Title :
Intelligent Vibration Signal Diagnostic System Using Artificial Neural Network
Author :
Lin, Chang-Ching ; Shieh, Shien-Chii
Author_Institution :
Grad. Inst. of Manage. Sci., Tamkang Univ., Tamshui, Taiwan
Abstract :
In this paper artificial neural network (ANN) technologies and analytical models have been investigated and incorporated to increase the effectiveness and efficiency of machinery self diagnostic system. Several advanced vibration trending methods have been studied and used to quantify machine operating conditions. An on-line, multi-channel condition monitoring procedure has been developed and coded. The major technique used for self diagnostic is a modified ARTMAP neural network. The objective is to provide a rigid solution for condition-based intelligent self diagnostic system.
Keywords :
ART neural nets; condition monitoring; fault diagnosis; mechanical engineering computing; vibrations; ARTMAP neural network; artificial neural network; condition based self diagnostic system; intelligent vibration signal diagnostic system; multichannel condition monitoring; online conditioning monitoring procedure; vibration trending methods; Artificial intelligence; Artificial neural networks; Condition monitoring; Data acquisition; Intelligent networks; Logic programming; Machine intelligence; Machinery; Parameter estimation; Sensor systems; artificial neural network; fault diagnosis; intelligent system; self diagnostic; vibration signals diagnostic;
Conference_Titel :
Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
Conference_Location :
Changsha, Hunan
Print_ISBN :
978-0-7695-3804-4
DOI :
10.1109/ICICTA.2009.91
