Imbalanced satellite telemetry data anomaly detection model based on Bayesian LSTM

Abstract

Anomaly detection of satellite telemetry data has always been a significant issue in the development of aeronautics and astronautics. Timely and effective anomaly detection method of satellite telemetry data is a research hotspot in academia and aerospace industry. For satellite telemetry data, we propose an anomaly detection model based on Bayesian deep learning without domain knowledge. In this model, we show the feasibility of implementing MC-dropout on the Long Short-Term Memory Network (LSTM), and establish the Bayesian LSTM. First, we perform a preliminary anomaly detection task through our model—Monte Carlo Dropout Bidirectional Long Short-term Memory Network (MCD-BiLSTM). Then, Monte Carlo (MC) Sampling Variance, Prediction Entropy and Mutual Information are taken to measure the uncertainty of output through MCD-BiLSTM. What's more, we further explore and exploit the three types of uncertainties, and utilize the variational auto-encoder (VAE) to reevaluate the high uncertainty samples to improve the anomaly detection capability. To our knowledge, it is the first time that Bayesian neural networks have been introduced into the field of satellite telemetry data anomaly detection. The experimental results on an imbalanced satellite telemetry dataset show that our proposed model can add effective regularization constraints, and obtain great robustness on imbalanced data, which performs better than popular traditional neural networks and other Bayesian neural networks.