Data Reduction in Proportional Hazards Models Applied to Reliability Prediction of Centrifugal Pumps
This paper presents the use of proportional hazards regression models for predicting the Mean Time Between Failures (MTBF) of centrifugal pumps in the oil and gas industry. To that end, a dataset collected over 8 years including both design and operational variables from 675 pumps in an oil refinery...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2075-1702/13/3/215 |
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| author | Marc Vila Forteza Diego Galar Uday Kumar Kai Goebel |
| author_facet | Marc Vila Forteza Diego Galar Uday Kumar Kai Goebel |
| author_sort | Marc Vila Forteza |
| collection | DOAJ |
| description | This paper presents the use of proportional hazards regression models for predicting the Mean Time Between Failures (MTBF) of centrifugal pumps in the oil and gas industry. To that end, a dataset collected over 8 years including both design and operational variables from 675 pumps in an oil refinery was used to fit statistical models. Parametric and non-parametric transformations and restricted cubic splines were used to fit the covariates, thereby relaxing linearity assumptions and potentiating predictors with strong nonlinear effects on the outcome. Standard Principal Component Analysis (PCA) and sparse robust PCA methods were used for data reduction to simplify the fitted models and minimize overfitting. Models fitted with sparse robust PCA on non-parametrically transformed variables using an additive variance stabilizing (AVAS) method are suggested for further investigation. The complexity of the fitted models was reduced by 85% while at the same time providing for a more robust model as indicated by an improvement of the calibration slope from 0.830 to 0.936 with an essentially stable Akaike information criterion (AIC) (0.34% increase). |
| format | Article |
| id | doaj-art-e4d166ca44614b0e96798e2ee72c1818 |
| institution | OA Journals |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-e4d166ca44614b0e96798e2ee72c18182025-08-20T02:11:03ZengMDPI AGMachines2075-17022025-03-0113321510.3390/machines13030215Data Reduction in Proportional Hazards Models Applied to Reliability Prediction of Centrifugal PumpsMarc Vila Forteza0Diego Galar1Uday Kumar2Kai Goebel3Division of Operation and Maintenance Engineering, Luleå University of Technology, 97187 Luleå, SwedenDivision of Operation and Maintenance Engineering, Luleå University of Technology, 97187 Luleå, SwedenDivision of Operation and Maintenance Engineering, Luleå University of Technology, 97187 Luleå, SwedenDivision of Operation and Maintenance Engineering, Luleå University of Technology, 97187 Luleå, SwedenThis paper presents the use of proportional hazards regression models for predicting the Mean Time Between Failures (MTBF) of centrifugal pumps in the oil and gas industry. To that end, a dataset collected over 8 years including both design and operational variables from 675 pumps in an oil refinery was used to fit statistical models. Parametric and non-parametric transformations and restricted cubic splines were used to fit the covariates, thereby relaxing linearity assumptions and potentiating predictors with strong nonlinear effects on the outcome. Standard Principal Component Analysis (PCA) and sparse robust PCA methods were used for data reduction to simplify the fitted models and minimize overfitting. Models fitted with sparse robust PCA on non-parametrically transformed variables using an additive variance stabilizing (AVAS) method are suggested for further investigation. The complexity of the fitted models was reduced by 85% while at the same time providing for a more robust model as indicated by an improvement of the calibration slope from 0.830 to 0.936 with an essentially stable Akaike information criterion (AIC) (0.34% increase).https://www.mdpi.com/2075-1702/13/3/215centrifugal pumpsMTBFAPI standardreliability predictionproportional hazards modeldata reduction |
| spellingShingle | Marc Vila Forteza Diego Galar Uday Kumar Kai Goebel Data Reduction in Proportional Hazards Models Applied to Reliability Prediction of Centrifugal Pumps Machines centrifugal pumps MTBF API standard reliability prediction proportional hazards model data reduction |
| title | Data Reduction in Proportional Hazards Models Applied to Reliability Prediction of Centrifugal Pumps |
| title_full | Data Reduction in Proportional Hazards Models Applied to Reliability Prediction of Centrifugal Pumps |
| title_fullStr | Data Reduction in Proportional Hazards Models Applied to Reliability Prediction of Centrifugal Pumps |
| title_full_unstemmed | Data Reduction in Proportional Hazards Models Applied to Reliability Prediction of Centrifugal Pumps |
| title_short | Data Reduction in Proportional Hazards Models Applied to Reliability Prediction of Centrifugal Pumps |
| title_sort | data reduction in proportional hazards models applied to reliability prediction of centrifugal pumps |
| topic | centrifugal pumps MTBF API standard reliability prediction proportional hazards model data reduction |
| url | https://www.mdpi.com/2075-1702/13/3/215 |
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