Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerful computational model for classification and estimation, have been used in many application fields since their birth. These two techniques are somewhat supplementary to each other in a way that what one is lacking of the other can provide. This led to the creation of Neuro-Fuzzy systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. Generally speaking all type of systems that integrate these two techniques can be called Neuro-Fuzzy systems. Key feature of these systems is that they use input-output patterns to adjust the fuzzy sets and rules inside the model. The paper reviews the principles of a Neuro-Fuzzy system and the key methods presented in this field, furthermore provides survey on their applications for technical diagnostics and measurement. © 2015 Elsevier Ltd

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

Bagged ensemble of Fuzzy C-Means classifiers for nuclear transient identification

This paper presents an ensemble-based scheme for nuclear transient identification. The approach adopted to construct the ensemble of classifiers is bagging; the novelty consists in using supervised fuzzy C-means (FCM) classifiers as base classifiers of the ensemble. The performance of the proposed classification scheme has been verified by comparison with a single supervised, evolutionary-optimized FCM classifier with respect of the task of classifying artificial datasets. The results obtained indicate that in the cases of datasets of large or very small sizes and/or complex decision boundaries, the bagging ensembles can improve classification accuracy. Then, the approach has been applied to the identification of simulated transients in the feedwater system of a boiling water reactor (BWR)

Bagged ensemble of Fuzzy C-Means classifiers for nuclear transient identification

This paper presents an ensemble-based scheme for nuclear transient identification. The approach adopted to construct the ensemble of classifiers is bagging; the novelty consists in using supervised fuzzy C-means (FCM) classifiers as base classifiers of the ensemble. The performance of the proposed classification scheme has been verified by comparison with a single supervised, evolutionary-optimized FCM classifier with respect of the task of classifying artificial datasets. The results obtained indicate that in the cases of datasets of large or very small sizes and/or complex decision boundaries, the bagging ensembles can improve classification accuracy. Then, the approach has been applied to the identification of simulated transients in the feedwater system of a boiling water reactor (BWR)

Evaluation of Manufactured Product Performance Using Neural Networks

This paper discusses some of the several successful applications of neural networks which have made them a useful simulation tool. After several years of neglect, confidence in the accuracy of neural networks began to grow from the 1980s with applications in power, control and instrumentation and robotics to mention a few. Several successful industrial implementations of neural networks in the field of electrical engineering will be reviewed and results of the authors’ research in the areas of food security and health will also be presented. The research results will show that successful neural simulation results using Neurosolutions software also translated to successful realtime implementation of cost-effective products with reliable overall performance of up to 90%

Fault Detection and Isolation (Fdi) Via Neural Networks

Abstract Recent approaches to fault detection and isolation for dynamic systems using methods of integrating quantitative and qualitative model information, based upon soft computing (SC) methods are used. In this study, the use of SC methods is considered an important extension to the quantitative model-based approach for residual generation in FDI. When quantitative models are not readily available, a correctly trained neural network (NN) can be used as a non-linear dynamic model of the system. However, the neural network does not easily provide insight into model. This main difficulty can be overcome using qualitative modeling or rule-based inference methods. The paper presents the properties of several methods of combining quantitative and qualitative system information and their practical value for fault diagnosis of Neural network

Evaluación de técnicas de inteligencia artificial utilizadas en el diagnóstico de fallas en plantas de potencia

This article presents an evaluation about the research related to the development of computational tools based on artificial intelligence techniques, which focus on the detection and diagnosis of faults in the different processes associated with a power generation plant such as: hydroelectric, thermoelectric and nuclear power plants. Initially, the main techniques of artificial intelligence that allow the construction of intelligent systems in the area of fault diagnosis is described in a general way, techniques such as: fuzzy logic, neural networks, knowledge-based systems and hybrid techniques Subsequently A summary of the research based on each of these techniques is presented. Subsequently, the different articles found for each of the techniques are presented in tables, illustrating the year of publication and the description of the research carried out. The result of this work is the comparison and evaluation of each technique focused on the diagnosis of failures in power plants. The novelty of this work is that it presents an extensive bibliography of the applications of the different intelligent techniques in solving the problem of detection and diagnosis of failure in power plantsEste artículo presenta una evaluación de herramientas computacionales basadas en técnicas de inteligencia artificial, las cuales se enfocan en la detección y diagnóstico de fallas en los diferentes procesos asociados a una central de generación de energía tal como: hidroeléctricas, termoeléctricas y centrales nucleares. Inicialmente, se describen de manera general las principales técnicas de inteligencia artificial que permiten la construcción de sistemas inteligentes para el diagnóstico de fallas en centrales eléctricas, se presentan técnicas como: lógica difusa, redes neuronales, sistemas basados en el conocimiento y técnicas hibridas.  Posteriormente se presentan en tablas los diferentes artículos encontrados para cada una de las técnicas, ilustrando el año de publicación y una descripción de cada publicación. El resultado de este trabajo es la comparación y evaluación de cada técnica enfocada al diagnóstico de fallas en centrales eléctricas.  Lo novedoso de este trabajo, es que presenta una extensa bibliografía de las aplicaciones de las diferentes técnicas inteligentes en la solución del problema de detección y diagnóstico de falla en centrales de generación eléctric

An Integrated Fuzzy Inference Based Monitoring, Diagnostic, and Prognostic System

To date the majority of the research related to the development and application of monitoring, diagnostic, and prognostic systems has been exclusive in the sense that only one of the three areas is the focus of the work. While previous research progresses each of the respective fields, the end result is a variable grab bag of techniques that address each problem independently. Also, the new field of prognostics is lacking in the sense that few methods have been proposed that produce estimates of the remaining useful life (RUL) of a device or can be realistically applied to real-world systems. This work addresses both problems by developing the nonparametric fuzzy inference system (NFIS) which is adapted for monitoring, diagnosis, and prognosis and then proposing the path classification and estimation (PACE) model that can be used to predict the RUL of a device that does or does not have a well defined failure threshold. To test and evaluate the proposed methods, they were applied to detect, diagnose, and prognose faults and failures in the hydraulic steering system of a deep oil exploration drill. The monitoring system implementing an NFIS predictor and sequential probability ratio test (SPRT) detector produced comparable detection rates to a monitoring system implementing an autoassociative kernel regression (AAKR) predictor and SPRT detector, specifically 80% vs. 85% for the NFIS and AAKR monitor respectively. It was also found that the NFIS monitor produced fewer false alarms. Next, the monitoring system outputs were used to generate symptom patterns for k-nearest neighbor (kNN) and NFIS classifiers that were trained to diagnose different fault classes. The NFIS diagnoser was shown to significantly outperform the kNN diagnoser, with overall accuracies of 96% vs. 89% respectively. Finally, the PACE implementing the NFIS was used to predict the RUL for different failure modes. The errors of the RUL estimates produced by the PACE-NFIS prognosers ranged from 1.2-11.4 hours with 95% confidence intervals (CI) from 0.67-32.02 hours, which are significantly better than the population based prognoser estimates with errors of ~45 hours and 95% CIs of ~162 hours

FAULT DIAGNOSIS OF GENERATION IV NUCLEAR HTGR COMPONENTS USING THE ENTHALPY-ENTROPY GRAPH APPROACH

Abstract: Fault diagnosis (FD) is an important component in modern nuclear power plant (NPP) supervision to improve safety, reliability, and availability. In this regard, a significant amount of experience has been gained in FD of generation II and III water-cooled nuclear energy systems through active research. However, new energy conversion methodologies as well as advances in reactor and component technology support the study of different FD methods in modern NPPs. This paper presents the application of the enthalpy-entropy (h-s) graph for FD of generation IV nuclear high temperature gas-cooled reactor (HTGR) components. The h-s graph is adapted for fault signature generation by comparing actual operating plant graphs with reference models. Multiple input feature sets (patterns) are generated for the fault classification algorithm based on the error, area, and direction of the fault residuals. The effectiveness of the FD method is demonstrated by classifying 24 non-critical single faults in the main power system of the Pebble Bed Modular Reactor (PBMR) during normal steady state operation as well as load following of the plant. Reference and fault data are calculated for the thermo-hydraulic network by means of a simulation model in Flownex ® Nuclear. The results show that the proposed FD method produces different uncorrelated fault signatures for all the examined fault conditions