Bearing residual life prediction method based on DRSN and optimized BiLSTM
In view of the problems of traditional bearing life prediction methods such as excessive dependence on prior knowledge,lack of adaptability and large prediction error caused by difficult extraction of degradation characteris-tics,a bearing residual life prediction method based on Deep Residual Shrinkage Network(DRSN)and Bidirectional Long-Short-Term Memory network(BiLSTM)with adaptive feature extraction was proposed.Without any prior knowledge,DRSN was used to automatically learn the characteristics of the original signal of the bearing,extract the degradation characteristics and construct the health index.Then,the number of hidden layer neurons and learn-ing rate of BiLSTM were optimized by sparrow search algorithm,and the remaining life prediction model of bearing was established based on the optimized BiLSTM.The performance of health index extracted by DRSN,residual net-work and mean feature and different bearing residual life prediction models were compared.The experimental results showed that the health index extracted by DRSN network had the best performance,and the error of the optimized BiLSTM bearing residual life prediction model was the smallest.The root means square errors of the three bearing residual life prediction models based on the optimized BiLSTM,BiLSTM and Long-Short-Term Memory network(LSTM)were 1.41％,2.71％and 5.64％respectively,which verified the effectiveness of the proposed method.
deep residual shrinkage networkbidirectional long short term memory networkresidual life predictionsparrow search algorithm
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