Dealing with missing data for prognostic purposes

Simple item page
dc.contributor.authorLoukopoulos, Panagiotis
dc.contributor.authorSampath, Suresh
dc.contributor.authorPilidis, Pericles
dc.contributor.authorZolkiewski, G.
dc.contributor.authorBennett, I.
dc.contributor.authorDuan, F.
dc.contributor.authorMba, David
dc.date.accessioned2017-03-16T10:13:58Z
dc.date.available2017-03-16T10:13:58Z
dc.date.issued2017-01-19
dc.description.abstractCentrifugal compressors are considered one of the most critical components in oil industry, making the minimization of their downtime and the maximization of their availability a major target. Maintenance is thought to be a key aspect towards achieving this goal, leading to various maintenance schemes being proposed over the years. Condition based maintenance and prognostics and health management (CBM/PHM), which is relying on the concepts of diagnostics and prognostics, has been gaining ground over the last years due to its ability of being able to plan the maintenance schedule in advance. The successful application of this policy is heavily dependent on the quality of data used and a major issue affecting it, is that of missing data. Missing data's presence may compromise the information contained within a set, thus having a significant effect on the conclusions that can be drawn from the data, as there might be bias or misleading results. Consequently, it is important to address this matter. A number of methodologies to recover the data, called imputation techniques, have been proposed. This paper reviews the most widely used techniques and presents a case study with the use of actual industrial centrifugal compressor data, in order to identify the most suitable ones.en_UK
dc.identifier.citationLoukopoulos P, Sampath S, Pilidis P, et al., Dealing with missing data for prognostic purposes, 2016 Prognostics and System Health Management Conference (PHM-Chengdu), 19/10/2016 - 21/10/2016. DOI: 10.1109/PHM.2016.7819934.en_UK
dc.identifier.issn2166-5656
dc.identifier.urihttp://dx.doi.org/10.1109/PHM.2016.7819934
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/11607
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rights©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.subjectcompressorsen_UK
dc.subjectpetroleum industryen_UK
dc.subjectpreventive maintenanceen_UK
dc.subjectcondition based maintenanceen_UK
dc.subjectdata recoveryen_UK
dc.subjectimputation techniquesen_UK
dc.subjectindustrial centrifugal compressor dataen_UK
dc.subjectmaintenance scheduleen_UK
dc.subjectoil industryen_UK
dc.subjectprognostics-health managementen_UK
dc.subjectCompressorsen_UK
dc.subjectInterpolationen_UK
dc.subjectMATLABen_UK
dc.subjectMaintenance engineeringen_UK
dc.subjectMathematical modelen_UK
dc.subjectPrincipal component analysisen_UK
dc.subjectTime series analysisen_UK
dc.subjectcentrifugal compressoren_UK
dc.subjectimputation techniquesen_UK
dc.subjectmissing dataen_UK
dc.subjectprognosticsen_UK
dc.titleDealing with missing data for prognostic purposesen_UK
dc.typeConference paperen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Dealing_with_missing_data_for_prognostics_purposes-2017.pdf
Size:
599.41 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.79 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)