensors and computer-controlled actuators. The size, weight and budget constraints leave little or no room for redundant systems. So all components must be reliable and any fault must be detected as early as possible. In this paper, we propose an adaptive, real-time, on-board system to continuously monitor sensors, software, and hardware components for the detection and diagnosis of failures by means of Bayesian networks. In particular, we propose an optimized hardware implementation of Bayesian Networks (BNs) for monitoring and exploiting the evidence. We consider FPGA for both performances and the ability to dynamically configure the hardware according to mission applications. Finally, we introduce an off-line framework that can generate FPGA implementations of the monitors for embedded systems under time and resource constraints.