The aim of my diploma thesis was to assess possibilities of tracing and, consequently, identifying radionuclides in transported materials detected by detection frames on the protected area border of the nuclear power plant of Temelín, by means of the portable spectrometer Inspector 1000. To achieve this, I designed a series of measurements based on a simulated passage of a motor vehicle fitted with ionizining radiation sources of various activities at the designed points of measurement so that these measurements corresponded as close as possible to the actual passage of vehicles with large-sized loads. Recorded values were analysed and marginal activities for the particular measuring points were fixed. Computation correctness was then verified by another passage of the vehicle through the measuring device. Specified values of the marginal activities at the particular measuring points were, for comparison, consequently measured by the portable spectrometer Inspector 1000. Measurement results proved that the portable spectrometer Inspector 1000 can efficiently detect, identify and locate the sources of ionizing radiation causing the alarm level to be exceeded on a stable measuring device at the fixed measuring points, with a higher sensitivity than a stable measuring device. Conclusions of this diploma thesis will be given to the radiation protection unit of the nuclear power plant of Temelín as a basis for specification or even expansion of the methods of measuring ionizining radiation sources when transporting large-sized loads in the nuclear power plant, and their introduction into the monitoring programme.

An islanded power system indicates a geographical and logical detach between a portion<br />of a power system and the major grid, and often accompanies with the loss of system<br />observability. A power system islanding contingency could be one of the most severe<br />consequences of wide-area system failures. It might result in enormous losses to both the power utilities and the consumers. Even those relatively small and stable islanding events may largely disturb the consumers\' normal operation in the island. On the other hand, the power consumption in the U.S. has been largely increasing since 1970s with the respect to the bloom of global economy and mass manufacturing, and the daily increased requirements from the modern customers. Along with the extreme weather and natural disaster factors, the century old U.S. power grid is under severely tests for potential islanding disturbances. After 1980s, the invention of synchronized phasor measurement units (PMU) has broadened the horizon for system monitoring, control and protection. Its real time feature and reliable measurements has made possible many online system schemes. The recent revolution of computers and electronic devices enables the implementation of complex methods (such as data mining methods) requiring large databases in power system analysis. The proposed method presented in this dissertation is primarily focused on two studies: one power system islanding contingency detection, identification, classification and state evaluation algorithm using a decision tree algorithm and topology approach, and its application in Dominion Virginia power system; and one optimal PMU placement strategy using a binary integral programming algorithm with the consideration of system islanding and redundancy issues. / Ph. D.

Phishing websites are forged web pages that are created by malicious people to mimic web pages of real websites and it attempts to defraud people of their personal information. Detecting and identifying Phishing websites is really a complex and dynamic problem involving many factors and criteria, and because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Logic model can be an effective tool in assessing and identifying phishing websites than any other traditional tool since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the `fuzziness¿ in traditional website phishing risk assessment and propose an intelligent resilient and effective model for detecting phishing websites. The proposed model is based on FL operators which is used to characterize the website phishing factors and indicators as fuzzy variables and produces six measures and criteria¿s of website phishing attack dimensions with a layer structure. Our experimental results showed the significance and importance of the phishing website criteria (URL & Domain Identity) represented by layer one, and the variety influence of the phishing characteristic layers on the final phishing website rate.

No description available.

Hybrid systems have been identified as one of the main directions in control theory and attracted increasing attention in recent years due to their huge diversity of engineering applications. Multiplemodel (MM) estimation is the state-of-the-art approach to many hybrid estimation problems. Existing MM methods with fixed structure usually perform well for problems that can be handled by a small set of models. However, their performance is limited when the required number of models to achieve a satisfactory accuracy is large due to time evolution of the true mode over a large continuous space. In this research, variable-structure multiple model (VSMM) estimation was investigated, further developed and evaluated. A fundamental solution for on-line adaptation of model sets was developed as well as several VSMM algorithms. These algorithms have been successfully applied to the fields of fault detection and identification as well as target tracking in this thesis. In particular, an integrated framework to detect, identify and estimate failures is developed based on the VSMM. It can handle sequential failures and multiple failures by sensors or actuators. Fault detection and target maneuver detection can be formulated as change-point detection problems in statistics. It is of great importance to have the quickest detection of such mode changes in a hybrid system. Traditional maneuver detectors based on simplistic models are not optimal and are computationally demanding due to the requirement of batch processing. In this presentation, a general sequential testing procedure is proposed for maneuver detection based on advanced sequential tests. It uses a likelihood marginalization technique to cope with the difficulty that the target accelerations are unknown. The approach essentially utilizes a priori information about the accelerations in typical tracking engagements and thus allows improved detection performance. The proposed approach is applicable to change-point detection problems under similar formulation, such as fault detection.

Cracks often exist in structural members that are exposed to repeated loading, which will certainly lower the structural integrity. A crack on a structural member introduces a local flexibility which is a function of the crack depth and location. This may cause nonlinear dynamic response of the structure. In this thesis, a new method is suggested to detect and locate a crack in a structural component. The method is based on the fact that nonlinear response of a structure with a crack will be a function of the crack location and crack magnitude. The method suggested is the extension of a recently developed technique for identification of non-linearity in vibrating multi degree of freedom system. In this method, experimentally measured receptances at different forcing levels are used as input, and the existence and location of a nonlinearity are sought. In order to validate the method, simulated experimental data is used. Characteristics of a cracked beam are simulated by using experimentally obtained analytical expressions, given in the literature. The structure itself is modelled by using finite element method. Several case studies are performed to test and demonstrate the applicability, efficiency and sensitivity of the method suggested. The effect of crack depth on nonlinear system response is also studied in numerical examples.

<p dir="ltr">Resilient engine operation hugely depends on proper functioning of the engine’s sensors, enabling efficient feedback control of the engine systems operation. When the sensors on the engine measure a physical quantity incorrectly, it leads the engine control system to determine that the sensor measuring the physical quantity has failed. This failure may be attributed to a sensor stick failure, bias failure, drift failure, or failure occurring due to physical wear and tear of the sensor. Failure of crucial engine sensors may have adverse effects on engine operation, and in most cases leading into a limp home mode or a torque limitation mode. This affects the engine performance and efficiency. The engine under study in this work is a medium duty marine engine with diesel fuel. Sensor failures in the middle of a marine operation can hugely impact its mission. Therefore, fault tolerant control systems are essential to counter these challenges occurring due to sensor failures. In this thesis, an advanced nonlinear fault detection and state estimation algorithm is developed and implemented on a GT-Power engine model, employing a sophisticated co-simulation approach. The focus is on a 6.7L Cummins diesel engine, for which a detailed nonlinear state space model is constructed. This model accurately replicates critical engine parameters, such as pressures, temperatures, and engine speed, by integrating various submodels. These sub-models estimate key parameters like cylinder inlet charge flow, valve flow, cylinder outlet temperature, turbocharger turbine flow, and charge air cooler flow. To assess the model’s accuracy and reliability, it is rigorously validated against a truth reference GT-Power engine model. The results demonstrate exceptional performance, with the nonlinear model exhibiting a minimal percentage performance error of less than 5% under steady-state conditions and less than 15% during transient conditions. The core of the Fault Detection and State Estimation (FDSE) modules consists of a bank of Extended Kalman Filters (EKF). These filters are meticulously designed to estimate vital engine states, generate residuals, and assess these residuals even in the presence of process and measurement noise. This approach enables the detection of sensor faults and facilitates controller reconfiguration, ensuring the engine’s robustness in the face of unexpected sensor failures. Crucially, the nonlinear physics-based model serves as the foundation for the state transition functions utilized in the design of the observer bank. Residuals generated by the EKFs are evaluated using both fixed and adaptive thresholding techniques masking the sensor faults at the time step at which it is detected, ensuring robust performance not only in steady-state conditions but also during varying transient load conditions. To comprehensively evaluate the system’s resilience in practical scenarios, multiple sensor stuck failures are introduced into the GT-Power model. A software-in-the-loop co-simulation strategy is meticulously established, employing both the GT-Power truth reference engine model and the nonlinear Fault Detection and State Estimation (FDSE) model within the Simulink environment. This unique co-simulation approach provides a platform to assess the FDSE performance and its effect on engine performance in simulated sensor fault scenarios. The FDSE module is able to detect sensor failures which deviate at least 5% from their actual values. The percentage estimation error is less than 10% under steady state conditions and less than 20% under transient load conditions. Ultimately, this process creates analytical redundancy, not only forming the basis of state estimation but also empowering the engine to maintain its performance in the presence of sensor faults.</p>

No description available.

Unmanned Underwater Vehicles (UUVs) are widely used in commercial, scientific, and military missions for various purposes. What makes this technology challenging is the increasing mission duration and unknown environment. It is necessary to embed fault-tolerant control paradigms into UUVs to increase the reliability of the vehicles and enable them to execute and finalize complex missions. Specifically, fault-tolerant control (FTC) comprises fault detection, identification, and control reconfiguration for fault compensation. Literature review shows that there have been no systematic methods for fault-tolerant control of UUVs in earlier investigations. This study presents a hierarchical methodology of fault detection, identification and compensation (HFDIC) that integrates these functions systematically in different levels. The method uses adaptive finite-impulse-response (FIR) modeling and analysis in its first level to detect failure occurrences. Specifically, it incorporates a FIR filter for on-line adaptive modeling, and a least-mean-squares (LMS) algorithm to minimize the output error between the monitored system and the filter in the modeling process. By analyzing the resulting adaptive filter coefficients, we extract the information on the fault occurrence. The HFDIC also includes a two-stage design of parallel Kalman filters in levels two and three for fault identification using the multiple-model adaptive estimation (MMAE). The algorithm activates latter levels only when the failure is detected, and can return back to the monitoring loop in case of false failures. On the basis of MMAE, we use multiple sliding-mode controllers and reconfigure the control law with a probability-weighted average of all the elemental control signals, in order to compensate for the fault. We validate the HFDIC on the steering and diving subsystems of Naval Postgraduate School (NPS) UUVs for various simulated actuator and/or sensor failures, and test the hierarchical fault detection and identification (HFDI) with realistic data from at-sea experiment of the Florida Atlantic University (FAU) Autonomous Underwater Vehicles (AUVs). For both occasions, we model actuator and sensor failures as additive parameter changes in the observation matrix and the output equation, respectively. Simulation results demonstrate the ability of the HFDIC to detect failures in real time, identify failures accurately with a low computational overhead, and compensate actuator and sensor failures with control reconfiguration. In particular, verification of HFDI with FAU data confirms the performance of the fault detection and identification methodology, and provides important information on the vehicle performance. / Ph. D.

Neste trabalho propõe-se uma metodologia off-line, prática e eficiente, para detectar, identificar e corrigir erros em parâmetros série e shunt de linhas de transmissão. As linhas de transmissão, ou ramos do modelo barra-ramo, suspeitas de estarem com EPs são identificadas através do Índice de Suspeita (IS). O IS de um ramo é a relação entre o número de medidas incidentes a esse ramo, cujos resíduos normalizados são maiores que um valor pré-estabelecido, e o número total de medidas incidentes a esse ramo. Usando várias amostras de medidas, os parâmetros dos ramos suspeitos são estimados, de forma seqüencial, via um estimador de estado e parâmetros baseado nas equações normais, que aumenta o vetor de variáveis de estado para inclusão dos parâmetros suspeitos. Resultados numéricos de diversas simulações, com os sistemas de 14, 30 e 57 barras do IEEE, têm demonstrado a alta precisão e confiabilidade da metodologia proposta, mesmo na ocorrência de erros múltiplos (em mais de um parâmetro) em ramos adjacentes, como também em linhas de transmissão paralelas com compensação série. Comprovou-se a viabilidade prática da metodologia proposta através da aplicação da mesma, para depuração (detecção, identificação e correção) dos valores dos parâmetros de dois subsistemas da Hydro-Québec Trans-Énergie. / A practical and efficient off-line approach to detect, identify and correct series and shunt branch parameter errors is proposed in this thesis. The branches suspected of having parameter errors are identified by means of the Suspicious Index (SI). The SI of a branch is the ratio between the number of measurements incident to that branch, whose normalized residuals are larger than one specified threshold value, and the total number of measurements incident to that branch. Using several measurement snapshots, the suspicious parameters are sequentially estimated, via an augmented state and parameter estimator which increases the V-\'teta\' state vector for the inclusion of suspicious parameters. Several simulation results (with IEEE 14, 30 and 57 bus systems) have demonstrated the high accuracy and reliability of the proposed approach to de al with single and multiple parameter errors in adjacent and non-adjacent branches, as well as in parallel transmission lines with series compensation. The proposed approach is confirmed by tests performed in two subsystems of the Hydro-Québec Trans-Énergie.