Organic Computing with Artificial DNA for Reliable Dynamic Systems based on Semantic Models and Evolutionary Algorithms for Fault Diagnosis and Adaptation

基于语义模型和故障诊断和适应进化算法的可靠动态系统的人工 DNA 有机计算

• 批准号: 445555232
• 负责人: Professor Dr. Uwe Brinkschulte
• 金额: --
• 依托单位: Department Elektrotechnik und Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/445555232?language=en
• 关键词: Organic; Computing; Artificial; DNA; Reliable

Organic Computing leads to significant advantages for complex dynamic systems like reduced development efforts, increased adaptability and robustness. However, for safety-critical systems which have to maintain functionality even in the presence of faults or failures (fail-operational) further properties are necessary. This includes the maintenance of the major core functionality even if non redundant system resources fail, the organic computing run-time environment is harmed or the remaining resources are insufficient to maintain all services. These failure scenarios require semantic knowledge of the system combined with fault-diagnosis and adaption techniques to properly degrade and reconfigure the system.The proposed research project addresses the corresponding research gaps and their interactions based on artificial DNA: (1) semantic description methods for organic computing systems based on artificial DNA, (2) diagnosis techniques with a high level of automation for organic computing systems using artificial DNA, and (3) adaptation techniques for such systems in highly safety-critical applications.Semantic description methods for organic computing systems with artificial DNA are the foundation for higher semantic-based failure detection and adaptation techniques.Diagnosis techniques for organic computing systems with artificial DNA can exploit the semantic descriptions to automatically build diagnosis models. Furthermore, these models can be optimized by evolutionary algorithms to improve their failure detection rates.Adaptation techniques modify the artificial DNA based on the recognized failures and the semantic description to realize the reconfiguration and degradation concepts.Within the project, the models and algorithms will be incrementally developed, prototypically implemented and evaluated using sample scenarios and failure injection experiments.

有机计算可为复杂的动态系统带来显着的优势，例如减少开发工作、提高适应性和鲁棒性。然而，对于安全关键系统来说，即使在出现故障或故障（故障操作）的情况下也必须保持功能，进一步的属性是必要的。这包括即使非冗余系统资源发生故障、有机计算运行时环境受到损害或剩余资源不足以维持所有服务，也要维护主要核心功能。这些故障场景需要系统的语义知识与故障诊断和适应技术相结合，以正确地降级和重新配置系统。所提出的研究项目解决了基于人工DNA的相应研究空白及其相互作用：（1）基于人工DNA的有机计算系统的语义描述方法，（2）使用人工DNA的有机计算系统的高度自动化诊断技术，以及（3） 具有人工DNA的有机计算系统的语义描述方法是基于语义的更高级别的故障检测和适应技术的基础。具有人工DNA的有机计算系统的诊断技术可以利用语义描述来自动构建诊断模型。此外，这些模型可以通过进化算法进行优化，以提高其故障检测率。适应技术根据识别的故障和语义描述修改人工DNA，以实现重构和退化概念。在项目内，模型和算法将使用样本场景和故障注入实验进行增量开发、原型实现和评估。