| Citation: | TAN Na, GUO Jiaxi, LI Yaohua, et al. Aero-engine remaining life prediction model based on improved autoencoder and TFT[J]. Journal of Aerospace Power, 2026, 41（2）：20240822 doi: 10.13224/j.cnki.jasp.20240822
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Considering the problem of difficult characterization of degraded features in time-varying operating conditions of aero-engine multi-source sensor data, a prediction model fusing an improved convolutional autoencoder and temporal fusion transformer (TFT) decoder was proposed to enhance the accuracy of single-point prediction of remaining life and the quantification of time-varying uncertainty through multi-scale temporal and spatial feature fusion. The improved convolutional autoencoder utilized its multi-scale convolution unit (MSCU) to extract feature information at different scales from multi-dimensional sensor time-series data, and flexibly captured local dependencies among information in the sequence while avoiding the information loss problem. The TFT decoder efficiently captured the global dependencies in the data and revealed the importance of the features through these mechanisms, thus providing an explanation for the degree of influence of the data features. Test validation using publicly available datasets and comparative analysis with state-of-the-art prediction models showed that the MS1DCAE_TFT model improved the root mean squared error and score index by at least 0.2% and 65.5% on the FD001 and FD003 datasets, while quantile regression predicted the uncertainty quantification of its life intervals, which can provide a reliable engine remaining life prediction solution.
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