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Monitoring Model of Coal Mill in Power Plant Based on Big Data and BP Neural Network

机译：基于大数据和BP神经网络的电厂煤轧机监测模型

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摘要

In order to monitor the wear condition of grinding roller of coal mill in power plant and improve the reliability of production equipment, it is necessary to establish a state monitoring model with high accuracy and good prediction effect. It has been shown that the power of coal mill can reflect the wear degree of grinding roller. If the voltage and power factor of coal mill are constant, grinding current can be used to replace the power of coal mill. In this paper, through collecting field historical operation data and data preprocessing, the current model of coal mill is established by using double hidden layers BP (Back Propagation) neural network to predict the wear state of grinding roller. The simulation results show that compared with single hidden layer, double hidden layers BP neural network can improve the performance of the network, so as to improve the prediction accuracy of the model and provide basis for the follow-up maintenance of coal mill, which has certain practical engineering significance.
机译：为了监测电厂煤磨厂磨削辊的磨损条件，提高生产设备的可靠性，有必要具有高精度和良好预测效果的状态监测模型。已经表明，煤磨机的功率可以反映磨削辊的磨损程度。如果煤磨机的电压和功率因数是恒定的，则磨削电流可用于更换煤磨机的功率。本文通过收集现场历史操作数据和数据预处理，通过使用双隐藏层BP（后传播）神经网络来建立煤磨机的当前模型来预测研磨辊的磨损状态。仿真结果表明，与单层隐藏层相比，双隐藏层BP神经网络可以提高网络的性能，从而提高模型的预测准确性，为煤磨机进行后续维护提供依据某些实用工程意义。

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