On-Line Monitoring to Detect Sensor and Process Degradation under Normal Operational Transients - 327
- Department of Nuclear Engineering, University of Tennessee, 1004, Estabrook Dr (United States)
Calibration assessment of nuclear plant sensors and transmitters is currently performed through periodic maintenance actions during plant refueling outages. On-line monitoring (OLM) can be used to reduce the time and cost of this maintenance process by using empirical models to evaluate sensor calibration. The implementation of OLM can also mitigate human error associated with unnecessary maintenance on sensors operating within calibration tolerances. OLM has been studied and validated for plants under steady-state baseload operation, but the extension to load-following operation is not well-documented in the literature. Auto-associative kernel regression (AAKR) provides an error-corrected prediction of process sensor measurements for OLM through a weighted average of historic data. The current study evaluates the performance of AAKR for prediction under normal operational transients (such as expected under load-following operation). Performance of the AAKR model with two anomaly detection routines, error uncertainty limit monitoring (EULM) and sequential probability ratio test (SPRT), was evaluated for normal operation, sensor faults, and process degradation. The data used to develop and evaluate the OLM system were collected from a forced flow loop with temperature and flow sensors. Three models of well-correlated groups of sensors are trained to predict normal system behavior under planned transients. The anomaly detection routines are tested for sensor faults and process faults. The results of this experimental study suggest AAKR and an appropriate anomaly detection routine can be applied for sensor calibration and process fault detection under transient operation. (authors)
- Research Organization:
- American Nuclear Society - ANS, 555 North Kensington Avenue, La Grange Park, IL 60526 (United States)
- OSTI ID:
- 23035424
- Resource Relation:
- Conference: NPIC and HIMIT 2017: 10. International Conference on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, San Francisco, CA (United States), 11-15 Jun 2017; Other Information: Country of input: France; 7 refs.; available from American Nuclear Society - ANS, 555 North Kensington Avenue, La Grange Park, IL 60526 (US)
- Country of Publication:
- United States
- Language:
- English
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