Life Analysis of Rolling Bearing Based on K-means Degradation Identification and Random Forest
The problem that the life label in the prediction method of residual useful life(RUL)of rolling bearing is difficult to reflect the actual situation and the prediction accuracy is not high,a analysis method of residual life of rolling bearing based on PCA-Kmeans-RF is proposed.Firstly,the original horizontal vi-bration signal is denoised by wavelet denoising and fast fourier transform(FFT),and various features in time domain and frequency domain are extracted.Secondly,through variance filtering(VF)and principal component analysis(PCA)for feature processing,better degraded features are selected and fused to con-struct health indicators(HI).Then,K-means clustering is used to determine the degradation point and fail-ure point of the sample,and a more realistic degradation label of the remaining life is constructed,and the health index and time label are input into the random forest for training and testing.Finally,the IEEE PHM 2012 rolling bearing data set is verified and compared by the proposed method.The results show that this method has achieved results.
rolling bearinglife analysismulti-stage degradationrandom forest
NETL
NSTL
万方数据
