Real-time Prediction Method for Response of Difficult-to-measure Points of Rotor System Under Variable Working Conditions Driven by Digital Twin

doi:10.3901/JME.260373

Abstract

Abstract: Aiming at the problem that the vibration response of some key positions of the rotor system under different working conditions is difficult to obtain in real time through the sensor, a real-time prediction method for the response of the rotor system under variable working conditions driven by digital twin is proposed. Firstly, based on the dynamic model, the vibration characteristics of the rotor system are analyzed, and a high-precision simplified model based on genetic algorithm-BP neural network is established as the data-driven image of the dynamic model, which can improve the response calculation speed of the model and ensure the real-time performance of the digital twin model. Secondly, a model dynamic updating method based on RBF surrogate model is proposed. The genetic algorithm-BP neural network is used to realize the real-time and efficient iterative optimization of the model, which solves the fusion problem of the measured vibration signal and the mechanism model. Finally, the digital twin experimental platform is built to carry out the real-time prediction experiment of the vibration response of the difficult point of the rotor system under different working conditions, and the validity of the digital twin response prediction method is verified. The experimental results show that the digital twin model can realize the real-time mapping of the operating state of the rotor system, and can better solve the problem of difficult point response prediction of the rotor system.

Key words: rotor system, digital twin, BP neural network, RBF surrogate model, response prediction

CLC Number:

TH113

LUO Zhong, LI Hongyu, LI Lei, DONG Yinzhe. Real-time Prediction Method for Response of Difficult-to-measure Points of Rotor System Under Variable Working Conditions Driven by Digital Twin[J]. Journal of Mechanical Engineering, 2026, 62(7): 221-233.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260373

http://www.cjmenet.com.cn/EN/Y2026/V62/I7/221

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