Degradation equipment residual life prediction method considering multiple uncertainties

Abstract

Aiming at the situation that long-life and high-reliability equipment lacks sufficient degradation data, the invention provides a degradation equipment residual life prediction method considering multiple uncertainties, and the method comprises a stepping accelerated degradation model based on a nonlinear diffusion process, and the model has the advantages that only a small sample size and short test time are needed. For inherent characteristics, individual differences and multiple uncertainties caused by human deviation in the process of measuring equipment performance and degradation of a degradation model, the model considers time-varying uncertainties, individual differences and performance degradation and covariable measurement uncertainties. In order to estimate the residual life of the degraded equipment, an analytic approximate solution that the nonlinear diffusion process passes through a preset threshold value under the sense of first arrival time is obtained through derivation. A maximum likelihood estimation (MLE) method and a simulation extrapolation (SIMEX) method are combined to obtain an MME-SIMEX method which is used for estimating unknown parameters in a model. The effectiveness of the model provided by the invention is proved through simulation and actual cases. The result shows that the method has higher residual life estimation precision and has certain engineering practical value.

Description

technical field [0001] The invention belongs to the technical field of reliability engineering, and in particular relates to a method for predicting the remaining life of degraded equipment considering multiple uncertainties. Background technique [0002] With the continuous improvement of the current design level and manufacturing process, people's demand for high-quality products is also increasing day by day, and more and more equipment with long service life and high reliability have appeared, especially in the aerospace and military fields. Due to the long test time and high test cost, the traditional residual life prediction method is difficult to be effectively applied to this kind of equipment. In order to obtain more degradation data, engineers accelerate the degradation of equipment by increasing the severity of the test environment, such as high temperature and high pressure, random vibration, etc. Therefore, accelerated degradation test has become an effective m...

AI Technical Summary

Problems solved by technology

Solve the time-varying uncertainty of equipment degradation by establishing a degradation model based on random processes; consider the random effect of the drift coefficient in the degradation model to describe the individual differences of degraded equipment; Artifacts, measurement errors in the measurement of most covariates and degradation performance

At present, the existing methods only consider part of the multiple uncertainties, and there is a lack of related research on nonlinear accelerated degradation modeling and remaining life prediction methods considering multiple uncertainties

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