Belief Reliability for Uncertain Random Systems

Zhang, Qingyuan; Kang, Rui; Wen, Meilin

doi:10.1109/tfuzz.2018.2838560

Cited by 94 publications

(53 citation statements)

References 30 publications

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“…Zeng et al [33] integrated the belief reliability into the existing model-based reliability framework. Zhang et al [35] utilized the belief reliability to describe the states in the uncertain random system. In this paper, we adopt the concept of the belief reliability and apply it in ALBP-UT.…”

Section: Belief Reliability Of the Assembly Linementioning

confidence: 99%

Optimizing the Reliability and Efficiency for an Assembly Line That Considers Uncertain Task Time Attributes

Yao

Tang

et al. 2019

IEEE Access

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An assembly line is an industrial arrangement of machines, equipment, and operators for continuous flow of workpieces in mass-production operations. In an assembly line balancing problem, tasks are allocated to workstations according to their processing times and precedence relationships amongst tasks. Nowadays, some research investigated the reliability of assembly production by taking account of task time uncertainties. Our research utilizes uncertainty theory to model task time uncertainties and introduces the belief reliability measure to the assembly line production for the first time. We proposed a multi-objective optimization model that aimed at maximizing the belief reliability and minimizing the cycle time. The problem is solved using a newly developed restart neighborhood search method. The numerical experiments are conducted to verify its efficiency. The methodology proposed in this paper is applicable to any industry (including the automotive industry) when the historical data on task processing times are very scarce. INDEX TERMS Assembly line balancing, belief reliability, neighborhood search, uncertain task times. A. ASSEMBLY LINE BALANCING WITH UNCERTAIN TASK TIMES Nowadays, ALBP with uncertain task times (ALBP-UT) receives a lot of attention in academia. Task time uncertainty may result from instability of the operator's work rates, the varied skills and motivations of workers, and the failure sensitivity of complex processes' uncertainty [3]. Suresh and Sahu [29] first introduced the task time uncertainty into ALBP and proposed a simulated annealing algorithm. Baykasoğlu and Özbakir [2] optimized a U-shaped ALBP-UT by genetic algorithm. Cakir et al. [6] proposed a hybrid simulated annealing algorithm to solve the multi-objective ALBP-UT. More recently, Delice et al. [7] developed a genetic algorithm to solve the two-sided U-shaped ALBP-UT. Li [12] solved the type-II ALBP-UT by a branch-and-bound

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Section: Belief Reliability Of the Assembly Linementioning

confidence: 99%

Optimizing the Reliability and Efficiency for an Assembly Line That Considers Uncertain Task Time Attributes

Yao

Tang

et al. 2019

IEEE Access

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“…or components or systems that do not have enough time to generate sufficient amounts of reliability data, the frequentist approaches are not suitable for use [10 -12]. For this reason, several methods were proposed to manage epistemic uncertainties such as interval theory-based reliability methods [13,14], possibility theory-based reliability methods [15,16], evidence theory-based reliability [17 -19] and belief reliability [20].…”

Section: Introductionmentioning

confidence: 99%

“…Belief reliability is a new reliability metric proposed by Zhang et al [20] to describe the reliability of components or systems affected by both aleatory uncertainty and epistemic uncertainty by the chance theory. The chance theory is founded by Liu [21] as a mixture of the uncertainty theory and the probability theory, to deal with problems affected by both aleatory uncertainty and epistemic uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Graduation formula: A new method to construct belief reliability distribution under epistemic uncertainty

Kang

Wen

2020

J. of Syst. Eng. Electron.

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In reliability engineering, the observations of the variables of interest are always limited due to cost or schedule constraints. Consequently, the epistemic uncertainty, which derives from lack of knowledge and information, plays a vital influence on the reliability evaluation. Belief reliability is a new reliability metric that takes the impact of epistemic uncertainty into consideration and belief reliability distribution is fundamental to belief reliability application. This paper develops a new method called graduation formula to construct belief reliability distribution with limited observations. The developed method constructs the belief reliability distribution by determining the corresponding belief degrees of the observations. An algorithm is designed for the graduation formula as it is a set of transcendental equations, which is difficult to determine the analytical solution. The developed method and the proposed algorithm are illustrated by two numerical examples to show their efficiency and future application.

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“…Thus, uncertainty theory is more suitable for situations with limited/scarce data for statistical analysis. It has been introduced to various domains, such as reliability analysis [19] [20], risk analysis [21], supply chain [22], accelerated degradation testing [23], data development analysis [24], etc.…”

Section: Introductionmentioning

confidence: 99%

“…And, thus, the estimated value of b is expressed as Equation (2), the inverse uncertainty distribution Ψ of the RUL in Equation (18) with respect to a belief degree α is expressed as the sum of the inverse uncertainty distributions of * and , as shown in the following equation. Ψ (α) = Φ * ( ) + ∑ Φ ( ) −(19)…”

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confidence: 99%

First-Order Uncertain Hidden Semi-Markov Process for Failure Prognostics With Scarce Data

Liu

2020

IEEE Access

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Failure prognostics aims at predicting the object equipment's future degradation trend and derives the remaining useful life with a predefined failure threshold. Hidden semi-Markov process (HSMP) is widely adopted for failure prognostics of degradation process with discrete states. The effective estimation of the holding time distribution on each degradation state is of critical importance for the prediction performance of a HSMP model. The distributions are generally estimated with frequencies-based probabilities given a large amount of degradation data. In practical engineering applications, it is difficult to collect enough data and the data can even be scarce. In such situations, the estimated distributions are no longer reliable. First-order uncertain hidden semi-Markov process (1-UHSMP) based on uncertain statistics is defined in this work. The holding time distributions in 1-UHSMP are described with uncertainty theory and are adaptively updated with conditional uncertainty distributions given observations related to the true degradation states. Analytical expressions are derived for the expected remaining useful life for 1-UHSMP with regular uncertainty distributions, i.e. normal and linear uncertainty distribution. The proposed method can build a degradation model from scarce data and derive adaptively the remaining useful life with associated uncertainty interval. A case study concerning centrifugal pumps in a nuclear power plant is considered to verify the effectiveness of 1-UHSMP.

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Belief Reliability for Uncertain Random Systems

Cited by 94 publications

References 30 publications

Optimizing the Reliability and Efficiency for an Assembly Line That Considers Uncertain Task Time Attributes

Optimizing the Reliability and Efficiency for an Assembly Line That Considers Uncertain Task Time Attributes

Graduation formula: A new method to construct belief reliability distribution under epistemic uncertainty

First-Order Uncertain Hidden Semi-Markov Process for Failure Prognostics With Scarce Data

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