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

Abhimanyu Kapuria () and Daniel G. Cole
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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

Energies, 2023, vol. 16, issue 9, 1-16

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.

Keywords: machine learning; remaining useful life; condition monitoring; probabilistic estimation; Bayesian networks; survival analysis; vibration analysis; fault analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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