Learning Bayesian networks for fault detection

Abstract. The correct detection of a fault can save worthy resources or even prevent the destruction of key equipment, but, mainly, the correct detection of a single fault can save lives, as, for example, in the case of spaceships, aircraft and nuclear plants. In this work a new fault detection method, based on the learning of a Bayesian network, is applied to the longitudinal motion of the 747 aircraft. A linearized model of the 747 flying under the control of an autopilot and subjected to gusts of wind is used and faults at the altitude sensor are simulated. Such faults, if not detected, could make the autopilot lower the flight level causing a collision. A Bayesian network is learned from data collected from the aircraft flying under normal conditions and is used in the fault detection. The simulation results comparing the correct fault detection ratio, the false alarm ratio and the average time of detection of the proposed method and of the Luenberger observer residue approach show a clear advantage of the application of proposed method