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Anomaly detection in aircraft data using Recurrent Neural Networks (RNN)

Anomaly Detection in multivariate, time-series data collected from aircraft's Flight Data Recorder (FDR) or Flight Operational Quality Assurance (FOQA) data provide a powerful means for identifying events and trends that reduce safety margins. The industry standard “Exceedance Detection” algorithm uses a list of specified parameters and their thresholds to identify known deviations. In contrast, Machine Learning algorithms detect unknown unusual patterns in the data either through semi-supervised or unsupervised learning. The Multiple Kernel Anomaly Detection (MKAD) algorithm based on One-class SVM identified 6 of 11 canonical anomalies in a large dataset but is limited by the need for dimensionality reduction, poor sensitivity to short term anomalies, and inability to detect anomalies in latent features. This paper describes the application of Recurrent Neural Networks (RNN) with Long Term Short Term Memory (LTSM) and Gated Recurrent Units (GRU) architectures which can overcome the limitations described above. The RNN algorithms detected 9 out the 11 anomalies in the test dataset with Precision = 1, Recall = 0.818 and F1 score = 0.89. RNN architectures, designed for time-series data, are suited for implementation on the flight deck to provide real-time anomaly detection. The implications of these results are discussed.