Bearing Residual Life Prediction Based on RMS Sublinear Interval Fitting Method
The prediction accuracy of the residual life (RL) prediction model of bearings depends largely on the trend consistency of the recession characteristics.Due to the individual variability of bearings,different bearings of the same type exhibit different recession trends of the same characteristics,which leads to the mismatch between the RL prediction model established for the training set bearings and the test set bearings.Aiming at the above problems,this paper proposed a bearing life prediction method based on the trend consistency of the root-mean-square (RMS) of vibration signals for the subline interval fitting method.According to the principle of μ±3σ,a singular value replacement method was proposed to effectively enhance the trend consistency of the RMS recession curve,and the subline interval fitting method was proposed based on the trend consistency of the height of the RMS recession curve.This proposed method was tested and validated using the XJTU-SY dataset,and the results of this method were compared with those of the common life prediction methods,such as back propagation neural network (BPNN) and long short-term memory network (LSTM).And the prediction accuracy of the RL of bearings was found to be effectively improved.
fault diagnosisrolling bearingstrend consistencysingular value substitutionsublinear interval fittinglife prediction
万方数据
维普
