Remaining life predictions of rolling bearing based on relative features and multivariable support vector machine

Novel prediction method is proposed based on the relative features and multivariable support vector machine (MSVM) to estimate the rolling bearing remaining life under limited condition data. The relative root mean square (RRMS) with ineffectiveness of the bearing individual difference is used to assess the performance degradation, and sensitive features are selected as input by correlation analysis. Meanwhile, MSVM is structured to predict the remaining life, which has the advantages of multivariable prediction and the small samples prediction. Unlike univariate SVM, MSVM overcomes the simple structure and the lack of information, and excavates the potential information of small sample as much as possible. The simulation and the bearing run-to-failure tests are carried out to inspect the prediction model, and the results demonstrate that MSVM can utilize the effective information as much as possible for the more precise results with the practical values and generality.