Robust Anomaly Detection for Multivariate Data of Spacecraft Through Recurrent Neural Networks and Extreme Value Theory
Gang Xiang, Ruishi Lin
Abstract
Spacecraft anomaly detection which could find anomalies in the telemetry or test data in advance and avoid the occurrence of catastrophic failures after taking corresponding measures has elicited the attention of researchers both in academia and aerospace industry. Current spacecraft anomaly detection systems require costly knowledge and human expertise to identify a true anomaly. Moreover, some new problems and challenges such as large volume of test data, imbalanced data distribution and the scarcity of faulty labeled samples have emerged. In this work, we propose an unsupervised anomaly detection algorithm combining Gated Recurrent Unit (GRU) based Recurrent Neural Network (RNN) and Extreme Value Theory (EVT). First, we develop a two-layer ensemble learning based predictor framework which stacks three GRU-based networks with different architectures to learn and capture the normal behavior of multiple channels of data. Then, the prediction errors are calculated and smoothed using Exponentially Weighted Moving Average (EWMA) algorithm. Next, we propose a detection rule setting anomaly threshold automatically through EVT which does not assume any parent distribution on the prediction errors. To the best of our knowledge, it is the first attempt that stacked GRU-based predictors with EVT has been employed into the spacecraft anomaly detection. Through extensive experiments conducted on public datasets as well as real data sampled from a launch vehicle, we show that the proposed detection algorithm is superior to other state-of-the-art anomaly detection approaches in terms of model performance and robustness.