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A physics-based parametric regression approach for feedwater pump system diagnosis

Journal Article · · Annals of Nuclear Energy
 [1];  [1];  [2];  [2];  [2];  [1]
  1. Argonne National Laboratory (ANL), Lemont, IL (United States)
  2. Xcel Energy, Minneapolis, MN (United States)
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Here 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.
Research Organization:
Argonne National Laboratory (ANL), Lemont, IL (United States)
Sponsoring Organization:
USDOE Office of Nuclear Energy; USDOE Office of Nuclear Energy (NE); USDOE Office of Science (SC)
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1962058
Alternate ID(s):
OSTI ID: 1868590
Journal Information:
Annals of Nuclear Energy, Journal Name: Annals of Nuclear Energy Vol. 166; ISSN 0306-4549
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

References (8)

The Unbearable Shallow Understanding of Deep Learning journal December 2019
A probabilistic model-based diagnostic framework for nuclear engineering systems journal December 2020
A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis journal August 2012
A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art journal September 2017
Moisture-carryover performance optimization using physics-constrained machine learning journal May 2021
Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump journal September 2017
Explainable and Trustworthy Diagnostics Achievable Through Process-Based Automated Reasoning
  • Vilim, R.; Nguyen, T.; Ponciroli, R.
  • 12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021) https://doi.org/10.13182/T124-34543
conference January 2021
Physics-Based Automated Reasoning for Health Monitoring: Sensor Set Selection
  • Vilim, R.; Nguyen, T.; Ponciroli, R.
  • 12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021) https://doi.org/10.13182/T124-34545
conference January 2021

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Related Subjects

73 NUCLEAR PHYSICS AND RADIATION PHYSICS
diagnostics
feedwater system
model-based diagnosis
performance degradation
physics-based