Intelligent Fault Diagnosis of New Energy Vehicles Based on Gated Neural Networks
The gearbox is the main component of a car's engine,and as the core component of the gearbox,gears are prone to malfunctions due to the influence of vehicle conditions.Therefore,in response to this issue,this experiment proposes a hybrid neural network training algorithm based on gated neural networks for fault diagnosis of gearbox gears.The experimental results show that the proposed model can effectively reduce gradient vanishing and time series changes.Compared to traditional neural networks,the pro-posed model consistently maintains an accuracy of over 96%under strong noise interference.When the normal sample size is 40,the accuracy of the proposed model is higher than other traditional neural network models,reaching 94.86%.In summary,the gear fault diagnosis model used in this study has good performance and can be used in practical new energy vehicle gear fault diagnosis prob-lems.