A novel end-to-end anomaly detection framework for spacecraft using MINE and LSTM-VAE with attention mechanism

Abstract Anomaly detection in spacecraft plays a pivotal role in ensuring the safety and reliability of aerospace missions. As contemporary spacecraft systems become intricate with the integration of numerous subsystems and components, it is crucial to have effective methods to detect anomalies to maintain the normal operation of the spacecraft and prevent potential failures. However, due to the complexity of the space environment and the high level of integration of systems, conventional anomaly detection methods are often inadequate in dealing with multi-source, high-dimensional, and nonlinear data within spacecraft. To address these problems, this paper proposes a spacecraft end-to-end anomaly detection framework based on Mutual Information Neural Estimation (MINE) and long short-term memory networks-variational autoencoder (LSTM-VAE). This framework employs MINE to capture complex dependencies between features and utilizes LSTM-VAE for temporal modeling and anomaly detection, while an attention mechanism is also incorporated to enhance model interpretability. Specifically, MINE is used to learn nonlinear, high-order dependencies among variables in the aerospace system. LSTM-VAE manages to blend the temporal modeling capability of recurrent neural networks and the advantaged latent representation learning of variational autoencoders, providing deeply integrated modeling of the telemetric data dynamics and latent structure. The introduction of the attention mechanism elevates the model’s interpretability, making it possible to trace anomaly detection results back to specific time points and related features. The research conducted comparative experiments on two novel aerospace telemetry datasets. The results indicate that the proposed framework significantly outperforms existing methods in terms of anomaly detection accuracy, precision, recall rate, and F 1 score. Additionally, through case study analysis, we demonstrate this framework’s application in actual spacecraft anomaly detection tasks, thereby illustrating its potential in aiding anomaly source tracing in spacecraft.