Nuclear Power Plant Maintenance Research Articles

A Novel Ultrasonic Leak Detection System in Nuclear Power Plants Using Rigid Guide Tubes with FCOG and SNR.

Leak detection in nuclear reactor coolant systems is crucial for maintaining the safety and operational integrity of nuclear power plants. Traditional leak detection methods, such as acoustic emission sensors and spectroscopy, face challenges in sensitivity, response time, and accurate leak localization, particularly in complex piping systems. In this study, we propose a novel leak detection approach that incorporates a rigid guide tube into the insulation layer surrounding reactor coolant pipes and combines this with an advanced detection criterion based on Frequency Center of Gravity shifts and Signal-to-Noise Ratio analysis. This dual-method strategy significantly improves the sensitivity and accuracy of leak detection by providing a stable transmission path for ultrasonic signals and enabling robust signal analysis. The rigid guide tube-based system, along with the integrated criteria, addresses several limitations of existing technologies, including the detection of minor leaks and the complexity of installation and maintenance. By enhancing the early detection of leaks and enabling precise localization, this approach contributes to increased reactor safety, reduced downtime, and lower operational costs. Experimental evaluations demonstrate the system's effectiveness, focusing on its potential as a valuable addition to the current array of nuclear power plant maintenance technologies. Future research will focus on optimizing key parameters, such as the threshold frequency shift (Δf) and the number of randomly selected frequencies (N), using machine learning techniques to further enhance the system's accuracy and reliability in various reactor environments.

Explainable, Deep Reinforcement Learning–Based Decision Making for Operations and Maintenance

This paper presents research that integrates condition monitoring and prognostics with decision making for nuclear power plant operations and maintenance aimed at reducing lifetime maintenance and repair costs. Additionally, a focal point of this research is to make the decisions explainable to operators, improving the trustworthiness of the decisions from what can be considered a black box model. In this work, we develop and evaluate an explainable, online asset management methodology to help reduce lifetime maintenance and repair costs. Using the latest advancements in condition monitoring, inventory management, deep reinforcement learning, and explainable artificial intelligence methods, we create a predictive maintenance methodology that can optimize the maintenance and spare part management of a repairable nuclear power plant system. To demonstrate these methods, preliminary studies were conducted on a representative maintenance system undergoing a stochastic degradation process that requires repairs or replacement to continue operation. Using deep reinforcement learning, we were able to reduce maintenance spending by approximately 50% compared to optimized, time-based maintenance strategies for the chosen system. A key component of our methodology is the integration of Shapley values to quantify the contribution of various factors to the decision-making process. This addition enhances the explainability and trustworthiness of our decisions, providing operators with transparent and understandable insights into the rationale behind maintenance strategies. The robustness and resiliency of our decision policy against observation noise were also thoroughly evaluated, demonstrating its effectiveness in uncertain operational environments.

An estimation method for bias error of measurements by utilizing process data, an incidence matrix and a reference instrument for data validation and reconciliation: Part 2 Extension to the energy balance relation

Abstract Ensuring data reliability is becoming increasingly important for further applications of AI, IoT, and digital twins. One promising technology for ensuring data reliability is data validation and reconciliation (DVR), which minimizes the uncertainty of measurements based on statistics. DVR has been widely used for the operation and maintenance of nuclear power plants in Europe and the United States in recent years. The most important input for DVR analysis is measurement uncertainty. The catalog value provided by sensor manufacturers includes the measurement uncertainty, but in reality, the actual measurement uncertainty is often smaller. Previous studies have proposed several methods for evaluating the actual measurement uncertainty based on process data, which have been confirmed to be effective for evaluating random errors. It is important to note that bias errors also contribute significantly to the measurement uncertainty. In our previous paper, we proposed a method for estimating bias error using process data, an incidence matrix, and a reference instrument. The proposed method was limited to a mass balance relation, i.e., flow rate measurements. In this paper, we extend the method to include an energy balance relation by considering energy conservation in addition to mass conservation. This extension enables the evaluation of measurement uncertainty for flow rate and temperature. The proposed method was validated with two benchmark problems. It was found to be applicable to various flow conditions, including physically fluctuating flow, such as that observed in the feedwater flow in nuclear power plants.

The Evolving Technological Framework and Emerging Trends in Electrical Intelligence within Nuclear Power Facilities

This paper thoroughly explores the feasibility of integrating a variety of intelligent electrical equipment and smart maintenance technologies within nuclear power plants to enhance the currently limited level of intelligence of these systems and better support operational and maintenance tasks. Initially, this paper outlines the demands and challenges of intelligent electrical systems in nuclear power plants, highlighting the current state of development of intelligent electrical systems, including new applications of artificial intelligence and big data technologies in power grid companies, such as intelligent defect recognition through image recognition, intelligence-assisted inspections, and intelligent production commands. This paper then provides a detailed introduction to the architecture of intelligent electrical equipment, encompassing the smart electrical equipment layer, the smart control system layer, and the cloud platform layer. It discusses the intelligentization of medium- and low-voltage electrical equipment, such as smart circuit breakers, smart switchgear, and low-voltage distribution systems, emphasizing the importance of intelligentization in improving the safety, reliability, and maintenance efficiency of medium- and low-voltage distribution equipment in nuclear power plants. Furthermore, this paper addresses issues in the intelligentization of nuclear power plant electrical systems, such as information silos, the inefficiency of traditional manual inspection processes, and the lack of comprehensive intelligent design and evaluation standards, proposing corresponding solutions. Additionally, this paper presents the trends in intelligent operation and maintenance technology and applications, including primary and secondary fusion technology, intelligent patrol system architecture, intelligent inspection based on non-destructive testing, and a comprehensive solution based on inspection robots. The application of these technologies aids in achieving automated inspection, real-time monitoring, and the intelligent diagnosis of electrical equipment in nuclear power plants. Finally, this paper proposes basic principles for the development of intelligent electrical systems in nuclear power plants, including intelligent architecture, the evolutionary path, and phased goals and key technologies. It emphasizes the gradual transition from automation to digitization and then to intelligentization and presents a specific implementation plan for the intelligentization of the electrical systems in nuclear power plants. This paper concludes with a summary of short-term and long-term goals for improving the performance of nuclear power plant electrical systems through intelligent technologies and prospects for the application of intelligent technologies in the operation and maintenance of nuclear power plants in the future.

Research and Analysis on the Optimization of the Preventive Disassembly Inspection Intervals of Important Wedge Gate Valves in the Primary Cooling System of CPR1000 Nuclear Power Plant

At present, the important wedge gate valve of the primary coolant system of the CPR1000 nuclear power plant performs preventive disassembly inspection according to the intervals of 5 years, the average dose of a single disassembly is about 10mSv, the average time of each overhaul occupies the critical path is 1 hour, and the valve has been disassembly inspection in good condition. Based on the historical maintenance data, operating environment, failure mode, failure mechanism, operation and maintenance experience feedback of the same type of valve, and failure risk control of the important wedge gate valve of the primary coolant system, this paper demonstrates and analyzes the feasibility of extending the disassembly inspection intervals of the important wedge gate valve of the primary coolant system from 5 years to 10 years. This work plays an important role in improving the economic efficiency of nuclear power plant maintenance and reducing the radiation dose of maintenance.

Amplicon-based sequencing revealed potential microbiologically influenced corrosion in the interim storage for spent fuel of RSG-GAS, Serpong, Indonesia

Abstract. Sugoro I, Fadila DSR, Pikoli MR, Hermanto FE, Ramadhan F, Shalsabilla SE, Cici A, Rijal MS, Rahayu DS, Puspito MJ, Haribowo DR, Tetriana D. 2023. Amplicon-based sequencing revealed potential microbiologically influenced corrosion in the interim storage for spent fuel of RSG-GAS, Serpong, Indonesia. Biodiversitas 24: 5391-5398. Interim storage of spent fuel (ISSF) corrosion can negatively impact nuclear power plant maintenance, specifically concerning safety, financial costs, and regulatory compliance. Besides chemical reactions, microbiologically influenced corrosion (MIC) also primarily occurred. Sulfate-reducing bacteria (SRB) plays a significant role in the development of MIC. The quantitative of MIC research has never been conducted in the ISSF - Multipurpose nuclear reactor of G.A. Siwabessy (RSG-GAS). So, this study aims to monitor the potential MIC development in the ISSF of RSG-GAS pool and canal through bacterial diversity analysis using total plate count (TPC) and metagenomic approach. TPC results showed that SRB was identified in the canal of ISSF but not in the pool site. However, metagenomics analysis revealed higher diversity measures in the pool than in the canal sites. Staphylococcus sp. was the most abundant species in both sites, with a relative abundance greater than 85%. The number of SRB and MIC-related bacteria was higher in the canal than in the pool, both from TPC and metagenomic analysis. Several SRB taxa were identified, such as Desulfobulbus mediterraneus, Desulfobulbus sp., Desulfofustis sp., Desulfovibrio sp., and Truepera sp. The potential MIC development also strengthens by diversity-based metabolic pathway prediction, which mainly displayed sulfur oxidation and sulfate reduction pathways in the canal system.

Selection of a nickel-specific DNA aptamer and development of a protocol for radioactive nickel removal

Development of technology for removal of radioactive particles including nickel from liquid nuclear waste is necessary for maintenance of nuclear power plants. In this study, we focused on development of a method for trapping radioactive nickel ions using an aptamer capable of capturing small molecules such as metal ions. Nine rounds of SELEX were performed resulting in selection of two aptamers with high affinity for nickel. Liquid containing 3 nmol stable nickel was passed through a column containing aptamer/bead, and a reduction in the concentration of nickel of 95% or more in the liquid passed through column was confirmed by measurement using ICP-OES. Based on the interaction between the FA-N1 aptamer-bead composite and the nickel-containing surrogates, the specificity for nickel was at least three times higher than that for other metal ions such as cobalt and chrome. Examination of the interaction of the aptamer with stable nickel, a non-radioactive isotope, and nickel-63, a radioactive isotope, was performed in order to determine whether it has similar specificity for radioactive and non-radioactive metal ions. In addition, because recovery of more than 99.5% of nickel bound to the aptamer/bead complex was achieved, the aptamer could be reused. Based on these results, we predict that the aptamer, which is capable of removing radioactive nickel through capture of radioactive isotopes of nickel, can be utilized effectively in the effort to reduce liquid nuclear waste.

A fault diagnosis method for nuclear power plant rotating machinery based on adaptive deep feature extraction and multiple support vector machines

Rotating machinery is the essential component in nuclear power plants (NPPs). Effective fault detection and diagnosis is a main challenge in the operation and maintenance of NPPs rotating machinery. The performance of traditional fault diagnosis methods mainly depends on complex manual feature extraction and sufficient expert prior knowledge. This study proposes a fault diagnosis method based on adaptive feature extraction and multiple support vector machines (ResNet-SVMs) to overcome the limitations of the traditional intelligent fault diagnostics. First, the vibration information from different locations of the rotating machinery is collected and used as input data for the algorithm model. Then, the deep residual neural network adaptively extracts fault features from input data to obtain feature data of different depths. Finally, multiple support vector machines identify the feature data to realize the fault diagnosis. The effectiveness verification is carried out based on the experimental cases of induction motor and rolling bearing fault diagnosis. Compared with other advanced intelligent fault diagnosis methods, ResNet-SVMs model provides better diagnostic performance, which demonstrates its potential value for NPPs rotating machinery fault diagnosis.

ADQ—Anomaly Detection and Quantification From Delayed Neutron Monitoring Data of Nuclear Power Plants

Identifying anomalies and quantifying their severity from delayed neutron monitoring data are of great significance to ascertain the reason of anomalous behavior and for predictive maintenance of nuclear power plants (NPPs). An existing anomaly detection technique is primarily aimed to identify anomalies and does not consider quantification of anomalies. In this work, an anomaly detection and quantification (ADQ) technique has been proposed. It detects anomalies based on the fact that abnormal behavior occurs rarely in the real world as opposed to normal behavior. Thereby, the dissimilarity of an anomalous data instance happens to be large from a normal data instance in comparison with dissimilarity between normal data instances and anomalies in itself. This has been exploited by the proposed technique by employing the concept of candidate anomaly pair. It computes a quantified value for severity of given anomaly as intensity in reference to underlying normal behavior, representative of which is extracted from data itself. The proposed technique has been applied on delayed neutron monitoring data of 28 sensors for an NPP, which was provided by Nuclear Power Corporation of India Ltd. (NPCIL), Mumbai, India. The detected anomalies are categorized as point and interval anomalies, and sensors are ranked based on them. The performance of the proposed technique has been found highly promising on validating results with domain experts. The proposed technique has outperformed other three state-of-the-art techniques in terms of anomaly detection rate (ADR), false alarm rate (FAR), and precision.

X-ray Computed Tomography for Nuclear Power Plant Maintenance

As part of the scheduled maintenance of nuclear power plants, specialist tools are deployed into the reactor core, for example to inspect the moderator. It is imperative that these tools operate correctly, and that no element of the tool remains in the reactor when the reactor resumes operation. The current processes for ensuring this are hugely labour intensive, and hence costly, involving a full teardown before and after deployment. This paper describes the development of a novel X-ray Computed Tomography (CT) system and workflow for ensuring the integrity of specialist reactor tools without the need for disassembly. The system hardware must be able to deal with the challenge of tools that are up to 6 metres in length and contain a significant amount of dense componentry. On the other hand, the system software must be able to confirm the correct and comprehensive assembly of the tool based on the obtained CT scan, and despite numerous potential, but benign, differences in the tool appearance. The presented approach overcomes both challenges: the hardware uses a gantry design with a high-powered X-ray source (see Fig. 1), the software employs a machine learning implementation.

Study of radiation protection vestments for maintenance of mobile nuclear power plants

Most radiation exposure to personnel comes from inspection, maintenance, and repair within the reactor compartment. The objective of this paper was to discover the garment that, at the same time, presents the best result for the attenuation of ionizing radiation, as well as good ergonomics for the maintenance professional of mobile nuclear plants. For this, market research was carried out and, as a result, nine Radiation Protection Vestments (VPRs) were found, from five different manufacturers and from three countries, the United States, Japan and China; and which are feasible to be acquired. To choose the VPR, the optimization techniques of CIPR 55 were used: Multi-Attribute Utility Analysis and Multi-Criteria Outranking Analysis. Based on the information provided by the manufacturers, five attributes were chosen for comparison: protection cost, percentage of ionizing radiation attenuation, weight, discomfort, and surface decontamination of the vestment. To verify the robustness of the analytical solution, the values of the scaling constants were re-calculated, where it was observed that the analytical solution found is strongly influenced when the protection cost is changed, as it is the highest cost VPR among all those surveyed. The VPR chosen by both optimization techniques was the STEMRAD 360, which has the highest attenuation of ionizing radiation, as well as being the VPR with greater emphasis on ergonomics.

Application of EDG AOT Extension Based on the Risk-Informed Method in NPPs

At present, the allowed outage time (AOT) of an M310 unit emergency diesel generator (EDG) was 3 days, which can be extended to 14 days through replacement of additional diesel units; although it provides a certain online maintenance time, it cannot meet the needs of ten-years overhauls. In order to avoid stopping the reactor for maintenance of NPP due to insufficient of EDG AOT, based on risk-informed method analysis feasibility of extending AOT for EDG to 28 days, we quantitatively calculate the impact of extension of AOT on risk level of nuclear power plants (NPPs). Analysis shows that extension of EDG AOT to 28 days has less impact on NPPs, and safety of NPPs can be further ensured through temporary risk control measures, so the extension of AOT to 28 days is acceptable. By using risk-informed technology to extend AOT for EDG, unnecessary shutdown and maintenance is avoided and the economy of NPPs and flexibility of maintenance work arrangement is greatly improved while ensuring safety, which is of great significance to operation and maintenance of NPPs.

A point cloud semantic segmentation method for nuclear power reactors based on RandLA-Net Model

The detection of foreign objects in nuclear power plant reactor is a key task in the operation and maintenance of nuclear power plants. Loose and falling foreign objects such as bolts can lead to fuel component damage and unplanned shutdown, posing serious hazards. Therefore, we propose a point cloud semantic segmentation method for foreign objects in nuclear power plant reactor based on the RandLA-Net model. Considering the correlation between point cloud collection error and curvature, the data augmentation method is improved to reduce the risk of model overfitting. By treating the boundary points of different classes as the hard examples, the hard example mining is designed to improve model generalization performance. Adding an improved test time augmentation method during model inference, the more reliable segmentation results are performed by multiple prediction on points. The experimental results indicate that the proposed method can achieve high-accuracy reactor point cloud semantic segmentation with mIoU of 0.992 and mAcc of 0.997.

Sensitivity study of thermal hydraulics to corrosion of heat exchange tubes in steam generator

A leakage accident occurs in the circulating water system in PWRs, impurity ions enter the SG with the secondary circuit feed water and cause electrochemical corrosion of the heat transfer tubes (HTTs). Based on the hide out and return mathematical model in the SG, a coupled mathematical model of mass transfer-electromigration-hydrodynamics of impurity ions in the secondary boiling channel of SG is deduced. The accuracy of the mathematical model is verified by experiments, and the effects of heat flux density (Peq), flow velocity (Peu), impurity ion concentration (Pec) and relative solubility of impurity ions (Kp) on impurity ions migration and electrochemical corrosion are analyzed and discussed. The results show that Peq mainly controls the diffusion of impurity ions and thus affects the migration rate of impurity ions. The mass transfer of impurity ions driven by Peq can enhance the reduction of H+, thereby accelerating the corrosion rate of HTTs. Peu mainly affects the synergistic effect of convective mass transfer and electromigration during the migration of impurity ions, and charge transfer mainly controls the electrochemical corrosion of HTTs surface with the increase of Peu. The Pec on the surface of HTTs changes linearly with time, and the corrosion rate increases with the increase of Pec. The corrosion rate of HTTs is proportional to Kp, and Kp is a main factor that causes different corrosion rates in different location of HTTs. In this paper, a coupled model for the sensitivity of thermal-hydraulic parameters (Peq, Peu, Pec and Kp) in SG to electrochemical corrosion of HTTs is proposed for the first time. The research results can provide important support for the development of corresponding response strategies, schemes for the life prediction of HTTs and the safety maintenance of nuclear power plants.

Methodology for Online Sensor Recalibration

A significant contributor to nuclear power plant operations and maintenance (O&M) costs is the periodic calibration check of sensors. Periodic calibration checks provide the necessary confidence that the measurements from these sensors are correct, with the data used to monitor and verify proper operation of the reactor. The periodicity of calibrations in the nuclear industry can range from once in several weeks for some instrument channels to once every refueling outage (~18 months) for certain safety-significant pressure and level transmitters. With expected longer refueling intervals in many advanced reactor/small modular reactor concepts, fewer opportunities are expected to be available for manual calibration checks and recalibration for many instrument channels.
 Presently, periodic sensor calibration checks are performed manually, with manual recalibration performed if the sensor is found to be out of calibration. Although studies have shown that most (over 90%) sensors are found to stay within calibration specifications over a calibration cycle (~18 months), labor must still be spent to verify that these sensors are within calibration. Given the large number of sensors (anywhere between 100–2400 sensors) in a typical nuclear power plant, the ability to identify sensors that are failing/failed or drifting out of calibration and limit recalibration to those specific sensors has the potential to save between $0.5–1M per year per plant. The cost of calibration checks and recalibration are expected to be of greater significance to advanced reactors and small modular reactors, given the industry focus on reducing O&M costs for these reactor concepts as part of improving the economic viability of advanced nuclear power.
 Methods that can compensate for calibration drift by adjusting the calibration automatically and in real-time may be able to further reduce costs associated with recalibration. Such autocalibrations or online automated recalibrations reduce unavailability of instrumentation and improve online maintenance planning flexibility from both resource allocation and online risk evaluation perspectives.
 This paper describes an initial set of algorithms developed for the purpose of detecting drift and correcting for it through an online recalibration method. Initial results on laboratory-scale experimental data indicate the potential of these algorithms to detect calibration drift and update calibrations with prediction and drift correction accuracy exceeding 95%.

Human-centered Evaluation of Shared Teleoperation System for Maintenance and Repair Tasks in Nuclear Power Plants

Human-centered Evaluation of Shared Teleoperation System for Maintenance and Repair Tasks in Nuclear Power Plants

Graded Approach Establishment for the HTGR Maintenance Activities Using Modified Fuzzy FMEA & Expert Judgement Methodology

Grading is an important step of the Nuclear Power Plant (NPP) operation & maintenance activities. However, there are several grading difficulties for the High Temperature Gas Reactor (HTGR) as well as the other type of NPPs causing by the lack of operational experiences and availability of the reliability data. Failure Mode & Effects Analysis (FMEA) is one of the mature techniques that are commonly used to solve such kinds of difficulties. Nevertheless, traditional FMEA has several issues and possibly become an obstacle in the grading process. The modified FMEA by utilizing expert judgment elicitation techniques combined with the fuzzy logic theory is proposed to solve those issues. As a study practice, the proposed methodology is applied by examining Japanese’s HTGR, Gas Turbine High Temperature Reactor 300 for Cogeneration (GTHTR300C) design carefully. This study establishing good practice especially for the future advanced NPP maintenance activities development.

Systematic Design of a 3-DOF Dual-Segment Continuum Robot for In Situ Maintenance in Nuclear Power Plants

In situ maintenance works for nuclear power plants are highly beneficial as they can significantly reduce the current maintenance cycle and cost. However, removing absorber balls in a constrained environment through an inspection port is fairly challenging. In this article, a 3-DOF dual-segment continuum robot system is proposed which is equipped with an end-effector to remove absorber balls by pneumatic conveying. Then, according to the symmetrical layout of actuation ropes, the kinematics of the single-segment continuum robot are extended, and the kinematics equation which is universal to the continuum robot with the dual segment is summarized. In addition, some special kinematics solutions can be obtained according to opposite-bending and feeding characteristics. Finally, the functions of the device are verified by tests. The results show that the continuum robot can smoothly pass through the divider plug and reach any position at the bottom of a ball-storage tank where absorber balls are located with only two segments. In a gas environment, the efficiency of absorber ball removal can reach 58.96 kg/h with a lift of 7.5 m and 48.54 kg/h with a lift of 10 m. This result undoubtedly paves the way for the in-service maintenance of nuclear power plants.

Ideological Extremism and Political Participation in Japan

Abstract Do the policy preferences expressed through political participation represent the citizens as a whole? Previous studies argue that there is no ideological bias in voting participation in Japan. However, previous studies have only analyzed Japan up to 2010, and it is unclear whether ideological bias was consistently absent in voting participation in the 2010s. In the 2010s, ideological issues, such as the maintenance of nuclear power plants or the acceptance of collective self-defense, emerged in Japan, and the two major political parties, the Liberal Democratic Party and Democratic Party of Japan, became increasingly polarized. Considering these changes, the influence of ideology on political participation may be growing. Therefore, this paper examines the relationship between people’s ideological positions and political participation using voter surveys conducted between 2001 and 2017. I find that since 2012, the more ideologically extreme Japanese voters are, the more they participate in voting. Furthermore, the same is true for other forms of participation. In general, the voices of ideologically moderate Japanese are becoming less represented by political parties and politicians.

Maintenance scheduling of nuclear components under reliability constraints using adaptive parallel particle swarm optimization

Preventive maintenance is a critical element of maintenance policies in a wide range of industries, including the power sector. To achieve reasonable and effective maintenance of nuclear power plants (NPPs), proper aging management is critical and should be optimized from both safety and economic perspectives. Thus, in this paper, we propose a maintenance-scheduling model based on an adaptive parallel particle swarm optimization (PSO) to minimize the total number of maintenance activities over the lifetime of an NPP while ensuring the reliability of safety-critical functions. The proposed model recognizes that effective maintenance activities differ depending on the cause of the latent failure. In addition, the applied PSO algorithm, which is based on the dynamic exchange of hyperparameters between adjacent swarms, allows us to optimize inertia factor and learning factors adaptively during the solution search process. The proposed model is verified by applying it to a representative case in which the best maintenance schedules for the components constituting a water injection function are produced.