Fuzzy reliability evaluation of linear &lt;i&gt;m&lt;/i&gt;-consecutive weighted-&lt;i&gt;k&lt;/i&gt;-out-of-&lt;i&gt;r&lt;/i&gt;-from-&lt;i&gt;n&lt;/i&gt;: F systems

Negi, Seema; Singh, Sunil

doi:10.1504/ijcsm.2019.104027

Cited by 7 publications

(4 citation statements)

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“…Generally, typical multi-objective optimization problems(MOPs) [4,33,34] have two or three objectives, which are formulated with the following expression:…”

Section: Preliminaries 21 Basic Definitionsmentioning

confidence: 99%

Pareto fronts-driven Multi-Objective Cuckoo Search for 5G Network Optimization

2020

KSII TIIS

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5G network optimization problem is a challenging optimization problem in the practical engineering applications. In this paper, to tackle this issue, Pareto fronts-driven Multi-Objective Cuckoo Search (PMOCS) is proposed based on Cuckoo Search. Firstly, the original global search manner is upgraded to a new form, which is aimed to strengthening the convergence. Then, the original local search manner is modified to highlight the diversity. To test the overall performance of PMOCS, PMOCS is test on three test suits against several classical comparison methods. Experimental results demonstrate that PMOCS exhibits outstanding performance. Further experiments on the 5G network optimization problem indicates that PMOCS is promising compared with other methods.

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“…Generally, typical multi-objective optimization problems(MOPs) [4,33,34] have two or three objectives, which are formulated with the following expression:…”

Section: Preliminaries 21 Basic Definitionsmentioning

confidence: 99%

Pareto fronts-driven Multi-Objective Cuckoo Search for 5G Network Optimization

2020

KSII TIIS

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“…Initially, the reliability analysis of different type of systems have been done using the classical crisp set theory as Kumar and Singh (2016) analysed complex repairable system with deliberate failure emphasising reboot delay and Negi and Singh (2019) introduced the method of reliability assessment of linear m-consecutive weighted-k-out-of-r-from-n: F systems. Later, FS theory has been used by several researchers like Singer (1990) used FS approach to fault tree and reliability analysis, Cai et al (1991) used fuzzy variables as a basis for a theory of fuzzy reliability in the possibility context, Chen (1994) studied fuzzy system reliability analysis using fuzzy number arithmetic operations, Chen (1996) introduced a new method for fuzzy system reliability and Ram et al (2013) analysed a standby system with waiting repair strategy.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy reliability appraisal of a system using probabilistic dual hesitant fuzzy element emphasising score function

Kumar¹,

Singh²,

Kumar³

2022

IJMOR

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Present paper introduces a new technique for analysing fuzzy reliability of a system under probabilistic fuzzy environment, where the reliability of a component/unit of a system is represented by probabilistic dual hesitant fuzzy element for handling uncertainty like hesitancy in real life situation. With the help of the proposed approach, in this study, we have evaluated the fuzzy reliability of some complex systems having series, parallel and bridge configurations using score function and deviation degree. Additionally, a numerical example is taken for better demonstration of the proposed technique.

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“…Qin et al [26] proposed a combined method for reliability analysis of MSS of minor-repairable components. Negi and Singh [25] provided the fuzzy reliability evaluation method of linear m-consecutive weighted-k-out-of-r-from-n: F systems. Chen et al [2] performed the reliability analysis and optimization of equal load-sharing k-out-of-n phased-mission systems.…”

Section: Introductionmentioning

confidence: 99%

Reliability assessment of wind turbine generators by fuzzy universal generating function

Huang¹,

Xiahou²,

Li³

et al. 2021

Eksploatacja i Niezawodność – Maintenance and Reliability

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Wind power has been widely used in the past decade because of its safety and cleanness. Double fed induction generator (DFIG), as one of the most popular wind turbine generators, suffers from degradation. Therefore, reliability assessment for this type of generator is of great significance. The DFIG can be characterized as a multi-state system (MSS) whose components have more than two states. However, due to the limited data and/or vague judgments from experts, it is difficult to obtain the accurate values of the states and thus it inevitably contains epistemic uncertainty. In this paper, the fuzzy universal generating function (FUGF) method is utilized to conduct the reliability assessment of the DFIG by describing the states using fuzzy numbers. First, the fuzzy states of the DFIG system’s components are defined and the entire system state is calculated based the system structure function. Second, all components’ states are determined as triangular fuzzy numbers (TFN) according to experts’ experiences. Finally, the reliability assessment of the DFIG based on the FUGF is conducted.

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Fuzzy reliability evaluation of linear <i>m</i>-consecutive weighted-<i>k</i>-out-of-<i>r</i>-from-<i>n</i>: F systems

Cited by 7 publications

References 0 publications

Pareto fronts-driven Multi-Objective Cuckoo Search for 5G Network Optimization

Pareto fronts-driven Multi-Objective Cuckoo Search for 5G Network Optimization

Fuzzy reliability appraisal of a system using probabilistic dual hesitant fuzzy element emphasising score function

Reliability assessment of wind turbine generators by fuzzy universal generating function

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