A Multistage Physics-Informed Neural Network for Fault Detection in Regulating Valves of Nuclear Power Plants

Chenyang Lai, Ibrahim Ahmed (), Enrico Zio (), Wei Li, Yiwang Zhang, Wenqing Yao and Juan Chen
Additional contact information
Chenyang Lai: Energy Department, Politecnico di Milano, 20156 Milan, Italy
Ibrahim Ahmed: Energy Department, Politecnico di Milano, 20156 Milan, Italy
Enrico Zio: Energy Department, Politecnico di Milano, 20156 Milan, Italy
Wei Li: China Nuclear Power Engineering Co., Ltd., Beijing 100822, China
Yiwang Zhang: China Nuclear Power Engineering Co., Ltd., Beijing 100822, China
Wenqing Yao: China Nuclear Power Engineering Co., Ltd., Beijing 100822, China
Juan Chen: School of Mechanical Engineering & Automation, Beihang University (BUAA), Beijing 100191, China

Energies, 2024, vol. 17, issue 11, 1-23

Abstract: In Nuclear Power Plants (NPPs), online condition monitoring and the fault detection of structures, systems and components (SSCs) can aid in guaranteeing safe operation. The use of data-driven methods for these tasks is limited by the requirement of physically consistent outcomes, particularly in safety-critical systems. Considering the importance of regulating valves (e.g., safety relief valves and main steam isolation valves), this work proposes a multistage Physics-Informed Neural Network (PINN) for fault detection in such components. Two stages of the PINN are built by developing the process model of the regulating valve, which integrates the basic valve sizing equation into the loss function to jointly train the two stages of the PINN. In the 1st stage, a shallow Neural Network (NN) with only one hidden layer is developed to estimate the equivalent flow coefficient (a key performance indicator of regulating valves) using the displacement of the valve as input. In the 2nd stage, a Deep Neural Network (DNN) is developed to estimate the flow rate expected in normal conditions using inputs such as the estimated flow coefficient from the 1st stage, the differential pressure, and the fluid temperature. Then, the residual, i.e., the difference between the estimated and measured flow rates, is fed into a Deep Support Vector Data Description (DeepSVDD) to detect the occurrence of faults. Moreover, the deviation between the estimated flow coefficients of normal and faulty conditions is used to interpret the consistency of the detection result with physics. The proposed method is, first, applied to a simulation case implemented to emulate the operating characteristics of regulating the valves of NPPs and then validated on a real-world case study based on the DAMADICS benchmark. Compared to state-of-the-art fault detection methods, the obtained results from the proposed method show effective fault detection performance and reasonable flow coefficient estimation, thus guaranteeing the physical consistency of the detection results.

Keywords: physics-informed neural network; deep neural network; fault detection; regulating valves; nuclear power plant; DeepSVDD (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/11/2647/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/11/2647/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:11:p:2647-:d:1405151

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().