Optimizing the performance of a repairable system under a maintenance and repair contract

Lugtigheid, Diederik; Jardine, Andrew; Jiang, Xiang

doi:10.1002/qre.859

Cited by 38 publications

(28 citation statements)

References 28 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PIM is similar to PHM, but with the underlying failure mechanism following a stochastic point process rather than a probabilistic distribution [63]. PIM is used to model the intensity process of failures and repairs of a repairable system which incorporates explanatory variables [54,64]. Volk et al [65] introduces PIM for both non-repairable and repairable systems utilising historic failure data and corresponding diagnostic measurements.…”

Section: Proportional Intensity Modelmentioning

confidence: 99%

See 1 more Smart Citation

A review on reliability models with covariates

Gorjian

Mittinty

et al. 2010

Engineering Asset Lifecycle Management

View full text Add to dashboard Cite

Modern Engineering Asset Management (EAM) requires the accurate assessment of current and the prediction of future asset health condition. Suitable mathematical models that are capable of predicting Time-to-Failure (TTF) and the probability of failure in future time are essential. In traditional reliability models, the lifetime of assets is estimated using failure time data. However, in most real-life situations and industry applications, the lifetime of assets is influenced by different risk factors, which are called covariates. The fundamental notion in reliability theory is the failure time of a system and its covariates. These covariates change stochastically and may influence and/or indicate the failure time. Research shows that many statistical models have been developed to estimate the hazard of assets or individuals with covariates. An extensive amount of literature on hazard models with covariates (also termed covariate models), including theory and practical applications, has emerged. This paper is a state-of-the-art review of the existing literature on these covariate models in both the reliability and biomedical fields. One of the major purposes of this expository paper is to synthesise these models from both industrial reliability and biomedical fields and then contextually group them into non-parametric and semiparametric models. Comments on their merits and limitations are also presented. Another main purpose of this paper is to comprehensively review and summarise the current research on the development of the covariate models so as to facilitate the application of more covariate modelling techniques into prognostics and asset health management.

show abstract

Section: Proportional Intensity Modelmentioning

confidence: 99%

“…Maximum likelihood estimation is used to estimate regression coefficients. This model is applied in the reliability field [31, 34,64,66].…”

Section: Proportional Intensity Modelmentioning

confidence: 99%

A review on reliability models with covariates

Gorjian

Mittinty

et al. 2010

Engineering Asset Lifecycle Management

View full text Add to dashboard Cite

show abstract

“…Subsequently to these works, many extensions of the previous models were proposed on finite time span [3,4] and also on infinite time horizon in the literature. For survey, the reader may refer, for example, to [5][6][7][8] and the references therein. We note that in most cited works, the authors assumed that the equipment lifetime distribution is parametrically characterized and well known.…”

Section: Introductionmentioning

confidence: 99%

Optimum Maintenance Policy for Equipment over Changing of the Operation Environment

Sidibe¹,

Djelloul²

2017

System Reliability

View full text Add to dashboard Cite

This chapter investigates optimization of maintenance policy of a repairable equipment whose lifetime distribution depends on the operating environment severity. The considered equipment is undergone to a maintenance policy which consists of repairing minimally at failure and maintaining after operating periods. The periodic maintenance is preventive maintenance (PM) and allows reducing consequently the equipment age but with higher cost than minimal repair. In addition, the equipment has to operate at least in two operating environments with different severity. Therefore, in this analysis, the equipment lifetime distribution function depends on the operating severity. Under these hypotheses, a mathematical modeling of the maintenance cost per unit of time is proposed and discussed. This cost is mathematically analyzed in order to derive optimal periods between preventive maintenance (PM) and the optimal condition under which these exist.

show abstract

“…Held and Fritz (2009) claim that avionics field reliability data were used to compare two failure rate prediction models but disclose no details about the data used and how the analysis was carried out. Works like Lugtigheid et al (2007) and Jolly and Singh (2014) combine models for repairable and non-repairable items within the same case study. However, they present the empirical data aggregately and limit the explanation of their analysis to how the models were fitted using a particular software package.…”

Section: Assumptions and "Forgotten" Lessons In Field Data Analysismentioning

confidence: 99%

“…Thus, how field reliability data analysis is performed impacts whether or not a business will embark on a long-term equipment support contract and the financial conditions they impose. Example case studies are available on fleets of aircraft engines (Bowman and Schmee, 2001) and industrial machinery (Huang et al, 2013;Lugtigheid et al, 2007).…”

Section: Introductionmentioning

confidence: 99%