A framework for failure prediction models of medical electron linear accelerators

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AuteurMEDENOU, DATON
AuteurIDJIWOLE, FRANCOIS B A
AuteurHOUESSOUVO, C ROLAND
Date d'ajout2026-06-02T16:06:57Z
Date de disponibilite2026-06-02T16:06:57Z
Date de publication2019
ResumeAmong the available maintenance strategies, predictive maintenance seems to be the most promising for medical linear accelerators (linacs). Predictive Maintenance predicts failures and allows timely reaction. Input data and model are important to implement predictive maintenance. The aim of this study is to provide a new framework including workflow, data and models that can be used for developing a predictive maintenance approach for medical linear accelerators. In this paper, 51 operational parameters and output performances data related to 15 systems of linacs, 8 environment data, proces sing data methods and 29 prediction models are identified. This work shows there is no standard failure prediction model apply to medicallinacs.
DOI10.1109/AFRICON46755.2019.9133833
Autre identifiantBECDB-9193
URIhttps://dspace.uac.bj/handle/123456789/8220
Languefr
Fait partie deIEEE AFRICON
Sujetdata
Sujetfailure
Sujetlinear accelerator
Sujetmaintenance
Sujetmodel
Sujetprédiction
TitreA framework for failure prediction models of medical electron linear accelerators
TypeArticle

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