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

INCREMENTAL FAULT DIAGNOSABILITY AND SECURITY/PRIVACY VERIFICATION

Dynamical systems can be classified into two groups. One group is continuoustime systems that describe the physical system behavior, and therefore are typically modeled by differential equations. The other group is discrete event systems (DES)s that represent the sequential and logical behavior of a system. DESs are therefore modeled by discrete state/event models.DESs are widely used for formal verification and enforcement of desired behaviors in embedded systems. Such systems are naturally prone to faults, and the knowledge about each single fault is crucial from safety and economical point of view. Fault diagnosability verification, which is the ability to deduce about the occurrence of all failures, is one of the problems that is investigated in this thesis. Another verification problem that is addressed in this thesis is security/privacy. The two notions currentstate opacity and current-state anonymity that lie within this category, have attracted great attention in recent years, due to the progress of communication networks and mobile devices.Usually, DESs are modular and consist of interacting subsystems. The interaction is achieved by means of synchronous composition of these components. This synchronization results in large monolithic models of the total DES. Also, the complex computations, related to each specific verification problem, add even more computational complexity, resulting in the well-known state-space explosion problem.To circumvent the state-space explosion problem, one efficient approach is to exploit the modular structure of systems and apply incremental abstraction. In this thesis, a unified abstraction method that preserves temporal logic properties and possible silent loops is presented. The abstraction method is incrementally applied on the local subsystems, and it is proved that this abstraction preserves the main characteristics of the system that needs to be verified.The existence of shared unobservable events means that ordinary incremental abstraction does not work for security/privacy verification of modular DESs. To solve this problem, a combined incremental abstraction and observer generation is proposed and analyzed. Evaluations show the great impact of the proposed incremental abstraction on diagnosability and security/privacy verification, as well as verification of generic safety and liveness properties. Thus, this incremental strategy makes formal verification of large complex systems feasible

Discrete and hybrid methods for the diagnosis of distributed systems

Many important activities of modern society rely on the proper functioning of complex systems such as electricity networks, telecommunication networks, manufacturing plants and aircrafts. The supervision of such systems must include strong diagnosis capability to be able to effectively detect the occurrence of faults and ensure appropriate corrective measures can be taken in order to recover from the faults or prevent total failure. This thesis addresses issues in the diagnosis of large complex systems. Such systems are usually distributed in nature, i.e. they consist of many interconnected components each having their own local behaviour. These components interact together to produce an emergent global behaviour that is complex. As those systems increase in complexity and size, their diagnosis becomes increasingly challenging. In the first part of this thesis, a method is proposed for diagnosis on distributed systems that avoids a monolithic global computation. The method, based on converting the graph of the system into a junction tree, takes into account the topology of the system in choosing how to merge local diagnoses on the components while still obtaining a globally consistent result. The method is shown to work well for systems with tree or near-tree structures. This method is further extended to handle systems with high clustering by selectively ignoring some connections that would still allow an accurate diagnosis to be obtained. A hybrid system approach is explored in the second part of the thesis, where continuous dynamics information on the system is also retained to help better isolate or identify faults. A hybrid system framework is presented that models both continuous dynamics and discrete evolution in dynamical systems, based on detecting changes in the fundamental governing dynamics of the system rather than on residual estimation. This makes it possible to handle systems that might not be well characterised and where parameter drift is present. The discrete aspect of the hybrid system model is used to derive diagnosability conditions using indicator functions for the detection and isolation of multiple, arbitrary sequential or simultaneous events in hybrid dynamical networks. Issues with diagnosis in the presence of uncertainty in measurements due sensor or actuator noise are addressed. Faults may generate symptoms that are in the same order of magnitude as the latter. The use of statistical techniques,within a hybrid system framework, is proposed to detect these elusive fault symptoms and translate this information into probabilities for the actual operational mode and possibility of transition between modes which makes it possible to apply probabilistic analysis on the system to handle the underlying uncertainty present

Reconfigurability level assessment in Portuguese companies

O conceito de sistemas de produção reconfiguráveis (SPRs) surgiu como uma estratégia para alcançar sistemas de produção mais ágeis, capazes de ajustar a funcionalidade e capacidade quando necessário. Este tópico é um problema atual para empresas porque a viabilidade de SPRs foi alcançada recentemente devido às novas tecnologias promovidas pela Indústria 4.0. Em SPRs, a reconfigurabilidade é a capacidade que permite a mudança de um produto para outro, a adição ou remoção de recursos, com mínimo esforço e sem demora. Por esta razão, a avaliação do nível de reconfigurabilidade é de extrema importância para as indústrias. O objetivo desta pesquisa é descrever o desenvolvimento de um índice de reconfigu rabilidade (RI) que pode ser utilizado por empresas para definir o quão reconfiguráveis são seus sistemas de manufatura. Especificamente, este estudo pretende determinar em que medida cada característica fundamental contribui para a composição da reconfi gurabilidade e o nível atual de reconfigurabilidade presente nas empresas portuguesas. Adicionalmente, este trabalho tenta estabelecer uma relação entre as características es senciais e o desempenho operacional dos sistemas de manufatura, e a extensão em que cada característica básica é implementada em diferentes setores industriais. Para construir o IR, uma pesquisa por questionário foi usada para selecionar as va riáveis e uma análise de componentes principais (ACP) foi aplicada aos resultados da pesquisa para determinar as contribuições das características centrais. O IR foi usado para estabelecer um ranking dos setores industriais das empresas respondentes e para discutir o nível de implementação das características centrais de reconfigurabilidade. Os resultados mostram que cada característica central contribui com uma quantidade diferente para a composição da reconfigurabilidade. A adaptabilidade e a diagnostica bilidade são as que mais contribuem, com 25% cada. As empresas portuguesas têm um nível moderado de reconfigurabilidade implementado. Em relação ao desempenho ope racional, a modularidade parece contribuir para a qualidade e entrega; integrabilidade para entrega e flexibilidade; adaptabilidade para custo e qualidade e capacidade de di agnóstico para qualidade e entrega. Entre os setores industriais, a reconfigurabilidade varia de níveis baixos a moderados. A implementação das características centrais variam significativamente, mas o RI parece estar relacionado aos níveis de flutuações do mercado

Layout level design for testability strategy applied to a CMOS cell library

The layout level design for testability (LLDFT) rules used here allow to avoid some hard to detect faults or even undetectable faults on a cell library by modifying the cell layout without changing their behavior and achieving a good level of reliability. These rules avoid some open faults or reduce their appearance probability. The main purpose has been to apply that set of LLDFT rules on the cells of the library designed at the Centre Nacional de Microelectronica (CNM) in order to obtain a highly testable cell library. The authors summarize the main results (area overhead and performance degradation) of the application of the LLDFT rules on the cell

Intermittent/transient faults in computer systems: Executive summary

An overview of an approach for diagnosing intermittent/transient (I/T) faults is presented. The development of an interrelated theory and experimental methodology to be used in a laboratory situation to measure the capability of a fault tolerant computing system to diagnose I/T faults, is discussed. To the extent that such diagnosing capability is important to reliability in fault tolerant computing systems, this theory and supporting methodology serves as a foundation for validation efforts