Title :
Health monitoring of aeroplane structural component based on K-means clustering
Author :
Jianguo, Cui ; Yingyu, Wang ; Zhonghai, Li ; Liqiu, Liu ; Yun, Zhao ; Guangyan, Xu
Author_Institution :
Automatization Coll., Shenyang Inst. of Aeronaut. Eng., Shenyang, China
Abstract :
As the health status of aeroplane structural components has direct influence on the flight safety, it is important to monitor the health status of structural components timely. In this paper, acoustic emission technology is used to monitor the health status of the aeroplane structural component. The acoustic emission health information from the aeroplane structural component is analyzed and disposed. The fault inference engine that bases on rules and the forward chaining control strategies is designed. The K-means clustering analysis algorithm is used to monitor the health status of aeroplane structural component. Experiments show that the method has good performance on monitoring the status of aeroplane structural components. It presents an effective health monitoring method of aeroplane structural components, which can also be directly applied to other structural systems in machine equipments.
Keywords :
acoustic emission testing; aerospace components; aerospace safety; condition monitoring; fault diagnosis; learning (artificial intelligence); pattern clustering; structural engineering computing; acoustic emission health information; aeroplane structural component health monitoring; fault inference engine; flight safety; k-means clustering; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Cognition; Engines; Euclidean distance; Monitoring; K-means clustering; fault inference engine; forward chaining control strategy;
Conference_Titel :
Systems and Control in Aeronautics and Astronautics (ISSCAA), 2010 3rd International Symposium on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-6043-4
Electronic_ISBN :
978-1-4244-7505-6
DOI :
10.1109/ISSCAA.2010.5633092
