Remaining life prediction methods using operating data and knowledge on mechanisms

Lee, Bom Soon; Chung, Han Sub; Kim, Ki-Tae; Ford, F. P.; Andersen, P.L.

doi:10.1016/s0029-5493(99)00138-7

Cited by 15 publications

(3 citation statements)

References 5 publications

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“…For gear fault diagnosis and prognosis, they explain the wavelet-based techniques. Using plant operating data, Bom et al explore the Weibull statistics as a useful tool in estimating the residual life of a component [68].…”

Section: B Prognostics Of Electronic Componentsmentioning

confidence: 99%

Review of Health Prognostics and Condition Monitoring of Electronic Components

et al. 2020

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To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' visa -vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted. INDEX TERMS Reliability methods, condition monitoring, faults and failures, prognostics, diagnostics.

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Section: B Prognostics Of Electronic Componentsmentioning

confidence: 99%

Review of Health Prognostics and Condition Monitoring of Electronic Components

et al. 2020

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“…Estes modelos são de simples desenvolvimento e entendimento, cujas vantagens já são apresentadas e discutidas há anos, no entanto lidam com informações subjetivas e dependem dos conhecimentos de especialistas (ØIEN, 1998;LEE et al, 1999;SIKORSKA et al, 2011 As redes neurais artificiais são a terceira categoria, nas quais a RUL é estimada pela representação matemática do sistema oriunda de dados e observações. Sendo assim, esta abordagem requer banco de dados e conhecimento de programação de redes neurais, e consegue modelar sistemas complexos (SIKORSKA et al, 2011).…”

Section: Vida úTil Restanteunclassified

Modelo para vida útil restante de ativos em terminais aeroportuários

Beuren

Ribeiro

2017

Revista PO: R. Eletr. de Eng. de Produção e Correlatas

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Resumo: Estudos destacam a importância da previsão da vida útil de ativos para redução de custos e aumento de produtividade nas companhias, em especial, a vida útil restante. No entanto, esta previsão é muito sensível ao método utilizado e à quantidade de dados disponível para a análise. Apesar de existirem diversos modelos para vida útil restante de ativos, nenhum deles foi desenvolvido considerando especificamente o ambiente aeroportuário, que apresenta problemas na gestão de seus ativos. O objetivo principal deste trabalho é propor um modelo para estimativa de vida útil restante de ativos em aeroportos, além da sua aplicação em um aeroporto brasileiro na forma de estudo de caso. O trabalho concluiu que modelo conseguiu identificar ativos com um amplo intervalo de vida útil restante, apoiando-se em informações subjetivas e poucos dados históricos. Como principal contribuição, cita-se o estudo das variáveis presentes em ambientes aeroportuários, o que permitiu a proposição de um modelo adequado para lidar com as especificidades do setor. Palavras-chave:Vida útil de ativos. Vida útil restante. Aeroportos. Abstract:Studies highlight the importance of accurate asset life estimation in order to reduce costs and increase productivity in companies, in particular the Remaining Useful Life (RUL). However, this estimation is very sensitive to the method used and the amount of data available for analysis. Although there are several models for remaining useful life of assets, none of those were developed specifically to airports, which present problems in asset management. The main objective of this work is to develop a model to estimate the remaining life of assets in airports, in addition to its application in a Brazilian airport. It can be concluded that model was able to identify assets with a wide range of remaining useful life, relying on subjective information and few historical data. In addition, the study of the airports variables appears as a main contribution, which allows the development of models more appropriated to the specificities of the sector.

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“…However the existing literature (Heng et al, 2009;Huang et al, 2007;Lee et al, 1999) largely focuses on the degradation mechanism and remaining useful life from the component or single equipment deterioration point of view; for example, ball bearing, pump, and steam generator. Very few models have taken into account not only the interdependencies among the components and subsystems, but also the impact of degradations and the influence of exogenous variables on the degradation processes.…”

Section: Introductionmentioning

confidence: 99%

An integrated safety prognosis model for complex system based on dynamic Bayesian network and ant colony algorithm

Zhang

et al. 2011

Expert Systems with Applications

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Remaining life prediction methods using operating data and knowledge on mechanisms

Cited by 15 publications

References 5 publications

Review of Health Prognostics and Condition Monitoring of Electronic Components

Review of Health Prognostics and Condition Monitoring of Electronic Components

Modelo para vida útil restante de ativos em terminais aeroportuários

An integrated safety prognosis model for complex system based on dynamic Bayesian network and ant colony algorithm

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