Repairable k-out-n system work model analysis from time response

Mathematical Problems in Engineering

Cheng

et al. 2021

Self Cite

For series, parallel, and k-out-of-n voting system reliability calculation methods, the six σ principles have been proposed in this study to derive the interchange relationship between interval parameters and random parameters. The interval reliability index can be expressed in the function of the random reliability index. The interval reliability index can then be transformed into a random reliability index. The computational method of the reliability for series, parallel, and k-out-of-n voting systems with interval parameters is established. Finally, it has been shown that the proposed method is rational, practical, and applicable with two engineering practical simulations.

Section: Proposed Reliability Computational Approachmentioning

confidence: 99%

“…In the paper by Fang et al [14,15], the dynamic reliability of the repairable k/n vote is studied, and its reliability computational model is proposed. e reliability and maintenance of SPKS are also studied, and the optimal maintenance plan with repair time is proposed [16].…”

Section: Introductionmentioning

confidence: 99%

Computational Method and Simulation of Reliability for Series, Parallel, and k-Out-of-n Systems with Interval Parameters

Fang

Mathematical Problems in Engineering

Cheng

et al. 2021

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“…ROC curves are two-dimensional graphs that visually depict the performance and performance trade-off of a classification model [36]. (11) Throughout this paper it is assumed that the two groups (failure and non-failure) are fully independent, i.e., no information about any aspect related to one group contains information about any aspect of the other group. If there are 1 n conditions data from a failure group and 0 n data from non-failure group, then these can be denoted by…”

Section: Basic Of Roc Curvementioning

confidence: 99%

“…Methods of reliability analysis such as first-order reliability method, second-order reliability method, point estimate method, Monte Carlo simulation, gamma process, probability density evolution method, subset simulation and dynamic reliability are available in literature. [7][8][9][10][11][12][13] The correlation coefficients between different failure modes show that all the failure modes are strongly correlated positively, that is, where the failure modes might happen concurrently within a buried pipeline system. 14 The determination of threshold value for a particular pipe failure mode provides useful information on reliability analysis.…”

Section: Introductionmentioning

confidence: 99%

Application of receiver operating characteristic curve for pipeline reliability analysis

Proceedings of the Institution of Mechanical Engineers, Part O:

Khan

Coolen‐Maturi

2015

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Structural reliability analysis of buried pipeline systems is one of the fundamental issues for water and wastewater asset managers. Measuring the accuracy of a reliability analysis or a failure prediction technique is an effective approach to enhancing its applicability and provides guidance on selection of reliability or failure prediction methods. The determination of threshold value for a particular pipe failure criterion provides useful information on reliability analysis. However, this threshold value is not always known. In this paper, Receiver Operating Characteristic (ROC) curve has been applied where empirical and Nonparametric Predictive Inference (NPI) techniques are used to evaluate the accuracy of pipeline reliability analysis and to predict the failure threshold value. Multi-failure conditions, namely, corrosion induced deflection, buckling, wall thrust and bending stress have been assessed in this paper. It is hoped that choosing the optimal operating point on the ROC curve which involves both maintenance and financial issues, can be ideally implemented by combining the ROC analysis with a formal risk-cost management of underground pipelines.

“…Methods of reliability analysis such as first order reliability method (FORM), second-order reliability method (SORM), point estimate method (PEM), Monte Carlo simulation (MCS), gamma process, probability density evolution method (PDEM), etc. are available in literature (Sivakumar Babu and Srivastava, 2010;Mahmoodian et al, 2012;Fang et al, 2013aFang et al, , 2013b. In this context, a robust uncertainty propagation method whose applicability is insensitive to complexity nature of the problem is most desirable.…”

Section: Introductionmentioning

confidence: 99%

Application of subset simulation in reliability estimation of underground pipelines

Reliability Engineering & System Safety

Khan

2014

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This paper presents a computational framework for implementing an advanced Monte Carlo simulation method, called Subset Simulation (SS) for time-dependent reliability prediction of underground flexible pipelines. The SS can provide better resolution for low failure probability level of rare failure events which are commonly encountered in pipeline engineering applications. Random samples of statistical variables are generated efficiently and used for computing probabilistic reliability model. It gains its efficiency by expressing a small probability event as a product of a sequence of intermediate events with larger conditional probabilities. The efficiency of SS has been demonstrated by numerical studies and attention in this work is devoted to scrutinise the robustness of the SS application in pipe reliability assessment and compared with direct Monte Carlo simulation (MCS) method. Reliability of a buried flexible steel pipe with time-dependent failure modes, namely, corrosion induced deflection, buckling, wall thrust and bending stress has been assessed in this study. The analysis indicates that corrosion induced excessive deflection is the most critical failure event whereas buckling is the least susceptible during the whole service life of the pipe. The study also shows that SS is robust method to estimate the reliability of buried pipelines and it is more efficient than MCS, especially in small failure probability prediction.