Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types
To improve the viability of nuclear power plants, there is a need to reduce their operational costs. Operational costs account for a significant portion of a plant’s yearly budget, due to their scheduled-based maintenance approach. In order to reduce these costs, proactive methods are required that...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/9/3707 |
| _version_ | 1827743008934592512 |
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| author | Abhimanyu Kapuria Daniel G. Cole |
| author_facet | Abhimanyu Kapuria Daniel G. Cole |
| author_sort | Abhimanyu Kapuria |
| collection | DOAJ |
| description | To improve the viability of nuclear power plants, there is a need to reduce their operational costs. Operational costs account for a significant portion of a plant’s yearly budget, due to their scheduled-based maintenance approach. In order to reduce these costs, proactive methods are required that estimate and forecast the state of a machine in real time to optimize maintenance schedules. In this research, we use Bayesian networks to develop a framework that can forecast the remaining useful life of a centrifugal pump. To do so, we integrate survival analysis with Bayesian statistics to forecast the health of the pump conditional to its current state. We complete our research by successfully using the Bayesian network on a case study. This solution provides an informed probabilistic viewpoint of the pumping system for the purpose of predictive maintenance. |
| first_indexed | 2024-03-11T04:19:56Z |
| format | Article |
| id | doaj.art-2e78f9ad264f453b80cef9951b6fc6ba |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T04:19:56Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-2e78f9ad264f453b80cef9951b6fc6ba2023-11-17T22:50:39ZengMDPI AGEnergies1996-10732023-04-01169370710.3390/en16093707Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault TypesAbhimanyu Kapuria0Daniel G. Cole1Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USASwanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USATo improve the viability of nuclear power plants, there is a need to reduce their operational costs. Operational costs account for a significant portion of a plant’s yearly budget, due to their scheduled-based maintenance approach. In order to reduce these costs, proactive methods are required that estimate and forecast the state of a machine in real time to optimize maintenance schedules. In this research, we use Bayesian networks to develop a framework that can forecast the remaining useful life of a centrifugal pump. To do so, we integrate survival analysis with Bayesian statistics to forecast the health of the pump conditional to its current state. We complete our research by successfully using the Bayesian network on a case study. This solution provides an informed probabilistic viewpoint of the pumping system for the purpose of predictive maintenance.https://www.mdpi.com/1996-1073/16/9/3707machine learningremaining useful lifecondition monitoringprobabilistic estimationBayesian networkssurvival analysis |
| spellingShingle | Abhimanyu Kapuria Daniel G. Cole Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types Energies machine learning remaining useful life condition monitoring probabilistic estimation Bayesian networks survival analysis |
| title | Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types |
| title_full | Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types |
| title_fullStr | Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types |
| title_full_unstemmed | Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types |
| title_short | Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types |
| title_sort | integrating survival analysis with bayesian statistics to forecast the remaining useful life of a centrifugal pump conditional to multiple fault types |
| topic | machine learning remaining useful life condition monitoring probabilistic estimation Bayesian networks survival analysis |
| url | https://www.mdpi.com/1996-1073/16/9/3707 |
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