DocumentCode :
2922509
Title :
QoS Adaptive ISHM Systems
Author :
Zhang, Yansheng ; Fu, Jicheng ; Yen, I-Ling ; Bastani, Farokh ; Tai, Ann T. ; Chau, Savio ; Vatan, Farrokh ; Fijany, Amir
Author_Institution :
Texas Univ., Dallas, TX
fYear :
2006
fDate :
Nov. 2006
Firstpage :
47
Lastpage :
54
Abstract :
Embedded systems are becoming highly complex and increasingly being used in critical applications. Integrated system health management (ISHM) techniques have therefore been developed to ensure the proper operation of these systems. However, some ISHM systems are relatively complex and may consume a significant amount of resources. In some situations, activating the full ISHM system may cause resource contention and prevents the target system from timely completing critical tasks. Thus, it is imperative to introduce the notion of adaptivity into ISHM systems. This paper systematically discusses the issues that need to be addressed in an adaptive ISHM system with a focus on adaptation in terms of QoS aspects. A novel model, adaptive diagnosis quality-oriented system model (ADQSM), is proposed to model the QoS specification and fault diagnosis quality measurement issues as well as the abstraction of the adaptation problem. We then present the method to evaluate various diagnosability attributes based on a modified fault signature matrix. We further map the ADQSM model to the particle swarm optimization (PSO) problem model and use PSO for rapid configuration decision making
Keywords :
decision making; embedded systems; fault diagnosis; particle swarm optimisation; quality of service; software fault tolerance; software quality; QoS specification; adaptive diagnosis quality-oriented system model; configuration decision making; embedded system; fault diagnosis quality measurement; fault signature matrix; integrated system health management; particle swarm optimization; resource contention; Adaptive systems; Costs; Decision making; Embedded system; Fault diagnosis; Frequency; Particle swarm optimization; Propulsion; Sensor phenomena and characterization; Sensor systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence, 2006. ICTAI '06. 18th IEEE International Conference on
Conference_Location :
Arlington, VA
ISSN :
1082-3409
Print_ISBN :
0-7695-2728-0
Type :
conf
DOI :
10.1109/ICTAI.2006.99
Filename :
4031879
Link To Document :
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