A full history proportional hazards model for preventive maintenance scheduling

Kobbacy, Khairy A. H.; Fawzi, B. B.; Percy, David F.; Ascher, Harold

doi:10.1002/(sici)1099-1638(199707)13:4<187::aid-qre98>3.0.co;2-l

Cited by 53 publications

(33 citation statements)

References 2 publications

(4 reference statements)

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“…The work by Kobbacy and associates [43] is concerned with the development of a realistic preventive maintenance (PM) scheduling model. A heuristic approach to implementing the semi-parametric proportional-hazards model (PHM) to schedule the next preventive maintenance interval on the basis of the equipment's full condition history was introduced.…”

Section: Ms Literaturementioning

confidence: 99%

“…Various optimisation studies have also been conducted, including by Billinton and colleague [9], Charles-Owaba [13], Christiaanse [15], El-Sharkh [28], Pan [69], Yamayee and associate [86], and Zunn and Quintana [91,92]. Models have been developed in different situations: proportional intensities [64], full history [43], warranted products [41], finite source [43], uncertainty [54], deregulated system [59], and planning [79]. The constraints under which models operated include: sparse data [63], risk [33], and network [60].…”

Section: Important Fields Of Msmentioning

confidence: 99%

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Maintenance Scheduling: Description, Status and Future Directions

Oke

2012

The South African Journal of Industrial Engineering

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In the past several decades, there have been diverse and significant research activities on maintenance scheduling (MS) due to the multi-disciplinary dimension of the field. As a consequence of this diversity and multi-disciplinary involvement, the MS literature is at present fragmented, somewhat misleading, and deficient in informative articles that cover the length, breadth, and depth of scientific investigations in the field. This paper reviews the scientific discipline of MS, covering general surveys, models, and significant empirical studies and cases in MS research. The research reflects the multidisciplinary nature of work in the field and suggests areas for future investigation. OPSOMMINGIn die afgelope aantal dekades was daar uiteenlopende en aansienlike navorsingsbedrywigheid op die gebied van instandhoudingskedulering (IS) weens die multidissiplinêre dimensie van die gebied. As gevolg van hierdie diversiteit en multidissiplinêre betrokkenheid is die ISliteratuur tans gefragmenteer, ietwat misleidend en gebrekkig aan insiggewende artikels wat die lengte, breedte en diepte van wetenskaplike ondersoeke op die gebied dek. Hierdie artikel bied 'n oorsig oor die wetenskaplike vakgebied van IS en dek algemene opnames, modelle en aansienlike empiriese ondersoeke en gevalle in IS-navorsing. Die navorsing weerspieël die multidissiplinêre uitkyk van werk op dié gebied en open gebiede vir toekomstige navorsing.

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Section: Ms Literaturementioning

confidence: 99%

Section: Important Fields Of Msmentioning

confidence: 99%

Maintenance Scheduling: Description, Status and Future Directions

Oke

2012

The South African Journal of Industrial Engineering

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“…In parametric models, the form of degradation paths or distribution of degradation measure is specified and/or partially specified [6,74]. These models can be classified as:…”

Section: Semi-parametric Modelsmentioning

confidence: 99%

A review on reliability models with covariates

Gorjian

Mittinty

et al. 2010

Engineering Asset Lifecycle Management

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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.

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“…The inverse Laplace transforms (28) and (29) were obtained by coding a numerical Laplace transform inversion algorithm due to Abate and Whitt [1]. This algorithm and the discrete-event simulation models were coded in the MATLAB R computing environment and executed on a personal computer equipped with an Intel R Core TM 2 Duo CPU operating at 3.00 GHz with 2.00 GB of RAM.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The PHM has been implemented in a variety of engineering applications, such as aircraft, marine systems, and machinery [19,20,21,49]. An excellent review of proportional hazard models for preventive maintenance can be found in [28]. Gebraeel, et al [17,18] proposed models that use actual signals to fit linear, exponential, and polynomial degradation curves from which they estimated normal residual lifetime distributions.…”

Section: Introductionmentioning

confidence: 99%

Stochastic models for degradation-based reliability

2005

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We present hybrid, degradation-based reliability models for a single-unit system whose degradation is driven by a semi-Markov environment. The primary objective is to develop a mathematical framework and associated computational techniques that unite environmental data and stochastic failure models to assess the current or future health of the system. By employing phase-type distributions, we construct a surrogate environment process that is amenable to analysis by exact Markovian techniques to obtain reliability estimates. By way of two numerical experiments, we illustrate the viability of our approach and assess the quality of the approximations. The numerical results indicate that remarkably accurate lifetime distribution and moment approximations are attainable.

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A full history proportional hazards model for preventive maintenance scheduling

Cited by 53 publications

References 2 publications

Maintenance Scheduling: Description, Status and Future Directions

Maintenance Scheduling: Description, Status and Future Directions

A review on reliability models with covariates

Stochastic models for degradation-based reliability

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