Degradation evaluation method for communication satellite traveling wave tubes based on full life cycle data

Traveling Wave Tube (TWT) is a key component in communication satellites, typically operating in high-temperature and high-pressure environments for extended periods, making its degradation evaluation particularly important. Traditional mechanism models and threshold rule methods struggle to effectively represent degradation levels under complex electro-thermal coupling effects. To address this challenge, we propose a deep neural network degradation evaluation method based on a Multi-Scale Attention Encoder (MSA-Encoder). First, the complex characteristics of telemetry data are analyzed in detail, exploring the difficulties these characteristics pose for training deep learning models and providing solutions. Second, the MSA-Encoder network is designed, which precisely captures the temporal and channel dependencies in multi-dimensional telemetry data through auto-correlation and Global-Local Channel Attention (GLCA) mechanisms, respectively, achieving accurate data reconstruction. Third, we propose a TWT degradation index calculation method based on Mahalanobis distance and moving average seasonal decomposition. This method calculates data drift index through reconstruction difference theory, followed by moving average seasonal decomposition to compute the degradation index. Finally, using the full life cycle TWT data from a certain communication satellite, a TWT degradation dataset is constructed, and numerical experiments are conducted on this dataset. The experimental results show that the MSA-Encoder surpasses benchmark models of the same parameter scale in telemetry data extraction capability, proving the reliability of the degradation index calculation.