Integration for degradation analysis with multi-source ADT datasets considering dataset discrepancies and epistemic uncertainties

Degradation data collected from a single accelerated degradation testing (ADT) are usually non-sufficient, which causes a lack of knowledge and leads to epistemic uncertainties. So multi-source ADT datasets are usually integrated for degradation analysis and reliability evaluations. Nevertheless, the discrepancies among multi-source ADT datasets are ignored in current integration methods, which cannot make full use of all information in the integration process. To address these problems, an integration method of multi-source ADT datasets is developed for degradation analysis and reliability evaluations. Firstly, the evaluation index system for an ADT dataset is constructed. Then, the relative qualities of ADT datasets are computed through an additive model and a weight formula. After that, the degradation process is described by an uncertain accelerated degradation model to quantify epistemic uncertainties, and the related statistical analysis is given through a proposed uncertain least weighted squared error to consider dataset discrepancies. A simulation study and an application case are utilized to illustrate the proposed method. Results show that the proposed method can well capture the discrepancies among multi-source ADT datasets and make a quantitative comparison; additionally, it can notably decrease uncertainties in the degradation analysis.