Prediction of Residual Life of Coal Mining Machine Rocker Arm Bearings Based on Long Short-Term Memory Neural Network
To solve the failure problem of key components of coal mining machine rocker arms,an innovative method based on Long Short-Term Memory(LSTM)neural network is proposed to predict the residual life of the coal mining machine rocker arm bearing.Based on the theory of Long Short-Term Memory neural network,by establishing a degradation index for bearing life,the residual life of the bearing is conducted to predict.Isomorphism uses stratified sampling method to partition data sets;By introducing particle swarm algorithm to optimize LSTM,the problem of selecting the optimal hyperparameter in LSTM algorithm is solved,and the accuracy of predicting the residual life of the bearing is improved.The research results indicate that the residual life predicted results of the bearing based on LSTM is basically consistent with the actual changes situations in bearing life,and the predicted results are all within the confidence interval,which can provide reference for bearing maintenance and upkeep work.
Long Short-Term Memory neural networkcoal mining machine rocker arm bearingresidual life
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