A deep reinforcement learning-based method for parameter inversion of digital twin model of planetary gear system

HE Guolin; HONG Liming; LIN Huibin; CHEN Huayuan; XU Zhongsheng

doi:10.16578/j.issn.1004.2539.2026.06.008

Abstract：[Objective]Rapid parameter inversion of planetary gear system digital twin models is the core difficulty in real-time monitoring of their operating conditions.Traditional optimization and heuristic algorithms have defects such as long time consumption and inability to retain experience.A deep reinforcement learning-driven parameter updating method for planetary gear system digital twin models was proposed.[Methods]Firstly,a rigid-flexible coupled digital twin model of the planetary gear system was established by the lumped parameter method and the finite element method,and encapsulated into a deep reinforcement learning environment to provide a simulation carrier for parameter optimization.Secondly,the amplitudes of the first three meshing frequency harmonics and their sidebands in the frequency domain were extracted as optimization objectives,an error-related reward function was constructed,and a two-layer fully connected neural network with ReLU activation was selected to fit state-action pairs.Finally,a planetary gear transmission test bench was built,and parameter inversion verification tests were carried out under different speed conditions.[Results]The results show that the proposed method only takes 4.9 min for parameter inversion under 2 000 r/min working condition with an amplitude error of 0.45,which shortens the inversion time by more than 95%compared with the particle swarm optimization algorithm.The inversion time across 1 500 r/min working condition is 5.2 min with an amplitude error of 1.83.It can retain training experience to achieve rapid parameter updating,and provides a reference for real-time correction of planetary gear system digital twin models.