Fault Prediction of P-R-N-D Shifting Force Based on BP Neural Network

As an important reference indicator for transmissions, it is necessary to conduct offline testing of PRND shifting force. By studying the shift force of PRND(Parking, Reverse, Neutral, and Drive), a fault prediction method for PRND shift force based on BP neural network is proposed to improve the of...

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Bibliographic Details
Published in2023 IEEE 16th International Conference on Electronic Measurement & Instruments (ICEMI) pp. 487 - 491
Main Authors Wang, Shuxian, Liu, Shiyou, Su, Zuqiang, Wang, Xin, Liu, Linlin, Zhu, Hang
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.08.2023
Subjects
Analytical models
BP neural network
fault prediction
Force
Neural networks
offline inspection
Predictive models
shift force
Simulation
Time series analysis
Training
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Summary:As an important reference indicator for transmissions, it is necessary to conduct offline testing of PRND shifting force. By studying the shift force of PRND(Parking, Reverse, Neutral, and Drive), a fault prediction method for PRND shift force based on BP neural network is proposed to improve the offline detection efficiency of PRND shift force. Based on the data obtained from the offline detection platform, a dataset for network training is obtained after processing; Build a PRND shift force fault prediction model, design and train a BP neural network, and after error backpropagation, the next layer gradient determines the gradient of the current layer to obtain the prediction network; Bringing the dataset into the network for simulation and comparing the simulation results, it was found that the network can achieve ideal results.
DOI:10.1109/ICEMI59194.2023.10270041