Definitions of generalized multi-performance weighted multi-state K¯-out-of-n system and its reliability evaluations

Larsen, Emil Mahler; Ding, Yi; Li, Yan‐Fu; Zio, Enrico

doi:10.1016/j.ress.2017.06.009

Cited by 39 publications

(11 citation statements)

References 22 publications

(33 reference statements)

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“…is will be helpful for analyzing some real systems such as electric systems, aerospace navigation systems, and military systems which may have some errors results in many problems. As a future extension to this work, we can apply the proposed method for analyzing other complex repairable and nonrepairable systems such as linear and circular consecutive k-out-of-n with hot, cold, or warm standby having different types of fuzzy time varying failure and repair rates [55][56][57][58][59][60][61][62][63][64][65][66][67][68].…”

Section: Discussionmentioning

confidence: 99%

“…ese systems operate (perform their operational function) provided at least k units operate. Such systems occur in many contexts: in process industries, e.g., in desalination plants [1]; in transportation systems, e.g., electric traction motors on commuter trains [2]; in power generation, e.g., secondary-cooling pumping systems [3]; in distribution networks, e.g., electricity supply [4]; Larsen et al (2017); in manufacturing e.g. soft drinks production [5]; in aerospace navigation systems [6]; and in oil and gas production infrastructure, e.g., in [7] and in the military [8].…”

Section: Introductionmentioning

confidence: 99%

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Availability and Reliability Analysis of a k-Out-of-n Warm Standby System with Common-Cause Failure and Fuzzy Failure and Repair Rates

El-Ghamry

Muse²,

Aldallal

et al. 2022

Mathematical Problems in Engineering

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In the real life, there exists limited information or uncertainty in knowledge about failure and repair rates which follow one of the standard distributions as exponential distribution and Weibull distribution with some parameters. We suppose that these parameters are fuzzy which allow one to specify a system design for a “worst-case scenario.” In this paper, the fuzzy availability and the fuzzy reliability of a redundant repairable parallel k-out-of-n warm standby system with common-cause failure are evaluated. We assume that the failure time of each operating unit or warm standby unit follows Weibull distribution with two fuzzy parameters and the repair time of any failed unit follows exponential distribution with one fuzzy parameter. Each fuzzy parameter is represented by triangular membership function estimated from statistical data taken from random samples of each unit. Also, we give a numerical example for a fuzzy repairable parallel 3-out-of-5 warm standby system with two active and three warm standby units to get analytically and represent graphically the fuzzy availability and reliability function of this fuzzy system.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Availability and Reliability Analysis of a k-Out-of-n Warm Standby System with Common-Cause Failure and Fuzzy Failure and Repair Rates

El-Ghamry

Muse²,

Aldallal

et al. 2022

Mathematical Problems in Engineering

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“…In this paper, the multi-state systems [34][35][36][37][38][39] refers to the discrete multi-state series systems composed of m multi-performance state components. Suppose S i ¼ f0; 1; 2 .…”

Section: Discretization Of Component Performance Statementioning

confidence: 99%

Selective maintenance of multi-state series systems considering maintenance quality uncertainty and failure effects

Ran

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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In order to improve the lifetime and reliability of equipment operation stage, it is necessary to carry out maintenance for components. The typical selective maintenance model does not consider the maintenance quality uncertainty and the failure effects, which may make the decision-maker overestimates the reliability of completing the next task after system maintenance, resulting in incorrect maintenance decision and prone to serious consequences of failure. In this paper, the potential discrepancy between the target maintenance quality and the actual maintenance quality of the decision maker is considered. At the same time, a multi-state FMECA is proposed to measure the failure effects under different states. Finally, the selective maintenance model of the multi-state series systems is established and solved by the genetic algorithm. The results show that considering the effect of component failure and the uncertainty of maintenance quality has an influence on maintenance decision. The maintenance decision made in this way is more consistent with the engineering practice.

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“…Song et al [12] established the stochastic multi-valued (SMV) models, which avoid the large computational overhead of the universal generating function (UGF) and Monte Carlo simulation methods, to evaluate the reliability of the MSS with dependent multi-state components (MSCs). Larsen et al [13] presented the multi-performance weighted multi-state components and two generalized multi-performance multi-state K-out-of-n system models to study the reliability evaluation of the multi-performance weighted multi-state K -out-of-n system.…”

Section: Literature Reviewmentioning

confidence: 99%

A Reliability Statistical Evaluation Method of CNC Machine Tools Considering the Mission and Load Profile

Zhang

Ran

et al. 2019

IEEE Access

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Reliability of the CNC machine tool (abbreviated as machine tool) relates to the product's reliability and has great influence on the manufacturing process. The traditional reliability statistical evaluation method of the machine tool neglects the influence of the mission profile and load profile (M&L), it causes that the evaluation result is not accurate enough to provide accurate references for the reliability-related works under specific M&Ls such as the preventive maintenance, product improvement, etc. To address these defects, this paper proposes an improved reliability statistical evaluation method considering the M&L. Firstly, the machine tool is decomposed by the meta-action decomposition method, and the mission profile of the machine tool is represented by the meta-action chain (MC). Secondly, load profile representation indicators of the machine tool are extracted based on the load composition and cutting force calculation. Then, the mapping model between the M&L and the machine tool's reliability is established using the radial basis function (RBF) neural network. Finally, the improved reliability statistical evaluation method is illustrated and validated by the engineering practical application. Comparing the evaluation results of the two statistical evaluation methods, it shows that the improved reliability statistical evaluation method is more accurate than the traditional reliability statistical evaluation method, under specific M&Ls, so that it can provide more accurate references for the reliability-related works.INDEX TERMS CNC machine tool, reliability evaluation, mission and load profile, radial basis function neural network.

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Definitions of generalized multi-performance weighted multi-state K¯-out-of-n system and its reliability evaluations

Cited by 39 publications

References 22 publications

Availability and Reliability Analysis of a k-Out-of-n Warm Standby System with Common-Cause Failure and Fuzzy Failure and Repair Rates

Availability and Reliability Analysis of a k-Out-of-n Warm Standby System with Common-Cause Failure and Fuzzy Failure and Repair Rates

Selective maintenance of multi-state series systems considering maintenance quality uncertainty and failure effects

A Reliability Statistical Evaluation Method of CNC Machine Tools Considering the Mission and Load Profile

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