Title :
Research of intelligent condition monitoring model for AUV
Author :
Yujia Wang ; Mingjun Zhang ; Ruichen Sun
Author_Institution :
College of Mechanical and Electrical Engineering, Harbin Engineering University
Abstract :
An intelligent condition monitoring model for propellers and rudder of autonomous underwater vehicles (AUVs) was proposed, which was based on the FALCON with a 3-step learning algorithm. The steps of the algorithm included initialization with fuzzy C clustering, rules extraction with maximum weights matrix and parameters fine-tuning with GA. It constructed the configuration of the model, analyzed the process of the monitoring, and discussed the method of evaluation for the model. The results of the computer simulation by actual experiment data of a certain AUV shows that the condition monitoring model proposed in this article is feasible and prove that the learning algorithm for the FALCON is effective.
Keywords :
Algorithm design and analysis; Clustering algorithms; Computerized monitoring; Condition monitoring; Control system synthesis; Data mining; Fuzzy logic; Neural networks; Propellers; Underwater vehicles; FALCON; autonomous underwater vehicle; condition monitoring; learning algorithm;
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Conference_Location :
Hangzhou, China
Print_ISBN :
0-7803-8273-0
DOI :
10.1109/WCICA.2004.1340963