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dc.contributor.authorLoukopoulosa, Panagiotisen
dc.contributor.authorPilidisa, Periclesen
dc.contributor.authorBennett, Ianen
dc.contributor.authorZolkiewski, Georgeen
dc.contributor.authorLi, Xiaochuanen
dc.contributor.authorMba, Daviden
dc.date.accessioned2018-11-27T09:19:55Z
dc.date.available2018-11-27T09:19:55Z
dc.date.issued2018-11-24
dc.identifier.citationLoukopoulos, P., Pilidis, P., Bennett, I., Zolkiewski, G., Li, X., Mba, D. (2019) Abrupt fault remaining useful life estimation using measurements from a reciprocating compressor valve failure. Mechanical Systems and Signal Processing, 121, pp. 359-372en
dc.identifier.issn0888-3270
dc.identifier.urihttp://hdl.handle.net/2086/17292
dc.descriptionThe file attached to this record is the author's final peer reviewed version.en
dc.description.abstractOne of the major targets in industry is minimisation of downtime and cost, and maximisation of availability and safety, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) which is founded on the principles of diagnostics and prognostics, is a step towards this direction as it offers a proactive means for scheduling maintenance. Reciprocating compressors are vital components in oil and gas industry, though their maintenance cost is known to be relatively high. Compressor valves are the weakest part, being the most frequent failing component, accounting for almost half maintenance cost. To date, there has been limited information on estimating Remaining Useful Life (RUL) of reciprocating compressor in the open literature. This paper compares the prognostic performance of several methods (multiple linear regression, polynomial regression, Self-Organising Map (SOM), K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and precision, using actual valve failure data captured from an operating industrial compressor. SOM technique is proposed to be employed for the first time as a standalone tool for RUL estimation. Furthermore, two variations on estimating RUL based on SOM and KNNR respectively are proposed. Finally, an ensemble method by combining the output of all aforementioned algorithms is proposed and tested. Principal components analysis and statistical process control were implemented to create T^2 and Q metrics, which were proposed to be used as health indicators reflecting degradation processes and were employed for direct RUL estimation for the first time. It was shown that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectreciprocating compressoren
dc.subjectvalveen
dc.subjectprognosticsen
dc.subjectremaining useful lifeen
dc.subjectmultiple linear regressionen
dc.subjectpolynomial regressionen
dc.subjectself-organising mapen
dc.subjectK-nearest neighboursen
dc.subjectinstantaneous failureen
dc.subjectprincipal components analysisen
dc.subjectstatistical process controlen
dc.titleAbrupt fault remaining useful life estimation using measurements from a reciprocating compressor valve failureen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.ymssp.2018.09.033
dc.researchgroupInstitute of Artificial Intelligence (IAI)en
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2018-09-25en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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