Abstract
Purpose
Centrifugal compressors are integral components in oil industry, thus effective maintenance is required. Condition-based maintenance and prognostics and health management (CBM/PHM) have been gaining popularity. CBM/PHM can also be performed remotely leading to e-maintenance. Its success depends on the quality of the data used for analysis and decision making. A major issue associated with it is the missing data. Their presence may compromise the information within a set, causing bias or misleading results. Addressing this matter is crucial. The purpose of this paper is to review and compare the most widely used imputation techniques in a case study using condition monitoring measurements from an operational industrial centrifugal compressor.
Design/methodology/approach
Brief overview and comparison of most widely used imputation techniques using a complete set with artificial missing values. They were tested regarding the effects of the amount, the location within the set and the variable containing the missing values.
Findings
Univariate and multivariate imputation techniques were compared, with the latter offering the smallest error levels. They seemed unaffected by the amount or location of the missing data although they were affected by the variable containing them.
Research limitations/implications
During the analysis, it was assumed that at any time only one variable contained missing data. Further research is still required to address this point.
Originality/value
This study can serve as a guide for selecting the appropriate imputation method for missing values in centrifugal compressor condition monitoring data.
Centrifugal compressors are integral components in oil industry, thus effective maintenance is required. Condition-based maintenance and prognostics and health management (CBM/PHM) have been gaining popularity. CBM/PHM can also be performed remotely leading to e-maintenance. Its success depends on the quality of the data used for analysis and decision making. A major issue associated with it is the missing data. Their presence may compromise the information within a set, causing bias or misleading results. Addressing this matter is crucial. The purpose of this paper is to review and compare the most widely used imputation techniques in a case study using condition monitoring measurements from an operational industrial centrifugal compressor.
Design/methodology/approach
Brief overview and comparison of most widely used imputation techniques using a complete set with artificial missing values. They were tested regarding the effects of the amount, the location within the set and the variable containing the missing values.
Findings
Univariate and multivariate imputation techniques were compared, with the latter offering the smallest error levels. They seemed unaffected by the amount or location of the missing data although they were affected by the variable containing them.
Research limitations/implications
During the analysis, it was assumed that at any time only one variable contained missing data. Further research is still required to address this point.
Originality/value
This study can serve as a guide for selecting the appropriate imputation method for missing values in centrifugal compressor condition monitoring data.
| Original language | English |
|---|---|
| Pages (from-to) | 260-278 |
| Number of pages | 19 |
| Journal | Journal of Quality in Maintenance Engineering |
| Volume | 23 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 14 Aug 2017 |