Prognosis of a Degradable Hydraulic System: Application on a Centrifugal Pump

This article proposes a preliminary diagnostic/prognostic method for the identification of a critical system, undergoing a continuous evolutionary degradation, in a production area, and the determination of the component responsible for its degradation, called the failing element. Using for this, a...

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Main Authors: Imad El Adraoui, Hassan Gziri, Ahmed Mousrij
Format: Article
Language:English
Published: The Prognostics and Health Management Society 2020-06-01
Series:International Journal of Prognostics and Health Management
Subjects:
mlp
Online Access:https://papers.phmsociety.org/index.php/ijphm/issue/view/34
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spelling doaj-919af53dece34f69b233686fa90ee3882021-07-02T20:50:20ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482153-26482020-06-01112doi:10.36001/ijphm.2020.v11i2.2926Prognosis of a Degradable Hydraulic System: Application on a Centrifugal PumpImad El Adraoui0Hassan Gziri1Ahmed Mousrij2Hassan Ist University, FST, IMII Laboratory, Settat, 26000, MoroccoHassan Ist University, FST, IMII Laboratory, Settat, 26000, MoroccoHassan Ist University, FST, IMII Laboratory, Settat, 26000, MoroccoThis article proposes a preliminary diagnostic/prognostic method for the identification of a critical system, undergoing a continuous evolutionary degradation, in a production area, and the determination of the component responsible for its degradation, called the failing element. Using for this, a model based on learning by multilayer perception (MLP). The purpose of this paper is to provide a modeling approach that makes it possible to determine the level of degradation reached by the system at any given point of time, in a precise way. Thus, the horizon of the failure will be produced with a minimum error compared to the discrete jump model used in the literature. The proposed approach consists of using a neural network with fewer layers and optimal computing time. We performed data learning (tests) in order to illustrate a regression of good correlation of these data (tests) on a centrifugal pump with satisfactory performance parameters and compared it with other commonly used methods.https://papers.phmsociety.org/index.php/ijphm/issue/view/34diagnosticsprognosticsdegradationcentrifugal pumpmlp
collection DOAJ
language English
format Article
sources DOAJ
author Imad El Adraoui
Hassan Gziri
Ahmed Mousrij
spellingShingle Imad El Adraoui
Hassan Gziri
Ahmed Mousrij
Prognosis of a Degradable Hydraulic System: Application on a Centrifugal Pump
International Journal of Prognostics and Health Management
diagnostics
prognostics
degradation
centrifugal pump
mlp
author_facet Imad El Adraoui
Hassan Gziri
Ahmed Mousrij
author_sort Imad El Adraoui
title Prognosis of a Degradable Hydraulic System: Application on a Centrifugal Pump
title_short Prognosis of a Degradable Hydraulic System: Application on a Centrifugal Pump
title_full Prognosis of a Degradable Hydraulic System: Application on a Centrifugal Pump
title_fullStr Prognosis of a Degradable Hydraulic System: Application on a Centrifugal Pump
title_full_unstemmed Prognosis of a Degradable Hydraulic System: Application on a Centrifugal Pump
title_sort prognosis of a degradable hydraulic system: application on a centrifugal pump
publisher The Prognostics and Health Management Society
series International Journal of Prognostics and Health Management
issn 2153-2648
2153-2648
publishDate 2020-06-01
description This article proposes a preliminary diagnostic/prognostic method for the identification of a critical system, undergoing a continuous evolutionary degradation, in a production area, and the determination of the component responsible for its degradation, called the failing element. Using for this, a model based on learning by multilayer perception (MLP). The purpose of this paper is to provide a modeling approach that makes it possible to determine the level of degradation reached by the system at any given point of time, in a precise way. Thus, the horizon of the failure will be produced with a minimum error compared to the discrete jump model used in the literature. The proposed approach consists of using a neural network with fewer layers and optimal computing time. We performed data learning (tests) in order to illustrate a regression of good correlation of these data (tests) on a centrifugal pump with satisfactory performance parameters and compared it with other commonly used methods.
topic diagnostics
prognostics
degradation
centrifugal pump
mlp
url https://papers.phmsociety.org/index.php/ijphm/issue/view/34
work_keys_str_mv AT imadeladraoui prognosisofadegradablehydraulicsystemapplicationonacentrifugalpump
AT hassangziri prognosisofadegradablehydraulicsystemapplicationonacentrifugalpump
AT ahmedmousrij prognosisofadegradablehydraulicsystemapplicationonacentrifugalpump
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