Active Diagnosis based on Semantic Web Technologies for Dis-tributed Embedded Real-Time Systems (ADISTES)

主动诊断的目的是通过在运行时利用诊断信息进行故障隔离和在线错误恢复，从而显著提高系统的可靠性。开放式嵌入式实时系统的主动诊断（例如，健康管理和医疗系统）是一个开放的研究问题，由于严格的实时性和可靠性的要求相结合的组成部分，是未知的在设计时。拟议的项目将扩展语义技术，通常用于大型IT系统，在开放的嵌入式实时系统的主动诊断。我们将开发用于表达诊断功能、症状、故障和恢复操作的建模技术。分布式知识管理的方法将建立宽松的一致性，同时确保实时约束。实时推理将调查的时间触发调度的诊断查询的基础上。查询转换、语义转换和面向目标的学习的目标是提高可扩展性和可靠性。这些方法和算法将被原型实现，以及实验和分析评估有关的可靠性和及时性。超越国家的最先进的主要贡献包括（1）建模技术的诊断知识库，（2）时间触发调度和优化诊断查询的实时推理（3）分布式知识管理与宽松的一致性，（4）开放式嵌入式系统中面向目标的自学习主动诊断方法。

Active diagnosis aims at significantly improving system reliability by using diagnostic information at run-time for fault isolation and online error recovery. Active diagnosis for open embedded real-time systems (e.g., health management and medical systems) is an open research problem due to stringent real-time and reliability requirements in combination with constituent components that are unknown at design time.The proposed project will extend semantic techniques, usually used in large-scale IT systems, for active diagnosis in open embedded real-time systems. We will develop modeling techniques for expressing diagnostic features, symptoms, faults and recovery actions. Methods for distributed knowledge management will establish relaxed consistency while ensuring real-time constraints. Real-time inference will be investigated based on the time-triggered scheduling of diagnostic queries. The goal of query transformations, semantic transformations and goal-oriented learning will be improved schedulability and reliability. The methods and algorithms will be prototypically implemented, as well as experimentally and analytically evaluated concerning reliability and timeliness.Major contributions beyond the state-of-the-art include (1) modeling techniques for a diagnostic knowledge base, (2) time-triggered scheduling and optimizations of diagnostic queries for real-time inference (3) distributed knowledge management with relaxed consistency, and (4) goal-oriented self-learning for active diagnosis in open embedded systems.