System Safety Analysis for Critical System Applications Using Bayesian Networks

Abstract

Safety Assessment (SA) is a well-established process for assuring the safety and reliability of critical systems. It uses probabilistic analysis to provide precise measures about the safety requirements of a system. This research work introduces SA into safety critical systems like unmanned aerial vehicles (UAV) that have prominent roles in both the commercial and military aerospace industries. The absence of thinking, observing, reacting and decision making pilot reduces the capability of UAVs to manage adverse situations such as faults and failures. There are number of various sensors in UAV, any major faults or failures in such critical sensors affect the total functionality of the system and hence will make the system unsafe. So for safety analysis of UAV here the functionality of different sensors is considered. The goal is to monitor the safety critical sensor outputs and ensure successful performance of UAV sensors using Bayesian Networks (BN), and this is implemented using the software BayesiaLab 5.4.3 DE. Simulation results comprise the influence analysis on the system safety by the sensor functionality modes and the total precision of the network using samples.