The reactor coolant pump is a key equipment in a nuclear power plant. If the leakage exceeds a certain threshold, it may cause reactor overheating and shutdown. The reactor coolant pump leakage fault usually has two problems: corrosion and scaling. Accurately and efficiently diagnosing the leakage fault mode as early as possible and predicting its remaining useful life (RUL) are important for taking timely maintenance measures. In this paper, an integrated method is proposed. First, the cross-sectional area of the first seal is extracted as a fault indicator. The motivation is that corrosion may enlarge the cross-sectional area, and scaling may reduce the cross-sectional area. Based on the fluid mechanics theory, an integrated model with several uncertain parameters is established among the cross-sectional area, temperature, and leakage at the inlet and outlet of the first seal. In the diagnosing process, a modified change-detection method is proposed to detect the starting point of degradation. Then, the unknown parameters in the previous relation are estimated, and the degrading data before the starting point of degradation are used to diagnose the leakage fault mode. Second, a time-series model of the autoregressive integrated moving average (ARIMA) is established to predict the remaining useful life based on the degrading data after the starting point of degradation. Finally, the leakage degrading data from six reactor coolant pumps of a nuclear power plant is used to perform the leakage fault mode diagnosis and life prediction with degradation point detection error rates not exceeding 4%, fault mode diagnosis correction rates 100% and practical RUL predicting results, which proves that the proposed integrated method is accurate and efficient. The proposed integrated method combines the advantages of both the physical model diagnosis and the data-driven model diagnosis and innovatively make use of the quantity of flow from the output side of the primary pump as the monitoring indicator and the cross-sectional area as the characteristic index together to diagnose the leakage fault mode happened to the seal and predict its RUL, which can meet the needs of actual operation and maintenance to ensure a healthy and stable operation of the pump and prevent unexpected shutdowns of nuclear power plants and serious accidents.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11213310 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0304652 | PLOS |
Sci Rep
March 2025
School of Energy and Power Engineering, Xihua University, Chengdu, 610039, China.
Effective identification of damage characteristics and failure modes for buried pipelines subjected to fault movements is crucial for early design and disaster assessment. In the preceding companion paper, the structural responses of large-diameter prestressed concrete cylinder pipeline (PCCP) subjected to fault displacement were initially investigated under the condition where faulting crosses pipe barrel vertically, and the deterioration process and failure modes were summarized. However, the structural responses of jointed pipelines are closely tied to faulting parameters.
View Article and Find Full Text PDFNat Commun
February 2025
School of Transportation Science and Engineering, Beihang University, 10191, Beijing, China.
For the intricate and infrequent safety issues of batteries, online safety fault diagnosis over stochastic working conditions is indispensable. In this work, we employ deep learning methods to develop an online fault diagnosis network for lithium-ion batteries operating under unpredictable conditions. The network integrates battery model constraints and employs a framework designed to manage the evolution of stochastic systems, thereby enabling fault real-time determination.
View Article and Find Full Text PDFSci Rep
February 2025
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China.
The hydraulic system is crucial for the safety of the aircraft, which is the key to ensuring the safety of both the aircraft and passengers. It is necessary to study and analyze the normal and fault mode of the system to provide a way for evaluating the fault degree of the hydraulic system. Therefore an improved ResNet based fault degree evaluation method was proposed to evaluate the fault degree of the aircraft hydraulic system.
View Article and Find Full Text PDFSci Rep
January 2025
School of Mechanical Engineering, University of Ulsan, Ulsan, 44610, Republic of Korea.
This paper proposes an adaptive output feedback full state constrain (FSC) controller based on the adaptive neural disturbance observer (ANDO) for a nonlinear electro-hydraulic system (NEHS) with unmodeled dynamics. The Barrier Lyapunov Functions (BLFs) are utilized to ensure that all states of the system are specified within the constraints, and the approximation ability of radial basis function neural networks (RBFNNs) is used to cope with the unknown nonlinear functions. An adaptive neural compensation disturbance observer is elaborated to estimate the compound disturbance and oil leakage fault, effectively addressing these negative effects.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan.
This paper proposes a hybrid algorithm combining the symmetrized dot pattern (SDP) method and a convolutional neural network (CNN) for fault detection in lithium battery modules. The study focuses on four fault types: overcharge, over-discharge, aging, and leakage caused by manual perforation. An 80.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!