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Self-optimizing mechatronic systems are a new class of technical systems. On the one hand, new challenges regarding dependability arise from their additional complexity and adaptivity. On the other hand, their abilities enable new concepts and methods to improve the dependability of mechatronic systems. This paper introduces a multi-level dependability concept for selfoptimizing mechatronic systems and shows how probabilistic planning can be used to improve the availability and reliability of systems in the operating phase. The general idea to improve the availability of autonomous systems by applying probabilistic planning methods to avoid energy shortages is exemplified on the example of an innovative railway vehicle.

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New mechatronic systems, called self-optimizing systems, are able to adapt their behavior according to environmental, user and system specific influences. Self-optimizing systems are complex and due to their non-deterministic behavior comprise hidden risks, which cannot be foreseen in the design phase of the system. Therefore, this paper presents modifications of the current condition monitoring policy, to be able to cope with this new kind of systems. Beside avoiding critical situations evoked by self-optimization, the proposed concept uses self-optimization to increase the dependability of the system. In this case, the concept is applied to the active guidance module of an innovative rail-bound vehicle.

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