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 Model of Concrete Dam Deformation Based on Adaptive Unscented Kalman Filter and BP Neural Network

2014 ◽  
Vol 513-517 ◽  
pp. 4076-4079 ◽  
Author(s):  
Liang Hui Li ◽  
Sheng Jun Peng ◽  
Zhen Xiang Jiang ◽  
Bo Wen Wei
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Unscented Kalman Filter ◽  
Safety Monitoring ◽  
Hydraulic Engineering ◽  
Concrete Dam ◽  
Adaptive Unscented Kalman Filter ◽  
Dam Deformation

By using unscented kalman filter (UKF) theory and introducing adaptive factor into BP neural network, a new prediction model of concrete dam deformation was proposed. Example shows that this model can improve the convergence speed of BP neural network, and the calculation precision of this model meets engineering requirements. Meanwhile, this model can be applied in the safety monitoring of other hydraulic engineering structure.

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 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.

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.

Prediction Model of Passenger Waiting Time in High-Speed Rail Hub Based on Bp Neural Network

2013 ◽  
Vol 321-324 ◽  
pp. 1903-1906 ◽  
Author(s):  
Yu Long Pei ◽  
Kan Zhou ◽  
Ting Peng
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Influencing Factors ◽  
Waiting Time ◽  
Bp Neural Network ◽  
High Speed ◽  
Influencing Factor ◽  
Educational Background ◽  
High Speed Rail ◽  
Trip Distance

In order to explore the characteristics of passenger waiting time in high-speed rail hub, this paper analyzed the influencing factors of passenger waiting time, based on the survey of passenger waiting time in high-speed rail hub. And the main influencing factors were screened out using variance analysis. Then the prediction model of passenger waiting time based on BP neural network was established, the parameters of the model were calibrated and the validity was verified. The results show that, travel time in urban area, trip distance, familiarity toward the hub, educational background of passengers, and the type of transportation is the main influencing factor of passenger waiting time in high-speed rail hub, and the average relative error is only 9.2% using the proposed prediction model of passenger waiting time based on BP neural network.

Dynamic Performance of High-Speed Electric Multiple Unit within Multi-Body System Simulation

2019 ◽  
Vol 12 (4) ◽  
pp. 339-349
Author(s):  
Junguo Wang ◽  
Daoping Gong ◽  
Rui Sun ◽  
Yongxiang Zhao
Keyword(s):  
High Speed ◽  
Rapid Development ◽  
Dynamic Performance ◽  
Body System ◽  
High Speed Railway ◽  
Evaluation Indexes ◽  
Actual Operation ◽  
Multiple Unit ◽  
The Stability ◽  
Multi Body

Background: With the rapid development of the high-speed railway, the dynamic performance such as running stability and safety of the high-speed train is increasingly important. This paper focuses on the dynamic performance of high-speed Electric Multiple Unit (EMU), especially the dynamic characteristics of the bogie frame and car body. Various patents have been discussed in this article. Objective: To develop the Multi-Body System (MBS) model of EMU, verify whether the dynamic performance meets the actual operation requirements, and provide some useful information for dynamics and structural design of the proposed EMU. Methods: According to the technical characteristics of a typical EMU, a MBS model is established via SIMPACK, and the measured data of China high-speed railway is taken as the excitation of track random irregularity. To test the dynamic performance of the EMU, including the stability and safety, some evaluation indexes such as wheel-axle lateral forces, wheel-axle lateral vertical forces, derailment coefficients and wheel unloading rates are also calculated and analyzed in detail. Results: The MBS model of EMU has better dynamic performance especially curving performance, and some evaluation indexes of the stability and safety have also reached China’s high-speed railway standards. Conclusion: The effectiveness of the proposed MBS model is verified, and the dynamic performance of the MBS model can meet the design requirements of high-speed EMU.

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