Optimal Design of Traction Gear Modification of High-Speed EMU Based on Radial Basis Function Neural Network

Tang, Zhaoping; Wang, Manyu; Hu, Yutao; Mei, Ziyuan; Sun, Jianjun; Li, Yan

doi:10.1109/access.2020.3007449

Cited by 9 publications

(11 citation statements)

References 11 publications

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“…A gear modification parameter optimization model is to minimize noise is constructed, and the model is solved by the particle swarm optimization (PSO) algorithm [22]. The optimization goal of the model is shown in Equation (19), which represents the acoustic power level RMS value of the transmission system radiated noise; the constraint conditions are shown in Equation (20), which represents the modification range of the pinion drum modification amount, the tooth inclination, and the tooth profile modification amount.…”

Section: Establishing An Optimization Model Of Gear Modification Para...mentioning

confidence: 99%

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Modification and Noise Reduction Design of Gear Transmission System of EMU Based on Generalized Regression Neural Network

et al. 2022

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In view of traction gear vibration and noise affecting the performance of the transmission system and the comfort of passengers when the electric multiple units (EMU) is running at high speed, taking the traction gear transmission system of an EMU as the research object by using Romax software to construct the parametric modification model of the gear transmission system based on gear modification theory. Combined with multibody dynamics, the vibration response characteristics of the transmission system are simulated and analyzed. A radiated noise prediction model is established using the acoustic boundary element method, based on the generalized regression neural network (GRNN). To further explore the influence of gear modification methods and parameters on vibration and noise characteristics and minimize gear transmission’s radiation noise. A particle swarm optimization (PSO) algorithm is designed to solve the optimal modification parameters. The simulation results reveal that after the optimization and modification, the gear transmission error is significantly reduced, the contact status is considerably improved, and the root mean square value of the acoustic power level is reduced by 13.10 dB, which is a reduction of 14%. It shows that the design can effectively reduce the radiation noise of EMU gear trans-mission system.

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Section: Establishing An Optimization Model Of Gear Modification Para...mentioning

confidence: 99%

“…Tang et al used RBF neural network to build a predictive model of radiated noise from EMU gear transmission. The optimization model of modification parameters is constructed, then the multi-island genetic algorithm is taken to solve the model to obtain the minimum noise [19]. To solve the gear noise problem, Phi.…”

Section: Introductionmentioning

confidence: 99%

Modification and Noise Reduction Design of Gear Transmission System of EMU Based on Generalized Regression Neural Network

et al. 2022

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“…where N represents the number of input variables. (2) RBF RBF is used to fit multivariate functions using discrete data [15]- [17]. Typically, RBF is used as a Gaussian function, expressed as follows:…”

Section: A Surrogate Modelmentioning

confidence: 99%

An Improved Dynamic Model Updating Method for Multistage Gearbox Based on Surrogate Model and Sensitivity Analysis

Qin

Liu

et al. 2021

IEEE Access

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Model updating can improve the accuracy of the multistage gearbox dynamic model (MGDM). However, owing to the time-consuming calculations and numerous parameters of the MGDM, previous updating methods are not suitable for updating the MGDM. To solve the above problems, an improved dynamic model updating method that is suitable for updating the MGDM is proposed in this paper. Compared with previous methods, this updating method presents two improvements: (1) In the iterative calculation process, the surrogate model is used to replace the time-consuming MGDM, thereby solving the problem of low computational efficiency; (2) before the updating calculation, Sobol sensitivity analysis is performed to screen out updating parameters from numerous parameters. This study shows that the accuracy of the updated MGDM improved by 18.52% compared with the initial model, thereby enabling a more accurate prediction of the dynamic characteristics of the actual system. This proves the effectiveness of the improved updating method.INDEX TERMS Gear, gearbox, dynamic model, updating method, sensitivity analysis, surrogate model.

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“…Because of the complexity of the design space, the manual selection and verification method based on the experience of personnel is time-consuming and laborious, and the accuracy and the optimization effect of the method are insufficient. In recent decades, the response surface method (RSM), [11][12][13] hierarchical kriging (HK) method, [14][15][16][17] and radial basis function (RBF) method [18][19][20][21] have been proposed. These methods construct a proxy model between design variables and responses 22 for predicting the response value and avoiding the verification of data one by one.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective structural optimization of the aluminum alloy subway car body based on an approximate proxy model

Zhang

2022

Advances in Mechanical Engineering

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To improve the comprehensive structural performance and optimize the structural quality of the aluminum alloy subway car body, a multi-objective structural optimization method based on a response surface approximate proxy model was proposed. On the basis of the sensitivity calculation of the objective response to the thickness changes of profiles in different regions of the car body, 21 groups of plate variables with the highest optimization potential were selected for optimization. The optimization experiments were performed on the design variables, and sufficient sample points were selected for the target analysis. Based on the calculated data, second-order response surface models for the bending modal frequency, bending stiffness, and complete mass of the subway car body were constructed considering the optimized profile thickness. After verifying the accuracy of the model, it was used to replace the finite element model for optimization analysis. To achieve feasible optimization results, two schemes with the profile thickness varied in steps of 0.1 and 0.5 mm in the design space were used. After the completion of the calculation, experimental evaluation of the two optimal schemes indicated that the structural performance was consistent with the numerical results.

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Optimal Design of Traction Gear Modification of High-Speed EMU Based on Radial Basis Function Neural Network

Cited by 9 publications

References 11 publications

Modification and Noise Reduction Design of Gear Transmission System of EMU Based on Generalized Regression Neural Network

Modification and Noise Reduction Design of Gear Transmission System of EMU Based on Generalized Regression Neural Network

An Improved Dynamic Model Updating Method for Multistage Gearbox Based on Surrogate Model and Sensitivity Analysis

Multi-objective structural optimization of the aluminum alloy subway car body based on an approximate proxy model

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