DOI: 10.11799/ce202505020
Fault diagnosis of hoist braking system based on improved Transformer
WANG Kaixuan
ZHANG Hongwei
Abstract：In order to reduce the dependence on expert experience and fully exploit the complex relationship between data,a fault diagnosis method of hoist braking system based on improved Transformer neural network was proposed.Firstly,the fault phenomena and causes of the braking system were analyzed,and the monitoring parameters were determined.Secondly,an improved Transformer fault diagnosis model was built,and a multi-layer self-attention mechanism was used to capture the correlation and fault relationship between the monitoring data of the mine hoist.The pooling layer was introduced into the Transformer model to reduce the parameters of the model and alleviate the risk of over-fitting.Finally,the experiment was carried out based on the actual operation data of the hoist.The Adam optimizer was used to update the model parameters.The results show that the accuracy of the improved Transformer fault classification prediction reached 97.5%,which was 6.1%,10.0%and 14.8%higher than that of Transformer,CNN and LSTM neural network,respectively.
Keywords：mine hoistbrake systemTransformer neural networkfault diagnosisself-attention mechanism
Publication Date：2025-05-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 148-155 )