Research on engine cooling system condition monitoring based on deep digital twin

Cooling system is a crucial subsystem essential for the engines, and its condition monitoring plays an important role in the engine safety and reliability. This paper proposes an innovative deep digital twin (DDT) model that combines Gradient Boosting Decision Tree (GBDT) based ensemble learning and Stacked Sparse Autoencoder (SSAE) based deep learning to enhance the sensitivity and accuracy of engine cooling system condition monitoring (CM). The Gradient Boosting Decision Tree (GBDT) algorithm is employed to generate coolant temperature baselines of healthy state under varying operational conditions. Then, taking the coolant temperature as the characteristic parameter, the health feature extraction model is constructed using Stacked Sparse Autoencoder (SSAE) network to extract the feature representations from coolant temperature under different health states. Specifically, to enhance the efficiency of feature extraction, this paper introduced modifications to the structure of the SSAE. To assess the health state of the cooling system quantitively, the probability density function (PDF) of the feature representations was calculated, with Kullback-Leibler (KL) divergence serving as the health indicators (HI). The severity of cooling system’s degradation was indicated by comparing the deviation in KL divergence between healthy and degradation states. Simulation and experimental data validation demonstrate the capability of the proposed method in cooling system condition monitoring.