Remaining Life Prediction And Reliability Evaluation Methods Based On Wiener Process

The prediction of remaining life and reliability assessment are important methods for preventing equipment failures,and they have gained significant attention in fields such as aerospace,industrial manufacturing,healthcare,and transportation.Due to its favorable mathematical properties,the Wiener process has been widely applied by researchers in degradation models that consider stochastic factors.This dissertation focuses on devices with long service life and limited sample sizes,and conducts research on degradation modeling,remaining life prediction,and reliability assessment methods.The aim is to provide relevant references for equipment health management and maintenance decision-making.The main research content of this dissertation is as follows:(1)A nonlinear degradation model considering the random effect of the diffusion coefficient is established based on the Wiener process.This model describes the impact of external random factors on the device.The concept of first-passage time is utilized to define the device’s life and residual life.The probability density functions of the first-passage time and residual life are derived.The Expectation Maximization algorithm is employed to estimate the model parameters.The proposed method is validated using a dataset of gyroscopes from an inertial navigation platform.The constructed model demonstrates higher predictive accuracy compared to the traditional fixed coefficient model.(2)A double-random-effect nonlinear Wiener process degradation model is developed,considering the combined influence of individual differences and external factors on the device.The analytical expressions for the first-passage time and residual life probability density functions are derived.The EM algorithm is employed to estimate the five unknown parameters of the model.The T50 sensor monitoring data from the C-MAPSS aircraft engine dataset is chosen for validation and analysis.Compared to a degradation model that only considers individual differences,the constructed model provides a more comprehensive description of the device’s degradation behavior and achieves higher prediction accuracy.(3)Based on the nonlinear Wiener process,a reliability assessment model based on threshold transformation method is established.The analytic expression of the reliability function is derived using the tangent line approximation principle.Additionally,the Wiener process is combined with fuzzy theory to establish a fuzzy reliability assessment model.A degradation dataset of marine engine cylinder liners is selected as a case study to validate and compare the two reliability assessment models with the conventional model.The results indicate that the fuzzy reliability assessment model provides more conservative evaluation results,with a smoother reliability function curve.On the other hand,the reliability function expressed by the threshold transformation method is more intuitive,resulting in a steeper reliability function curve.