Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded

Methods and Systems for Fault Diagnosis in Nuclear Power Plants

This research mainly deals with fault diagnosis in nuclear power plants (NPP), based on a framework that integrates contributions from fault scope identification, optimal sensor placement, sensor validation, equipment condition monitoring, and diagnostic reasoning based on pattern analysis. The research has a particular focus on applications where data collected from the existing SCADA (supervisory, control, and data acquisition) system is not sufficient for the fault diagnosis system. Specifically, the following methods and systems are developed. A sensor placement model is developed to guide optimal placement of sensors in NPPs. The model includes 1) a method to extract a quantitative fault-sensor incidence matrix for a system; 2) a fault diagnosability criterion based on the degree of singularities of the incidence matrix; and 3) procedures to place additional sensors to meet the diagnosability criterion. Usefulness of the proposed method is demonstrated on a nuclear power plant process control test facility (NPCTF). Experimental results show that three pairs of undiagnosable faults can be effectively distinguished with three additional sensors selected by the proposed model. A wireless sensor network (WSN) is designed and a prototype is implemented on the NPCTF. WSN is an effective tool to collect data for fault diagnosis, especially for systems where additional measurements are needed. The WSN has distributed data processing and information fusion for fault diagnosis. Experimental results on the NPCTF show that the WSN system can be used to diagnose all six fault scenarios considered for the system. A fault diagnosis method based on semi-supervised pattern classification is developed which requires significantly fewer training data than is typically required in existing fault diagnosis models. It is a promising tool for applications in NPPs, where it is usually difficult to obtain training data under fault conditions for a conventional fault diagnosis model. The proposed method has successfully diagnosed nine types of faults physically simulated on the NPCTF. For equipment condition monitoring, a modified S-transform (MST) algorithm is developed by using shaping functions, particularly sigmoid functions, to modify the window width of the existing standard S-transform. The MST can achieve superior time-frequency resolution for applications that involves non-stationary multi-modal signals, where classical methods may fail. Effectiveness of the proposed algorithm is demonstrated using a vibration test system as well as applications to detect a collapsed pipe support in the NPCTF. The experimental results show that by observing changes in time-frequency characteristics of vibration signals, one can effectively detect faults occurred in components of an industrial system. To ensure that a fault diagnosis system does not suffer from erroneous data, a fault detection and isolation (FDI) method based on kernel principal component analysis (KPCA) is extended for sensor validations, where sensor faults are detected and isolated from the reconstruction errors of a KPCA model. The method is validated using measurement data from a physical NPP. The NPCTF is designed and constructed in this research for experimental validations of fault diagnosis methods and systems. Faults can be physically simulated on the NPCTF. In addition, the NPCTF is designed to support systems based on different instrumentation and control technologies such as WSN and distributed control systems. The NPCTF has been successfully utilized to validate the algorithms and WSN system developed in this research. In a real world application, it is seldom the case that one single fault diagnostic scheme can meet all the requirements of a fault diagnostic system in a nuclear power. In fact, the values and performance of the diagnosis system can potentially be enhanced if some of the methods developed in this thesis can be integrated into a suite of diagnostic tools. In such an integrated system, WSN nodes can be used to collect additional data deemed necessary by sensor placement models. These data can be integrated with those from existing SCADA systems for more comprehensive fault diagnosis. An online performance monitoring system monitors the conditions of the equipment and provides key information for the tasks of condition-based maintenance. When a fault is detected, the measured data are subsequently acquired and analyzed by pattern classification models to identify the nature of the fault. By analyzing the symptoms of the fault, root causes of the fault can eventually be identified

Nuclear plant diagnostics using neural networks with dynamic input selection

The work presented in this dissertation explores the design and development of a large scale nuclear power plant (NPP) fault diagnostic system based on artificial neural networks (ANNs). The viability of detecting a large number of transients in a NPP using ANNs is demonstrated. A new adviser design is subsequently presented where the diagnostic task is divided into component parts, and each part is solved by an individual ANN. This new design allows the expansion of the diagnostic capabilities of an existing adviser by modifying the existing ANNs and adding new ANNs to the adviser;This dissertation also presents an architecture optimization scheme called the dynamic input selection (DIS) scheme. DIS analyzes the training data for any problem and ranks the available input variables in order of their importance to the input-output relationship. Training is initiated with the most important input and one hidden node. As the network training progresses, input and hidden nodes are added as required until the networks have learned the problem. Any hidden or input nodes that were added during training but are unnecessary for subsequent recall are now removed from the network. The DIS scheme can be applied to any ANN learning paradigm;The DIS scheme is used to train the ANNs that form the NPP fault diagnostic adviser. DIS completely eliminates any guesswork related to architecture selection, thus decreasing the time taken to train each ANN. Each ANN uses only a small subset of the available input variables that is required to solve its particular task. This reduction in the dimensionality of the problem leads to a drastic reduction in training time;Data used in this work was collected during the simulation of transients on the operator training simulator at Duane Arnold Energy Center, a boiling water reactor nuclear power plant. An adviser was developed to detect and classify 30 distinct transients based on the simulation of 47 scenarios at different severities. This adviser was then expanded to detect and classify a total of 36 transients based on the simulation of 58 transient scenarios. The noise tolerant characteristics of the adviser are demonstrated

A Review of Prognostics and Health Management Applications in Nuclear Power Plants

The US operating fleet of light water reactors (LWRs) is currently undergoing life extensions from the original 40-year license to 60 years of operation. In the US, 74 reactors have been approved for the first round license extension, and 19 additional applications are currently under review. Safe and economic operation of these plants beyond 60 years is now being considered in anticipation of a second round of license extensions to 80 years of operation.Greater situational awareness of key systems, structures, and components (SSCs) can provide the technical basis for extending the life of SSCs beyond the original design life and supports improvements in both safety and economics by supporting optimized maintenance planning and power uprates. These issues are not specific to the aging LWRs; future reactors (including Generation III+ LWRs, advanced reactors, small modular reactors, and fast reactors) can benefit from the same situational awareness. In fact, many SMR and advanced reactor designs have increased operating cycles (typically four years up to forty years), which reduce the opportunities for inspection and maintenance at frequent, scheduled outages. Understanding of the current condition of key equipment and the expected evolution of degradation during the next operating cycle allows for targeted inspection and maintenance activities. This article reviews the state of the art and the state of practice of prognostics and health management (PHM) for nuclear power systems. Key research needs and technical gaps are highlighted that must be addressed in order to fully realize the benefits of PHM in nuclear facilities

Incipient fault detection and isolation of sensors and field devices

The purpose of this research is to develop a robust fault detection and isolation method, for detecting faults in process sensors, actuators, controllers and other field devices. The approach to the solution to this problem is summarized below. A novel approach for the validation of control system components and sensors was developed in this research. The process is composed of detecting a system anomaly, isolating the faulty component (such as sensors, actuators, and controllers), computing its deviation from expected value for a given system\u27s normal condition, and finally reconstructing its output when applicable. A variant of the Group Method of Data Handling (GMDH) was developed in this research for generating analytical redundancy from relationships among different system components. A rational function approximation was used for the data-driven modeling scheme. This analytical redundancy is necessary for detecting system anomalies and isolating faulty components. A rule-base expert system was developed in order to isolate the faulty component. The rule-based was established from model-simulated data. A fuzzy-logic estimator was implemented to compute the magnitude of the loop component fault so that the operator or the controller might take corrective actions. This latter engine allows the system to be operated in a normal condition until the next scheduled shutdown, even if a critical component were detected as degrading. The effectiveness of the method developed in this research was demonstrated through simulation and by implementation to an experimental control loop. The test loop consisted of a level control system, flow, pressure, level and temperature measuring sensors, motor-operated valves, and a pump. Commonly observed device faults were imposed in different system components such as pressure transmitters, pumps, and motor-operated valves. This research has resulted in a framework for system component failure detection and isolation, allowing easy implementation of this method in any process control system (power plants, chemical industry, and other manufacturing industry). The technique would also aid the plant personnel in defining the minimal number of sensors to be installed in a process system, necessary for reliable component validation

The generation and classification of small leaks in a high pressure water system

This report investigates the detection of small leaks from the primary system of a Nuclear Pressurised Water Reactor. Leak rates of 12 g/s are invariably difficult to detect and locate. The typical leak indicators in a nuclear reactor control room are a drop in pressure and level from the pressuriser, and the air sampler detecting particulate matter. However, in both cases the leak is normally quite substantial by the time any parameters or values are obviously outside the normal operating conditions. Therefore, a small leak could go undetected for a significant amount of time. As part of the reactor safety studies, it is important to have more information about small leaks. Due to the lack of small leak data, the solution was to construct a high pressure water rig producing temperatures and pressures close to those experienced in the primary circuit, these being 200ºC and 100 bar respectively. Pressure is maintained by a vane water pump and heating is achieved by passing a high current through a small diameter, thin walled pipe. To reproduce different size cracks, various size carburettor jets are used. The water on exiting this crack, flashes to steam and immediately meets metallic pipe lagging, which is typical of most primary systems. With the typical crack scenario recreated it is now important to add sensors that will detect conditions associated with a small leak. These sensors are either mounted on or around the lagging material. The parameters that are monitored include vibrations, acoustics, thermal variations, moisture change, air flow and pressure adjustment leaving a predetermined outlet. The sensor outputs are pre-processed and the nonlinear data are applied to an artificial neural network, whereas the other data are applied to a digital logic system. The results showed that with 13 different leak rates, separated by only 1.4 g/s the ANN was able to correctly differentiate and identify different leak sizes with a certainty of over 97%. The results from all the analysis are further presented graphically through an Operator Advisory System. This informs the operator of the predicted leak size and location. All of the available sensor data relevant to the leak can be viewed and location of the leak is presented by a three dimensional model of the reactor system