Modeling And Predicting For The Remaining Useful Life Based On Statistical Analysis

Remaining useful life(RUL) prediction is one of core problems of the Condition Based Maintenance(CBM) process, and it is also an inevitable key problem during product remanufacturing process. This issue has become a research focus and challenge in Prognostics and Health Management(PHM) areas. According to the analysis of RUL results can improve availability and reliability of device, reduces maintenance and support costs, reduces the risk of failure events, and reduces or avoids failure caused significant damage, so it has important research and practical value.Firstly, this paper outlines some methods about the remaining useful life prediction based on statistical analysis, the method mainly include regression model, the Markov model, Wiener process and stochastic filtering. The literatures present a linear degradation model and a nonlinear exponential degradation model based on the Wiener process. The parameters to be estimated by Bayesian estimation and EM algorithm, and the probability density function of remaining useful life is obtained.Secondly, on the basis of regression model with random coefficients, in order to obtain more accurate residual useful life. This paper uses Bayesian estimation and EM algorithm to estimate parameters. Numerical experiment shows the effectiveness of the method of RUL model with measurement error.Finally, due to shock load impacts on the performance degradation of device, the remaining useful life model considering the shock load and natural degradation rules is established. This paper uses Bayesian estimation and EM algorithm to estimate parameters, and gets the probability density function of residual useful life. Experiment shows that model is more consistent with the actual situation, the predicted results are more accurate, and it also shows the reliability and superiority.