DocumentCode :
2094940
Title :
A SVM-based method for engine maintenance strategy optimization
Author :
Jia, Qing-Shan ; Zhao, Qian-Chuan
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing
fYear :
2006
fDate :
15-19 May 2006
Firstpage :
1066
Lastpage :
1071
Abstract :
Due to the abundant application background, the optimization of maintenance problem has been extensively studied in the past decades. Besides the well-known difficulty of large state space and large action space, the pervasive application of digital computers forces us to consider the new constraint of limited memory space. The given memory space restricts what strategies can be explored during the optimization procedure. By explicitly quantifying the minimal memory space to store a strategy using support vector machine, we propose to describe simple strategies exactly and only approximate complex strategies. This selective approximation can best utilize the given memory space for any description mechanism. We use numerical results on illustrative examples to show how the selective approximation improves the solution quality. We hope this work sheds some insights to best utilize the memory space for practical engine maintenance strategy optimization problems
Keywords :
engines; maintenance engineering; optimisation; support vector machines; approximate complex strategies; description mechanism; engine maintenance strategy optimization; support vector machine; Application software; Computer aided manufacturing; Contracts; Costs; Engines; Intelligent systems; Memory management; Optimization methods; Pervasive computing; State-space methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on
Conference_Location :
Orlando, FL
ISSN :
1050-4729
Print_ISBN :
0-7803-9505-0
Type :
conf
DOI :
10.1109/ROBOT.2006.1641851
Filename :
1641851
Link To Document :
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