Aktive Diagnose basierend auf semantischen Webtechnologien für verteilte eingebettete Echtzeitsysteme
Zusammenfassung der Projektergebnisse
In recent years, the field of embedded systems has evolved towards novel application areas that combine stringent realtime constraints, reliability requirements and the need for an open-world assumption. These systems are called open embedded real-time systems. Examples are Ambient-Assisted Living (AAL) systems for elderly care, networked medical devices and health management systems, applications for electrical power distribution and command/control systems. These systems are based on an open-world assumption where new components are integrated at run-time in order to dynamically realize emerging global services. At the same time, reliable operation and support for stringent real-time requirements are essential to support closed-loop control and guaranteed response times. For example, physicians need to dynamically integrate medical devices into an in-home AAL system for emergency treatment, while ensuring predictable response times and reliable interaction with in-home devices (e.g., medical sensors) and remote sites (e.g., hospital). In this context, the research project ADISTES introduced models and algorithms for active diagnosis in open embedded real-time systems with improved reliability and safety. The project results enable cost-effective fault isolation and recovery actions at run-time based on the time-triggered execution of diagnostic queries. In contrast to other fault-tolerance techniques based on spatial and temporal redundancy, the incurred overhead is significantly reduced. At the same time, we cope with dynamic system structures, where components enter and leave at run-time, interact among each other in variable setups and realize different global application services. In this context, diagnostic relationships cannot be expressed absolutely, e.g., by defining assertions on specific state variables. Diagnostic relationships need to be modelled indirectly by referring to semantic categories and recurring diagnostic patterns. Systems with a dynamic structure must be observed and analysed upon the occurrence of anomalous behaviours and states. Likewise, the definition and execution of recovery actions must consider the dynamic nature of an open system. The ADISTES project extended semantic techniques, usually used in large-scale IT systems, for active diagnosis in open embedded real-time systems. Based on this we developed novel modelling techniques for expressing diagnostic features, symptoms, faults, and recovery actions. Methods for the management of the semantic knowledge with a relaxed consistency and methods for automatically identifying and modelling faults were developed. Connecting the semantic and real-time environment, a diagnostic multi-query graph (DMG) enables to track and conclude on faults, which may lead to failures. The resulting real-time inference is established based on the time-triggered scheduling and optimization of diagnostic queries. The methods and algorithms were prototypically implemented, as well as experimentally and analytically evaluated concerning reliability and timeliness. The ADISTES results have resulted in four PhD theses, several master theses, and numerous publications at scientific conferences. The ADISTES results are also leading to follow-up projects with companies in different domains such as automotive diagnosis and medical services funded by KMU Innovativ. A project in the automotive domain serves for the maintenance-oriented diagnosis for garages, while the medical projects aims at a wearable computing device for improved medical diagnosis of ambulance teams.
Projektbezogene Publikationen (Auswahl)
- “Class-based query-optimization for minimizing worst-case execution times of diagnostic queries in embedded real-time systems,” in IEEE 15th International Conference on Industrial Informatics (INDIN). IEEE, 2017, pp. 653–658
N. Tabassam and R. Obermaisser
(Siehe online unter https://doi.org/10.1109/INDIN.2017.8104849) - “Time-triggered scheduling of query executions for active diagnosis in distributed realtime systems,” in 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 2017, pp. 1–9
S. Amin and R. Obermaisser
(Siehe online unter https://doi.org/10.1109/ETFA.2017.8247610) - “Time-triggered scheduling of query executions for active diagnosis in distributed realtime systems. In 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) 2017, (pp. 1-9). IEEE
S. Amin, R. Obermaisser
(Siehe online unter https://doi.org/10.1109/ETFA.2017.8247610) - A graph-based sensor fault detection and diagnosis for demand-controlled ventilation systems extracted from a semantic ontology, In IEEE 22nd International Conference on Intelligent Engineering Systems (INES) (pp. 000377-000382), 2018
A. Mallak, A. Behravan, C. Weber, M. Fathi, R. Obermaisser
(Siehe online unter https://doi.org/10.1109/INES.2018.8523895) - “Minimizing the make span of diagnostic multi-query graphs using graph pruning and query merging,” in IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), vol. 1. IEEE, 2018, pp. 368–375
N. Tabassam and R. Obermaisser
(Siehe online unter https://doi.org/10.1109/ETFA.2018.8502626) - "SenGen: A Two-Phase Dynamic Simulation and Toolbox of an Indoor Mobile Wireless Sensor Network for Sensor Monitoring and Dataset Generation," International Conference on Computational Science and Computational Intelligence (CSCI), 2019, pp. 1190-1195
A. Mallak, A. Sonnad and M. Fathi
(Siehe online unter https://doi.org/10.1109/CSCI49370.2019.00224) - ADISTES Ontology for Active Diagnosis of Sensors and Actuators in Distributed Embedded Systems, In: IEEE International Conference on Electro Information Technology (EIT), 2019
N.T.M. Saeed, C. Weber, A. Mallak, M. Fathi, R. Obermaisser, K.D. Kuhnert
(Siehe online unter https://doi.org/10.1109/EIT.2019.8834013) - “Minimizing the Worst Case Execution Time of Diagnostic Fault Queries in Real Time Systems Using Genetic Algorithms”. In Science and Information Conference 2019 Apr 25 (pp. 564-582). Springer
N. Tabassam, S. Amin, R. Obermaisser
(Siehe online unter https://doi.org/10.1007/978-3-030-17798-0_46)
