Dealing with missing data for prognostic purposes

Show simple item record

dc.contributor.author Loukopoulos, Panagiotis
dc.contributor.author Sampath, Suresh
dc.contributor.author Pilidis, Pericles
dc.contributor.author Zolkiewski, G.
dc.contributor.author Bennett, I.
dc.contributor.author Duan, F.
dc.contributor.author Mba, David
dc.date.accessioned 2017-03-16T10:13:58Z
dc.date.available 2017-03-16T10:13:58Z
dc.date.issued 2017-01-19
dc.identifier.citation Loukopoulos 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.issn 2166-5656
dc.identifier.uri http://dx.doi.org/10.1109/PHM.2016.7819934
dc.identifier.uri https://dspace.lib.cranfield.ac.uk/handle/1826/11607
dc.description.abstract Centrifugal 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.language.iso en en_UK
dc.publisher IEEE en_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.subject compressors en_UK
dc.subject petroleum industry en_UK
dc.subject preventive maintenance en_UK
dc.subject condition based maintenance en_UK
dc.subject data recovery en_UK
dc.subject imputation techniques en_UK
dc.subject industrial centrifugal compressor data en_UK
dc.subject maintenance schedule en_UK
dc.subject oil industry en_UK
dc.subject prognostics-health management en_UK
dc.subject Compressors en_UK
dc.subject Interpolation en_UK
dc.subject MATLAB en_UK
dc.subject Maintenance engineering en_UK
dc.subject Mathematical model en_UK
dc.subject Principal component analysis en_UK
dc.subject Time series analysis en_UK
dc.subject centrifugal compressor en_UK
dc.subject imputation techniques en_UK
dc.subject missing data en_UK
dc.subject prognostics en_UK
dc.title Dealing with missing data for prognostic purposes en_UK
dc.type Conference paper en_UK


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search CERES


Browse

My Account

Statistics