Title :
Fault feature extraction of rotating machinery based on wavelet transform and Self-organizing map network
Author :
Gong, Maofa ; Zhang, Xiaoming ; Liu, Qingxue ; Zhao, Zidong ; Zhang, Xiaoli
Author_Institution :
Coll. of Inf. & Electr. Eng., Shandong Univ. of Sci. & Technol., Qingdao, China
Abstract :
In this paper, it expounded in detail the principle of energy spectrum analysis based on Discrete Wavelet Transform and Multi-resolution Analysis. In the aspect of study on feature extraction method, with investigating the feature of impact factor in vibration signals and considering the non-placidity and nonlinear of vibration diagnosis signals, this paper imported wavelet analysis and fractal theory as the tools of faulty signal feature description. Experimental results proved the validity of this method. To some extent, this method provides a good approach of solving the problem that fault feature symptom is described comprehensively.
Keywords :
discrete wavelet transforms; fault diagnosis; feature extraction; fractals; machinery; self-organising feature maps; discrete wavelet transform; energy spectrum analysis; fault feature extraction; faulty signal feature description; fractal theory; impact factor feature; multiresolution analysis; rotating machinery; self-organizing map network; vibration diagnosis signals; wavelet analysis; Correlation; Fault diagnosis; Feature extraction; Rotors; Wavelet analysis; Wavelet transforms; Discrete Wavelet Transform (DWT); Feature Extraction; Rotating Machinery; Self-organizing Map Network;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
DOI :
10.1109/WCICA.2010.5554545