PropMat-DAE:An Avionics System Fault Diagnosis Algorithm based on Graph Anomaly Detection

Fault diagnosis of sensors in avionics system is one of the important ways to ensure the normal operation of modern aircraft. However, in this process, most studies only consider the time anomaly of a single sensor and seldom consider the hidden spatial position relationship between sensors, so they cannot fully consider the spatiotemporal anomaly of sensors.To solve the above problem, in this paper, we propose PropMat-DAE, which is a fault diagnosis framework that comprehensively considers sensor attribute and structural anomalies. In each iteration, in addition to calculating the anomaly scores of the two parts separately, it can also optimize the combined loss on the basis of considering the full attention, which represents the reconstruction error of the sensor under the spatiotemporal fusion. Experimental results on open source aerospace sensor datasets show that the proposed method is superior to 14 new baseline methods in overall performance, and it is also superior to the mainstream attention mechanisms in the design of attention.