A mechanism and data-driven adaptive update framework for digital twin model of electric drive assembly gear transmission system

Abstract

Digital twin technology (DT) provides a transformative solution for condition monitoring and fault diagnosis (FD) of complex electromechanical systems by constructing virtual mirrors of physical systems. As the key carrier of torque transmission in the powertrain system of electric vehicles (EV), the dynamic characteristics of reduction gears directly affect the safety, efficiency, and service life of EV. In practical engineering applications, the failure of reduction gears not only leads to vehicle performance degradation, but also may cause safety accidents and high maintenance costs. However, accurately modeling and updating the DT of reduction gears remains a challenging task due to the complexity and dynamics of these systems. In this study, a joint mechanism-data-driven adaptive (JMDDA) updating method is proposed to improve the accuracy of the DT model of electrically driven assembly reduction gears. First, a mechanism-based model is constructed to simulate the dynamic response of gears under different operating conditions; then, adaptive least squares estimation is used to update the model parameters and simulate the changing characteristics of the gears under different load and speed conditions; further, a long and short-term memory neural network (LSTM) is employed to learn the error between the simulation results and the measured data to narrow the gap between the simulation and the actual performance to realize the accurate prediction of various operating conditions. The effectiveness of the proposed method is verified by comprehensively comparing the time and frequency domain results between the measured data and the model simulation. The results indicated that the JMDDA method significantly improves the accuracy of the DT model, and provides a reliable solution for real-time monitoring of gear system health status, predicting potential failures and optimizing maintenance schedules, thus extending equipment life and reducing operating costs.