A physics-based parametric regression approach for feedwater pump system diagnosis. (February 2022)

• Record Type: Journal Article
• Authors: Nguyen, Tat Nghia; Ponciroli, Roberto; Kibler, Timothy; Anderson, Marc; Strasser, Molly J.; Vilim, Richard B.
• Is Part Of: Annals of nuclear energy. Volume 166(2022)
• Publication Date: 2022-02
• Subjects: Physics-based -- Model-based diagnosis -- Feedwater system -- Diagnostics -- Performance degradation; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2021.108692
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: A diagnostic tool developed for operations and maintenance cost reduction application. Physics-based diagnostic models for reactor feed pumps and motors. Real-time plant data were used for model training and multiple test cases. All component faults and sensor faults were detected and identified correctly. Abstract: In this paper, we assessed the performance of a model-based approach for fault diagnosis of a nuclear power plant feedwater pump system. Physics-based models were constructed to monitor the performance of the system components. Plant data were used for the calibration of the models and subsequently in the diagnosis of abnormal events. We considered two real-time events representing scenarios of a pump fault and component performance degradation. The two events were correctly diagnosed, and the results demonstrated the high detection sensitivity of the physics-based models. Various sensor fault scenarios were simulated to show the capability of the approach to detect and uniquely identify sensor faults. Results for a scenario in which the plant operated in flexible power mode also showed that the diagnostic approach is insensitive to changes of operating conditions, which is one of the advantages of the model-based approach using physics-based models over purely data-driven approach.