@inproceedings{9760,
  abstract     = {{Self-optimizing systems are able to adapt their behavior autonomously according to their current self-determined objectives. Unforeseen influences could lead to dependability-critical behavior of the system. Methods are required which secure self-optimizing systems during operation. These methods to increase the dependability of the system should already be taken into consideration in the design process. This paper presents a guideline for the dependability-oriented design of self-optimizing systems, which integrates established classical methods like failure mode and effects analysis as well as methods based on self-optimization. On the one hand self-optimization is used to increase the dependability of the system by integrating objectives like safety, availability, and reliability to the objectives of the system. On the other hand methods are required to ensure the self-optimization itself. As basis for this guideline serves the principle solution of the system. The six phases of the guideline extend the design process and lead to an enhanced principle solution. Additionally, the guideline illustrates phases to implement and validate the self-optimizing system. The proposed guideline is applied to an innovative rail-bound vehicle, called RailCab, which is equipped with self-optimizing function modules.}},
  author       = {{Sondermann-Wölke, Christoph and Hemsel, Tobias and Sextro, Walter and Gausemeier, Jürgen and Pook, Sebastian}},
  booktitle    = {{Industrial Informatics (INDIN), 2010 8th IEEE International Conference on}},
  keywords     = {{RailCab, dependability-critical behavior, dependability-oriented design, failure mode, rail-bound vehicle, secure self-optimizing systems, self-optimizing function modules, optimisation, railways, self-adjusting systems}},
  pages        = {{739 --744}},
  title        = {{{Guideline for the dependability-oriented design of self-optimizing systems}}},
  doi          = {{10.1109/INDIN.2010.5549490}},
  year         = {{2010}},
}

@inproceedings{9763,
  abstract     = {{Recent advances in information processing enable new kinds of technical systems, called self-optimizing systems. These systems are able to adapt their objectives and their behavior according to the current situation and influences autonomously. This behavior adaptation is non-deterministic and hence self-optimization is a risk to the system, e.g. if the result of the self-optimization process does not match the suddenly changed situation. In contrary, self-optimization could be used to increase the dependability by pursuing objectives like reliability and availability. In our preceding publications we introduced the so called multi-level dependability concept to cope with this new kind of systems (cf. [6]). This concept comprises the monitoring of the system behavior, the classification of the current situation, and the selection of the appropriate measure, if reliability limits are exceeded. In this paper we present for the first time experimental results. The dependability concept is implemented in the self-optimizing active guidance system of a railway vehicle. The test drives illustrate clearly that the proposed concept is able to cope with, e.g., sensor failures, and is able to increase the reliability and availability of the active guidance module.}},
  author       = {{Sondermann-Wölke, Christoph and Geisler, Jens and Sextro, Walter}},
  booktitle    = {{Reliability and Maintainability Symposium (RAMS), 2010 Proceedings - Annual}},
  issn         = {{0149-144X}},
  keywords     = {{availability, dependability concept, multilevel dependability concept, railway vehicle, reliability, self optimizing active guidance system, self optimizing railway guidance system, situation classification, system behavior monitoring, optimal control, railways, reliability theory, self-adjusting systems}},
  pages        = {{1 --6}},
  title        = {{{Increasing the reliability of a self-optimizing railway guidance system}}},
  doi          = {{10.1109/RAMS.2010.5448080}},
  year         = {{2010}},
}

@inproceedings{9736,
  abstract     = {{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 self-optimizing mechatronic systems and shows how planning can be used to improve the availability and reliability of systems in the operating stages.}},
  author       = {{Klöpper, Benjamin and Sondermann-Wölke, Christoph and Romaus, Christoph and Vöcking, Henner}},
  booktitle    = {{Computational Intelligence in Control and Automation, 2009. CICA 2009. IEEE Symposium on}},
  keywords     = {{multilevel dependability concept, probabilistic planning, self-optimizing mechatronic systems, systems reliability, mechatronics, planning (artificial intelligence), self-adjusting systems}},
  pages        = {{104 --111}},
  title        = {{{Probabilistic planning integrated in a multi-level dependability concept for mechatronic systems}}},
  doi          = {{10.1109/CICA.2009.4982790}},
  year         = {{2009}},
}

@inproceedings{9742,
  abstract     = {{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.}},
  author       = {{Sondermann-Wölke, Christoph and Sextro, Walter}},
  booktitle    = {{Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, 2009. COMPUTATIONWORLD '09. Computation World:}},
  keywords     = {{condition monitoring, mechatronic systems, rail bound vehicle, rail guidance module, self-optimization, self-optimizing function modules, condition monitoring, mechatronics, railway rolling stock, self-adjusting systems}},
  pages        = {{15 --20}},
  title        = {{{Towards the Integration of Condition Monitoring in Self-Optimizing Function Modules}}},
  doi          = {{10.1109/ComputationWorld.2009.47}},
  year         = {{2009}},
}