Life Prediction of Digital Analog Linkage Rolling Bearings Based on Mahalanobis Distance
As a key component of the high-speed train running gear system,the prediction of the remaining life of rolling bearings directly determines the remaining life of the system.Therefore,monitoring the degradation status and predicting the remaining useful life(RUL)of rolling bearings is of great engineering significance.Using Mahalanobis distance as a measure of the vibration signal and temperature signal characteristics of rolling bearings,a corresponding performance index sequence is established.The degradation state of rolling bearings is observed from the index sequence,and RUL prediction is carried out using the exponential model as the degradation model.A first predicting time(FPT)based on 3σ criteria is introduced to trigger the RUL prediction process,which can achieve reliable prediction of RUL of rolling bearings.A comparative experiment was conducted between this method and the traditional neural network life prediction method,and the results showed that the deviation index based on Mahalanobis distance in this paper has a high prediction accuracy for the numerical model linkage life evaluation method.
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