Research on scraper conveyor load prediction method based on wavelet transform and BP neural network

Scraper conveyor load prediction is crucial to realize the cooperative speed regulation of coal mining machine and scraper conveyor. In the synthesized mining face, due to the uncertainty of the coal fall, the load of the scraper conveyor fluctuates due to the change of the coal load, which shows a strong nonlinearity and non-smoothness, leading to the difficulty of prediction. To solve this problem, this paper proposes a BP neural network model combined with wavelet transform for scraper conveyor current prediction. By studying the mapping relationship between motor load and current based on the BP neural network algorithm, and taking the scraper conveyor current as the input condition, wavelet decomposition and data reconstruction of historical current data are carried out, and time series prediction is performed on the original data samples and reconstructed data samples, respectively. The simulation results show that the reconstructed BP neural network model using wavelet decomposition has higher prediction accuracy, in which the root mean square error is reduced by 13.26%, the average absolute error is reduced by 14.19%, and the percentage error is reduced by 17.43%. The model meets the accuracy requirements of scraper conveyor load prediction, and can provide theoretical basis for cooperative speed regulation of coal mining machine and scraper conveyor.