Fuzzy system reliability analysis for components with different membership functions

Mon, Don‐Lin; Cheng, Ching‐Hsue

doi:10.1016/0165-0114(94)90330-1

Cited by 120 publications

(55 citation statements)

References 5 publications

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“…Hong and Do [23] utilized T ω (the weakest t-norm)-based addition and multiplication while Fuh et al [24] used level ( ,1) interval-valued fuzzy numbers to examine the fuzzy reliability of the system. Mon and Cheng [25] considered different types of fuzzy membership functions and nonlinear programming approach to analyze the fuzzy reliability of a nonrepairable sump-pump system. Huang et al [26] proposed posbist FTA method for the situation when historical data are scarce or the failure probability is extremely small.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty

Komal

2015

Applied Soft Computing

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Please cite this article in press as: Komal, Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty, Appl. Soft Comput. J. (2015), http://dx.a b s t r a c t Nowadays healthcare becoming more important aspect for everybody. Healthcare institutions now giving more attention to their patients' safety by reducing the frequency of medical errors and trying to provide all kinds of best facilities to them. Clinical processes can be understood as a series of interactions between patients, providers, and technologies. Therefore, there are some chances exist for medical errors due to the involvement of human beings and machines. A number of tools exist to prospectively analyze processes in healthcare which generally needs precise numerical data. In general, available or extracted data is not precise and sufficient to assess the clinical processes upto a desired degree of accuracy due to various practical and economic reasons. Thus, collected data may have some sort of uncertainties and quantification of these uncertainties should be done very carefully before analysing further. In this paper, a new fuzzy fault tree approach has been presented for patient safety risk modeling in healthcare. This approach applies fault-tree, trapezoidal fuzzy numbers, ˛-cut set, and the weakest-t-norm (T ω ) based approximate arithmetic operations to obtain fuzzy failure probability of the system. The effectiveness of the developed approach is illustrated with two different kinds of problems taken from literature related to healthcare. Also, Tanaka et al.'s approach has been used to rank the critical basic events of the considered problems. Computed results have been compared with results obtained from other existing techniques.

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Section: Introductionmentioning

confidence: 99%

Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty

Komal

2015

Applied Soft Computing

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“…In order to handle the insufficient information, the fuzzy approach is used to evaluate the failure rate status. A great deal of literature works [2,21,22,24] has been carried out in fuzzy reliable analysis.…”

Section: Introductionmentioning

confidence: 99%

The reliability of general vague fault-tree analysis on weapon systems fault diagnosis

Chang

Liao

et al. 2005

Soft Comput

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An algorithm of vague fault-tree analysis is proposed in this paper to calculate fault interval of system components from integrating expert's knowledge and experience in terms of providing the possibility of failure of bottom events. We also modify Tanaka et al's definition and extend the new usage on vague fault-tree analysis in terms of finding most important basic system component for managerial decision-making. In numerical verification, the fault of automatic gun is presented as a numerical example. For advanced experiment, a fault tree for the reactor protective system is adopted as simulation example and we compare the results with other methods. This paper also develops vague fault tree decision support systems (VFTDSS) to generate fault-tree, fault-tree nodes, then directly compute the vague fault-tree interval, traditional reliability, and vague reliability interval.

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“…To handle the inevitably imprecise failure information in diversified real applications, many studies have taken uncertain situations into consideration. The fuzzy set theory has proven to be effective for solving the problems when there are no regular boundaries and precise values (Liang and Wang, 1993;Mon and Cheng, 1994). Therefore, it is applied to the reliability analysis and system safety to estimate the failure probabilities of basic events.…”

Section: Fuzzy Fault Tree Analysismentioning

confidence: 99%

Risk assessment of an oxygen-enhanced combustor using a structural model based on the FMEA and fuzzy fault tree

Chen

Qin

2014

Journal of Loss Prevention in the Process Industries

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Fuzzy system reliability analysis for components with different membership functions

Cited by 120 publications

References 5 publications

Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty

Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty

The reliability of general vague fault-tree analysis on weapon systems fault diagnosis

Risk assessment of an oxygen-enhanced combustor using a structural model based on the FMEA and fuzzy fault tree

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