M out of n inspected systems subject to shocks in random environment

Kenzin, Moshe; Frostig, Esther

doi:10.1016/j.ress.2009.02.005

Cited by 17 publications

(9 citation statements)

References 25 publications

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“…In Figure 1, the function Aðt, mÞ given by equations (15) and (16) is plotted for different values of the number of shocks m observed by the time t. We see the Ushape of this function with the positive value at t ¼ T as it follows from equation (16) thatAðT; mÞ = C R + C t . 0…”

Section: Degradation Modeled By the Shot-noise Process Of Shocksmentioning

confidence: 91%

On missions’ quality of performance for systems with partially or completely observable degradation

Finkelstein

Levitin

2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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At some instances, it is better to terminate operation of a system than to wait for its failure or completion. However, in this article, we are mostly interested in missions that are cost-effective during the whole mission time and, therefore, do not require termination. Moreover, some requirements for parameters of the considered models that guarantee this cost-effectiveness are analyzed. We consider two failure models for systems executing missions of the fixed duration. In the first model, degradation is partially observed via the number of shocks experienced by a system. Shocks act directly on the failure rate forming the shot-noise process. In the second model, degradation is completely observed and is modeled by the Poisson process. Thus, the number of shocks or the number of failed components is the degradation parameters in our models, respectively. The detailed numerical examples illustrate our findings. Specifically, the bounds for the number of events (shocks or component’s failures) observed at each instant of time that guarantee cost-effectiveness of a mission are obtained.

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Section: Degradation Modeled By the Shot-noise Process Of Shocksmentioning

confidence: 91%

On missions’ quality of performance for systems with partially or completely observable degradation

Finkelstein

Levitin

2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…Wang 1996a, 1997;Murthy and Whang 1996;Wang and Pham 1996). Recently, other studies have been developed about replacement policies and spare parts management under different conditions (Chien 2008;Kenzin and Frostig 2009;Chien et al 2009;Wang, Chu, and Mao 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

Age replacement policy in a random environment using systemability

Persona

Pham

Sgarbossa

2010

International Journal of Systems Science

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Preventive maintenance is a group of maintenance policies based on preventive actions in order to predate the failure of a component or a system. Usually, these policies are designed using a series of data related to the studied units. All policies do not consider the effect of the environment where the components or systems operate. In this article, one of the most used policies, the age replacement policy, is also discussed taking into consideration the environmental effects using an innovative concept, introduced by Pham, called systemability.Several numerical examples are carried out in order to illustrate the aim of this work. The importance of environmental factors is also demonstrated thanks to the application to a real case.

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“…In this viewpoint, we think of the components of a coherent system as dependent through an environmental random variable. For more information on system reliability and replacement policies subjected to random environments, we refer the interested reader to [16], [24], and [33]- [35].…”

Section: Introductionmentioning

confidence: 99%

Stochastic comparisons of coherent systems under different random environments

Amini-Seresht

Zhang

2018

J. Appl. Probab.

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For many practical situations in reliability engineering, components in the system are usually dependent since they generally work in a collaborative environment. In this paper we build sufficient conditions for comparing two coherent systems under different random environments in the sense of the usual stochastic, hazard rate, reversed hazard rate, and likelihood ratio orders. Applications and numerical examples are provided to illustrate all the theoretical results established here.

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M out of n inspected systems subject to shocks in random environment

Cited by 17 publications

References 25 publications

On missions’ quality of performance for systems with partially or completely observable degradation

On missions’ quality of performance for systems with partially or completely observable degradation

Age replacement policy in a random environment using systemability

Stochastic comparisons of coherent systems under different random environments

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