基于BP神经网络的刮板输送机中底板磨损量预测分析

Prediction and Analysis of Wear Amount of Middle and Bottom Plate of Scraper Conveyor Based on BP Neural Network

为了解决井下刮板输送机中部槽中底板磨损量难以测量的问题,依据某矿刮板输送机中部槽检验数据,使用BP神经网络建立了以齿轨座圆孔直径、上链道宽度、铲板上沿厚度、耳板厚度为输入参数,刮板输送机中部槽中底板磨损量为输出参数的预测模型。利用此模型对复杂工况下井下刮板输送机中部槽中底板的磨损量进行预测,预测值与实际测量值对比结果验证了预测模型的可行性。利用此预测模型提高了测量效率,极大地降低了测量成本,为刮板输送机中部槽使用寿命的预测提供了一定的理论依据。

In order to solve the problem that it is difficult to measure the wear amount of the middle and the bottom plate in the middle groove of the scraper conveyor in the mine,based on the inspection data of the middle groove of the scraper conveyor in a mine,the BP neural network was used to establish a prediction model based on the diameter of the round hole of the rack seat,the width of the upper chain,the thickness of the upper edge of the shovel plate and the thickness of the ear plate as the input parameter,the wear amount of the middle and the bottom plate in the middle groove of the scraper conveyor as the output parameter.This model was used to predict the wear amount of the middle and bottom plate of the scraper conveyor under complex working conditions.The comparison between the predicted value and the actual measured value verified the feasibility of the prediction model.By using this prediction model,the measurement efficiency is improved,the measurement cost is greatly reduced,and a certain theoretical basis is provided for the prediction of the service life of the middle groove of the scraper conveyor.