Neural network-based evaluation method for alignment state of air spring vibration isolation device
[Objectives]To address the difficulty in accurately describing the states of alignment under non-linear and time-varying conditions in existing monitoring models,a neural network-based method for evaluat-ing the states of alignment is proposed.[Method]A BP neural network-based prediction model is de-veloped.The typical working conditions for acquiring training and testing data are defined,and the data is de-noised using a moving average filter.The rules for adjusting the model's hyperparameters are summarized.Ex-perimental studies are then carried out on both small and large air spring isolation devices.[Results]The res-ults demonstrate that the neural network model can accurately predict the states of isolation devices alignment using only the air spring pressure data.The model exhibits strong generalizability across different device types,with a prediction error of less than 0.5 and an alignment prediction accuracy of 96.29%.[Conclusion]The proposed model does not rely on system parameters and performs well in predicting the states of align-ment for both small and large devices.The results of this study can provide theoretical support for the state prediction of alignment in a dynamic way and shaft alignment control of power equipment after startup.
shafts(machine components)alignmentair spring vibration isolation deviceneural net-worksPyTorch
国家科技期刊平台
NSTL
万方数据
