Semi-Markov models for degradation-based reliability

Kharoufeh, Jeffrey P.; Solo, Christopher J.; Ulukus, M. Yasin

doi:10.1080/07408170903394371

Cited by 88 publications

(54 citation statements)

References 35 publications

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“…The degradation process is also often modelled as a Markov process with discrete states. Optimal replacement policies were derived for observable Markov processes by Makis and Jiang [20] and Kharoufeh et al [14] and from the evolution of the hidden states by Bunks et al [4] and Lin and Makis [18]. Further, proportional hazards models are often used to relate the system's condition variables to the hazard function of a system, so that the maintenance policies can be optimized with respect to the optimal risk value of the hazard function; see Jardine et al [12] and Vlok et al [32].…”

Section: Introductionmentioning

confidence: 99%

A condition-based maintenance model for a single component in a system with scheduled and unscheduled downs

Zhu

Peng

Timmermans³

et al. 2017

International Journal of Production Economics

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Section: Introductionmentioning

confidence: 99%

A condition-based maintenance model for a single component in a system with scheduled and unscheduled downs

Zhu

Peng

Timmermans³

et al. 2017

International Journal of Production Economics

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“…Failures usually occur when the degradation level of a system reaches its failure threshold level, so that the condition monitoring data and stochastic models of the degradation processes are often necessary to estimate remaining useful lifetimes (RUL) or reliability functions. Si et al (2011) distinguished two types of probability models of RUL estimation: directly observed CBM models [e.g., regression-based models Lu andMeeker 1993, Wiener process Gebraeel et al 2005, Gamma processes (van Noortwijk 2009), Markovian-based models (Kharoufeh et al 2010)] and indirectly observed CBM models [e.g., stochastic filtering-based models (Wang and Christer 2000), covariation-based hazard model (Vlok et al 2002), hidden Markov model (Lin and Makis 2003)]. In this paper, we consider systems with continuously observable degradation processes, which is a typical feature of systems in the industry of advance capital goods.…”

Section: Introductionmentioning

confidence: 99%

“…If the degradation process could be modeled as discrete states, Markovian-based models were applied. The optimal replacement policies were derived from observable Markov processes (Kharoufeh et al 2010) or the evolution of the hidden states (Jiang et al 2013). Moreover, Proportional Hazards Models are also often used to relate the system's condition variables to the hazard function of a system (Vlok et al 2002), so that the maintenance policies can be optimized with respect to the optimal risk value of the hazard function.…”

Section: Introductionmentioning

confidence: 99%

A condition-based maintenance policy for multi-component systems with a high maintenance setup cost

Zhu¹,

Peng

Houtum

2015

OR Spectrum

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Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. Abstract Condition-based maintenance (CBM) is becoming increasingly important due to the development of advanced sensor and information technology, which facilitates the remote collection of condition data. We propose a new CBM policy for multi-component systems with continuous stochastic deteriorations. To reduce the high setup cost of maintenance, a joint maintenance interval is introduced. With this interval and the control limits of components as decision variables, we develop a model for the minimization of the average long-run maintenance cost rate of the systems. Moreover, a numerical study of a production system consisting of a large number of non-identical components is presented, including a sensitivity analysis. Finally, our policy is compared to a failure-based policy and an age-based policy, in order to evaluate the cost-saving potential.

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“…But there are many systems in which a unit cannot be kept as spare due to its high cost. Therefore, reliability models of single-unit systems with different failure modes have also been probed by the authors including Malik and Bansal [7], Malik [8], Kharoufeh J.P. et al [5], Malik et al [9] and Uematsu and Nishida [11] and Pawar and Malik [10] keeping in view of their practical utility and common man's affordability under the following assumptions:…”

Section: Introductionmentioning

confidence: 99%

Cost -Benefit Analysis of a Single-Unit System with Preventive Maintenance and Weibull Distribution for Failure and Repair Activities

Kumar

Saini

2014

Journal of Applied Mathematics, Statistics and Informatics

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This paper deals with a reliability model developed for a single-unit system which goes for preventive maintenance after a pre-specific time 't' up to which no failure occurs. There is a single server who takes some time to arrive at the system for doing repair activities. The unit does not work as new after repair at complete failure and so called the degraded unit. The degraded unit is replaced by new one after its failure with some replacement time. The failure time, preventive maintenance time, replacement time and repair time of the unit are taken as Weibull distributed with common shape parameter and different scale parameters. The switching devices are perfect. The system is observed at suitable regenerative epochs to obtain various measures of system effectiveness of interest to system designers and operation managers.

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Semi-Markov models for degradation-based reliability

Cited by 88 publications

References 35 publications

A condition-based maintenance model for a single component in a system with scheduled and unscheduled downs

A condition-based maintenance model for a single component in a system with scheduled and unscheduled downs

A condition-based maintenance policy for multi-component systems with a high maintenance setup cost

Cost -Benefit Analysis of a Single-Unit System with Preventive Maintenance and Weibull Distribution for Failure and Repair Activities

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