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

基于语义网技术的分布式嵌入式实时系统（ADISTES）的主动诊断

基本信息

批准号：
298610080
负责人：
Professor Dr.-Ing. Madjid Fathi
金额：
--
依托单位：
Department Elektrotechnik und Informatik
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2016
资助国家：
德国
起止时间：
2015-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/298610080?language=en
关键词：
Active Diagnosis based Semantic Web

项目摘要

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.

主动诊断的目的是利用运行时的诊断信息进行故障隔离和在线故障恢复，从而显著提高系统的可靠性。开放式嵌入式实时系统（如健康管理和医疗系统）的主动诊断是一个开放的研究问题，因为它具有严格的实时性和可靠性要求，并且在设计时不知道组成组件。该项目将扩展通常用于大型IT系统的语义技术，用于开放式嵌入式实时系统的主动诊断。我们将开发用于表示诊断特征、症状、故障和恢复操作的建模技术。分布式知识管理方法将在保证约束实时性的同时建立宽松的一致性。实时推理将基于时间触发的诊断查询调度进行研究。查询转换、语义转换和面向目标学习的目标是提高可调度性和可靠性。这些方法和算法将以原型实施，并对可靠性和及时性进行实验和分析评估。主要贡献包括(1)诊断知识库的建模技术，(2)实时推理的诊断查询的时间触发调度和优化，(3)具有宽松一致性的分布式知识管理，以及(4)开放式嵌入式系统中主动诊断的面向目标的自学习。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Class-based query-optimization for minimizing worst-case execution times of diagnostic queries in embedded real-time systems

基于类的查询优化，可最大限度地减少嵌入式实时系统中诊断查询的最坏情况执行时间

DOI：
10.1109/indin.2017.8104849
发表时间：
2017
期刊：
2017 IEEE 15th International Conference on Industrial Informatics (INDIN)
影响因子：
0
作者：
N. Tabassam;R. Obermaisser
通讯作者：
R. Obermaisser

Time-triggered scheduling of query executions for active diagnosis in distributed real-time systems

分布式实时系统中主动诊断的时间触发查询执行调度

DOI：
10.1109/etfa.2017.8247610
发表时间：
2017
期刊：
2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
影响因子：
0
作者：
S. Amin;R. Obermaisser
通讯作者：
R. Obermaisser

A Graph-Based Sensor Fault Detection and Diagnosis for Demand-Controlled Ventilation Systems Extracted from a Semantic Ontology

从语义本体中提取的基于图形的需求控制通风系统传感器故障检测和诊断

DOI：
10.1109/ines.2018.8523895
发表时间：
2018
期刊：
2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES)
影响因子：
0
作者：
A. Mallak;A. Behravan;C. Weber;M. Fathi;R. Obermaisser
通讯作者：
R. Obermaisser

Minimizing the Make Span of Diagnostic Multi-Query Graphs Using Graph Pruning and Query Merging

使用图修剪和查询合并最小化诊断多查询图的生成跨度

DOI：
10.1109/etfa.2018.8502626
发表时间：
2018
期刊：
2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)
影响因子：
0
作者：
N. Tabassam;R. Obermaisser
通讯作者：
R. Obermaisser

Minimizing the Worst Case Execution Time of Diagnostic Fault Queries in Real Time Systems Using Genetic Algorithm

使用遗传算法最小化实时系统中诊断故障查询的最坏情况执行时间