DocumentCode
1731256
Title
Intelligent condition monitoring and fault diagnosis of a gearbox based on Artificial Neural Network
Author
Shulian, Yang ; Wenhai, Li ; Hua, Zhen ; Fang, Xiang
Author_Institution
ShanDong Inst. of Bus. & Technol., Yantai
fYear
2007
Abstract
In this paper the vibration test system for the gearbox of mining machine , the wavelet denoising method , the artificial neural network´ s essential principles and its features, BP network structures model in the gearbox fault diagnosis are discussed.Tested vibration signals are disposed by the method of wavelet denoising and than as the inputs of BP neural network. By using classical BP neural network, four kinds of typical patterns of gearbox faults have been studied and diagnosed and satisfied results have been acquired. The research results indicate that BP neural network with the excellent abilities of parallel distributed processing, self-study, self-adaptation, self-organization,associative memory and its highly non-linear pattern recognition is an efficient and feasible tool to solve complicated state identification problems in the gearbox fault diagnosis simultaneously.
Keywords
backpropagation; condition monitoring; fault diagnosis; gears; neural nets; BP network structures; artificial neural network; associative memory; fault diagnosis; gearbox; intelligent condition monitoring; mining machine; nonlinear pattern recognition; parallel distributed processing; vibration test system; wavelet denoising; Artificial intelligence; Artificial neural networks; Condition monitoring; Distributed processing; Fault diagnosis; Intelligent networks; Machine intelligence; Neural networks; Noise reduction; System testing; Artifical Neural Network(ANN); Back Propagation(BP) Algorithm; Fault diagnosis; Gearbox; wavelet analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Electronic Measurement and Instruments, 2007. ICEMI '07. 8th International Conference on
Conference_Location
Xi´an
Print_ISBN
978-1-4244-1136-8
Electronic_ISBN
978-1-4244-1136-8
Type
conf
DOI
10.1109/ICEMI.2007.4350980
Filename
4350980
Link To Document