Reducing unscheduled maintenance is important for aircraft operators. There are significant costs if flights must be delayed or cancelled, for example, if spares are not available and have to be shipped across the world. This thesis describes three methods of aircraft health condition monitoring and prediction; one for system monitoring, one for forecasting and one combining the two other methods for a complete monitoring and prediction process. Together, the three methods allow organizations to forecast possible failures. The first two use decision trees for decision-making and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have several advantages: the generated code is quickly and easily processed, it can be altered by human experts without much work, it is readable by humans, and it requires few resources for learning and evaluation. The readability and the ability to modify the results are especially important; special knowledge can be gained and errors produced by the automated code generation can be removed. A large number of data sets is needed for meaningful predictions. This thesis uses two data sources: first, data from existing aircraft sensors, and second, sound and vibration data from additionally installed sensors. It draws on methods from the field of big data and machine learning to analyse and prepare the data sets for the prediction process.

This thesis aims to use vibration and acoustic noise analysis to help a production line of a certain motor type to ensure good quality. Noise from the gearbox is sometimes present and the way it is detected is with a human listening to it. This type of error detection is subjective, and it is possible for human error to be present. Therefore, an automatic test that pass or fail the produced Brush Less Direct Current (BLDC)-motors is wanted. Two measurement setups were used. One was based on an accelerometer which was used for vibration measurements, and the other based on a microphone for acoustic sound measurements. The acquisition and analysis of the measurements were implemented using the data acquisition device, compactDAQ NI 9171, and the graphical programming software, NI LabVIEW. Two methods, i.e., power spectrum analysis and machine learning, were used for the analyzing of vibration and acoustic signals, and identifying faults in the gearbox. The first method based on the Fast Fourier transform (FFT) was used to the recorded sound from the BLDC-motor with the integrated planetary gearbox to identify the peaks of the sound signals. The source of the acoustic sound is from a faulty planet gear, in which a flank of a tooth had an indentation. Which could be measured and analyzed. It sounded like noise, which can be used as the indications of faults in gears. The second method was based on the BLDC-motors vibration characteristics and uses supervised machine learning to separate healthy motors from the faulty ones. Support Vector Machine (SVM) is the suggested machine learning algorithm and 23 different features are used. The best performing model was a Coarse Gaussian SVM, with an overall accuracy of 92.25 % on the validation data.

Strukturüberwachung und Condition Monitoring spielen in vielen Gebieten der Technik eine große Rolle. Zur Überwachung von Leichtbaustrukturen aus faserverstärkten Kunststoffen bietet sich hierfür besonders die Körperschall-Analyse an. Am Markt etabliert sind hierfür piezoelektrische Signalaufnehmer. Diese Arbeit stellt eine kostengünstige Alternative in Form von mikromechanischen Körperschall-Sensoren vor. Eine Besonderheit stellt hierbei das Prinzip des mechanischen Bandpasses dar. Es wird die Elektronik- und Gehäuseentwicklung sowie die experimentelle Untersuchung dargelegt. / Structural health monitoring is of vital importance in many technical fields. For monitoring of lightweight structures made from fiber reinforced plastics especially acoustic emission testing is used. Commercially available transducers utilize the piezoelectric effect. This thesis introduces a cost efficient alternative in form of micromechanical sensors, in particular sensors using the principle of a mechanical bandpass. The design of electronics and the packaging as well as experimental investigations are provided.

In the hope to increase the detection rate of faults in combined heat and power plant boilers thus lowering unplanned maintenance three machine learning models are constructed and evaluated. The algorithms; k-Nearest Neighbor, One-Class Support Vector Machine, and Auto-encoder have a proven track record in research for anomaly detection, but are relatively unexplored for industrial applications such as this one due to the difficulty in collecting non-artificial labeled data in the field.The baseline versions of the k-Nearest Neighbor and Auto-encoder performed very similarly. Nevertheless, the Auto-encoder was slightly better and reached an area under the precision-recall curve (AUPRC) of 0.966 and 0.615 on the trainingand test period, respectively. However, no sufficiently good results were reached with the One-Class Support Vector Machine. The Auto-encoder was made more sophisticated to see how much performance could be increased. It was found that the AUPRC could be increased to 0.987 and 0.801 on the trainingand test period, respectively. Additionally, the model was able to detect and generate one alarm for each incident period that occurred under the test period.The conclusion is that ML can successfully be utilized to detect faults at an earlier stage and potentially circumvent otherwise costly unplanned maintenance. Nevertheless, there is still a lot of room for improvements in the model and the collection of the data. / I hopp om att öka identifieringsgraden av störningar i kraftvärmepannor och därigenom minska oplanerat underhåll konstrueras och evalueras tre maskininlärningsmodeller.Algoritmerna; k-Nearest Neighbor, One-Class Support Vector Machine, och Autoencoder har bevisad framgång inom forskning av anomalidetektion, men är relativt outforskade för industriella applikationer som denna på grund av svårigheten att samla in icke-artificiell uppmärkt data inom området.Grundversionerna av k-Nearest Neighbor och Auto-encoder presterade nästan likvärdigt. Dock var Auto-encoder-modellen lite bättre och nådde ett AUPRC-värde av 0.966 respektive 0.615 på träningsoch testperioden. Inget tillräckligt bra resultat nåddes med One-Class Support Vector Machine. Auto-encoder-modellen gjordes mer sofistikerad för att se hur mycket prestandan kunde ökas. Det visade sig att AUPRC-värdet kunde ökas till 0.987 respektive 0.801 under träningsoch testperioden. Dessutom lyckades modellen identifiera och generera ett larm vardera för alla incidenter under testperioden. Slutsatsen är att ML framgångsrikt kan användas för att identifiera störningar iett tidigare skede och därigenom potentiellt kringgå i annat fall dyra oplanerade underhåll. Emellertid finns det fortfarande mycket utrymme för förbättringar av modellen samt inom insamlingen av data.

[ES] Las bombas hidráulicas son el núcleo de muchos procesos en la industria y el sector servicios. Conviene tener en cuenta que los motores eléctricos son responsables del 69% del consumo de energía eléctrica en la industria, siendo en torno a un 22% de motores utilizados para el accionamiento de bombas. Los fallos de estas bombas pueden provocar averías en el proceso y, por lo tanto, implican altos costes económicos para el operador de la planta. Además, un funcionamiento defectuoso de las bombas conlleva una reducción de la eficiencia energética de la planta. De forma habitual, se utilizan principalmente dos tipos de estrategias orientadas al mantenimiento de maquinaria. Una estrategia de mantenimiento (mantenimiento preventivo) consiste en la sustitución de las piezas desgastadas en un intervalo de tiempo fijo. Este tipo de estrategia presenta muchas desventajas asociadas a la escasa optimización en el uso de los recursos y al consiguiente impacto económico. Por otro lado, la estrategia basada en la condición del equipo (mantenimiento basado en la condición) liga el reemplazo de las piezas desgastadas al estado del equipo, el cual es monitorizado a través de señales adquiridas mediante sensores. Sin embargo, el uso de sensores tiene algunos inconvenientes, como costes de inversión adicionales, posibles problemas en el montaje del sensor y posibles fallos del mismo. El análisis de la señal de corriente no se ha utilizado de forma habitual en la práctica para evaluar el estado de la bomba, aunque en muchas aplicaciones se dispone de sensores de corriente ya instalados que se podrían utilizar a tal fin. Se ha demostrado que técnicas basadas en el análisis de la corriente resultan de gran utilidad para diagnosticar varios tipos de fallos en motores eléctricos. De hecho, el análisis de la firma de corriente del motor se utiliza hoy en día ampliamente en la industria, especialmente para el diagnóstico de fallos en motores de inducción. En la presente tesis, se evalúa la utilización de la técnica de análisis de corrientes para el diagnóstico de fallos típicos relacionados con las bombas en diferentes aplicaciones. Se investigan tres tipos de bombas diferentes: bombas en línea de rotor húmedo, bombas de rotor seco y bombas sumergibles. En la tesis se han adaptado diversas técnicas, previamente empleadas para la detección de fallos en motores, al diagnóstico de fallos en la propia bomba. Los resultados indican que fallos como obstrucción de la bomba, fisura del impulsor y desgaste de los cojinetes influyen especialmente en dos frecuencias del espectro de corriente, las cuales pueden utilizarse como base de estrategias de mantenimiento basadas en la condición. En concreto, en las bombas de rotor húmedo, estos dos indicadores de fallo varían sensiblemente en función del punto de carga hidráulica de la bomba. Con la ayuda de un método de extracción de características basado en la motor reference frame theory, se demuestra que las mencionadas frecuencias pueden analizarse en tiempo real en un entorno industrial. Además, se presentan directrices para la monitorización en la nube y se valida con la ayuda de ensayos de laboratorio. Adicionalmente, se demuestra que los fallos son también detectables al analizar la corriente de arranque mediante herramientas de descomposición tiempo-frecuencia. Este hito no se había abordado anteriormente en la literatura técnica del área en lo referente a la detección de fallos en bombas. En conclusión, los resultados de este trabajo demuestran que los métodos de diagnóstico basados en la corriente pueden detectar con éxito diversos tipos de fallo en bombas, lo cual constituye un punto de gran interés para las industrias que utilicen estos activos en sus procesos. / [CA] Les bombes hidràuliques són el nucli de molts processos en la indústria i en el sector dels serveis. Cal mencionar que els motors elèctrics són responsables del 69% del consum de la energia elèctrica en la indústria, sent al voltant del 22% dels motors utilitzats per l'accionament de bombes. Les fallades d'aquestes bombes poden causar avaries en els processos, i per tant, representen un alt cost econòmic per a l'operador de la planta. A més a més, un funcionament defectuós en les bombes representa una reducció de l'eficiència energètica de la planta. De manera habitual, s'utilitzen principalment dos tipus d'estratègies orientades al manteniment de la maquinària. Una estratègia de manteniment (manteniment preventiu) consisteix en la canvi de les peces desgastades en un interval fixe de temps. Aquest tipus d'estratègia presenta molts desavantatges associats a la reduïda optimització en el ús dels recursos i el seu impacte econòmic. D'altra banda, la estratègia basada en la condició dels equipaments (manteniment basat en la condició) enllaça la substitució de les peces desgastades al estat de l'equip, el qual es monitoritzat per mig de senyals adquirides per sensors. No obstant això, el ús de sensors té alguns inconvenients com costos d'inversió addicionals, possibles problemes al muntatge i possibles fallades. L'anàlisi dels senyals de corrent no s'utilitzen de manera habitual en la pràctica per avaluar l'estat de la bomba, encara que en moltes aplicacions, estos sensors es troben instal·lats i es podrien fer servir per a aquesta finalitat. Ha estat demostrat que les tècniques basades en l'anàlisi de la corrent són de gran utilitat per el diagnosi de diversos tipus de fallades en motors elèctrics. De fet, l'anàlisi de la firma de la corrent del motor s'utilitza àmpliament en l'indústria, especialment per el diagnosi de fallades en motors d'inducció. En la present tesi, s'avalua l'utilització de la tècnica d'anàlisi de corrents per el diagnosi de fallades típiques relacionades en bombes per a diferents aplicacions. Se investiguen tres tipus de bombes diferents: bombes en línia de rotor humit, bombes de rotor sec i bombes submergibles. En aquesta tesi se han adaptat diverses tècniques, prèviament utilitzades en el diagnosi de màquines elèctriques, per al diagnosi de la pròpia bomba. Els resultat indiquen que les fallades com obstrucció de la bomba, la fissura de l'impulsor i el desgast dels coixinets influeixen especialment en dos freqüències de l'espectre de la corrent, les quals es poden utilitzar com a base per a una estratègia de manteniment basada en la condició. Particularment, en les bombes de rotor humit, aquestos dos indicadors de fallada varíen sensiblement en funció del punt de càrrega hidràulica de la bomba. En l'ajuda de un mètode d'extracció de característiques basat en la "motor reference frame theory", es demostra que les mencionades freqüències es poden analitzar en temps real en un entorn industrial. A més a més, es presenten directrius per la monitorització en el núvol i es valida en l'ajuda de assajos en el laboratori. Addicionalment, es demostra que les fallades són també detectables quan s'analitza la corrent d'arrancada mitjançant ferramentes de descomposició temps-freqüència. Aquest fet no ha estat analitzat prèviament en cap tipus de literatura tècnica dins del camp de detecció de fallades en bombes. En conclusió, els resultats d'aquest treball demostren que els mètodes de diagnosi basats en la corrent poden detectar en èxit diversos tipus de fallades en bombes, el qual constitueix un punt d'interés per a l'indústria que utilitzen aquest tipus de actiu en els seus processos. / [EN] Pumps are the heart of many processes in industry and service sector. Electric motors are responsible for 69% of electric energy consumption in industry, with 22% of them being used for the operation of pumps. Pump faults can lead to process breakdowns and are thus related to high costs for the plant operator. Furthermore, faulty operation of pumps reduces the energy efficiency of the plant. In many cases, a time-based maintenance strategy is applied, which means that typical wear parts are replaced within defined time cycles, which comes with some drawbacks such as poor resource efficiency and high costs. Condition-based maintenance strategies - meaning that the replacement of parts is planned based on the condition of the pump - are often based on the evaluation of sensor signals like vibration or noise. However, the use of sensors also has some drawbacks, such as additional investment costs, frequent problems with the sensor mounting, and possible sensor faults. There is no widespread use of the current signal to make statements about the pump condition, although current sensors are installed in many applications anyway. As for motor fault diagnosis, different current-based techniques have demonstrated their function. Today, motor current signature analysis is used in industry, especially for the diagnosis of induction motors. In this thesis, the current-based diagnosis of typical pump-related faults in different applications is evaluated. In total, three different pump types are investigated: a wet-rotor pump, a dry-runner inline pump, and a submersible pump. The techniques used for motor fault detection are adapted for the diagnosis of pump-related faults. The results indicate that the faults clogging, impeller crack, and bearing wear, in particular, influence two frequencies in the current spectrum, which can be used as a basis for a condition-based maintenance strategy. Especially in wet-rotor pumps, these two fault indicators strongly vary depending on the hydraulic load point of the pump. With the help of a feature extraction method based on the adapted reference frame theory, this work demonstrates that the two frequencies can be analyzed in real time in a field environment. Furthermore, a concept for cloud monitoring is presented and validated with the help of a laboratory test. Additionally, it is demonstrated that the faults are visible if the starting current is evaluated in a time-frequency map, which has not been considered before in the literature on pump-related faults. In summary, the findings of this work indicate that current-based diagnosis methods can successfully detect typical faults in pumps, a fact that is of high interest for companies using these assets in their industrial processes. / Becker, V. (2022). Current-based Techniques for Condition Monitoring of Pumps [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/190630

<p dir="ltr">Condition-based maintenance is a proactive maintenance strategy that prevents failures or diminished functionality in process systems through proper monitoring and management of process conditions. Despite being considered a mature maintenance management strategy in various industries, condition-based maintenance remains underutilized in pharmaceutical manufacturing. This situation needs to change, especially as the pharmaceutical industry continues to shift from batch to continuous manufacturing, where the implementation of CBM as a maintenance strategy assumes a greater importance.</p><p dir="ltr">This dissertation focused on addressing the challenges of implementing CBM in a continuous drug product manufacturing system. These challenges stem from the unique aspects of pharmaceutical drug product manufacturing, which includes the peculiar behavior of particulate materials and the evolutionary nature of pharmaceutical process development. The proposed solutions to address these challenges revolve around an innovative framework for the practical development of condition monitoring systems. Overall, this framework enables the incorporation of limited process knowledge in creating condition monitoring systems, which has the desired effect of empowering data-driven machine learning models.</p><p dir="ltr">A key feature of this framework is a formalized method to represent the process condition, which is usually vaguely defined in literature. This representation allows the proper mapping of preexisting condition monitoring systems, and the segmentation of the entire process condition model into smaller modules that have more manageable condition monitoring problems. Because this representation methodology is based on probabilistic graphical modelling, the smaller modules can then be holistically integrated via their probabilistic relationships, allowing the robust operation of the resulting condition monitoring system and the process it monitors.</p><p dir="ltr">Breaking down the process condition model into smaller segments is crucial for introducing novel fault detection capabilities, which enhances model prediction transparency and ensures prediction acceptance by a human operator. In this work, a methodology based on prediction probabilities was introduced for developing condition monitoring systems with novel fault detection capabilities. This approach relies on high-performing machine learning models capable of consistently classifying all the initially known conditions in the fault library with a high degree of certainty. Simplifying the condition monitoring problem through modularization facilitates this, as machine learning models tend to perform better on simpler systems. Performance indices were proposed to evaluate the novel fault detection capabilities of machine learning models, and a formal approach to managing novel faults was introduced.</p><p dir="ltr">Another benefit of modularization is the identification of condition monitoring blind spots. Applying it to the RC led to sensor development projects such as the virtual sensor for measuring granule flowability. This sensor concept was demonstrated successfully by using a data-driven model to predict granule flowability based on size and shape distribution measurements. With proper model selection and feature extraction guided by domain expertise, the resulting sensor achieved the best prediction performance reported in literature for granule flowability.</p><p dir="ltr">As a demonstration exercise in examining newly discovered faults, this work investigated a roll compaction phenomenon that is usually concealed from observation due to equipment design. This phenomenon results in the ribbon splitting along its thickness as it comes out of the rolls. In this work, important aspects of ribbon splitting were elucidated, particularly its predictability based on RC parameters and the composition of the powder blend used to form the ribbon. These findings have positive ramifications for the condition monitoring of the RC, as correspondence with industrial practitioners suggests that a split ribbon is desirable in some cases, despite being generally regarded as undesirable in the limited literature available on the subject.</p><p dir="ltr">Finally, this framework was primarily developed for the pharmaceutical dry granulation line, which consists of particle-based systems with a moderate level of complexity. However, it was also demonstrated to be feasible for the Tennessee Eastman Process (TEP), a more complex liquid-gas process system with a greater number of process faults, variables, and unit operations. Applying the framework resulted in machine learning models that yielded one of the best fault detection performances reported in literature for the TEP, while also introducing additional capabilities not yet normally reported in literature, such as fault diagnosis and novel fault detection.</p>

Die vorliegende Arbeit beschäftigt sich mit der Entwicklung eines neuartigen, autarken, folienbasierten Sensorsystems für die Belastungsdetektion an Leichtbaumaterialien. Das integrierte Sensorsystem ist in der Lage mechanische Belastungen über die Photolumineszenz von Quantum Dots visuell darzustellen, wodurch strukturelle Defekte in Leichtbaumaterialien frühzeitig erkannt und ein Totalausfall einer gesamten Leichtbaukonstruktion verhindert werden kann. Dies führt neben einer erhöhten Sicherheit einzelner Komponenten und kompletter Konstruktionen auch zu Gewichts-, Kosten- und Rohstoffersparnissen. Die gezielte Beeinflussung der Photolumineszenz von Quantum Dots durch Ladungsträgerinjektion als Hauptmechanismus des Sensorsystems erfordert spezielle Lagenaufbauten von Dünnschichtsystemen. Durch die Kombination dieser Dünnschichtsysteme mit piezoelektrischen Materialien entsteht ein autarkes Sensorsystem, wodurch eine Auswertung, Visualisierung und Speicherung der Information über eine stattgefundene mechanische Belastung an Leichtbaumaterialien auf kleinsten Raum erreicht wird.:Inhaltsverzeichnis Formelverzeichnis Abkürzungsverzeichnis Vorwort 1 Einleitung 1.1 Motivation 1.2 Zielstellung 2 Autarker Sensor für mechanische Beanspruchungen 2.1 Sensorkonzept, -aufbau und Funktionsweise 2.2 Anforderungen an die Funktionalität 2.3 Stand der Technik 3 Theoretische Grundlagen 3.1 Quantum Dots 3.1.1 Größenquantisierungseffekt 3.1.2 Photolumineszenz 3.1.3 Aufbau und Materialien 3.1.4 Kommerziell erhältliche Quantum Dots 3.2 Mechanismen zur Beeinflussung der Photolumineszenz 3.2.1 Ladungsträgerinjektion in den QD Kern 3.2.2 Feldinduzierte Ionisation des Exzitons 3.2.3 Weitere Mechanismen 3.3 Ladungsträgertransportschichten 3.3.1 Poly(N-vinylkarbazol) 3.3.2 N,N,N´,N´-Tetrakis(3-methylphenyl)-3,3´-dimethylbenzidin 3.3.3 Poly(3,4-ethylendioxythiophen)-poly(styrolsulfonat) 3.4 Lithiumfluorid als elektrischer Isolator 3.5 Modellsysteme 3.5.1 Einbettung der QDs in organische Lochtransportschichten 3.5.2 QDs zwischen Elektrode und organischer Lochtransportschicht 3.5.3 QDs zwischen Elektrode und Nichtleiter 4 Experimentelle Vorgehensweise 4.1 Layout und Kontaktierung von Teststrukturen 4.2 Verfahren zur Herstellung dünner Schichten 4.2.1 Physikalische Gasphasenabscheidung 4.2.2 Rotationsbeschichtung 4.2.3 Weitere Verfahren 4.3 Charakterisierung der Schichten und der Gesamtfunktionalität 4.3.1 Mikrospektroskopieaufbau 4.3.2 Weitere Messverfahren 4.4 Integration der Schichtstapel in Faserkunststoffverbund 5 Experimentelle Untersuchungen 5.1 Einordnung der einzelnen Schichten der Modellsysteme 5.1.1 Elektroden 5.1.2 Matrixmaterial und Quantum Dots 5.2 Einordnung des elektrischen Verhaltens der Modellsysteme 5.2.1 Modellsystem I 5.2.2 Modellsystem II 5.2.3 Modellsystem III 5.3 Einfluss externer Beleuchtung am Modellsystem II und III 5.3.1 Modellsystem II 5.3.2 Modellsystem III 5.4 Wiederholbarkeit der elektrischen Beanspruchung am Modellsystem III 5.4.1 Photolumineszenzintensität 5.4.2 Stromdichte 5.4.3 Gesamtwiderstand im Schichtstapel 5.5 Einfluss des elektrischen Feldes am Modellsystem III 5.5.1 Photolumineszenzintensität 5.5.2 Stromdichte 5.5.3 Widerstand 5.6 Einfluss der Integration auf das Verhalten von Modellsystem III 5.6.1 Optisches Verhalten der Laminiertasche und des Harzsystems 5.6.2 Funktionalität des Schichtstapels nach der Integration 5.7 Temperaturwechseltest am integrierten Schichtstapel 5.8 Speicherzeit elektrischer Ladungsträger am Modellsystem III 5.8.1 Stabilität des Lasers und der PL Intensität 5.8.2 Reproduzierbarkeit 5.8.3 Langzeitmessung 5.9 Kopplung des Schichtsystems mit piezoelektrischem Element 6 Zusammenfassung und Ausblick 6.1 Zusammenfassung 6.2 Ausblick Anhang A : Layouts für untere Elektrode E1 und obere Elektrode E2 Anhang B : Halter für die Kontaktierung der Teststrukturen Anhang C : Frontpanel zur Aufnahme der Photolumineszenz Anhang D : Messdaten Profilometer Veeco Dektak 150 Literaturverzeichnis Abbildungsverzeichnis Tabellenverzeichnis Lebenslauf / This work focuses on the development of a novel, self-sufficient, film-based sensor system for load detection on lightweight materials. The integrated sensor system is capable to visualize mechanical loads on lightweight structures by quenching the photoluminescence of quantum dots. Structural defects in lightweight materials can thus be detected at an early stage and total failure of an entire lightweight structure can be prevented. In addition to increased safety of individual components and complete structures, this also leads to weight, cost and raw material savings. The quenching of the photoluminescence of quantum dots by charge carrier injection as the main mechanism of the sensor system requires special thin-film layer stacks. By combining these thin-film layer stacks with piezoelectric materials, a self-sufficient sensor system is created. An evaluation, visualization and storage of the information about a mechanical load that has taken place on lightweight materials is thus achieved in a very small space.:Inhaltsverzeichnis Formelverzeichnis Abkürzungsverzeichnis Vorwort 1 Einleitung 1.1 Motivation 1.2 Zielstellung 2 Autarker Sensor für mechanische Beanspruchungen 2.1 Sensorkonzept, -aufbau und Funktionsweise 2.2 Anforderungen an die Funktionalität 2.3 Stand der Technik 3 Theoretische Grundlagen 3.1 Quantum Dots 3.1.1 Größenquantisierungseffekt 3.1.2 Photolumineszenz 3.1.3 Aufbau und Materialien 3.1.4 Kommerziell erhältliche Quantum Dots 3.2 Mechanismen zur Beeinflussung der Photolumineszenz 3.2.1 Ladungsträgerinjektion in den QD Kern 3.2.2 Feldinduzierte Ionisation des Exzitons 3.2.3 Weitere Mechanismen 3.3 Ladungsträgertransportschichten 3.3.1 Poly(N-vinylkarbazol) 3.3.2 N,N,N´,N´-Tetrakis(3-methylphenyl)-3,3´-dimethylbenzidin 3.3.3 Poly(3,4-ethylendioxythiophen)-poly(styrolsulfonat) 3.4 Lithiumfluorid als elektrischer Isolator 3.5 Modellsysteme 3.5.1 Einbettung der QDs in organische Lochtransportschichten 3.5.2 QDs zwischen Elektrode und organischer Lochtransportschicht 3.5.3 QDs zwischen Elektrode und Nichtleiter 4 Experimentelle Vorgehensweise 4.1 Layout und Kontaktierung von Teststrukturen 4.2 Verfahren zur Herstellung dünner Schichten 4.2.1 Physikalische Gasphasenabscheidung 4.2.2 Rotationsbeschichtung 4.2.3 Weitere Verfahren 4.3 Charakterisierung der Schichten und der Gesamtfunktionalität 4.3.1 Mikrospektroskopieaufbau 4.3.2 Weitere Messverfahren 4.4 Integration der Schichtstapel in Faserkunststoffverbund 5 Experimentelle Untersuchungen 5.1 Einordnung der einzelnen Schichten der Modellsysteme 5.1.1 Elektroden 5.1.2 Matrixmaterial und Quantum Dots 5.2 Einordnung des elektrischen Verhaltens der Modellsysteme 5.2.1 Modellsystem I 5.2.2 Modellsystem II 5.2.3 Modellsystem III 5.3 Einfluss externer Beleuchtung am Modellsystem II und III 5.3.1 Modellsystem II 5.3.2 Modellsystem III 5.4 Wiederholbarkeit der elektrischen Beanspruchung am Modellsystem III 5.4.1 Photolumineszenzintensität 5.4.2 Stromdichte 5.4.3 Gesamtwiderstand im Schichtstapel 5.5 Einfluss des elektrischen Feldes am Modellsystem III 5.5.1 Photolumineszenzintensität 5.5.2 Stromdichte 5.5.3 Widerstand 5.6 Einfluss der Integration auf das Verhalten von Modellsystem III 5.6.1 Optisches Verhalten der Laminiertasche und des Harzsystems 5.6.2 Funktionalität des Schichtstapels nach der Integration 5.7 Temperaturwechseltest am integrierten Schichtstapel 5.8 Speicherzeit elektrischer Ladungsträger am Modellsystem III 5.8.1 Stabilität des Lasers und der PL Intensität 5.8.2 Reproduzierbarkeit 5.8.3 Langzeitmessung 5.9 Kopplung des Schichtsystems mit piezoelektrischem Element 6 Zusammenfassung und Ausblick 6.1 Zusammenfassung 6.2 Ausblick Anhang A : Layouts für untere Elektrode E1 und obere Elektrode E2 Anhang B : Halter für die Kontaktierung der Teststrukturen Anhang C : Frontpanel zur Aufnahme der Photolumineszenz Anhang D : Messdaten Profilometer Veeco Dektak 150 Literaturverzeichnis Abbildungsverzeichnis Tabellenverzeichnis Lebenslauf

Die vorliegende Arbeit beschreibt die Entwicklung und Charakterisierung eines mikromechanischen kraftgekoppelten Schwingsystems für die resonante Detektion niederfrequenter Schwingungen. Es wird ein neuartiges Prinzip vorgestellt, das es ermöglicht, niederfrequente Vibrationen frequenzselektiv zu erfassen. Mittels Amplitudenmodulation wird das niederfrequente Signal in einen höheren Frequenzbereich umgesetzt. Durch Ausnutzung der mechanischen Resonanzüberhöhung wird aus dem breitbandigen Signal ein schmales Band herausgefiltert, die anderen Frequenzbereiche werden unterdrückt. Auf diese Weise wird direkt die spektrale Information des niederfrequenten Signals gewonnen. Eine Fourier-Transformation ist hierbei nicht notwendig. Die Abstimmung des Sensors erfolgt über eine Wechselspannung und führt dadurch zu einer einfachen Auswertung. Die Schwerpunkte der Arbeit liegen in den theoretischen Untersuchungen zum neuartigen Sensorprinzip, in der Entwicklung einer mikromechanischen Sensorstruktur zum Einsatz des neuen Prinzips sowie in der Entwicklung und Charakterisierung eines Messsystems zur Detektion niederfrequenter mechanischer Schwingungen mit dem neuen Sensor. / This thesis describes the development and characterization of a micromechanical force coupled oscillator system for the resonant detection of low frequency vibrations. It presents a novel working principle that enables spectral measurements of low frequency vibrations. The low frequency spectral content is converted into a higher frequency range by means of amplitude modulation. Due to the mechanical resonance a narrow band is filtered out of the wide band vibration signal. The remaining frequency content is suppressed. Hence, the spectral information is directly obtained with the sensor system without a fast Fourier transform. The tuning is done with an AC voltage resulting in a simple analysis. The main focuses of the work are the theoretical analysis of this novel sensor principle, the development of the micromechanical sensor structure for the use of the novel principle as well as the development and characterization of a measurement system for the spectral detection of low frequency mechanical vibrations with the developed sensor system.