Risk-based maintenance modeling. Prioritization of maintenance importances and quantification of maintenance effectiveness

Vesely, W.E.; Rezos, J.T.

doi:10.2172/106641

Cited by 5 publications

(6 citation statements)

References 6 publications

(4 reference statements)

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“…It is to be noted that the degradation rate is considered by varying the β parameter value introduced in Component Markov Model. This parameter indicates the transition from a degradation state to a failure state, which is calculated based on NUREG/CR-6002 as given in the study by Vesely and Rezos (1995):…”

Section: Coupling Markov Model With Fault Tree Methodsmentioning

confidence: 99%

“…According to NUREG/ CR-6002, a small value for f AF e.g. (f AF 0.1) is selected to represent a small fraction of catastrophic failures not passing through a degraded state (Vesely and Rezos, 1995).…”

Section: Coupling Markov Model With Fault Tree Methodsmentioning

confidence: 99%

“…Adopting a risk-based T and M strategy is an essential step toward evaluating the effects of maintenance activities (MAs) on risk measures at both the component level and the system level. In this context, the question of quantifying the effects of maintenance strategies on risk measures has been discussed repeatedly in the literature (Vesely and Rezos, 1995;Baraldi et al, 2011;Kumar et al, 2012;Veeramany, 2012;Joel and Kumar, 2014;Zio and Compare, 2013;Kumar and Joel, 2018). In this regard, both the positive and the negative aspects of MAs should be quantified (Vesely and Rezos, 1995), thereby optimizing the MAs to reduce the risks or increase the availability of safety systems.…”

Section: Introductionmentioning

confidence: 99%

“…These approaches are introduced to describe the random behavior of systems and the degradation/repair of the components. The Markov method has been widely applied to analyze the system reliability as well (Dugan et al, 1993;Vesely and Rezos, 1995;Chan and Asgarpoor, 2006;Veeramany, 2012;Hellmich and Berg, 2014;Dawid et al, 2015;Kumar et al, 2020;Mohammadhasani and Pirouzmand, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…It can be applied to NPPs where a huge number of components need to be analyzed. Despite the Markov model limitations, the literature shows that the Markov process is an affordable and a straightforward approach that can be easily applied to a large number of components with complicated maintenance policies with reasonable accuracy and computational costs (Alam, 1982;Sim and Endrenyi, 1993;Somani et al, 1993;Vesely and Rezos, 1995;Papazoglu, 2000;Bukowski, 2001;Chan and Asgarpoor, 2006;Kumar et al, 2013;Matsuoka, 2014). Therefore, in this article the Markov process models are developed and applied to quantify basic events unavailability.…”

Section: Introductionmentioning

confidence: 99%

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Multi-state Risk-Based Maintenance Analysis of Redundant Safety Systems Using the Markov Model and Fault Tree Method

Mohammadhasani

Pirouzmand

2021

Front. Energy Res.

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The risk-based maintenance strategy has received special attention in the safe operation of nuclear power plants. Simultaneous quantification of the positive and negative effects of maintenance activities and components degradation effect makes it possible to accurately evaluate the risk criterion for safety systems of nuclear power plants. However, it is difficult to integrate the effects of maintenance and components degradation into the standard reliability approaches. A straightforward approach for considering components degradation and different maintenance policies is to make use of Markov maintenance models. In this article, the effectiveness of maintenance activities (including changes in the surveillance test intervals and alteration in the different maintenance policies) on the components unavailability with considering aging effects is quantified using Markov maintenance models and then by coupling these models and the fault tree method, the risk measure is upgraded from the component level to the system level. The proposed models are applied to evaluate the unavailability of two safety systems of VVER-1000/V446 nuclear power plants as case studies. The results show that the Markov method due to its multi-state nature is effective in the conservative evaluation of risk measures so that the unavailability computed by the coupling process is higher than the original unavailability (calculated by system fault tree using PSA data of nuclear power plants) for all maintenance policies. In addition, this study illustrates that the developed Markov maintenance models could be applied to the large-scale whole plant level and provides a proper transition from the classical PSA methods to new techniques. This approach integrates the effects of maintenance strategies and components degradation. Also, it provides a practical and a more accurate tool to determine the technical specification of a real nuclear power plant from the risk point of view.

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Section: Coupling Markov Model With Fault Tree Methodsmentioning

confidence: 99%

Section: Coupling Markov Model With Fault Tree Methodsmentioning

confidence: 99%