An efficient algorithm for anomaly detection in a flight system using dynamic Bayesian networks

Despite the fact that Dynamic Bayesian Network models have become a popular modelling platform to many researchers in recent years, not many have ventured into the realms of data anomaly and its implications on DBN models. An abnormal change in the value of a hidden state of a DBN will cause a ripple-like effect on all descendent states in current and consecutive slices. Such a change could affect the outcomes expected of such models. In this paper we propose a method that will detect anomalous data of past states using a trained network and data of the current network slice. We will build a model of pilot actions during a flight, this model is trained using simulator data of similar flights. Then our algorithm is implemented to detect pilot errors in the past given only current actions and instruments data.