Residual useful life(RUL)prediction is one of the most important technical means to realize equipment health man-agement and predictive maintenance.In order to accurately predict the remaining service life of bearings,a bearing remaining life prediction method based on ensemble empirical mode decomposition(EEMD)and long short time memory network(LSTM)was proposed.First,the vibration signal is time domain,frequency domain and time frequency analysis and record correspond-ing features;second,screen selection,decomposition and reconstruction via EEMD;Finally,a health feature index is construct-ed by LSTM binding.Experiments prove that the method can effectively predict the remaining life of the bearing.
Ensemble Empirical Mode DecompositionLong Short Term Memory NetworkFeature ExtractionLi-fe Prediction
NETL
NSTL
万方数据
