Bivariate Reliability Evaluation Based on Semi-parametric Method Considering Degradation Dependencies and Random Effect

Accurate reliability evaluation is a crucial prerequisite for the development of maintenance strategies and the reduction of maintenance costs. However, the traditional parametric degradation modeling methods are difficult to address the challenges of unknown, complex and time-varying distribution of degradation increments, and most of the studies have not fully considered the bivariate reliability evaluation with degradation dependencies and unit-to-unit variability. To address above issues, this paper proposes a novel semi-parametric degradation model with degradation dependencies and random effect. Firstly, an improved log-change adaptive kernel density estimation (AKDE) method is developed to address the uncertainty of the degradation increment distribution and the boundary estimation bias. Secondly, the extrapolation prediction of degradation states is constructed based on two-dimensional AKDE. Furthermore, a semi-parametric bivariate reliability evaluation model is established based on the copula function, and the random effects are introduced into the copula function. In addition, a two-stage statistical inference method for unknown parameters estimation is developed. Finally, the wet-heat aging experiment of O-ring seals is used to verify the validity and accuracy of the proposed model. The results show that the proposed method outperforms the existing models. The proposed method provides a new idea for component reliability prediction with complex degradation mechanisms.