Probabilistic life analysis of vital components considering multistage degradation under variable working conditions

摘要

The lifespan of a mechanical product is related to its working conditions; the product's performance typically shows a multistage degradation pattern throughout its life profile. The performance degradation is generally researched under constant test conditions, while the effects of different working conditions on life are seldom considered. This paper proposes a staged recursive derivation method for the multistage degradation under variable working conditions. The proposed method works by merging measured degradation data with an empirical degradation model. The measured degradation data of a new prototype are utilized to update the staged degradation model based on a Bayesian posterior probability analysis. The staged degradation model is derived stage by stage, and then the probabilistic life of the new prototype is predicted. The degradation data of a machine-gun barrel are used as a case study to demonstrate and validate the proposed method. The results show that the probabilistic life of the test prototype can be predicted effectively in the case of relatively little measured degradation data at the product development stage. Furthermore, the proposed method appears to be especially suited to mechanical components requiring short test periods or low test costs.