Abstract: To improve the accuracy of remaining useful life estimation for machine parts， an interval estimation model was proposed based on the SVM (support vector machine). The linear theory and nonlinearity theory of SVM were briefly introduced， and the correlation between input variable and output variable was analyzed. Degraded index and remaining useful life of machine parts were treated as input variable and output variable， correspondingly. It was assumed that input variable and residual error were independent and the residual error’s distribution pattern was known. Distribution parameters of residual error were estimated by means of the MLE (maximum likelihood estimation). Then the confidence interval of SVM output variable was obtained under a certain confidence level. The MSE (mean squared error) was used to measure the prediction of SVM. The SVM parameters were gotten by the means of variable step size grid search. A numerical example was presented to show that the proposed model can estimate the remaining useful life confidence interval precisely with the engineering application values and generality.

Key words: remaining useful life, SVM(support vector machine), interval estimation, machine parts； confidence interval； MSE (mean squared error)