Remaining Life Prediction of Rolling Bearing Based on SAE and BiGRU
In order to improve the prediction accuracy of rolling bearing remaining life,a remaining life prediction method based on sparse auto encoder(SAE)and bi-directional gated recurrent unit(BiGRU)is proposed.Firstly,the time domain,fre-quency domain and time-frequency domain features extracted from rolling bearing vibration signals are screened by using four evalu-ation indexes,and the sensitive degradation feature set is constructed.Then,in order to solve the problem of information redundan-cy between features,SAE network is used to reduce the dimension of sensitive degraded feature set.Finally,the fusion sensitive degradation features are input into BiGRU model to complete the prediction of rolling bearing remaining life.The results show that,compared with LSTM and GRU,this method has higher accuracy of remaining life prediction.
rolling bearingremaining life predictionsparse auto encoderbi-directional gated recurrent unit
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