A facilities maintenance management process based on degradation prediction using sensed data

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dc.contributor.advisor Brown, Kenneth N. en
dc.contributor.advisor Damien Fay en
dc.contributor.author Tobin, Ena
dc.date.accessioned 2016-05-17T10:40:33Z
dc.date.available 2016-05-17T10:40:33Z
dc.date.issued 2014
dc.date.submitted 2014
dc.identifier.citation Tobin, E.C. 2014. A facilities maintenance management process based on degradation prediction using sensed data. PhD Thesis, University College Cork. en
dc.identifier.endpage 336 en
dc.identifier.uri http://hdl.handle.net/10468/2584
dc.description.abstract Energy efficiency and user comfort have recently become priorities in the Facility Management (FM) sector. This has resulted in the use of innovative building components, such as thermal solar panels, heat pumps, etc., as they have potential to provide better performance, energy savings and increased user comfort. However, as the complexity of components increases, the requirement for maintenance management also increases. The standard routine for building maintenance is inspection which results in repairs or replacement when a fault is found. This routine leads to unnecessary inspections which have a cost with respect to downtime of a component and work hours. This research proposes an alternative routine: performing building maintenance at the point in time when the component is degrading and requires maintenance, thus reducing the frequency of unnecessary inspections. This thesis demonstrates that statistical techniques can be used as part of a maintenance management methodology to invoke maintenance before failure occurs. The proposed FM process is presented through a scenario utilising current Building Information Modelling (BIM) technology and innovative contractual and organisational models. This FM scenario supports a Degradation based Maintenance (DbM) scheduling methodology, implemented using two statistical techniques, Particle Filters (PFs) and Gaussian Processes (GPs). DbM consists of extracting and tracking a degradation metric for a component. Limits for the degradation metric are identified based on one of a number of proposed processes. These processes determine the limits based on the maturity of the historical information available. DbM is implemented for three case study components: a heat exchanger; a heat pump; and a set of bearings. The identified degradation points for each case study, from a PF, a GP and a hybrid (PF and GP combined) DbM implementation are assessed against known degradation points. The GP implementations are successful for all components. For the PF implementations, the results presented in this thesis find that the extracted metrics and limits identify degradation occurrences accurately for components which are in continuous operation. For components which have seasonal operational periods, the PF may wrongly identify degradation. The GP performs more robustly than the PF, but the PF, on average, results in fewer false positives. The hybrid implementations, which are a combination of GP and PF results, are successful for 2 of 3 case studies and are not affected by seasonal data. Overall, DbM is effectively applied for the three case study components. The accuracy of the implementations is dependant on the relationships modelled by the PF and GP, and on the type and quantity of data available. This novel maintenance process can improve equipment performance and reduce energy wastage from BSCs operation. en
dc.description.sponsorship Science Foundation Ireland (SFI Grant 07.SRC.I1170) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2014, Ena C. Tobin. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject Maintenance processes en
dc.subject Degradation monitoring en
dc.subject Gaussian processes en
dc.subject Particle filter en
dc.subject HVAC data analysis en
dc.title A facilities maintenance management process based on degradation prediction using sensed data en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PHD (Engineering) en
dc.internal.availability Full text available en
dc.check.info No embargo required en
dc.description.version Accepted Version
dc.contributor.funder Science Foundation Ireland en
dc.description.status Not peer reviewed en
dc.internal.school Civil Engineering en
dc.internal.school Computer Science en
dc.check.type No Embargo Required
dc.check.reason No embargo required en
dc.check.opt-out No en
dc.thesis.opt-out false
dc.check.embargoformat Not applicable en
ucc.workflow.supervisor k.brown@cs.ucc.ie
dc.internal.conferring Summer 2015 en


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© 2014, Ena C. Tobin. Except where otherwise noted, this item's license is described as © 2014, Ena C. Tobin.
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