Multivariate degradation system reliability analysis with multiple sources of uncertainty

As the industrial Internet of Things evolves, multi-sensor monitoring becomes increasingly prevalent, resulting in degrading systems frequently exhibiting multivariate degradation characteristics. Multiple performance characteristics are frequently interdependent due to the consistency of the operating environment, operating conditions, and internal structure of the system. On the other hand, performance characteristic detection is also affected by numerous uncertainties. A multivariate stochastic degradation model that takes dependencies with multiple sources of uncertainty into account is developed for the analysis of multivariately dependent degradation systems. To represent linear or nonlinear degradation qualities, the marginal degradation process is referred to as a generalized stochastic degradation model. By using the D-vine copula function and pair-copula construction, dependencies between numerous variables are formed. The marginal and joint distribution parameters of the degradation model are derived using a two-step parameter estimation procedure. Lastly, the C-MAPSS dataset is used to analyze the applicability of the proposed model for bivariate and multivariate instances. The effectiveness of the proposed method is verified by experiments.