Search

The accelerated degradation testing (ADT) Bayesian evaluation method comprehensively utilizes product degradation data under accelerated stress levels collected over a short period of time and multiple sources of prior information, such as historical information, similar product information, simulation information, etc., to conduct life and reliability evaluation. Through the prior distribution, prior information affects the ADT Bayesian evaluation results ultimately. However, different evaluators may obtain different prior distributions based on the same prior information due to varying experiences or rules, which may lead to differences in the ADT Bayesian evaluation results. Therefore, it is necessary to analyze and study the impact of prior distribution uncertainty on the ADT Bayesian evaluation results while also finding criteria to judge the quality of prior distributions. This paper focuses on the ADT Bayesian evaluation method based on the Wiener process and the Arrhenius relation, studying the influence of different prior distributions on the robustness of ADT Bayesian evaluation results. Additionally, based on the deviance information criterion (DIC), a criterion for selecting prior distributions in the ADT Bayesian evaluation method is proposed. Through carrying out uncertainty analysis of prior distribution in the ADT Bayesian evaluation method, a theoretical system and framework for analyzing prior uncertainty in ADT Bayesian evaluation based on DIC are established, providing a better foundation for the practical application of the ADT Bayesian evaluation method in engineering.

Methods for fault diagnosis based on metric learning, in which a query sample is classified by picking the closest prototype from the support set based on their feature similarities, have been the subject of many studies. In real-world applications of in-orbit products, such as circulating pumps, the computation of similarity between different pairs is prone to different degrees of inaccuracy, especially epistemic uncertainty. Knowing and considering the uncertainty of similarity may improve fault detection accuracy. This article provides a unique approach to fault diagnosis based on Prototypical Network (Pro-Net) and Uncertainty Theory. In particular, we use epistemic uncertainty by altering the representation of prototypes from a deterministic scalar to an uncertain representation. To assess the similarity between a query and the prototypes in a support set, we calculate the uncertain distance between the pairs using cross-entropy. Experiments with symmetrical structures reveal that our proposed method significantly enhances classification precision and achieves state-of-the-art performance. It improves the reliability of fault diagnosis and reduces the risk of making erroneous judgments in safety-critical systems, decreasing the possibility of adverse consequences.

Organic/polymeric second-order nonlinear optical (NLO) materials, which rely on the poling-induced non-centrosymmetric arrangement of NLO chromophores, have played a very important role in laser technology and optical fiber communication, due to their ultra-fast response speed, excellent machining performance and low dielectric constant. However, the NLO chromophores have the large dipole moments with strong intramolecular charge transfer, which lead to the intermolecular electrostatic interactions to tend to the centrosymmetric arrangement and decrease the poling efficiency. Since the special three-dimensional spatial separation can minimize these strong intermolecular electrostatic interactions during poling process, dendrimers and hyperbranched polymers have been considered as better topology for the next generation of highly efficient NLO materials. In 2013, by the attachment of low generation dendrimers to the hyperbranched backbone, a new dendritic architecture of dendronized hyperbranched polymer (DHP) was proposed for improving the comprehensive performance of NLO materials. Recent results showed many advantages of DHPs in NLO field, such as easy syntheses, large NLO coefficients and high orientation stability, etc. In this review, the latest advancement of DHPs, including the design principle, synthesis, as well as their application as NLO materials is summarized. The new opportunities arising from DHPs are also summarized in the future perspective.