Satellite Failure Prognosis with Cascaded Temporal Convolution and Transformer Network for Multi-Scale Features
Yu Shi1, Yunfeng Dong1,*, Lu Tian2,3
1 School of Astronautics, Beihang University, Beijing, China
2 National Superior College for Engineers, Beihang University, Beijing, China
3 The 15th Research Institute of China Electronic Technology Corporation, Beijing, China
* Corresponding Author: Yunfeng Dong. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.080577
Received 12 February 2026; Accepted 03 May 2026; Published online 20 May 2026
Abstract
Failure prognosis provides critical decision-making support for Integrated System Health Management (ISHM), ensuring the operational safety of satellites in orbit. Temporal Convolutional Networks (TCNs), known for their capability in processing time-series data, have become an important approach for failure prognosis. The gradual performance degradation of satellites, combined with multi-physics coupling effects, gives rise to multi-scale features. However, existing TCN based failure prognosis methods remain limited in their ability to simultaneously capture both local and global features, posing challenges when processing such multi-scale features. To address this issue, a Cascaded Temporal Convolution and Transformer Network (CTCTN) framework is proposed for satellite failure prognosis and uncertainty quantification. The CTCTN first adaptively aligns the feature dimensions of the Depthwise Separable Temporal Convolution (DS-TC) block and the Transformer module through Adaptive Average Pooling (AAP), enabling both local feature extraction and global dependency modeling. A heteroscedastic Huber loss function is then designed to optimize the mean and variance of the CTCTN output. Finally, epistemic and aleatoric uncertainties are separately estimated and used to construct probabilistic prediction intervals. A satellite model is developed, and a run-to-failure dataset is constructed to validate the proposed CTCTN framework using a performance degradation scenario caused by damage to the Solar Array Paddle (SAP) of a Low Earth Orbit (LEO) satellite. Experimental results demonstrate that the proposed CTCTN method not only achieves more accurate Remaining Useful Life (RUL) predictions but also effectively quantifies uncertainty arising from multi-scale features. This work provides a reference case for failure prognosis in LEO satellites and offers decision support for ISHM.
Keywords
Failure prognosis; temporal convolution; transformer; uncertainty quantification; low earth orbit satellite; remaining useful life
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