Decline and repair, and covariate effects

Wu, Shaomin; Scarf, Philip

doi:10.1016/j.ejor.2015.01.041

Cited by 25 publications

(12 citation statements)

References 21 publications

(33 reference statements)

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“…One may notice that, in recent years, many authors have devoted considerable effort on developing novel methods to model repair processes, (see, e.g. Jain & Gupta, 2013;Liang & Parlikad, 2015;Sheu et al, 2015;Vu et al, 2015;Wu & Scarf, 2015, 2017Zhao et al, 2015;Wu, 2017). We consider the current paper and the process we describe as a further important contribution to the literature on the modelling the failure process of a repairable system.…”

Section: Contribution and Importance Of This Workmentioning

confidence: 99%

The semi-geometric process and some properties

Wang

2017

IMA Journal of Management Mathematics

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The geometric process has been widely applied in reliability engineering and other areas since its introduction. One of its assumptions is that the times between occurrences of events are independent. This assumption is rather restrictive and can limit its application in the real world. This paper extends the geometric process to a new process, which we call the semi-geometric process, by relaxing this assumption. Some probabilistic properties of the process are derived and parameter estimation is described. A numerical example, based on a real-world data set, is used to illustrate the model and validate the estimation methodology.

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Section: Contribution and Importance Of This Workmentioning

confidence: 99%

The semi-geometric process and some properties

Wang

2017

IMA Journal of Management Mathematics

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“…At the ends of this spectrum lie the renewal process (Ross, 1996) and the non-homogeneous Poisson process (Cox and Lewis, 1966). In between, and thus capturing imperfect repair, lie mixed processes (Brown and Proschan, 1983;Kallen, 2011;Doyen, 2014) virtual age-reduction models (Kijima and Sumita, 1986;Kijima, 1989;Jack, 1998;Tanwar et al, 2014), geometric processes (Lam, 1988;Wang and Pham, 1996;Wu and Clements-Croome, 2006;Wu and Scarf, 2015;Zhang et al, 2015;Wu and Wang, 2018), hazard-reduction models (Doyen and Gaudoin, 2004;Percy and Alkali, 2006;Percy et al, 2010), virtual component models (Wu and Scarf, 2017); and generalizations of these (Doyen et al, 2017). All these attempt to model multicomponent systems parsimoniously.…”

Section: Introductionmentioning

confidence: 99%

Virtual series-system models of imperfect repair

Nafisah

Shrahili

Alotaibi

et al. 2019

Reliability Engineering & System Safety

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Novel models of imperfect repair are fitted to classic reliability datasets. The models suppose that a virtual system comprises a component and a remainder in series. On failure of the component, the component is renewed, and on failure of the remainder, the component is renewed and the remainder is minimally repaired. It follows that the repair process is a counting process that is the superposition of a renewal process and a Poisson process. The repair effect, that is, the extent to the system is repaired by renewal of the component, depends on the relative intensities of the superposed processes. The repair effect may be negative, when the intensity of the part that is a renewal process is a decreasing function. Other special cases of the model exist (renewal process, Poisson process, superposed renewal process and homogeneous Poisson process). Model fit is important because the nature of the model and corresponding parameter values determine the effectiveness of maintenance, which we also consider. A cost-minimizing repair policy may be determined provided the cost of preventive-repair is less than the cost of corrective-repair and the repairable part is ageing. If the remainder is ageing, then policy needs to be adapted as it ages.

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“…Duan et al 38 investigated a selective maintenance model with stochastic maintenance quality for the multi-component system. Wu and Scarf 39 modelled the failure process under consideration of different modifications based on the failure intensity function λ 0 ( t ) = λ b ( t ) ϕ ( t ), where λ b ( t ) is the baseline failure intensity function and ϕ ( t ) is a function of covariates that quantify some extrinsic factors. Wang et al 40 presented a sequential imperfect PM policy with random maintenance quality and provided the maintenance optimization.…”

Section: Introductionmentioning

confidence: 99%

An extended geometric process and its application in replacement policy

Gao

2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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In this article, we develop an extended geometric process model with a recovery factor and propose a two-dimensional evaluation function for each maintenance effect. The developed model can describe the mean inter-failure time which can be improved by the first or several former repairs in some repairable systems. We also propose parameter estimation methods for the developed geometric process model and use some real failure datum to prove the model’s application. Focused on the developed geometric process model, two novel replacement policies and their optimizations are also presented in detail. Then, two real case studies are discussed to illustrate the modelling and optimizing process. Modelling results show that each recovery factor is related to the effect of each repair, and the developed geometric process model has a good-fitting property for some real cases. Optimization results display that the optimal ( N, m) replacement policy is applicable for some small-sized manufacturing systems, whereas the proposed ( N) replacement policy is suitable for some expensive systems.

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Decline and repair, and covariate effects

Cited by 25 publications

References 21 publications

The semi-geometric process and some properties

The semi-geometric process and some properties

Virtual series-system models of imperfect repair

An extended geometric process and its application in replacement policy

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