Tobias Wagner

• 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.