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