Application of AOA Optimization LSTM Method in the Remaining Life Prediction of Machine Tool Bearings
In order to improve the vibration control accuracy of industrial robots during operation,an algorithm based on arithmetic optimization algorithm(AOA)and improved Short time memory network(LSTM)method was designed to predict the residual service life of industrial robot bearings.RMSE and MAE indexes were used to evaluate the prediction model.The results show that the loss curve tends to be stable with the increase of the number of iterations,and the evaluation proves that the proposed method achieves better prediction results.Aaa-lstm has a higher fitting degree to the actual prediction results,and the smaller prediction error proves the effectiveness of the method.This method is helpful to improve the use efficiency of industrial robot bearings,and lays a foundation for the subsequent whole machine performance test.
industrial robot bearingremaining service lifearithmetic optimization algorithmlong term memory network
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