TEMPO: Time-evolving multi-period observational anomaly detection method for space probes

In space probes, anomaly detection of sequence data collected by various sensors is essential to help detect potential faults promptly, improve the reliability of equipment operation, and ensure the smooth operation of the mission. However, sensors’ signals often contain a superposition of various frequencies, changing fluctuations, and correlations between features. This complexity of data attributes makes building effective models challenging. This paper proposes a Time-Evolving Multi-Period Observational (TEMPO) anomaly detection method for space probes. First, fusing wavelet analysis and natural periods improves the ability to capture multi-period features in data. Then, the feature extraction framework proposed enhances the effectiveness of anomaly detection by comprehensively extracting the complex features of data through the multi-module synergy of temporal and channel. The results demonstrate that the proposed method enhances anomaly detection accuracy and its effectiveness is confirmed. Additionally, the ablation experiment results further validate the efficacy of each module. An evaluation of the algorithm's computational complexity confirms its suitability for real-time processing.