Aiming at the problem of predicting the gear remaining useful life (RUL) in rotating machinery,using the unique advantages of long short-term memory (LSTM) network in processing time series data,a gear remaining useful life prediction algorithm combining attention mechanism and LSTM is proposed in this paper. Firstly,four kinds of time domain features (root mean square value,kurtosis,variance,and margin index ) that can better reflect the health status are decomposed from gear vibration signal and taken as inputs to the RUL prediction network. Secondly,with the goal of improving the accuracy of RUL prediction results,a novel RUL prediction network is designed by combining LSTM and attention mechanism. Finally,the model was validated using real data generat-ed from gear full-life accelerated fatigue test bench of the laboratory. The results show that the attention LSTM algorithm proposed in this paper has high prediction accuracy in predicting the gear RUL.
gearprediction of remaining useful life (RUL)long short-term memory (LSTM) networkattention mechanismtime domain feature
维普
万方数据
