Condition monitoring of rotating machines under time-varying conditions based on adaptive canonical variate analysis

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dc.cclicenceCC-BY-NCen
dc.contributor.authorLi, Xiaochuan
dc.contributor.authorYang, Yingjie
dc.contributor.authorBennett, Ian
dc.contributor.authorMba, David
dc.date.acceptance2019-05-26
dc.date.accessioned2019-06-11T08:44:39Z
dc.date.available2019-06-11T08:44:39Z
dc.date.issued2019-06-06
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 link.en
dc.description.abstractCondition monitoring signals obtained from rotating machines often demonstrate a highly non-stationary and transient nature due to internal natural deterioration characteristics of their constituent components and external time-varying operational conditions. Traditional multivariate statistical monitoring approaches are based on the assumption that the underlying processes are linear and static and are apt to interpret the normal changes in operating conditions as faults, which would result in high false positive rates. On the other hand, the development of robust diagnostic techniques for the detection of incipient faults remains a challenge for researchers, given the difficulty of finding an appropriate trade-off between a low false positive ratio and early detection of emerging faults. To address these issues, this paper proposes a novel adaptive fault detection approach based on the canonical residuals (CR) induced by the combination of canonical variate analysis (CVA) and matrix perturbation theory for the monitoring of dynamic processes where variations in operating conditions are incurred. The canonical residuals are calculated based upon the distinctions between past and future measurements and are able to effectively detect emerging faults while still maintaining a low false positive rate. The effectiveness of the developed diagnostic model for the detection of abnormalities in industrial processes was demonstrated for slow involving faults in case studies of two operational industrial high-pressure pumps. In comparison with the variable-based and canonical correlation-based statistical monitoring approaches, the proposed canonical residuals-adaptive canonical variate analysis (CR-ACVA) fault detection method has demonstrated its superiorities by the detailed performance comparisons.en
dc.funderNo external funderen
dc.identifier.citationLi, X, Yang, Y., Bennett, I., Mba, D. (2019) Condition monitoring of rotating machines under time-varying conditions based on adaptive canonical variate analysis. Mechanical Systems and Signal Processing, 131, pp.348-363.en
dc.identifier.doihttps://doi.org/10.1016/j.ymssp.2019.05.048
dc.identifier.urihttps://www.dora.dmu.ac.uk/handle/2086/17995
dc.language.isoenen
dc.peerreviewedYesen
dc.publisherElsevieren
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectCondition monitoringen
dc.subjectFault detectionen
dc.subjectCanonical variable analysisen
dc.subjectRotating machineryen
dc.titleCondition monitoring of rotating machines under time-varying conditions based on adaptive canonical variate analysisen
dc.typeArticleen

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