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Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types

Author

Listed:
  • Abhimanyu Kapuria

    (Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA)

  • Daniel G. Cole

    (Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA)

Abstract

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.

Suggested Citation

  • Abhimanyu Kapuria & Daniel G. Cole, 2023. "Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types," Energies, MDPI, vol. 16(9), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3707-:d:1133202
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    References listed on IDEAS

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    1. Paolo Casoli & Mirko Pastori & Fabio Scolari & Massimo Rundo, 2019. "A Vibration Signal-Based Method for Fault Identification and Classification in Hydraulic Axial Piston Pumps," Energies, MDPI, vol. 12(5), pages 1-18, March.
    2. Li, Sai & Fang, Huajing & Shi, Bing, 2021. "Remaining useful life estimation of Lithium-ion battery based on interacting multiple model particle filter and support vector regression," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
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