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dc.contributor.authorLi, Xiaochuanen
dc.contributor.authorDuan, Fangen
dc.contributor.authorMba, Daviden
dc.contributor.authorBennett, Ianen
dc.contributor.authorLoukopoulosa, Panagiotisen
dc.date.accessioned2018-01-18T10:49:12Z
dc.date.available2018-01-18T10:49:12Z
dc.date.issued2018-01-03
dc.identifier.citationLi, X. et al. (2018) Canonical Variable Analysis and Long Short-term Memory for Fault Diagnosis and Performance Estimation of a Centrifugal Compressor. Control Engineering Practice, 72, pp.177-191.en
dc.identifier.issn0967-0661
dc.identifier.urihttp://hdl.handle.net/2086/15082
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linken
dc.description.abstractCentrifugal compressors are widely used for gas lift, re-injection and transport in the oil and gas industry. Critical compressors that compress flammable gases and operate at high speeds are prioritized on maintenance lists to minimize safety risks and operational downtime hazards. Identifying incipient faults and predicting fault evolution for centrifugal compressors could improve plant safety and efficiency and reduce maintenance and operation costs. This study proposes a dynamic process monitoring method based on canonical variable analysis (CVA) and long short-term memory (LSTM). CVA was used to perform fault detection and identification based on the abnormalities in the canonical state and the residual space. In addition, CVA combined with LSTM was used to estimate the behavior of a system after the occurrence of a fault using data captured from the early stages of deterioration. The approach was evaluated using process data obtained from an operational industrial centrifugal compressor. The results show that the proposed method can effectively detect process abnormalities and perform multi-step-ahead prediction of the system’s behavior after the appearance of a fault.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectPerformance estimationen
dc.subjectCanonical variable analysisen
dc.subjectLong short-term memoryen
dc.subjectCondition monitoringen
dc.titleCanonical Variable Analysis and Long Short-term Memory for Fault Diagnosis and Performance Estimation of a Centrifugal Compressoren
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.conengprac.2017.12.006
dc.researchgroupCentre for Engineering Science and Advanced Systemsen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017-12-16en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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