A Real Time Anomaly Detection Method Based on Variable N-Gram for Flight Data

Abstract

Anomaly detection of flight data is vital to aviation safety. A real time anomaly detection for flight data can identify safety issues as soon as anomalies occur and improve the safety of aviation system. However, existing anomaly detection methods for flight data either perform not very well on unknown issues or are limited in their capabilities of real time detection. This paper provides a method to detect real time flight data anomalies with comparable detection performance. Firstly it transforms discrete and continuous flight data into discrete sequences. And each sequence obtained from discrete and continuous data is segmented into several subsequences by sliding a window of the specified size. More importantly, the variable length n-grams are obtained from each subsequence. Then all of unique n-grams become the feature space of the training data set. In this way each subsequence is mapped to this feature space and becomes an input feature vector of one-class Support Vector Machine (SVM). Finally, anomalies in flight data can be detected in real time by the classifier learned on these feature vectors corresponding to subsequences. Experimental results show that it performs better to use linear kernel to train one-class SVM on our feature vectors than Gaussian kernel and the size of the sliding window takes a little effect on the performance of our detection method. Moreover, this method outperforms Multiple Kernel Anomaly Detection (MKAD) in real time anomaly detection.