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Deep-Learning and Dynamic Time Warping-Based Approaches for the Diagnosis of Reactor Systems.

Hoejun Jeong¹, Jihyun Kim¹, Doyun Jung²

¹Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Republic of Korea.

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|December 17, 2024

Summary

This study introduces a new method to pinpoint the size and location of clamping force degradation in nuclear power plant core support barrels. The approach combines deep learning and dynamic time warping for accurate internal structure monitoring.

Keywords:

deep learning dynamic time warping fault diagnosis fault monitoring reactor internals

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Area of Science:

Nuclear Engineering
Structural Health Monitoring
Artificial Intelligence in Engineering

Background:

Clamping force degradation in nuclear power plant core support barrels poses significant safety risks.
Existing methods for detecting degradation lack precision in identifying size and position.
Accurate monitoring of internal structures is crucial for nuclear power plant reliability.

Purpose of the Study:

To develop a novel methodology for diagnosing the size and position of clamping force degradation in core support barrels.
To improve the precision and efficiency of internal structure monitoring in nuclear power plants.
To address the limitations of previous studies in precisely locating degradation.

Main Methods:

Utilizing dynamic time warping (DTW) on frequency-domain ex-core neutron noise signals to learn data changes.
Employing autoencoder-based (AE-based) representation learning for robust feature extraction and to prevent overfitting.
Combining deep learning techniques with DTW for a comprehensive diagnostic approach.

Main Results:

The proposed methodology accurately predicts the size and position of clamping force degradation.
Experimental results validate the effectiveness of the combined deep learning and DTW approach.
Enhanced model robustness achieved through AE-based representation learning.

Conclusions:

The developed method offers a significant advancement in diagnosing clamping force degradation in core support barrels.
This research is expected to enhance the precision and efficiency of nuclear power plant internal structure monitoring.
The findings contribute to improving the overall safety and reliability of nuclear power facilities.