Modeling dependent series systems with q-Weibull distribution and Clayton copula

Xu, Meng; Herrmann, Jeffrey W.; Droguett, Enrique López

doi:10.1016/j.apm.2020.12.042

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…When q → 1, the q-model converges to the Weibull model (De Assis et al, 2020). The q-model is especially useful in modeling failure rates in a series system formed by dependent components (Xu et al, 2021). This is not the case of our study, as the involved equipment is composed of independent, autonomous machines.…”

Section: Introductionmentioning

confidence: 91%

“…Specifically, the asymptotic omega distribution is just the Weibull distribution (Dombi et al, 2019). Despite there being other models besides the omega distribution, such as gamma (parallel), q-Weibull (Jia, 2021;Xu et al, 2021), or Marshall-Olkin extended uniform (Jónás et al, 2018), estimating the shape factor of the Weibull model is still widely used to directly reveal the position in the bathtub curve (Jia, 2021). The second limitation concerns to scarcity of data in one of the machines.…”

Section: Introductionmentioning

confidence: 99%

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Maintenance Strategy Choice Supported by the Failure Rate Function: Application in a Serial Manufacturing Line

Sellitto

Pinho

2022

Period. Polytech. Soc. Man. Sci.

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The purpose of this article is to choose a maintenance procedure for the critical equipment of a forging production line with five machines. The research method is quantitative modelling and simulation. The main research technique includes retrieving time between failure and time to repair data and find the most likely distribution that has produced the data. The most likely failure rate function helps to define the maintenance strategy. The study includes two kinds of maintenance policies, reactive and anticipatory. Reactive policies include emergency and corrective procedures. Anticipatory policies include predictive and preventive ones combined with a total productive maintenance management approach. The most suitable combination for the first three machines is emergency and corrective choice. For the other machines, a combination of total productive maintenance and a predictive approach is optimal. The study encompasses the case of a serial production manufacturing line and maximum likelihood estimation. The failure rate function defines a combination of strategies for each machine. In addition, the study calculates the individual and systemic mean time to failure, mean time to repair, availability, and the most likely number of failures per production order, which follows a Poisson process. The main contribution of the article is a structured method to help define maintenance choices for critical equipment based on empirical data.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Maintenance Strategy Choice Supported by the Failure Rate Function: Application in a Serial Manufacturing Line

Sellitto

Pinho

2022

Period. Polytech. Soc. Man. Sci.

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“…The Gumbel Copula effectively describes upper tail correlation features without affecting lower tail correlation features. The Clayton Copula demonstrates robust lower tail correlation features without affecting overall upper tail correlations [26,33]. The Frank Copula can balance the imbalance in the upper and lower tails of the aforementioned Copulas while providing an accurate representation of correlation features in the intermediate portion [34].…”

Section: Failure Mechanism Correlationmentioning

confidence: 99%

A Structural Reliability Analysis Method Considering Multiple Correlation Features

Bai,

Li,

Zhang

et al. 2024

Machines

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The paper analyzes the correlation features between stress strength, multiple failure mechanisms, and multiple components. It investigates the effects of different correlation features on reliability and proposes a method for structural reliability analysis that considers the joint effects of multiple correlation features. To portray the stress–strength correlation structure, the Copula function is utilized and the influence of the correlation degree parameter on reliability is clarified. The text describes the introduction of time-varying characteristics of structural strength and correlation parameters. A time-varying Copula is then constructed to calculate the structural reliability under the stress–strength correlation characteristics. Additionally, a time-varying hybrid Copula is constructed to characterize the intricate and correlation features of multiple failure mechanisms and components. The article proposes the variational adaptive sparrow search algorithm (VASSA) to obtain optimal parameters for the time-varying hybrid Copula. The effectiveness and accuracy of the proposed method are verified through actual cases. The results indicate that multiple correlation features significantly influence structural reliability. Incorporating multiple correlation features into the solution of structural reliability yields safer results that align with engineering practice.

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基于碳纤维可控分布的复合材料定向传热模拟研究

Shi,

Huang,

et al. 2024

J. Cent. South Univ.

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Modeling dependent series systems with q-Weibull distribution and Clayton copula

Cited by 5 publications

References 14 publications

Maintenance Strategy Choice Supported by the Failure Rate Function: Application in a Serial Manufacturing Line

Maintenance Strategy Choice Supported by the Failure Rate Function: Application in a Serial Manufacturing Line

A Structural Reliability Analysis Method Considering Multiple Correlation Features

基于碳纤维可控分布的复合材料定向传热模拟研究

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