Semi-Markov Switching Vector Autoregressive Model-based Anomaly Detection in Aviation Systems
Igor Melnyk*, University of Minnesota; Arindam Banerjee, University of Minnesota; Bryan Matthews, Nasa Ames Research Center; Nikunj Oza, Nasa Ames Research Center
In this work we consider the problem of anomaly detection in heterogeneous, multivariate, variable-length time series datasets. Our focus is on the aviation safety domain, where data objects are ﬂights and time series are sensor readings and pilot switches. In this context the goal is to detect anomalous ﬂight segments, due to mechanical, environmental, or human factors in order to identifying operationally signiﬁcant events and highlight potential safety risks. For this purpose, we propose a framework which represents each ﬂight using a semi-Markov switching vector autoregressive (SMS-VAR) model. Detection of anomalies is then based on measuring dissimilarities between the model’s prediction and data observation. The framework is scalable, due to the inherent parallel nature of most computations, and can be used to perform online anomaly detection. Extensive experimental results on simulated and real datasets illustrate that the framework can detect various types of anomalies along with the key parameters involved.