Modification and Noise Reduction Design of Gear Transmission System of EMU Based on Generalized Regression Neural Network
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 co...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/10/2/157 |
| _version_ | 1797478529303052288 |
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| author | Zhaoping Tang Manyu Wang Min Zhao Jianping Sun |
| author_facet | Zhaoping Tang Manyu Wang Min Zhao Jianping Sun |
| author_sort | Zhaoping Tang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:33:07Z |
| format | Article |
| id | doaj.art-2a7fbb8178e4475dba64041f4ab9bed9 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T21:33:07Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-2a7fbb8178e4475dba64041f4ab9bed92023-11-23T20:49:09ZengMDPI AGMachines2075-17022022-02-0110215710.3390/machines10020157Modification and Noise Reduction Design of Gear Transmission System of EMU Based on Generalized Regression Neural NetworkZhaoping Tang0Manyu Wang1Min Zhao2Jianping Sun3School of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaCRRC Qishuyan Institute Company Ltd., Changzhou 213025, ChinaSchool of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaIn 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.https://www.mdpi.com/2075-1702/10/2/157gear transmission systemGRNNPSO algorithmmodification noise reductionoptimal design |
| spellingShingle | Zhaoping Tang Manyu Wang Min Zhao Jianping Sun Modification and Noise Reduction Design of Gear Transmission System of EMU Based on Generalized Regression Neural Network Machines gear transmission system GRNN PSO algorithm modification noise reduction optimal design |
| title | Modification and Noise Reduction Design of Gear Transmission System of EMU Based on Generalized Regression Neural Network |
| title_full | Modification and Noise Reduction Design of Gear Transmission System of EMU Based on Generalized Regression Neural Network |
| title_fullStr | Modification and Noise Reduction Design of Gear Transmission System of EMU Based on Generalized Regression Neural Network |
| title_full_unstemmed | Modification and Noise Reduction Design of Gear Transmission System of EMU Based on Generalized Regression Neural Network |
| title_short | Modification and Noise Reduction Design of Gear Transmission System of EMU Based on Generalized Regression Neural Network |
| title_sort | modification and noise reduction design of gear transmission system of emu based on generalized regression neural network |
| topic | gear transmission system GRNN PSO algorithm modification noise reduction optimal design |
| url | https://www.mdpi.com/2075-1702/10/2/157 |
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