基于多源特征融合的柴油机状态数据上采样方法

High time-resolution state data serves as the core foundation for digital twin system of diesel engines in operation and maintenance to achieve accurate physical mapping and millisecond-level fault early warning. However，in real-vehicle/real-equipment operating scenarios，constrained by storage capacity，power consumption，and wireless transmission bandwidth，state data of diesel engines generally adopts a low-frequency sampling mode，leading to mapping deviations between digital twins and physical entities. To address these issues，an upsampling method for diesel engine state data based on multi-source feature fusion was proposed. Firstly，a temporal stacked autoencoder was designed to extract temporal evolution features of driver behavior and road environment，and a power-flow-driven stacked autoencoder was constructed to extract interpretable vehicle dynamics features according to power transmission hierarchy. Secondly，a tensor-product-based feature fusion mechanism was proposed to map the 3 types of driver-vehicle-road heterogeneous features into a unified conditional vector. Finally，a feature fusion conditional generative adversarial network(FF-cGAN) was constructed to achieve high-fidelity reconstruction from low-frequency data to high-frequency data. Verification was carried out based on real-vehicle test data. The results show that at an upsampling frequency from 1 —100 Hz，the root mean square errors(RMSE) of the upsampling results under the fault-free starting condition in cold regions and the crankshaft bearing seizure fault condition in hot regions are 17.64 r/min and 17.68 r/min respectively，the average error is below 0.8%，and the Pearson correlation coefficient of frequency-domain signals in the fault stage reaches 0.783.