Permanently excited synchronous motors are the driving components in countless systems and applications. The most common cause of motor failures are the bearings. Data-driven approaches have been used for predictive defect detections since many years, to prevent motors from an unexpected breakdown. In this way, downtime costs can be reduced and maintenance intervals based on actual wear can be realized. Existing approaches are usually based on structure-borne sound sensors that have to be attached externally to the motors. The resulting costs reduce the economic attractiveness and scalability of the solution. Therefore, the focus of this dissertation is on fault detection based on internal motor current signals. Hurdles, arising from the choice of this signal sources, are to be tackled by the developed fault detection framework. By this, an adequate alternative to the use of external sensors is achieved. The core of the framework is the development of a fault detection pipeline, which is to be applicable under expected conditions of real-world applications. The main pillars are data transformation methods derived from expert knowledge of different domains. These are concatenated and parameterized in an automated manner to reduce the human induced bias on the solution generation process. Starting with a review of the state of research, existing research gaps are identified. From this, the research hypothesis and concrete research questions are derived and the general relevance of research is motivated. Subsequently, a conceptual description of the developed framework is given. In contrast to related work, the proposed approach focuses on the abstraction of the motors operating parameters from the pipeline hyperparameters uniquely at training time. This makes reparameterizations in the course of varied motor parameters obsolete, which increases the robustness with respect to real-world use cases. The data used for the validation of the framework was acquired under real-world operating conditions to enable extensive stress tests of the developed pipelines. The results confirm the suitability of the framework in terms of general current based bearing fault detection as well as the intended use cases, regarding the working condition transfers.

Familientherapie ist ein relevanter Ansatz psychosozialer Versorgung in Ländern des globalen Südens. Für einen fairen globalen Wissenstransfer im Rahmen der Ausbildung sind implizite Werte westlicher Konzepte transparent zu machen und emergente Irritationen als Hinweise für kulturellen Adaptionsbedarf aufzunehmen. Für systemisch-familientherapeutische Arbeit in Kambodscha wurden Aspekte kultureller und kontextueller Passung erforscht, um damit die lokale Entwicklung einer entsprechenden Weiterbildung in dem südostasiatischen Land zu unterstützen. Dazu wurden Schlüsselkonzepte systemischer Familientherapie mit einem multidimensionalen, ökosystemischen Ansatz kulturvergleichend exploriert. Weiterführend wurden wesentliche Kompetenzen einer kambodschanischen Familientherapie spezifiziert, um Anschlussfähigkeit herzustellen zu den Werten soziozentrischer Familienorganisation sowie kulturell geprägter Vorstellungen von Hilfe, Rat und Heilung. Schließlich werden aus den empirischen Ergebnissen inhaltliche und didaktische Empfehlungen für die systemische Weiterbildung in Kambodscha sowie grundsätzliche Implikationen für eine transkulturelle familientherapeutische Arbeit abgeleitet.

Abstract knowledge is deeply grounded in many computer-based applications. An important research area of Artificial Intelligence (AI) deals with the automatic derivation of knowledge from data. Machine learning offers the according algorithms. One area of research focuses on the development of biologically inspired learning algorithms. The respective machine learning methods are based on neurological concepts so that they can systematically derive knowledge from data and store it. One type of machine learning algorithms that can be categorized as "deep learning" model is referred to as Deep Neural Networks (DNNs). DNNs consist of multiple artificial neurons arranged in layers that are trained by using the backpropagation algorithm. These deep learning methods exhibit amazing capabilities for inferring and storing complex knowledge from high-dimensional data. However, DNNs are affected by a problem that prevents new knowledge from being added to an existing base. The ability to continuously accumulate knowledge is an important factor that contributed to evolution and is therefore a prerequisite for the development of strong AIs. The so-called "catastrophic forgetting" (CF) effect causes DNNs to immediately loose already derived knowledge after a few training iterations on a new data distribution. Only an energetically expensive retraining with the joint data distribution of past and new data enables the abstraction of the entire new set of knowledge. In order to counteract the effect, various techniques have been and are still being developed with the goal to mitigate or even solve the CF problem. These published CF avoidance studies usually imply the effectiveness of their approaches for various continual learning tasks. This dissertation is set in the context of continual machine learning with deep learning methods. The first part deals with the development of an application-oriented real-world evaluation protocol which can be used to investigate different machine learning models with regard to the suppression of the CF effect. In the second part, a comprehensive study indicates that under the application-oriented requirements none of the investigated models can exhibit satisfactory continual learning results. In the third part, a novel deep learning model is presented which is referred to as Deep Convolutional Gaussian Mixture Models (DCGMMs). DCGMMs build upon the unsupervised approach of Gaussian Mixture Models (GMMs). GMMs cannot be considered as deep learning method and they have to be initialized in a data-driven manner before training. These aspects limit the use of GMMs in continual learning scenarios. The training procedure proposed in this work enables the training of GMMs by using Stochastic Gradient Descent (SGD) (as applied to DNNs). The integrated annealing scheme solves the problem of a data-driven initialization, which has been a prerequisite for GMM training. It is experimentally proven that the novel training method enables equivalent results compared to conventional methods without iterating their disadvantages. Another innovation is the arrangement of GMMs in form of layers, which is similar to DNNs. The transformation of GMMs into layers enables the combination with existing layer types and thus the construction of deep architectures, which can derive more complex knowledge with less resources. In the final part of this work, the DCGMM model is examined with regard to its continual learning capabilities. In this context, a replay approach referred to as Gaussian Mixture Replay (GMR) is introduced. GMR describes the generation and replay of data samples by utilizing the DCGMM functionalities. Comparisons with existing CF avoidance models show that similar continual learning results can be achieved by using GMR under application-oriented conditions. All in all, the presented work implies that the identified application-oriented requirements are still an open issue with respect to "applied" continual learning research approaches. In addition, the novel deep learning model provides an interesting starting point for many other research areas.

Im Zentrum der ethnografischen Feldstudie steht die Lebenswelt der illegalen brothel-Prostitution in Mumbais red light area Grant Road. Damit verbunden stellt sich die Frage, wie die von mir beforschten Sexarbeiterinnen ihren Alltag in der brothel-Prostitution bewältigen und gestalten. Mit der vorliegenden Arbeit werden Sexarbeiterinnen in der brothel-Prostitution betrachtet. So wird auf dieses soziale Thema und die damit verbundene Soziale Arbeit über nationale Grenzen hinweg aufmerksam gemacht. Weil die brothel-Prostitution in Indien im Gesetzbuch Immoral Traffic Prevention (Act) 1965 als kriminell deklariert ist, handeln die betreffenden Subjekte illegal und haben sich ein eigenständiges Netzwerk geschaffen, um trotzdem handlungsfähig zu sein. Im Fokus dieser Studie stehen die Sexarbeiterinnen und die Frage, wie sie ihren Alltag in ihrer prekären Situation der brothel-Prostitution bewältigen und gestalten. Die von mir beforschten Sexarbeiterinnen haben soziale Ungleichheiten erfahren, die auf unterschiedliche Indikatoren zurückzuführen sind. In der Tätigkeit als Sexarbeiterinnen in der brothel-Prostitution sehen diese Frauen eine Hoffnung, die Lebensqualität für sich und ihre Herkunftsfamilie zu verbessern. So sind die Sexarbeiterinnen herausgefordert, ihre beruflichen und privaten Belange in der Vielschichtigkeit des Alltags miteinander zu vereinbaren. Einerseits werden auf der mentalen Ebene die sozialen Räume des Berufes und des Privaten miteinander verwoben, andererseits wird versucht, beides voneinander zu trennen. In meiner Arbeit wird hinterfragt, mit welchen Ressourcen, Potenzialen und subjektiven Strategien Sexarbeiterinnen ihre prekäre Situation bewältigen und wie sie autonom handeln. Zugleich sind diese Frauen auch Momenten von ambivalenten Situationen ausgesetzt – zwischen Handlungsmacht und Handlungseinschränkung. Im Rückgriff auf entworfene Strategien und im ressourcenorientierten Handeln kann dieses Spannungsverhältnis zwischen Handlungsmacht und Handlungseinschränkung ertragen werden, die beide im Alltag miteinander verflochten sind.