This research has developed several models and methodologies with the aim of improving the accuracy and applicability of reliability predictions for complex repairable systems. A repairable system is usually defined as one that will be repaired to recover its functions after each failure. Physical assets such as machines, buildings, vehicles are often repairable. Optimal maintenance strategies require the prediction of the reliability of complex repairable systems accurately. Numerous models and methods have been developed for predicting system reliability. After an extensive literature review, several limitations in the existing research and needs for future research have been identified. These include the follows: the need for an effective method to predict the reliability of an asset with multiple preventive maintenance intervals during its entire life span; the need for considering interactions among failures of components in a system; and the need for an effective method for predicting reliability with sparse or zero failure data. In this research, the Split System Approach (SSA), an Analytical Model for Interactive Failures (AMIF), the Extended SSA (ESSA) and the Proportional Covariate Model (PCM), were developed by the candidate to meet the needs identified previously, in an effective manner. These new methodologies/models are expected to rectify the identified limitations of current models and significantly improve the accuracy of the reliability prediction of existing models for repairable systems. The characteristics of the reliability of a system will alter after regular preventive maintenance. This alternation makes prediction of the reliability of complex repairable systems difficult, especially when the prediction covers a number of imperfect preventive maintenance actions over multiple intervals during the asset's lifetime. The SSA uses a new concept to address this issue effectively and splits a system into repaired and unrepaired parts virtually. SSA has been used to analyse system reliability at the component level and to address different states of a repairable system after single or multiple preventive maintenance activities over multiple intervals. The results obtained from this investigation demonstrate that SSA has an excellent ability to support the making of optimal asset preventive maintenance decisions over its whole life. It is noted that SSA, like most existing models, is based on the assumption that failures are independent of each other. This assumption is often unrealistic in industrial circumstances and may lead to unacceptable prediction errors. To ensure the accuracy of reliability prediction, interactive failures were considered. The concept of interactive failure presented in this thesis is a new variant of the definition of failure. The candidate has made several original contributions such as introducing and defining related concepts and terminologies, developing a model to analyse interactive failures quantitatively and revealing that interactive failure can be either stable or unstable. The research results effectively assist in avoiding unstable interactive relationship in machinery during its design phase. This research on interactive failures pioneers a new area of reliability prediction and enables the estimation of failure probabilities more precisely. ESSA was developed through an integration of SSA and AMIF. ESSA is the first effective method to address the reliability prediction of systems with interactive failures and with multiple preventive maintenance actions over multiple intervals. It enhances the capability of SSA and AMIF. PCM was developed to further enhance the capability of the above methodologies/models. It addresses the issue of reliability prediction using both failure data and condition data. The philosophy and procedure of PCM are different from existing models such as the Proportional Hazard Model (PHM). PCM has been used successfully to investigate the hazard of gearboxes and truck engines. The candidate demonstrated that PCM had several unique features: 1) it automatically tracks the changing characteristics of the hazard of a system using symptom indicators; 2) it estimates the hazard of a system using symptom indicators without historical failure data; 3) it reduces the influence of fluctuations in condition monitoring data on hazard estimation. These newly developed methodologies/models have been verified using simulations, industrial case studies and laboratory experiments. The research outcomes of this research are expected to enrich the body of knowledge in reliability prediction through effectively addressing some limitations of existing models and exploring the area of interactive failures.

Switching of shunt reactors and capacitor banks is known to cause a very high rate of rise of transient recovery voltage across the circuit breaker contacts. With improvements in circuit breaker technology, modern SF6 puffer circuits have been designed with less interrupter per pole than previous generations of SF6 circuit breakers. This has caused modern circuit breakers to operate with higher voltage stress in the dielectric recovery region after current interruption. Catastrophic failures of modern SF6 circuit breakers have been reported during shunt reactor and capacitor bank de-energisation. In those cases, evidence of cumulative re-strikes has been found to be the main cause of interrupter failure. Monitoring of voltage waveforms during switching would provide information about the magnitude and frequency of small re-ignitions and re-strikes. However, measuring waveforms at a moderately high frequency require plant outages to connect equipment. In recent years, there have been increasing interests in using RF measurements in condition monitoring of switchgear. The RF measurement technique used for measuring circuit breaker inter-pole switching time during capacitor bank closing is of particular interest. In this thesis, research has been carried out to investigate switching transients produced during circuit breaker switching capacitor banks and shunt reactors using a non-intrusive measurement technique. The proposed technique measures the high frequency and low frequency voltage waveforms during switching operations without the need of an outage. The principles of this measurement technique are discussed and field measurements were carried out at shunt rector and capacitor bank installation in two 275 kV air insulated substations. Results of the measurements are presented and discussed in this thesis. The proposed technique shows that it is relatively easy to monitor circuit breaker switching transients and useful information on switching instances can be extracted from the measured waveforms. Further research works are discussed to realise the full potential of the measuring technique.

Transformer ageing is inevitable and it is a challenge for utilities to manage a large fleet of ageing transformers. This means the need for monitoring transformer condition. One of the most widely used methods is oil sampling and testing. Databases of oil test records hence manifest as a great source of information for facilitating transformer ageing assessment and asset management. In this work, databases from three UK utilities including about 4,600 transformers and 65,000 oil test entries were processed, cleaned and analysed. The procedures used could help asset managers in how to approach databases, such as the need for addressing oil contamination, measurement procedure change and oil treatment discontinuities. An early degradation phenomenon was detected in multiple databases/utilities, which was investigated and found to be caused by the adoption of hydrotreatment oil refining technique in the late 1980s. Asset managers may need to monitor more frequently the affected units and restructure long term plans. The work subsequently focused on population analyses which indicated higher voltage transformers (275 kV and 400 kV) are tested more frequently and for more parameters compared with lower voltage units (33 kV and 132 kV). Acidity is the parameter that shows the highest correlation with transformer in-service age. In addition, the influence of the length of oil test records on population ageing trends was studied. It is found that it is possible to have a representative population ageing trend even with a short period (e.g. two years) of oil test results if the transformer age profile is representative of the whole transformer population. Leading from population analyses, seasonal influence on moisture was investigated which implies the importance of incorporating oil sampling temperature for better interpretation of moisture as well as indirectly breakdown voltage records. A condition mismatch between dielectric dissipation factor and resistivity was also discovered which could mean the need for revising the current IEC 60422 oil maintenance guide. Finally, insulation condition ranking was performed through principal component analysis (PCA) and analytic hierarchy process (AHP). These two techniques were demonstrated to be not just capable alternatives to traditional empirical formula but also allow fast, objective interpretation in PCA case, as well as flexible and comprehensive (objective and subjective incorporations) analysis in AHP case.

Cette thèse propose trois méthodes de traitement du signal orientées vers la surveillance d’état et le diagnostic. Les techniques proposées sont surtout adaptées pour la surveillance d’état, effectuée à la base de vibrations, des machines tournantes qui fonctionnent dans des conditions d’opération non-stationnaires comme par exemple les éoliennes mais elles ne sont pas limitées à un tel usage. Toutes les méthodes proposées sont des algorithmes automatiques et gérés par les données.La première technique proposée permet de sélectionner la partie la plus stationnaire d’un signal en cadrant la représentation temps-fréquence d’un signal.La deuxième méthode est un algorithme pour l’association des dispositions spectrales, des séries harmoniques et des séries à bandes latérales avec des fréquences caractéristiques provennant du cinématique d'un système analysé. Cette méthode propose une approche unique dédiée à l’élément roulant du roulement qui permet de surmonter les difficultés causées par le phénomène de glissement.La troisième technique est un algorithme de démodulation de bande latérale entière. Elle fonctionne à la base d’un filtre multiple et propose des indicateurs de santé pour faciliter une évaluation d'état du système sous l’analyse.Dans cette thèse, les méthodes proposées sont validées sur les signaux simulés et réels. Les résultats présentés montrent une bonne performance de toutes les méthodes. / This thesis proposes a three signal-processing methods oriented towards the condition monitoring and diagnosis. In particular the proposed techniques are suited for vibration-based condition monitoring of rotating machinery which works under highly non-stationary operational condition as wind turbines, but it is not limited to such a usage. All the proposed methods are automatic and data-driven algorithms.The first proposed technique enables a selection of the most stationary part of signal by cropping time-frequency representation of the signal.The second method is an algorithm for association of spectral patterns, harmonics and sidebands series, with characteristic frequencies arising from kinematic of a system under inspection. This method features in a unique approach dedicated for rolling-element bearing which enables to overcome difficulties caused by a slippage phenomenon.The third technique is an all-sideband demodulation algorithm. It features in a multi-rate filter and proposes health indicators to facilitate an evaluation of the condition of the investigated system.In this thesis the proposed methods are validated on both, simulated and real-world signals. The presented results show good performance of all the methods.

The automated industry is in a growing phase and the human tasks is increasingly replaced by robots and other automation solutions. The increasing industry entails that the automations must be reliable and condition monitoring plays an important role in achieving that ambition. By utilizing condition monitoring of a machine it is possible to detect a wear before it turns into a critical damage that could result in complete failure. A useful tool when monitoring the condition of a machine is by sampling and analyzing vibrations. Vibrations are generated by the moving parts of the machinery and high amplitude vibrations can often be seen as an indication of the developed faults. The frequency of these vibrations can be calculated and then detected in the sampled data. Today there is no condition monitoring system that monitor industrial robots by analyzing vibrations. The problem with analyzing robots, is that they operate with a varying speed. Since the running conditions are changing rapidly all the time, this means that the vibration frequencies also changes constantly. This is due to the fact that the vibration frequencies are dependent and affected of the operation speed. This research is a sequel and continuation of a research from previous year. The purpose of the research is to investigate the possibility to monitor the condition of a gearbox in a industrial robot, by utilizing vibration analysis. The robot that has been tested under tuff conditions in order to reach a failure, is an ABB IRB 6600. To sample data in a stationary way even tough the speed is changing during the sample time, the method order tracking has been utilized. This makes it possible to sample data with numbers of measurement per rotation instead of sampling according to time. This is processed by SKF:s condition monitoring system multilog IMx and the signal is then presented as a time waveform in the software @ptitude Observer. In Observer, it is also possible to show the signal in a spectrum by using Fast Fourier Transform. By utilizing MATLAB, the research has also resulted in a new analyzing method. This method is called Spectral Auto-Correlation. The methodology of this practice is to correlated the time waveform with itself in order to see which frequencies that are reappearing. The correlated result is then calculated with a Fast Fourier Transform to illustrate the signal in a spectrum for further analysis. During the analysis of the parts in the gearbox, critical defects were found on both the cycloidal disks. The fault frequency for the defects were calculated and analyzed from the data. This resulted in trends where the amplitude from the fault frequency had more than doubled over the time the robot has been operating in the project. This report also include a production simulation where a robot cell from SKF is simulated. The robot cell is simulated with and without a condition monitoring system. A comparison was then made to see what advantages there were with utilizing a condition monitoring system. The result of the simulation was an increased productivity with two to three percent.

A importância das novas tecnologias de informação, automação, monitoramento e sistemas eletrônicos inteligentes têm aumentado significativamente nos últimos anos. Essas tecnologias desempenham um papel fundamental na sociedade moderna e contribuem de forma decisiva para a resolução de importantes desafios para uma sociedade que quer ser mais próspera, internacionalmente competitiva, saudável, segura e sustentável. Como eixo de \"inovação\", essas tecnologias são fatores importantes para todos os setores produtivos da economia. O motor destas tecnologias, entretanto, é a energia, particularmente a eletricidade. Assim, em uma sociedade cujo estilo de vida é fortemente dependente dela, desenvolver tecnologias que permitam não somente a geração, mas também a distribuição de energia de forma barata e limpa e que garantam seu fornecimento ao longo do tempo com a máxima eficiência é uma questão prioritária. Os sistemas baseados em redes inteligentes (do inglês: Smart Grid) vêm, justamente, atender a esses requisitos, representando o que há de mais moderno no setor elétrico, com aumento e diversificação de fontes de geração distribuída na forma de pequenos geradores, maior interação consumidor-distribuidor de energia, integração de diferentes fontes de geração renováveis (ex.: solar, eólica etc.). O cenário energético nacional está avançando de forma muito rápida. Nas distribuidoras, o foco claramente está na redução de perdas comerciais e de custos operacionais, principalmente por meio da modernização dos ativos e da crescente instalação de dispositivos eletrônicos inteligentes nos clientes de baixa tensão (ex.: medidores eletrônicos, dispositivos eletrônicos inteligentes para monitoramento e diagnóstico, relés digitais etc.). Esta tese de doutorado apresenta uma solução com tecnologia nacional que disponibiliza todos os benefícios do Smart Grid através dos equipamentos mais importantes e estratégicos presentes na topologia das Redes de Distribuição Subterrânea Secundária Trifásica: os Protetores de Redes. A partir do centro nevrálgico das Redes de Distribuição Subterrâneas (RDS), cuja topologia está presente nos centros de alta concentração de carga das principais metrópoles do Brasil, a solução desenvolvida pode viabilizar técnica e economicamente a modernização da automação da RDS, com tecnologia nacional de baixo custo, proporcionando igualmente a incorporação dos avanços do Smart Grid e da Geração Distribuída. Este salto tecnológico significaria para as distribuidoras de energia elétrica entre outros benefícios: Melhor controle do processo para uma melhor otimização da rede, desde integração das intermitentes fontes renováveis até uma interação mais dinâmica com os consumidores; Maior flexibilidade às concessionárias em relação ao uso da energia para atingir o grande objetivo social de redução do efeito estufa e otimização do consumo de energia reduzindo perdas e desperdícios; No curto prazo, os benefícios diretos da melhoria do gerenciamento da indisponibilidade, gerenciamento otimizado dos ativos e do capital, melhoria no planejamento, processos e serviços de fornecimento e usos finais de energia, aumento de eficiência de manutenção, redução de perdas técnicas e comerciais, otimização do investimento na compra de novos protetores com menores custos podendo superar a demanda reprimida pelos altos custos de alternativas importadas. / The importance of new technologies in the field of, automation, monitoring, information technology and electronic systems have increased significantly in recent years. These technologies play a basic role in the modern society and contribute of decisive way for the resolution of important challenges for a society that is in search of a more prosperous life, internationally competitive, healthful, safe and sustainable. As a key of \"innovation\", these technologies are key factors for all the productive sectors of the economy in the society. The fuel for the engine of these technologies, however, is the energy, particularly the electricity. Thus, in a society whose life style is strongly dependent of electricity, to develop technologies that not only allow the generation, but also the distribution of energy in a cheap and clean way and which could guarantee its supply throughout the time with the maximum efficiency is a priority issue. The systems based on intelligent networks fully meet these requirements, representing what there is of most modern in the electric sector. The Brazilian energy scenario is quickly changing over the recent years toward modernization, with more distributed generation, in the form of smaller generators, more customer interaction, the integration of more variable resources such as wind and solar, and more renewables overall. For the Power Utilities, especially in the Distribution Sector, the focus is clearly in the reduction of commercial losses and operational costs, mainly by means of the modernization of the assets and an increase in the installation of intelligent electronic devices at consumers side (e.g.: electronic energy meters, intelligent electronic devices for condition monitoring, digital relays etc.). This work presents a solution developed based on Brazilian technology that incorporates all the benefits of smart grid to the most important equipment that is present in the topology of the Low-Voltage Secondary Network Distribution System: the Network Protector. From the neuralgic center of these Low-Voltage Secondary Network Systems, which topology is used in the most important cities in Brazil, which has a high load concentration, the solution presented here make it feasible technically and economically the use of smart grid topology profiting from its great benefits such as: Allow utilities to better optimize the grid to support a number of public policies, from intermittent renewable integration to more dynamic interfaces with customers; Offer utilities more flexibility relative to how they use energy toward the greater societal objectives of reducing greenhouse gases and energy consumption. In the short and mid term, a smarter grid offers utilities operational benefits (outage management, improved processes, maintenance and workforce efficiency, reduced losses, etc.) as well as benefits associated with improved asset management (system planning, better capital asset utilization, etc.), lower investment to acquire new Network Protectors.

<p>Artificial neural networks, including deep neural networks, play a central role in data-driven science due to their superior learning capacity and adaptability to different tasks and data structures. However, although quantitative uncertainty analysis is essential for training and deploying reliable data-driven models, the uncertainties in neural networks are often overlooked or underestimated in many studies, mainly due to the lack of a high-fidelity and computationally efficient uncertainty quantification approach. In this work, a novel uncertainty analysis scheme is developed. The Gaussian mixture model is used to characterize the probability distributions of uncertainties in arbitrary forms, which yields higher fidelity than the presumed distribution forms, like Gaussian, when the underlying uncertainty is multimodal, and is more compact and efficient than large-scale Monte Carlo sampling. The fidelity of the Gaussian mixture is refined through adaptive scheduling of the width of each Gaussian component based on the active assessment of the factors that could deteriorate the uncertainty representation quality, such as the nonlinearity of activation functions in the neural network. </p> <p>Following this idea, an adaptive Gaussian mixture scheme of nonlinear uncertainty propagation is proposed to effectively propagate the probability distributions of uncertainties through layers in deep neural networks or through time in recurrent neural networks. An adaptive Gaussian mixture filter (AGMF) is then designed based on this uncertainty propagation scheme. By approximating the dynamics of a highly nonlinear system with a feedforward neural network, the adaptive Gaussian mixture refinement is applied at both the state prediction and Bayesian update steps to closely track the distribution of unmeasurable states. As a result, this new AGMF exhibits state-of-the-art accuracy with a reasonable computational cost on highly nonlinear state estimation problems subject to high magnitudes of uncertainties. Next, a probabilistic neural network with Gaussian-mixture-distributed parameters (GM-PNN) is developed. The adaptive Gaussian mixture scheme is extended to refine intermediate layer states and ensure the fidelity of both linear and nonlinear transformations within the network so that the predictive distribution of output target can be inferred directly without sampling or approximation of integration. The derivatives of the loss function with respect to all the probabilistic parameters in this network are derived explicitly, and therefore, the GM-PNN can be easily trained with any backpropagation method to address practical data-driven problems subject to uncertainties.</p> <p>The GM-PNN is applied to two data-driven condition monitoring schemes of manufacturing processes. For tool wear monitoring in the turning process, a systematic feature normalization and selection scheme is proposed for the engineering of optimal feature sets extracted from sensor signals. The predictive tool wear models are established using two methods, one is a type-2 fuzzy network for interval-type uncertainty quantification and the other is the GM-PNN for probabilistic uncertainty quantification. For porosity monitoring in laser additive manufacturing processes, convolutional neural network (CNN) is used to directly learn patterns from melt-pool patterns to predict porosity. The classical CNN models without consideration of uncertainty are compared with the CNN models in which GM-PNN is embedded as an uncertainty quantification module. For both monitoring schemes, experimental results show that the GM-PNN not only achieves higher prediction accuracies of process conditions than the classical models but also provides more effective uncertainty quantification to facilitate the process-level decision-making in the manufacturing environment.</p><p>Based on the developed uncertainty analysis methods and their proven successes in practical applications, some directions for future studies are suggested. Closed-loop control systems may be synthesized by combining the AGMF with data-driven controller design. The AGMF can also be extended from a state estimator to the parameter estimation problems in data-driven models. In addition, the GM-PNN scheme may be expanded to directly build more complicated models like convolutional or recurrent neural networks.</p>

Brennstoffzellen werden durch Stapelung präziser Bipolarplatten mit komplexer Struktur von Fließkanälen für Flüssigkeiten und Leitungen für den Transfer von Gasen hergestellt. Die für derartige Platten notwendigen Bleche werden durch Stanzen und Umformen erzeugt. Dabei haben neben der Einrichtung von Presse und Werkzeug (z. B. Hubzahl, Stößelverstellung) auch deren Zustand Einfluss auf die Qualität der Platten (Ebenheit, Materialspannungen/Rissbildungen, Gratbildung). Obwohl moderne Pressen heute im Wesentlichen auf servoelektrischen Antrieben basieren, erfolgt die Kraftübertragung auf den Stößel vornehmlich über mehrere Antriebsdruckpunkte. Spezifische Schneide- und Umformwerkzeuge erzeugen auf einer entsprechenden Presse bei definierten Presseneinstellungen und Materialparametern spezifische Kraft-, Moment- und Kippungsverläufe. Die am IWU entwickelte Überwachungslösung ist in der Lage, die genannten Größen hubabhängig zu erfassen. Mittels eines im IO-Zustand von Presse und Werkzeug generierten „Fingerprints“ werden im laufenden Prozess gemessene Abweichungen erkannt und in Echtzeit analysiert. Dies ist insbesondere beim Einsatz dünnster Bleche von enormer Bedeutung, da hier bereits kleinste anlagenbedingte Änderungen zu kostenintensiven Ausfällen an Werkzeug und Produkt führen können.

Für den zielgerichteten Einsatz von präventiven und mitigativen Notfallmaßnahmen sowie zur Beurteilung ihrer Wirksamkeit während auslegungsüberschreitender Unfälle in Druckwasserreaktoren aber auch für Hinweise zum Störfallverlauf und für die Abschätzung der Auswirkungen auf die Anlagenumgebung müssen geeignete Störfallinstrumentierungen vorhanden sein. Insbesondere der Zustand des Reaktordruckbehälterinventars (RDB-Inventar) während der In-Vessel-Phase eines auslegungsüberschreitenden Unfalls lässt sich mit aktuellen Störfallinstrumentierungen nur stark eingeschränkt überwachen, wodurch die o. g. Forderungen nicht erfüllt werden können. Die vorliegende Arbeit beinhaltet detaillierte Untersuchungen für die Entwicklung einer Störfallinstrumentierung, welche eine durchgängige Zustandsüberwachung des RDB-Inventars während der In-Vessel-Phase eines auslegungsüberschreitenden Unfalls ermöglicht. Die Störfallinstrumentierung basiert auf der Messung und Klassifikation von unterschiedlichen Gammaflussverteilungen, welche während der In-Vessel-Phase außerhalb des Reaktordruckbehälters auftreten können. Ausgehend von der Analyse zum Stand von Wissenschaft und Technik wird der modell-basierte Ansatz für Structural Health Monitoring-Systeme genutzt, um eine grundlegende Vorgehensweise für die Entwicklung der Störfallinstrumentierung zu erarbeiten. Anschließend erfolgt eine detaillierte Analyse zu den Vorgängen während der In-Vessel-Phase und eine daraus abgeleitete Definition von Kernzuständen für einen generischen Kernschmelzunfall. Für die definierten Kernzustände werden mittels Simulationen (Monte-Carlo-Simulationen zum Gammastrahlungstransport in einem zu dieser Arbeit parallel laufenden Vorhaben) Gammaflüsse außerhalb des Reaktordruckbehälters berechnet. Die Simulationsergebnisse dienen dem Aufbau von Datenbasen für die Entwicklung und Analyse eines Modells zur Klassifikation von Gammaflussverteilungen. Für die Entwicklung des Klassifikationsmodells kommen drei diversitäre und unabhängig arbeitende Klassifikationsverfahren (Entscheidungsbaum, k-nächste-Nachbarn-Klassifikation, Multilayer Perzeptron) zur Anwendung, um die Identifikationsgenauigkeit und Robustheit der Störfallinstrumentierung zu erhöhen. Die abschließenden Betrachtungen umfassen die Validierung der Störfallinstrumentierung mittels eines Versuchstandes zur Erzeugung unterschiedlicher Gammaflussverteilungen. Im Ergebnis der Untersuchungen konnte die prinzipielle Funktionsweise der entwickelten Störfallinstrumentierung nachgewiesen werden. Unter der Voraussetzung, die Gültigkeit der definierten Kernzustände zu untermauern sowie weitere, nicht in dieser Arbeit betrachtete Kernschmelzszenarien mit in die Entwicklung der Störfallinstrumentierung einzubeziehen, steht somit insbesondere für zukünftige Kernkraftwerke mit Druckwasserreaktoren eine Möglichkeit für die messtechnische Überwachung des RDB-Inventars während auslegungsüberschreitender Unfälle bereit. Die Arbeit leistet einen wesentlichen Beitrag auf dem Gebiet der Reaktorsicherheitsforschung sowie für den sicheren Betrieb von kerntechnischen Anlagen.:1 Einleitung 2 Analyse zum Stand von Wissenschaft und Technik 2.1 Sicherheit in deutschen Kernkraftwerken mit Druckwasserreaktor 2.1.1 Mehrstufenkonzept 2.1.2 Störfallinstrumentierungen 2.2 Auslegungsüberschreitende Unfälle mit Kernschmelze in DWR 2.2.1 Auslösende Ereignisse 2.2.2 Grundlegender Ablauf eines auslegungsüberschreitenden Unfall mit Kernschmelze 2.3 Strahlungstechnik, Strahlungsmesstechnik 2.3.1 Grundlagen der Strahlungstechnik 2.3.2 Wechselwirkungen von Gammastrahlung mit Materie 2.3.3 Messung ionisierender Strahlung 2.4 Verfahren und Methoden der Zustandsüberwachung 2.4.1 Zustandsüberwachung 2.4.2 Structural Health Monitoring 2.4.3 Mustererkennung 2.4.4 Entscheidungsbäume 2.4.5 k-nächste-Nachbarn-Klassifikation 2.4.6 Künstliche neuronale Netze 2.5 Schlussfolgerungen aus der Analyse zum Stand von Wissenschaft und Technik 2.5.1 Zusammenfassung zum Kapitel 2 2.5.2 Zielstellung, Aufbau und Abgrenzung der Arbeit 3 Analyse der In-Vessel-Phase und Definition von Kernzuständen 3.1 Detaillierte Analyse der In-Vessel-Phase 3.1.1 Auftretende Temperaturbereiche 3.1.2 Vorgänge während der frühen In-Vessel-Phase 3.1.3 Vorgänge während der späten In-Vessel-Phase 3.1.4 Spaltproduktfreisetzung 3.2 Definition von Kernzuständen für einen generischen Kernschmelzunfall 3.3 Zusammenfassung zum Kapitel 3 4 Datenbasen zur Entwicklung und Analyse des Klassifikationsmodells 4.1 Beschreibung der Monte-Carlo-Simulationsmodell 4.2 Beschreibung der Simulationsergebnisse und Merkmalsextraktion 4.3 Datenbasis zur Entwicklung 4.4 Datenbasen zur Analyse 4.5 Zusammenfassung zum Kapitel 4 5 Entwicklung und Analyse des Klassifikationsmodells 5.1 Beschreibung des Klassifikationsmodells 5.2 Teilmodell 1 - Entscheidungsbaum 5.2.1 Entwicklung 5.2.2 Analyse der Identifikationsgenauigkeit 5.3 Teilmodell 3 - k-nächste-Nachbarn-Klassifikation 5.3.1 Entwicklung 5.3.2 Analyse der Identifikationsgenauigkeit 5.4 Teilmodell 3 - Multilayer Perzeptron 5.4.1 Trainings- und Testdatenbasis 5.4.2 Entwicklung 5.4.3 Analyse der Identifikationsgenauigkeit 5.5 Teilmodell 4 - Vergleichsalgorithmus 5.5.1 Entwicklung 5.5.2 Analyse der Identifikationsgenauigkeit 5.6 Analysen zur Robustheit des Klassifikationsmodells 5.6.1 Ausfall einzelner Gammastrahlungsdetektoren 5.6.2 Gleichzeitiger Ausfall mehrerer Gammastrahlungsdetektoren 5.7 Zusammenfassung und Schlussfolgerungen für das Kapitel 5 6 Validierung der Kernzustandsüberwachungsverfahren 6.1 Zielstellung und Vorgehensweise 6.2 Versuchstand zur Validierung 6.2.1 Aufbau 6.2.2 Funktionsweise 6.3 Anpassung der Kernzustandsüberwachungsverfahren an den Versuchsstand 6.4 Validierungsexperimente 6.4.1 Experiment 1 - Füllstandsänderungen 6.4.2 Experiment 2 - Quellenbewegungen 6.4.3 Experiment 3 - Füllstandsänderungen, Quellenbewegungen und Änderung von Profilkonturen 6.5 Zusammenfassung und Schlussfolgerungen für das Kapitel 6 7 Zusammenfassung und Ausblick

Electro-hydrostatic Actuation Systems (EAS) successfully combine the advantages of electro-mechanical actuation - such as high-energy efficiency and low noise emission - with the benefits of electro-hydraulic technology –which include robustness and the precise handling of large forces. This paper defines keywords like EAS and Electro-hydraulic pump unit (EPU), and provides a comparison of the two technologies. Given the wide range of EAS technology topics, it is only possible to briefly introduce and discuss these in this paper. This technology has reached a level that renders it a strong mechanism for machine manufacturers to support existing and future motion control requirements.