Advanced Prognostics for a Centrifugal Fan and Multistage Centrifugal Pump Using a Hybrid Model

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dc.contributor.authorVila-Forteza, Marc
dc.contributor.authorJimenez-Cortadi, Alberto
dc.contributor.authorDiez-Olivan, Alberto
dc.contributor.authorSeneviratne, Dammika
dc.contributor.authorGalar-Pascual, Diego
dc.contributor.editorJuuso, Esko
dc.contributor.editorGalar, Diego
dc.contributor.institutionTecnalia Research & Innovation
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T11:56:18Z
dc.date.available2024-07-24T11:56:18Z
dc.date.issued2023
dc.descriptionPublisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
dc.description.abstractPredictive maintenance is fully implemented in the oil and gas industry, and the impressive development of field sensors, big data, and digital twins offers a wide field for the ongoing experimentation and development of diagnostic and prognostic tools for machinery. Although a wide range of technologies and sensors is available, vibration analysis remains the preferred predictive technique for rotating machinery diagnostics. It is well-known, widely used, and has proven efficacious in evaluating the health of rotating machinery and preventing failures. Taking advantage of vibration analysis development and computing capabilities, this study develops three digital twins of one multistage centrifugal pump and two centrifugal fans using real vibration data and synthetic data. This hybrid model approach permits the use of failure data which are not usually found in the normal operation of these machines. The study improves and tunes the accuracy of those models using real operating data obtained from a distributed control system (DCS), thus obtaining results in accordance with process conditions. Maintenance decisions can be supported by these models. They are based on online vibration and process data; they diagnose the health of a machine and give its remaining useful life (RUL). The models may also be used for other API plant assets (multistage centrifugal pumps or centrifugal fans) by changing the configuration parameters and process DCS tags.en
dc.description.statusPeer reviewed
dc.format.extent13
dc.identifier.citationVila-Forteza , M , Jimenez-Cortadi , A , Diez-Olivan , A , Seneviratne , D & Galar-Pascual , D 2023 , Advanced Prognostics for a Centrifugal Fan and Multistage Centrifugal Pump Using a Hybrid Model . in E Juuso & D Galar (eds) , Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021 . Lecture Notes in Mechanical Engineering , Springer Science and Business Media Deutschland GmbH , pp. 153-165 , The 5th International Conference on Maintenance, Condition Monitoring and Diagnostics, MCMD 2021 , Oulu , Finland , 16/02/21 . https://doi.org/10.1007/978-981-99-1988-8_12
dc.identifier.citationconference
dc.identifier.doi10.1007/978-981-99-1988-8_12
dc.identifier.isbn9789819919871
dc.identifier.issn2195-4356
dc.identifier.urihttps://hdl.handle.net/11556/2609
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85172197449&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofProceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021
dc.relation.ispartofseriesLecture Notes in Mechanical Engineering
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsEnsemble methods
dc.subject.keywordsHybrid approach
dc.subject.keywordsIndustrial prognosis
dc.subject.keywordsPredictive maintenance
dc.subject.keywordsRemaining useful life
dc.subject.keywordsSynthetic data generation
dc.subject.keywordsAutomotive Engineering
dc.subject.keywordsAerospace Engineering
dc.subject.keywordsMechanical Engineering
dc.subject.keywordsFluid Flow and Transfer Processes
dc.subject.keywordsSDG 9 - Industry, Innovation, and Infrastructure
dc.titleAdvanced Prognostics for a Centrifugal Fan and Multistage Centrifugal Pump Using a Hybrid Modelen
dc.typeconference output
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