MAG: A Novel Approach for Effective Anomaly Detection in Spacecraft Telemetry Data

Anomaly detection is a crucial matter to ensure the spacecraft stability. During the spacecraft operation, sensors and controllers generate a large volume of multidimensional time series telemetry data with long periodicity, and one key point to detect the anomaly inside the spacecraft timely and precisely is to extract essential features from the sheer amount of telemetry data. However, great challenges exist owing to the complex coupling relationships and the temporal characteristics inside the telemetry data. To address this issue, we propose a novel approach called maximum information coefficient attention graph network (MAG). The basic frame is a graph neural network, which utilizes embedding vectors to describe the intrinsic properties of each dimension, correlation analysis to investigate long-term dependencies, an attention mechanism to determine short-term interactions among dimensions, and long short term memory (LSTM) to extract temporal features. The fusion of these modules through a graph neural network results in the construction of the MAG model, allowing for a comprehensive analysis of complex variable relationships and temporal characteristics leading to successful detection of various types of anomalies. Since telemetry data has heterogeneous characteristics, we adapt the loss function and design an unsupervised anomaly scoring method suitable for MAG. To verify the effectiveness of the proposed algorithm, we conducted experiments using two publicly and two new available spacecraft telemetry datasets, and the results demonstrate that our algorithm is more efficient and accurate in detecting spacecraft data anomalies than several other state-of-the-art methods.