Research on Bearing Remaining Useful Life Prediction Based on KPCA-PSO-LSSVM
In order to predict the remaining useful life(RUL)of rolling bearings under different working conditions,a rolling bearing RUL prediction framework based on kernel principal component analysis(KPCA)combined with particle swarm optimiza-tion least squares support vector machine(PSO-LSSVM)is proposed.Firstly,fault features from the time domain,frequency do-main and wavelet packet domain are extracted to obtain a series of degraded features.Secondly,on the premise of retaining as many degraded features as possible,KPCA method is used to reduce features.Finally,PSO-LSSVM is used to build a combined model to predict the RUL of rolling bearings.The model is verified by multiple sets of bearing recession vibration signals provided by the intel-ligent maintenance system(IMS).The experimental results show that compared with the PSO-LSSVM and KPCA-LSSVM models,the remaining life forecasting approach of the bearing of the KPCA-PSO-LSSVM proposed in this paper has a lower forecasting er-ror,which can accurately determine the degradation of the fitting rolling bearing.
remaining useful life predictionKPCA-PSO-LSSVMdegradation feature extraction
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