Reliability modelling incorporating load share and frailty

Asha, G.; Raja, Arvind; Равишанкер, Налини

doi:10.1002/asmb.2294

Cited by 13 publications

(8 citation statements)

References 42 publications

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“…Since 2006, some studies on reliability have used the frailty model for modeling the impact of unobservable risk factors on the reliability of the components, intending to make decisions about maintenance and repair [20][21][22][23]. For example, Asha [24] employed the frailty model for a load-sharing system and demonstrated that the reliability analysis for a heterogeneous case varies significantly compared to a homogeneous one. Asfaw and Lindqvist [25] utilized the frailty model for modeling the effect of observable and unobservable risk factors on wind turbine reliability, using Poisson's process.…”

Section: Introductionmentioning

confidence: 99%

Spare-part management in a heterogeneous environment

et al. 2021

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Spare-part management has a significant effect on the productivity of mining equipment. The required number of spare parts can be estimated using failure and repair data collected under the name of reliability data. In the mining industry, failure and repair times are decided by the operational environment, rock properties, and the technical and functional behavior of the system. These conditions are heterogeneous and may change significantly from time to time. Such heterogeneity can change equipment’s reliability performance and, consequently, the required number of spare parts. Hence, it is necessary for effective spare-part planning to check the heterogeneity among the reliability data. After that, if needed, such heterogeneity should be modeled using an adequate statistical model. Heterogeneity can be categorized into observed and unobserved caused by risk factors. Most spare-part estimation studies ignore the effect of heterogeneity, which can lead to unrealistic estimations. In this study, we introduce the application of a frailty model for modeling the effect of observed and unobserved risk factors on the required number of spare parts for mining equipment. Studies indicate that ignoring the effect of unobservable risk factors can cause a significant bias in estimation.

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Section: Introductionmentioning

confidence: 99%

Spare-part management in a heterogeneous environment

et al. 2021

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“…There is a large number of possible extensions of this current work. The proposed IGP frailty model can be extended to other distributions for frailty, for example, we may assign the Weibull distribution for frailty, following a similar approach as in Asha et al 41 ; the Birnbaum‐Saunders distribution can also be considered for frailty, as discussed in Leão et al 42 . Our approach should be investigated further in these contexts.…”

Section: Discussionmentioning

confidence: 98%

Inverse Gaussian process model with frailty term in reliability analysis

Morita

Tomazella

Ramos

et al. 2020

Quality & Reliability Eng

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Traditional reliability analysis techniques focus on the occurrence of failures over time. Nevertheless, in certain cases where the occurrence of failures is tiny or almost null, the estimation of the quantities that describe the failure process is compromised. In this context, we introduce a reliability model for systems adopting the degradation process using frailty. The evolved degradation model has as experimental data, not the failure, but a quality feature attached to it. Degradation analysis can provide information about the lifetime distribution components without actually observing failures. In this paper, we propose an inverse Gaussian process model with frailty as a possible tool to investigate the effect of unobserved covariates. Moreover, a comparative study with the classical inverse Gaussian process based on simulated data was performed, revealing that the asymptotic properties of the maximum likelihood estimators are compromised when the presence of frailty is ignored. The application was based on two real data sets in the literature, showing that the inverse Gaussian process frailty models are propitious to use; however, gamma and inverse Gaussian distributions for frailty present similar results.

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“…For example, in the presence of observable and unobservable risk factors, the frailty model can be used. Originally, this was developed by Asha et al [34] into load share systems and described the effect of observable and unobservable covariates on the reliability analysis. In later years, authors such as Xu and Li, Misra et al, and Giorgio et al discussed the properties of the frailty model [35][36][37].…”

Section: Methodology and Framework: Risk Factor-based Reliability Importance Measure (Rf-rim)mentioning

confidence: 99%

Resilience Assessment: A Performance-Based Importance Measure

et al. 2021

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The resilience of a system can be considered as a function of its reliability and recoverability. Hence, for effective resilience management, the reliability and recoverability of all components which build up the system need to be identified. After that, their importance should be identified using an appropriate model for future resource allocation. The critical infrastructures are under dynamic stress due to operational conditions. Such stress can significantly affect the recoverability and reliability of a system’s components, the system configuration, and consequently, the importance of components. Hence, their effect on the developed importance measure needs to be identified and then quantified appropriately. The dynamic operational condition can be modeled using the risk factors. However, in most of the available importance measures, the effect of risk factors has not been addressed properly. In this paper, a reliability importance measure has been used to determine the critical components considering the effect of risk factors. The application of the model has been shown through a case study.

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Reliability modelling incorporating load share and frailty

Cited by 13 publications

References 42 publications

Spare-part management in a heterogeneous environment

Spare-part management in a heterogeneous environment

Inverse Gaussian process model with frailty term in reliability analysis

Resilience Assessment: A Performance-Based Importance Measure

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