| Citation: | GUO Xiaojing, GUO Jiahao, XU Chen. Aero-engine remaining useful life prediction of VIT model re-parameterized optimization method based on data field mapping[J]. Journal of Aerospace Power, 2026, 41（3）：20240824 doi: 10.13224/j.cnki.jasp.20240824
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Time-series aero-engine parameters are originated from different data sources. Its feature might be relevant with the remaining useful life prediction of the aero-engine. During the whole periods from the airplane’s taking off to landing, different types of aircrafts have particular engine parameters and data scale. In order to improve the remaining useful life prediction precision and apply it into different dimension engine datasets, the new prediction model was constructed based on the revised VIT （vision transformer） with the re-parameterization. The algorithm of datasets mapping into the RGB image was given, and the model generalization got better. Re-parameterization was combined with multi-dimension attention of VIT, and the precision was improved after the transferring between the time series datasets and space feature. The result of the experiment in datasets （CMAPSS） of the datasets showed that the value of root mean square error （RMSE） lied in [10.83,14.68]. The prediction model and algorithm were better than the traditional method with the smaller RMSE of 4.3% decrease in the least. And also, it could be applicable in another datasets (N-CMAPSS ) with the RMSE value of 2.07.
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