Performance Analysis and Assessment of Safety Critical Systems Using Ann

Abstract

Many of the safety critical systems (SCS) require the failure analysis to ensure that the applications work properly and safely. Failure of such safety critical systems results in huge losses. Unmanned aerial vehicles (UAVs) are safety critical systems. System Safety Assessments (SSA) is employed for analyzing these systems. SSA monitors the degraded functionalities of systems. Sensor faults are the root causes for most of the system faults and need to be assessed properly. The availability of several tools for the design, implementation, and simulation of artificial neural networks (ANN) makes it easier to use in safety assessment applications. MATLAB provides ANN features to analyze the sensor faults. In this paper we propose ANN as a novel approach to ensure the system safety and analyze the failures due to the sensor faults. UAV groundspeed sensor is considered as a case study. MATLAB/Simulink tool suite is used to implement the sensor model. The model outputs are classified and the classified values of sensor are used for prediction. The predicted values are classified for verifying the future performance. Simulation results discuss the classified values, performance analysis, and predicted outputs of the sensor model.