Remaining Useful Life Prediction Algorithm for Rolling Bearing in the Early Stage
Traditional useful life prediction algorithms have achieved good results in predicting the useful life of rolling bearings containing degradation stage data.However,as data just running are similar to data running for a period of time,accurate prediction using only normal-working-stage data is difficult.The Reservoir Computer(RC)can predict future data after multiple time steps based on previous time data,raising the possibility of converting predictions in the early stage into traditional predictions by supplementing the degraded data through data simulation.An Echo State Network(ESN)can output the relevant dimensions of the current moment while fully utilizing the temporal information.In this study,a Recursive Reconstructible Neural(RRN)network algorithm based on RC and ESN is proposed for bearing useful life prediction in the early stage.First,an RC-based feature simulation network is designed to simulate the entire lifecycle of data containing degraded data based on early features.Subsequently,a useful life prediction network based on ESN,which outputs the Remaining Useful Life(RUL)based on the simulated features of the input,is proposed.The effectiveness of the algorithm is validated on the PHM 2012 dataset,and the experimental results showed that compared with current algorithms with good performance,the proposed algorithm reduced the average error of the RUL prediction in the original test data and early stage experiments by 61.35％and 53.14％,respectively,demonstrating superior prediction performance.
Remaining Useful Life(RUL)prediction in the early stagerolling bearingdata simulationReservoir Computer(RC)Echo State Network(ESN)Recursive Reconstructible Neural(RRN)network
万方数据
维普
