scholarly journals Optimal design of noise reduction and shape modification for traction gears of EMU based on improved BP neural network

2021 ◽  
Vol 69 (4) ◽  
pp. 373-388
Author(s):  
Zhaoping Tang ◽  
Min Wang ◽  
Xiaoying Xiong ◽  
Manyu Wang ◽  
Jianping Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
High Speed ◽  
Transmission System ◽  
Gear Transmission ◽  
Noise Prediction ◽  
Vibration Acceleration ◽  
Acceleration Level ◽  
Gear Transmission 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 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.

A Model Based on BP Neural Network for Audible Noise Prediction

2014 ◽  
Vol 986-987 ◽  
pp. 1356-1359
Author(s):  
You Xian Peng ◽  
Bo Tang ◽  
Hong Ying Cao ◽  
Bin Chen ◽  
Yu Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Transmission Lines ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Absolute Error ◽  
Research Area ◽  
Noise Prediction ◽  
Bp Network ◽  
Model Based

Audible noise prediction is a hot research area in power transmission engineering in recent years, especially come down to AC transmission lines. The conventional prediction models at present have got some problems such as big errors. In this paper, a prediction model is established based on BP network, in which the input variables are the four factors in the international common expression of power line audible noise and the noise value is the output. Take multiple measured power lines as an example, a train is made by the BP network and then the prediction model is set up in the hidden layer of the network. Using the trained model, the audible noise values are predicted. The final results show that the average absolute error in absolute terms of the values by the audible noise prediction model based on BP neural network is 1.6414 less than that predicted by the GE formula.

scholarly journals Research on Thermal Error Modeling of Motorized Spindle Based on BP Neural Network Optimized by Beetle Antennae Search Algorithm

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 286
Author(s):  
Zhaolong Li ◽  
Bo Zhu ◽  
Ye Dai ◽  
Wenming Zhu ◽  
Qinghai Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
High Speed ◽  
Search Algorithm ◽  
Thermal Error ◽  
Error Prediction ◽  
Machining Accuracy ◽  
Motorized Spindle ◽  
Thermal Error Model

High-speed motorized spindle heating will produce thermal error, which is an important factor affecting the machining accuracy of machine tools. The thermal error model of high-speed motorized spindles can compensate for thermal error and improve machining accuracy effectively. In order to confirm the high precision thermal error model, Beetle antennae search algorithm (BAS) is proposed to optimize the thermal error prediction model of motorized spindle based on BP neural network. Through the thermal characteristic experiment, the A02 motorized spindle is used as the research object to obtain the temperature and axial thermal drift data of the motorized spindle at different speeds. Using fuzzy clustering and grey relational analysis to screen temperature-sensitive points. Beetle antennae search algorithm (BAS) is used to optimize the weights and thresholds of the BP neural network. Finally, the BAS-BP thermal error prediction model is established. Compared with BP and GA-BP models, the results show that BAS-BP has higher prediction accuracy than BP and GA-BP models at different speeds. Therefore, the BAS-BP model is suitable for prediction and compensation of spindle thermal error.

scholarly journals An Improved Prediction Model of IGBT Junction Temperature Based on Backpropagation Neural Network and Kalman Filter

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yu Dou
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Prediction Model ◽  
Bp Neural Network ◽  
High Speed ◽  
Rapid Development ◽  
Junction Temperature ◽  
Insulated Gate Bipolar Transistor ◽  
Network Estimation ◽  
The Stability

With the rapid development of emerging technologies such as electric vehicles and high-speed railways, the insulated gate bipolar transistor (IGBT) is becoming increasingly important as the core of the power electronic devices. Therefore, it is imperative to maintain the stability and reliability of IGBT under different circumstances. By predicting the junction temperature of IGBT, the operating condition and aging degree can be roughly evaluated. However, the current predicting approaches such as optical, physical, and electrical methods have various shortcomings. Hence, the backpropagation (BP) neural network can be applied to avoid the difficulties encountered by conventional approaches. In this article, an advanced prediction model is proposed to obtain accurate IGBT junction temperature. This method can be divided into three phases, BP neural network estimation, interpolation, and Kalman filter prediction. First, the validities of the BP neural network and Kalman filter are verified, respectively. Then, the performances of them are compared, and the superiority of the Kalman filter is proved. In the future, the application of neural networks or deep learning in power electronics will create more possibilities.

Prediction for Percentage of Vehicle Entering Expressway Rest Area Based on BP Neural Network

2012 ◽  
Vol 178-181 ◽  
pp. 1956-1960
Author(s):  
Xiao Yan Shen ◽  
Hao Xue Liu ◽  
Jia Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Influencing Factors ◽  
High Precision ◽  
Bp Neural Network ◽  
High Speed ◽  
High Speed Railway ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Influence Degree

In order to scientifically decide the percentage of vehicle entering expressway rest area, based on analyzing the influencing factors relating to the percent of mainline traffic stopping, a BP neural network prediction model for it was put forward. Finally, The Xinzheng Rest Area (XRA) was taken as an example for verifying the feasibility of the prediction model and determining the influence degree of the Shijiazhuang-Wuhan high-speed railway on the percentage of mainline vehicles entering XRA. The result shows that the model had a high precision and reliability.

scholarly journals Research on matching conditions of coaxial six-branch split-torsion herringbone gear transmission system

2021 ◽  
Vol 39 (2) ◽  
pp. 341-349
Author(s):  
Zhibin Li ◽  
Sanmin Wang ◽  
Fei Li ◽  
Qi'an Peng ◽  
Jianfeng Li
Keyword(s):  
Calculation Method ◽  
High Speed ◽  
High Reliability ◽  
Three Dimensional ◽  
Matching Condition ◽  
Transmission System ◽  
Gear Transmission ◽  
Heavy Load ◽  
Gear Transmission System ◽  
Herringbone Gear

Compared with traditional gear transmission, the multi-branch split-torsion gear transmission system has the advantages of large transmission power, small size and high reliability, so it is more and more used in high-speed heavy load occasions such as ships and aircraft. Since the transmission system of multi-branch split torsional gears belongs to over-constrained configuration, it is necessary to meet strict tooth matching condition in the design process in order to realize the correct synchronous meshing of each branch, which is of great significance to ensure its uniform installation and motion synchronization.Aiming at the coaxial six-branch twisted herringbone gear transmission system, this paper establishes a calculation method for the proper meshing conditions of each branch on the basis of considering the movement synchronization of each branch and preventing geometric interference.In addition, the calculation of gear allocation was carried out for a ship's power transmission system, and a parameter scheme that satisfies the requirements of transmission ratio, concentricity and synchronous meshing was obtained.The correctness of the calculation method of tooth matching in this paper is verified by three-dimensional modeling. This method has universal application value to the tooth matching design of other coaxial multi-branch gear transmissions.

Rotordynamics analysis of a single helical gear transmission system for high speed applications

2020 ◽  
Vol 14 (3) ◽  
pp. 7040-7048
Author(s):  
Najeeb Ullah ◽  
C. Xi ◽  
T. Cong ◽  
H. Yucheng
Keyword(s):  
High Speed ◽  
Critical Speed ◽  
Transmission Error ◽  
Gear Transmission ◽  
Dynamic Mesh ◽  
Coupled Systems ◽  
Acceleration Response ◽  
Vibration Acceleration ◽  
Gear Transmission System ◽  
The Stability

Since the non-linear dynamic response under various high-speed conditions can directly affect the life of gear transmission systems. In addition, the transmission error and dynamic mesh force play a key role in noise and harshness analysis of gear bearing coupled systems. So, in this piece of work, a 12 degree of freedom dynamic model is developed to probe the vibration response by using finite element method and taking into account the bearing and flexible shafts in the first part. Subsequently, some meshing gear characteristics such as dynamic and vibration acceleration response under different rotational speeds (1000-9000 rev/minute) were analyzed whereas critical speed appeared at 6500 rev/minute. Then, the stability analysis is performed to investigate the dynamics behind the critical speed by using MASTA. It was observed that natural frequency of 0.45 kHz for a fourth harmonic order is analogous to critical speed which further causes sudden elevation in both dynamic mesh force and transmission error.

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