To evaluate the lifetime and reliability of long-life, high-reliability products under limited resources, accelerated degradation testing (ADT) technology has been widely applied. Furthermore, the Bayesian evaluation method for ADT can comprehensively utilize historical information and overcome the limitations caused by small sample sizes, garnering significant attention from scholars. However, the traditional ADT Bayesian evaluation method has inherent shortcomings and limitations. Due to the constraints of small samples and an incomplete understanding of degradation mechanisms or accelerated mechanisms, the selected evaluation model may be inaccurate, leading to potentially inaccurate evaluation results. Therefore, describing and quantifying the impact of model uncertainty on evaluation results is a challenging issue that urgently needs resolution in the theoretical research of ADT Bayesian methods. This article addresses the issue of model uncertainty in the ADT Bayesian evaluation process. It analyzes the modeling process of ADT Bayesian and proposes a new model averaging evaluation method for ADT Bayesian based on relative entropy, which, to a certain extent, can resolve the issue of evaluation inaccuracy caused by model selection uncertainty. This study holds certain theoretical and engineering application value for conducting ADT Bayesian evaluation under model uncertainty.

Although neural network approaches achieve remarkable success on QA, many of them struggle to answer questions that require retrieving relevant factual information from the given paragraph. Inferring from a given paragraph and answering the question to be true or false is an essential part of natural language understanding. To improve the model's inferring ability on Natural YES/NO Question, we provide a simple and effective method. First, we find the tasks related to main task Natural YES/NO Question to fine-tune the model by multi-task learning, then we fine-tune the model on the main task. Results on dataset BoolQ show this method is competitive with other recently published methods, which means transferring from the related datasets through multi-task learning in first stage can save more beneficial information about main task Natural YES/NO. Further analysis show that this method can not only have benefit in this task, it also can be used to other tasks.

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.

Small sample sizes cause epistemic uncertainties in reliability estimation and even result in potential risks in maintenance strategies. To explore the difference between stochastic- and uncertain-process-based degradation modeling in reliability estimation for small samples, this study proposes a comparative analysis methodology based on the range of quantile reliable lifetime (RQRL). First, considering both unit-to-unit variability and epistemic uncertainty, we proposed the Wiener and Liu process degradation models. Second, based on the RQRL, a comparative analysis method of different degradation models for reliability estimation under various sample sizes and measurement times was proposed. Third, based on a case study, the sensitivities of the Wiener and Liu process degradation models for various sample sizes and measurement times were compared and analyzed based on the RQRL. The results demonstrated that using the uncertain process degradation model improved the uniformity and stability of reliability estimation under small-sample conditions.