减速器传动系统作为新能源汽车的核心部件,承担着传递动力的重要任务。实际运行过程中,因传动系统本身结构、制造和装配误差、齿轮啮合冲击等原因,减速器传动系统成为汽车室内噪声的主要来源。以新能源汽车二级减速器的传动系统作为研

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.

Under high-speed operating conditions, the noise caused by the vibration of the traction gear transmission system of the Electric Multiple Units (EMU) will distinctly reduce the comfort of passengers. Therefore, analyzing the dynamic characteristics of traction gears and reducing noise from the root cause through comprehensive modification of gear pairs have become a hot research topic. Taking the G301 traction gear transmission system of the CRH380A high-speed EMU as the research object and then using Romax software to establish a parametric modifi- cation model of the gear transmission system, through dynamics, modal and Noise Vibration Harshness (NVH) simulation analysis, the law of howling noise of gear pair changes with modification parameters is studied. In the small sample training environment, the noise prediction model is constructed based on the priority weighted Back Propagation (BP) neural network of small noise samples. Taking the minimum noise of high-speed EMU traction gear transmission as the optimization goal, the simulated annealing (SA) algorithm is introduced to solve the model, and the optimal combination of modification parameters and noise data is obtained. The results show that the prediction accuracy of the prediction model is as high as 98.9%, and it can realize noise prediction under any combination of modification parameters. The optimal modification parameter combination obtained by solving the model through the SA algorithm is imported into the traction gear transmission system model. The vibration acceleration level obtained by the simulation is 89.647 dB, and the amplitude of the vibration acceleration level is reduced by 25%. It is verified that this modification optimization design can effectively reduce the gear transmission. (C) 2021 Institute of Noise Control Engineering.

Under high-speed operating conditions, the noise caused by the vibration of the traction gear transmission system of the Electric Multiple Units (EMU) will distinctly reduce the comfort of passengers. Therefore, analyzing the dynamic characteristics of traction gears and reducing noise from the root cause through comprehensive modification of gear pairs have become a hot research topic. Taking the G301 traction gear transmission system of the CRH380A high-speed EMU as the research object and then using Romax software to establish a parametric modification model of the gear transmission system, through dynamics, modal and Noise Vibration Harshness (NVH) simulation analysis, the law of howling noise of gear pair changes with modification parameters is studied. In the small sample training environment, the noise prediction model is constructed based on the priority weighted Back Propagation (BP) neural network of small noise samples. Taking the minimum noise of high-speed EMU traction gear transmission as the optimization goal, the simulated annealing (SA) algorithm is introduced to solve the model, and the optimal combination of modification parameters and noise data is obtained. The results show that the prediction accuracy of the prediction model is as high as 98.9%, and it can realize noise prediction under any combination of modification parameters. The optimal modification parameter combination obtained by solving the model through the SA algorithm is imported into the traction gear transmission system model. The vibration acceleration level obtained by the simulation is 89.647 dB, and the amplitude of the vibration acceleration level is reduced by 25%. It is verified that this modification optimization design can effectively reduce the gear transmission. © 2021 Institute of Noise Control Engineering.

为了减小振动和噪声,以某款新能源汽车二级减速器为例,利用Romax软件构建减速器斜齿轮传动系统的三维模型,分析该系统的单位长度载荷、传动误差以及噪声、振动与声振粗糙度(noise,vibration,harshness,NVH)。针对Romax对角修形的参数设

The dynamic characteristics of the traction gear transmission system have a great influence on the safety, comfort, and reliability of EMU (electric multiple units). Combining the methods of theoretical analysis, numerical simulation, and optimization design theory, establishing a parameterized gear modification model. Meanwhile, designing reasonable shape modification schemes and parameters. The dynamic characteristics, vibration response characteristics, and acoustic response characteristics of gear meshing of CRH380A high-speed EMU under continuous traction conditions are analyzed. The corresponding relationship between gear modification parameters and gear transmission radiation noise is approximated by finite element simulation data and RBF (radial basis function) neural network. Using a multi-island genetic algorithm to optimize gear modification parameters to minimize gear transmission noise, further seeking to meet the low-noise modification design of high-speed train traction helical gear transmission system under continuous operating conditions method.
© 2013 IEEE.

The NVH (Noise, Vibration, and Harshness) characteristics of new energy vehicles are the key indexes to measure interior comfort. The multi-stage gear reducer in the transmission system is the primary source of vibration and noise. The parameterized 3D model of the multi-stage gear transmission system of the new energy vehicle was established through Romax software, and the comprehensive gear Modification method of the tooth direction combined with the tooth profile was built. Then a complete simulation analysis process is established to solve the maximum vibration acceleration of the multi-stage gear transmission system under constant speed condition, to obtain the simulation data of two-stage gear set under different Modification parameters. The traditional BP (Back Propagation) neural network is optimized and improved through the optimal selection of network parameters combined with Bayesian regularization. Based on the optimized BP neural network, a Modified parameter-vibration noise prediction model is constructed. Finally, the GA (Genetic Algorithm) optimization algorithm is used to solve the prediction model to obtain the optimal combination of Modification parameters aiming at the minimum vibration acceleration, the effectiveness and reliability of the Modified design are verified through actual simulation. It provides ideas and a basis for the research on vibration and noise reduction of multi-stage gears.
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Attribute reduction plays an important role in knowledge discovery and data mining. Confronted with data characterized by the interval and missing values in many data analysis tasks, it is interesting to research the attribute reduction for interval-valued data with missing values. Uncertainty measures can supply efficient viewpoints, which help us to disclose the substantive characteristics of such data. Therefore, this paper addresses the attribute reduction problem based on uncertainty measure for interval-valued data with missing values. At first, an uncertainty measure is provided for measuring candidate attributes, and then an efficient attribute reduction algorithm is developed for the interval-valued data with missing values. To improve the efficiency of attribute reduction, the objects that fall within the positive region are deleted from the whole object set in the process of selecting attributes. Finally, experimental results demonstrate that the proposed algorithm can find a subset of attributes in much shorter time than existing attribute reduction algorithms without losing the classification performance.
© 2019 World Scientific Publishing Company.

牵引齿轮修形是减小振动噪声、改善传动性能、提高乘客舒适度的常见方法。以CRH380A高速动车组G301牵引齿轮为研究对象,利用Romax软件构建齿轮副动力学仿真模型,在高速运行工况下对齿轮传动系统进行动态接触分析,基于接触斑点、啮合错位量等分析结果,提出全齿廓结合齿向的综合修形方法,并以传动误差和单位长度法向载荷变化量作为修形效果的评价指标。仿真表明,采用提出的综合修形方法,齿轮副的传动误差幅值和最大单位长度载荷分别降低了16. 75%、9. 80%,有效改善了齿轮的传动性能,减小了传动的啮合冲击,从而降低振动噪声,提升了乘客的舒适度。