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For newly developing systems, the determination of the reliability index is an important task in the system demonstration process that determines both the system design goal and the verification requirement. However, due to the insufficient data at the beginning of the system life cycle, the problem of epistemic uncertainty cannot be ignored. Researchers have proposed several belief reliability metrics and analysis methods to quantify the epistemic uncertainty of a system, but to the best knowledge of the authors, no relevant studies have been performed on system reliability index determination. In this paper, we propose a belief reliability index determination method based on group decision‐making. Using a similar system‐based reliability index determination method, this method comprehensively considers the demands of users and the capabilities of contractors. In addition, the Delphi method and Hadamard multiplication convex combination are combined to effectively solve the epistemic uncertainty quantification problem. The Delphi method is used to find the conflicts caused by multi‐expert scoring, and the Hadamard multiplication convex combination can integrate the scoring matrix of all experts, which makes the results more reasonable. Finally, a Low‐Earth‐orbit (LEO) satellite communication system is used to verify the effectiveness of our belief reliability index determination method. [ABSTRACT FROM AUTHOR]

Software reliability plays an important role in modern society. To evaluate software reliability, software reliability growth models (SRGMs) investigate the number of software faults in the testing phase. Obviously, testing progresses are inevitably influenced by dynamic indeterministic fluctuations such as the testing effort expenditure, testing efficiency and skill, testing method, and strategy. To model these dynamic fluctuations, several probability theory-based SRGMs are proposed. However, probability theory is suitable for dealing with aleatory uncertainty, but fails to deal with epistemic uncertainty widely existing in software faults. Therefore, this article considers software reliability from a new perspective under the framework of uncertainty theory, which is a new mathematical system different from probability theory, and proposes a software belief reliability growth model (SBRGM) based on uncertain differential equations for the first time. Based on this SBRGM, properties of essential software reliability metrics are investigated under belief reliability theory, which is a brand-new reliability theory. Parameter estimations for unknown parameters in SBRGM are presented. Furthermore, some numerical examples and real data analyses illustrate our methodology in detail, and show that it performs better than several famous probability-based SRGMs in terms of fitting ability and prediction ability. Finally, an optimal software release policy is discussed. [ABSTRACT FROM AUTHOR]

Accelerated degradation testing (ADT) aids the reliability and lifetime evaluations for highly reliable products. In engineering applications, the number of test items is generally small due to finance or testing resource constraints, which leads to the rare knowledge to evaluate reliability and lifetime. Consequently, the epistemic uncertainty is embedded in ADT data and the large-sample based probability theory is no longer appropriate. In this paper, we introduce the uncertainty theory, which is a theory different from the probability theory, to account for such uncertainty due to small samples and build up a framework of ADT modeling. In this framework, an uncertain accelerated degradation model is first proposed based on the arithmetic Liu process. Then, the uncertain statistics for parameter estimations are presented correspondingly, which is completely constructed on objectively observed ADT data. An application case and a simulation case are used to illustrate the proposed methodology. With further comparisons to the Wiener process based accelerated degradation model (WADM) and the Bayesian-WADM, the sensitivities of these models to sample sizes are explored and the results show that the proposed model is superior to the other two probability-based models under the small sample size. [ABSTRACT FROM AUTHOR]

Condition‐based maintenance (CBM) has gradually gained more attention, and the degradation process has been increasingly applied to maintenance optimization models. The insufficient data and the complex degradation process of the equipment conditions will contribute to epistemic uncertainty. Besides, the implementation of maintenance introduces oscillatory features into the equipment degradation process, deviating from a monotonically decreasing trend, complicating the optimization of CBM. In this article, to simultaneously address the problem of epistemic uncertainty and consider the influence of inspection and maintenance, we establish a new type of degradation model based on uncertainty theory to deal with epistemic uncertainty. Then an uncertain maintenance optimization model is proposed to give an optimal CBM strategy. Finally, a case study is provided to illustrate the proposed CBM optimization method. [ABSTRACT FROM AUTHOR]