Semi-Markov Switching Vector Autoregressive Model-based Anomaly Detection in Aviation Systems

TL;DR

This paper introduces a semi-Markov switching vector autoregressive model for detecting anomalies in aviation flight data, enabling real-time identification of safety risks and operational issues.

Contribution

The work presents a novel SMS-VAR framework tailored for aviation anomaly detection, capable of online processing and handling heterogeneous, variable-length time series.

Findings

Effective detection of diverse anomalies in real and simulated datasets

Framework scalable for online deployment

Identifies safety-critical flight segments

Abstract

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 provide insights into the flight operations and highlight otherwise unavailable potential safety risks and precursors to accidents. 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.