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Developing predictive models for time to failure estimation

DOITrouver le DOI : https://doi.org/10.1109/CSCWD.2016.7565977
AuteurRechercher : 1; Rechercher : ; Rechercher : ; Rechercher :
Affiliation
  1. Conseil national de recherches Canada. Technologies de l'information et des communications
FormatTexte, Article
Conférence2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), May 4-6, 2016, Nanchang, China
SujetPHM; prognostics; time to failure estimation; classification; regression; on-demand regression
Résumé
The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management (PHM) systems. Taking advantage of advances in sensor technologies, PHM systems enable a predictive maintenance strategy through continuously monitoring the health of complex systems. The core of PHM technology is prognostic which is able to estimate time to failure (TTF) for the monitored components or systems using the built-in predictive models. In this paper, the state of the art of TTF estimation will be first reviewed. After introduction of traditional methods of TTF estimation, we will present the developed approaches for estimating TTF, including classification, regression, on-demand regression, Particle Filtering (PF)-based method, and so on. The main purpose of this paper is to summarize the work on TTF estimation technologies developed in the past decade.
Date de publication
Maison d’éditionIEEE
Dans
  • IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2016) : 133–138.
Langueanglais
Publications évaluées par des pairsOui
Numéro NPARC23000849
Exporter la noticeExporter en format RIS
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Identificateur de l’enregistrementd6121610-48ef-47f7-b5fc-a85029acf0bd
Enregistrement créé2016-10-19
Enregistrement modifié2020-03-16

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