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Active magnetic bearings make it possible to levitate a rotating shaft without friction or wear. Due to the functional principle of active magnetic bearings there are essential signals available without additional measurement equipment. The paper introduces a concept for the diagnosis of active magnetic bearings at rotating machines using inherently existing signals and knowledge-based methods of fuzzy logic. Features for the diagnosis are extracted from relevant signals with signal- and model-based methods. A knowledgebase on faults at active magnetic bearings and machinery (e.g. faults in sensors, Controllers and actors, unbalance, radial forces) and their characteristic features is generated by simu-lations and experiments. The knowledgebase serves as basis for the development of a system for monitoring and fault diagnosis. Additional to the monitoring of active magnetic bearings based on limit value checking, a concept using the combination of the extracted features and applying fuzzy logic is created for the identification and localisation of appearing faults. Results of simulations and experiments and the knowledge-based generation of diagnosis statements are exemplarily represented in the paper.