Abstract
In order to improve the accuracy of fault prediction and diagnosis of large-diameter auger rigs, and combined with the development trend of coal mine intelligence, a fault prediction and diagnosis method based on digital twin and BP neural network is proposed. Firstly, the digital twin model of the large-diameter auger rig is established. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$^{1}\text{On}$</tex> the basis of the digital twin model, the fault prediction and diagnosis model is established by using the BP neural network machine learning algorithm. Then, combined with expert knowledge, the data that identified the errors is continuously accumulated to form a new data set, and the model is optimized and trained through the new data set to continuously improve the accuracy of model fault prediction and diagnosis. The results show that the fault prediction and diagnosis system has high recognition accuracy and can meet the needs of fault prediction and diagnosis of large-diameter auger rigs.
Published Version
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