Abstract
The increasing openness and dynamism in embedded systems necessitate the continuous advancement of diagnostic methodologies, particularly in contexts where safety is paramount and system operability must persist despite faults or failures. The implementation of Organic Computing offers substantial benefits to these intricate, dynamic systems, such as decreased development effort, enhanced adaptability, and resilience. Nonetheless, safety-critical systems that must preserve functionality amid failure by maintaining a fail-operational status require additional characteristics. This paper presents approaches such as adaptive diagnostics employing neural networks for fault detection and localization, adaptive probing for fault identification, and strategies for degraded performance states and system reconfiguration to circumvent complete service disruption when computational resources are insufficient.
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This work was supported by the DFG research grants BR 2024/25-1 and OB 384/11-1.
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Raj, U., Meckel, S., Koschowoj, A., Pacher, M., Obermaisser, R., Brinkschulte, U. (2023). Self-adaptive Diagnosis and Reconfiguration in ADNA-Based Organic Computing. In: Goumas, G., Tomforde, S., Brehm, J., Wildermann, S., Pionteck, T. (eds) Architecture of Computing Systems. ARCS 2023. Lecture Notes in Computer Science, vol 13949. Springer, Cham. https://doi.org/10.1007/978-3-031-42785-5_5
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