One of the benefits of the supercritical CO ${}_2$ Brayton cycle in Sodium-cooled Fast Reactors is an enhanced plant safety, since potential reactions of CO ${}_2$ with liquid sodium have been reported to be less stringent than a sodium-water reaction found in the Rankine cycle. However, moderate chemical interactions between CO ${}_2$ and liquid sodium make detecting CO ${}_2$ ingress accidents harder. Thus, this paper proposes a new physics-based detection algorithm by comparing the real-time pressure measurements of two identical heat exchangers for the early detection. The CO ${}_2$ ingress occurs owing to a crack at the pressure boundary wall, a certain self-recovery of structural damage does not happen over time, and an accident probabilistically starts at only one component of two. The proposed physics-based method with the probabilistic analysis was compared to the pure data-based method. Finally, the damage degradation was developed with a simplified mass and energy transfer model, and the proposed algorithm was verified with experimental data. The results show that a 99.99% detection probability can be achieved for the air ingress of 30 cc/s, which is equivalent to the 0.12 g/s CO ${}_2$ ingress, in a 70 s detection time, limiting down to 0.1% false alarms due to sensor noise.

The analysis of acoustic emission (AE) signals produced during object leakage is promising for condition monitoring of the components. In this study, an advanced condition monitoring technique based on acoustic emission detection and artificial neural networks was applied to a check valve, one of the components being used extensively in a safety system of a nuclear power plant. AE testing for a check valve under controlled flow loop conditions was performed to detect and evaluate disk movement for valve degradation such as wear and leakage due to foreign object interference in a check valve. It is clearly demonstrated that the evaluation of different types of failure modes such as disk wear and check valve leakage were successful by systematically analyzing the characteristics of various AE parameters. It is also shown that the leak size can be determined with an artificial neural network.

This article presents a model‐based fault diagnosis method to detect and isolate faults in the robot arm control system. The proposed algorithm is composed functionally of three main parts: parameter estimation, fault detection, and isolation. When a change in the system occurs, the errors between the system output and the estimated output cross a predetermined threshold, and once a fault in the system is detected, the estimated parameters are transferred to the fault classifier by the adaptive resonance theory 2 neural network (ART2 NN) with uneven vigilance parameters for fault isolation. The simulation results show the effectiveness of the proposed ART2 NN–based fault diagnosis method.

A time-frequency analysis (TFA) is applied to the analysis of the vibration signals from a pipe where some chemical corrosion is likely to occur by an acidic material being mixed in the coolant of nuclear power plants. The spalling out of internal material pieces by the so-called flow-accelerated corrosion (FAC) is expected to change the structural vibration of a local point in the pipe, but this effect is too tiny to be recognized in the result of the Fourier transform. From the analysis by TFA, it is identified that the TFA can provide important information such as the amplitude fluctuations in the instantaneous frequency of each characteristic frequency. The analysis results show that the peak or ridge pattern of the TFA varied according to the status of the chemical corrosion within the pipe

In this paper, we describe our previous studies for the development of an analysis algorithm and the application of a fault monitoring technique. Various signal processing methods have been implemented in the so-called monitoring tools to monitor and analyze abnormal conditions of components in nuclear power plants (NPPs). One of the analysis methods were devised by us for the efficient analysis of transient signals from NPP process components. This method, the adaptive cone-kernel distribution, is presented in this paper along with the description of the monitoring tool. Then, some application results using the monitoring tool are presented. As another application, the fault monitoring technique applied to the agitator driving system of a thermal chemical reduction reactor is also presented though this technique is not integrated in the monitoring tool yet.

Recently, the concerns of Nuclear Power Plants (NPPs) safety and reliability have increasing because of Fukushima disaster and NPPs increasing operating years. Operational experience has shown that greater situational awareness of the state of safety-critical nuclear plant System, Structure, and Components (SSCs) is necessary, particularly as they age due to exposure to harsh service conditions. While replacement of a subset of components is possible, and may even be economically attractive, it may be economically prohibitive to replace several of the lager components, including the reactor pressure vessel and primary piping. Thus, characterization, management, and mitigation of aging-related degradation in these critical passive components become important to maintain safety margins. In order to deal with these problem above mentioned, thus, it is necessary to develop Prognostics and health Management (PHM) technology. The key technology in PHM is to detect degradation and anomalies and to determine Remaining Useful Life (RUL) and Probability of Failure (POF) of SSCs. The prognostics results can be used to manage the evolving health and condition of nuclear plant SSCs. The prognostics information is used in a Probabilistic Safety Assessment (PSA) model to assess the risk significance of the degradation and the corresponding reduced safety margin. In this paper, the review of the state of the art in PHM for nuclear industry was summarized. In addition, the prognostics application in case of Nuclear filed and technical gap for NPPs weredescribed.