Transformer-Based Time-Series Forecasting for Telemetry Data in an Environmental Control and Life Support System of Spacecraft
Bin Song, Boyu Guo, Wei Hu, Zhen Zhang, Nan Zhang, Junpeng Bao, Jianji Wang, Jingmin Xin
Abstract
Safety and stability are critical in manned space missions, requiring an environmental control and life support system (ECLSS) of spacecraft to operate reliably. This study analyzed the time-series characteristics of telemetry data, including total pressure, temperature, and humidity, to predict the ECLSS’s operational state. Existing algorithms for time-series forecasting, including ARIMA, LSTM, TCN, and NBEATS, often struggle with long-sequence forecasting and discrepancies in data distribution, which hinder their ability to deliver accurate predictions. To address these challenges, this study introduces a two-stage normalization method, mean instance normalization (MeanIN), designed to adjust input data distributions and restore output data distributions, thereby significantly enhancing predictive performance. Experimental evaluations on ECLSS telemetry data demonstrate that MeanIN consistently improves model accuracy, with the informer model achieving superior results in long-sequence forecasting tasks. These results underscore the efficacy of MeanIN and its potential to support critical applications in anomaly detection and predictive analysis for spacecraft telemetry data.