Remaining Useful Lifetime Prediction of Reducer Gear for Roadheader Based on Gated Dual Attention Unit Model

The gears of the roadheader cutting reducer are prone to premature damage. A gated dual attention unit (GDAU) model based on gear oil abrasive particle information is proposed, which can be used to solve the remaining life prediction problem of gears. The model was tested and verified using the roadheader cutting reducer test bench. By comparing with the long short-term memory network (LSTM) and gated recurrent unit (GRU) neural network, the results show that the GDAU model has a better prediction effect on the remaining life of the gear, and can converge quickly in a short time, and its loss is only 0.0016, which has a faster convergence speed and higher prediction accuracy than the LSTM and GRU models.