Fuzzy system reliability analysis by interval of confidence

Cheng, Ching‐Hsue; Mon, Don‐Lin

doi:10.1016/0165-0114(93)90182-h

Cited by 196 publications

(73 citation statements)

References 6 publications

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“…You and Chen (2005) proposed an efficient heuristic approach for series-parallel redundant reliability problems. Cheng and Mon (1993) presented a method for fuzzy system reliability analysis by interval of confidence. Chen (1994) presented a method for fuzzy system reliability analysis using fuzzy number arithmetic operations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Singer (1990) developed a new methodology to find out various reliability parameters using fuzzy set approach and fault tree in which the failure rate and repair time were represented using triangular fuzzy numbers (TFN). Cheng and Mon (1993) used the confidence interval for analyzing the fuzzy system reliability. Chen (1994) presented a new method for analyzing system reliability using fuzzy number arithmetic operations.…”

Section: Literature Reviewmentioning

confidence: 99%

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Mathematical modeling and fuzzy availability analysis for serial processes in the crystallization system of a sugar plant

Aggarwal¹,

Kumar

Singh

2016

J Ind Eng Int

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The binary states, i.e., success or failed state assumptions used in conventional reliability are inappropriate for reliability analysis of complex industrial systems due to lack of sufficient probabilistic information. For large complex systems, the uncertainty of each individual parameter enhances the uncertainty of the system reliability. In this paper, the concept of fuzzy reliability has been used for reliability analysis of the system, and the effect of coverage factor, failure and repair rates of subsystems on fuzzy availability for fault-tolerant crystallization system of sugar plant is analyzed. Mathematical modeling of the system is carried out using the mnemonic rule to derive Chapman-Kolmogorov differential equations. These governing differential equations are solved with Runge-Kutta fourth-order method.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Mathematical modeling and fuzzy availability analysis for serial processes in the crystallization system of a sugar plant

Aggarwal¹,

Kumar

Singh

2016

J Ind Eng Int

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show abstract

“…Cheng and Mon [22] describe fuzzy number A as a fuzzy subset in support R (real number), which is both normal and convex, where supp(…”

Section: Fuzzy Numbersmentioning

confidence: 99%

An Application of Fuzzy Set Theory to the Weighted Average Cost of Capital and Capital Structure Decision

Wang¹,

Hwang²

2010

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The purpose of this paper is to present the use of fuzzy logic to improve the calculation of weighted average cost of capital (WACC). The fuzzy WACC approach not only allows the pre-tax cost of debt, the effective tax rate, the tax benefit, and cost of equity to be treated as fuzzy numbers, it also offers ranking means to find the optimal debt ratio. This paper contributes to the literature by offering alternative methods to calculate the WACC and the optimal debt ratio for firms under uncertainty. Compared with the traditional WACC, the fuzzy WACC model can overcome the problems pertinent to uncertainty, complexity and imprecision. This paper thus sheds light on capital structure decision making.

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“…Singer [34] and Cheng and Mon [35] have used fuzzy numbers to propagate the uncertainty of reliability in the fault tree analysis. Fuzzy numbers have been used to propagate parametric uncertainty in the finite element analysis [36,37].…”

Section: Introductionmentioning

confidence: 99%

Interval Estimations of Building Heating Energy Consumption using the Degree-Day Method and Fuzzy Numbers

Cheng¹,

Li²

2018

Buildings

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Abstract:The purpose of this paper is to propagate the input uncertainties of the degree-day method to estimate the building heating energy consumption as numerical intervals. While it is common to use average or expected values (e.g., Typical Meteorological Year) to address the input uncertainties, this practice can only yield the best estimates as single-point values without informing the possible range of variations. After classifying two types of uncertainty as weather variability and imprecision in the degree-day method, this paper proposes the adoption of fuzzy numbers and their arithmetic as the theoretical approach to handle uncertainty. As the degree-day method mainly involves elementary arithmetic (e.g., addition and multiplication), fuzzy number arithmetic can be directly applied to formally process numerical intervals. The proposed method is demonstrated and verified via a building example in Canada, and the interval results are comparable to the variation of heating energy consumption based on the data of outdoor ambient temperatures in 52 years.

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Fuzzy system reliability analysis by interval of confidence

Cited by 196 publications

References 6 publications

Mathematical modeling and fuzzy availability analysis for serial processes in the crystallization system of a sugar plant

Mathematical modeling and fuzzy availability analysis for serial processes in the crystallization system of a sugar plant

An Application of Fuzzy Set Theory to the Weighted Average Cost of Capital and Capital Structure Decision

Interval Estimations of Building Heating Energy Consumption using the Degree-Day Method and Fuzzy Numbers

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