Uncertain accelerated degradation modeling and analysis considering epistemic uncertainties in time and unit dimension. (September 2020)

• Record Type: Journal Article
• Authors: Wu, Ji-Peng; Kang, Rui; Li, Xiao-Yang
• Is Part Of: Reliability engineering & system safety. Volume 201(2020)
• Publication Date: 2020-09
• Subjects: Accelerated degradation testing -- Epistemic uncertainties -- Time dimension -- Unit dimension -- Uncertainty theory -- Belief reliability theory; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2020.106967
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: This paper proposes a new uncertain accelerated degradation model. The model considers the epistemic uncertainties in time and unit dimensions. This paper presents the uncertain statistical method for parameter estimations. The model is suitable for limited data sizes in time and unit dimensions. Abstract: Accelerated degradation testing (ADT) has been widely used for the reliability and lifetime evaluations of highly reliable products. Generally speaking, the obtained ADT data are composed of two parts - the deterministic degradation trend and uncertainties. When only limited ADT data can be obtained, there will be a lack of knowledge on recognizing the population and lead to epistemic uncertainties. However, current accelerated degradation models fail to clearly distinguish and properly quantify the epistemic uncertainties in time and unit dimensions. Motivated by this problem, this paper constructs a new uncertain accelerated degradation model based on uncertainty theory and belief reliability theory, and presents the uncertain statistical method for parameter estimations. An application case is used to illustrate the proposed methodology and conduct discussions for the deterministic degradation trend and uncertainty analyses of the proposed methodology and the analyses of the proposed methodology to the data sizes in time and unit dimensions. Results show that under limited data sizes in time and unit dimensions, the proposed model can clearly distinguish and properly quantify the epistemic uncertainties in time and unit dimensions and is more suitable for the ADT modeling and analysis than the other two widely used accelerated degradation models.