Structural Health Monitoring (SHM) is the technology of monitoring and assessing the condition of aerospace, civil, and mechanical infrastructures using a sensing system integrated into the structure. Among variety of SHM approaches, impedance-based method is efficient for local damage detection. This thesis focuses on system level concerns for impedance-based SHM. Two essential requirements are reached in the thesis: reduction of power consumption of wireless SHM sensor, and compensation of temperature dependency on impedance. The proposed design minimizes power by employing on-board signal processing, and by eliminating power hungry components such as ADC and DAC. The prototype implemented with MSP430 micro controller is verified to be able to handle SHM operation and wireless communication with extremely low-power: 0.15 mW during the inactive mode and 18 mW during the active mode. Each SHM operation takes about 13 seconds to consume 236 mJ. When our ASN-2 operates once in every four hours, it can run for about 2.5 years with two AAA-size batteries. To compensate for temperature change, we proposed an algorithm to select a small subset of baseline profiles for some critical temperatures and to estimate the baseline profile for a given ambient temperature through interpolation. Experimental results show that our method reduces the number of baseline profiles to be stored by 45%, and estimates the baseline profile of a given temperature accurately. / Master of Science

Structural health monitoring (SHM) is the process of using measurements of a structure's response to known excitations and trying to determine if damage has occurred to the structure. This also fits the description of non-destructive evaluation (NDE). The main difference is that NDE takes place while the structure is out of service and SHM is intended to take place while the structure is in service. As such, SHM provides the opportunity to provide early warning against structural failure. This thesis intends to advance the state of the art in SHM by examining two approaches to SHM: vibration based and impedance based, and to associate these with the NDE method of stress intensity factors. By examining these methods the goal is to try and answer some of the important questions in SHM process. The first is to experimentally validate a crack model and to see how small of a crack can be detected by vibration methods. The second is to use the concept of stress intensity factor to perform an SHM type of measurement to determine the remaining life of a structure once the impedance method has determined that damage has occurred. The measurement system considered consists of using several different piezoceramic materials as self-sensing actuators and sensors. The structures are a simple beam and a more complex lug element used in aircraft applications. The approach suggested here is to use the impedance and vibration methods to detect crack initiation and then to use the proposed stress intensity method to measure the stress intensity factor of the structure under consideration. / Master of Science

Smart infrastructure has become increasingly prevalent in recent decades due to the emergence of sophisticated and affordable sensing technologies. As sensors are deployed more widely and higher sampling rates are feasible, managing the massive scale of real-time data collected by these systems has become fundamental to providing relevant and timely information to decision-makers. To address this task, a novel open-source framework has been developed to manage and intuitively present high-volume data in near real-time. This design is centered around the goals of making data accessible, supporting decision-making, and providing flexibility to modify and reuse this framework in the future. In this work, the framework is tailored to vibration-based structural health monitoring, which can be used in near real-time to screen building condition. To promote timely intervention, distributed computing technologies are employed to accelerate the processing, storage, and visualization of data. Vibration data is processed in parallel using a publish-subscribe messaging queue and then inserted into a NoSQL database that stores heterogeneous data across several nodes. A REST-based web application allows interaction with this stored data via customizable visualization interfaces. To illustrate the utility of this framework design, it has been implemented to support a frequency domain monitoring dashboard for a 5-story classroom building instrumented with 224 accelerometers. A simulated scenario is presented to capture how the dashboard can aid decisions about occupant safety and structural maintenance. / Master of Science / Advances in technology have made it affordable and accessible to collect information about the world around us using sensors. When sensors are used to aid decision-making about structures, it is frequently referred to as Structural Health Monitoring (SHM). SHM can be used to monitor long-term structural health, inform maintenance decisions, and rapidly screen structural conditions following extreme events. Accelerometers can be used in SHM to capture vibration data that give insight into deflection patterns and natural frequencies in a structure. The challenge with vibration-based SHM and many other applications that leverage sensors is that the amount of data collected has the potential to grow to massive scales. To communicate relevant information to decision-makers, data must be processed quickly and presented intuitively. To facilitate this process, a novel open-source framework was developed for processing, storing, and visualizing high-volume data in near real-time. This framework combines multiple computers to extend the processing and storage capacity of our system. Data is processed in parallel and stored in a database that supports efficient data retrieval. A web application enables interaction with stored data via customizable visualization interfaces. To demonstrate the framework functionality, it was implemented in a 5-story classroom building instrumented with 224 accelerometers. A frequency-domain dashboard was developed for the building, and a simulated scenario was conducted to capture how the dashboard can aid decisions about occupant safety and structural maintenance.

A self-sensing and self-healing bolted joint has been developed. This concept encompasses the areas of health monitoring, joint dynamics and smart materials. In order to detect looseness in a joint the impedance health monitoring method is used. A new method of making impedance measurements for health monitoring that greatly reduces the equipment cost and equipment size was developed. This facilitates implementation of the impedance technique in real-life field applications. Several proof of concept experiments are presented and compared to the traditional method of making impedance measurements. Investigations of bolted joint dynamics were conducted. A literature review of bolted joints and their diagnostics is presented. The application of the transfer impedance method is compared to standard modal tests on various bolt tensions. An investigation of damping in bolted joints was also made comparing a bolted and monolithic beam. Practical issues in adaptive bolted joints are investigated. This includes issues on activating/heating SMA actuators, connecting the actuators to the power source, size selection of SMA actuators and insulations. These issues are examined both experimentally and theoretically. / Master of Science

This thesis presents a generic passive non-contact based acoustic health monitoring approach using ultrasonic acoustic emissions (UAE) to facilitate classification of bearing health via neural networks. This generic approach is applied to classifying the operating condition of conventional ball bearings. The acoustic emission signals used in this study are in the ultrasonic range (20-120 kHz), which is significantly higher than the majority of the research in this area thus far. A direct benefit of working in this frequency range is the inherent directionality of the microphones capable of measurement in this range, which becomes particularly useful when operating in environments with low signal-to-noise ratios. Using the UAE power spectrum signature, it is possible to pose the health monitoring problem as a multi-class classification problem, and make use of a multi-layer artificial neural network (ANN) to classify the UAE signature. One major problem limiting the usefulness of ANN's for failure classification is the need for large quantities of training data. Artificial training data, based on statistical properties of a significantly smaller experimental data set is created using the combination of a normal distribution and a coordinate transformation. The artificial training data provides a sufficient sized data set to train the neural network, as well as overcome the curse of dimensionality. The combination of the artificial training methods and ultrasonic frequency range being used results in an approach generic enough to suggest that this particular method is applicable to a variety of systems and components where persistent UAE exist. / Master of Science

Condition assessment of civil engineering infrastructure systems is of growing importance as they face aging and degradation due to both human-made activities and environmental factors. Nevertheless, challenges persist in data collection, leading to "data scarcity", and the need for frequent site visits in inspections, presenting significant obstacles in the assessment of the civil infrastructure systems. This dissertation aims to overcome these challenges by exploring the potential of two emerging technologies: Generative Artificial Intelligence (AI) and Virtual Reality (VR). In tackling the issue of data scarcity, the research question revolves around how Generative AI can be utilized to mitigate data collection-related constraints and increase data availability, thus facilitating health monitoring applications of infrastructure systems. For that, using various Generative AI models, the dissertation works on acceleration response data generation, including data augmentation and domain translation applications on different structures. In addressing the site visit challenge, the dissertation focuses on the use of VR to bring the infrastructure to the experts in a single collaborative immersive environment and investigate its impact on decision-making in inspections. For that, using VR technology, the dissertation develops a Virtual Meeting Environment (VME) integrated with the infrastructure data and models presented through novel immersive visualization techniques. The dissertation further investigates the impact of VME on decision-making in infrastructure inspections through experimentation with engineers. These investigations of the use of Generative AI and VR demonstrate various contributions. Generative AI effectively tackles the need for vast datasets in data-intensive damage detection applications. It also demonstrates its potential in estimating representative response data for various structural conditions across dissimilar infrastructures. VME, on the other hand, offers an increased understanding of the material along with a safer, practical, and cost-effective complementing alternative to traditional in-person site visits. It further reveals how VME improves decision-making in infrastructure inspections.

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.

This thesis is about using structural-dynamics based methods to address the existing challenges in the field of Structural Health Monitoring (SHM). Particularly, new structural-dynamics based methods are presented, to model areas of damage, to do damage diagnosis and to estimate and predict the sensitivity of structural vibration properties like natural frequencies to the presence of damage. Towards these objectives, a general analytical procedure, which yields nth-order expressions governing mode shapes and natural frequencies and for damaged elastic structures such as rods, beams, plates and shells of any shape is presented. Features of the procedure include the following: 1. Rather than modeling the damage as a fictitious elastic element or localized or global change in constitutive properties, it is modeled in a mathematically rigorous manner as a geometric discontinuity. 2. The inertia effect (kinetic energy), which, unlike the stiffness effect (strain energy), of the damage has been neglected by researchers, is included in it. 3. The framework is generic and is applicable to wide variety of engineering structures of different shapes with arbitrary boundary conditions which constitute self adjoint systems and also to a wide variety of damage profiles and even multiple areas of damage. To illustrate the ability of the procedure to effectively model the damage, it is applied to beams using Euler-Bernoulli and Timoshenko theories and to plates using Kirchhoff's theory, supported on different types of boundary conditions. Analytical results are compared with experiments using piezoelectric actuators and non-contact Laser-Doppler Vibrometer sensors. Next, the step of damage diagnosis is approached. Damage diagnosis is done using two methodologies. One, the modes and natural frequencies that are determined are used to formulate analytical expressions for a strain energy based damage index. Two, a new damage detection parameter are identified. Assuming the damaged structure to be a linear system, the response is expressed as the summation of the responses of the corresponding undamaged structure and the response (negative response) of the damage alone. If the second part of the response is isolated, it forms what can be regarded as the damage signature. The damage signature gives a clear indication of the damage. In this thesis, the existence of the damage signature is investigated when the damaged structure is excited at one of its natural frequencies and therefore it is called ``partial mode contribution". The second damage detection method is based on this new physical parameter as determined using the partial mode contribution. The physical reasoning is verified analytically, thereupon it is verified using finite element models and experiments. The limits of damage size that can be determined using the method are also investigated. There is no requirement of having a baseline data with this damage detection method. Since the partial mode contribution is a local parameter, it is thus very sensitive to the presence of damage. The parameter is also shown to be not affected by noise in the detection ambience.

Aerospace structures comprising of metals and composites are exposed to extreme loading and environmental conditions which necessitates regular inspection and maintenance to verify and monitor overall structural integrity. The timely and accurate detection, characterization and monitoring of structural cracking, corrosion, delaminating, material degradation and other types of damage are of major concern in the operational environment. Along with these, stringent requirements of safety and operational reliability have lead to evolutionary methods for evaluation of structural integrity. As a result, conventional nondestructive evaluation methods have moved towards a new concept, Structural Health Monitoring (SHM). SHM provides in-situ information a bout the occurrence of damage if any, location and severity of damage and residual life of the structure and also helps in improving the safety, reliability and confidence levels of critical engineering structures. While the concepts underlying SHM are well understood, development of methods is still in a nascent stage which requires extensive research that is challenging and has been the main motivating factor for undertaking the work reported in the thesis. Under the scope of the investigations carried out in this thesis, an integrated approach using Ultrasonic (active) and Acoustic Emission (passive) methods has been explored for SHM of metallic and composite plate structures using a distributed array of surface bonded circular piezoelectric wafer active sensors(PWAS). In ultrasonic method, PWAS is used for actuation and reception of Lamb waves in plate structures. The damage detection is based on the interaction of waves with defects resulting in reflection, mode conversion and scattering. In acoustic emission (AE) technique, the same sensor is used to pick up the stress waves generated by initiation or growth of defects or damage. Thus, both the active and passive damage detection methods are used in this work for detection, location and characterization of defects and damage in metallic and composite plates with complex geometries and structural discontinuities. And, thus the strategy adopted is to use time-frequency analysis and time reversal technique to extract the information from Lamb wave signals for damage detection and a geodesic based Lamb wave approach for location of the damage in the structure. To start with experiments were conducted on aluminum plates to study the interaction of Lamb waves with cracks oriented at different angles and on a titanium turbine blade of complex geometry with a fine surface crack. Further, the interaction of Lamb wave modes with multiple layer delaminations in glass fiber epoxy composite laminates was studied. The data acquired from these experiments yielded complex sets of signals which were not easily discern able for obtaining the information required regarding the defects and damage. So, to obtain a basic understanding of the wave patterns, Spectral finite element method has been employed for simulation of wave propagation in composite beams with damages like delamination and material degradation. Following this, time-frequency analysis of a number of simulated and experimental signals due to elastic wave scattering from defects and damage were performed using wavelet transform (WT) and Hilbert-Huang transform(HHT).And, a comparison of their performances in the context of quantifying the damages has given detailed insight into the problem of identifying localized damages, dispersion of multi-frequency non-stationary signals after their interaction with different types of defects and damage, finally leading to quantification. Conventional Lamb wave based damage detection methods look for the presence of defects and damage in a structure by comparing the signal obtained with the baseline signal acquired under healthy conditions. The environmental conditions like change in temperature can alter the Lamb wave signals and when compared with baseline signals may lead to false damage prediction. So, in order to make Lamb wave based damage detection baseline free, in the present work, the time reversal technique has been utilized. And, experiments were conducted on metallic and composite plates to study the time reversal behavior ofA0 andS0Lamb wave modes. Damage in the form of a notch was introduced in an aluminum plate to study the changes in the characteristics of the time reversed Lamb wave modes experimentally. This experimental study showed that there is no change in the shape of the time reversed Lamb wave in the presence of defect implying no breakage of time reversibility. Time reversal experiments were further carried out on a carbon/epoxy composite T-pull specimen representing a typical structure. And, the specimen was subjected to a tensile loading in a Universal testing machine. PWAS sensor measurements were carried out at no load as also during different stages of delamination due to tensile loading. Application of time reversed A 0 and S0 modes for both healthy and delaminated specimens and studying the change in shape of the time reversed Lamb wave signals has resulted in successful detection of the presence of delamination. The aim of this study has been to show the effectiveness of Lamb wave time reversal technique for damage detection in health monitoring applications. The next step in SHM is to identify the damage location after the confirmation of presence of damage in the structure. Wave based acoustic damage detection methods (UT and AE) employing triangulation technique are not suitable for locating damage in a structure which has complicated geometry and contains structural discontinuities. And, the problem further gets compounded if the material of the structure is anisotropic warranting complex analytical velocity models. In this work, a novel geodesic approach using Lamb waves is proposed to locate the AE source/damage in plate like structures. The approach is based on the fact that the wave takes minimum energy path to travel from the source to any other point in the connected domain. The geodesics are computed numerically on the meshed surface of the structure using Dijkstra’s algorithm. By propagating the waves in reverse virtually from these sensors along the geodesic path and by locating the first inter section point of these waves, one can get the AE source/damage location. Experiments have been conducted on metallic and composite plate specimens of simple and complex geometry to validate this approach. And, the results obtained using this approach has demonstrated the advantages for a practicable source location solution with arbitrary surfaces containing finite discontinuities. The drawback of Dijkstra’s algorithm is that the geodesics are allowed to travel along the edges of the triangular mesh and not inside them. To overcome this limitation, the simpler Dijkstra’s algorithm has been replaced by a Fast Marching Method (FMM) which allows geodesic path to travel inside the triangular domain. The results obtained using FMM showed that one can accurately compute the geodesic path taken by the elastic waves in composite plates from the AE source/damage to the sensor array, thus obtaining a more accurate damage location. Finally, a new triangulation technique based on geodesic concept is proposed to locate damage in metallic and composite plates. The performances of triangulaton technique are then compared with the geodesic approach in terms of damage location results and their suitability to health monitoring applications is studied.

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