Performance of wavelet analysis and neural network for detection and diagnosis of rotating machine fault

Shanlin Kang; Yuzhe Kang; Jingwei Chen

doi:10.1117/12.806444

13 October 2008 Performance of wavelet analysis and neural network for detection and diagnosis of rotating machine fault

Shanlin Kang, Yuzhe Kang, Jingwei Chen

Author Affiliations +

Proceedings Volume 7128, Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment; 71280B (2008) https://doi.org/10.1117/12.806444
Event: Seventh International Symposium on Instrumentation and Control Technology, 2008, Beijing, China

Abstract

A novel approach combining wavelet transform with neural network is proposed for vibration fault diagnosis of turbo-generator set in power system. The multi-resolution analysis technology is used to acquire the feature vectors which are applied to train and test the neural network. Feature extraction involves preliminary processing of measurements to obtain suitable parameters which reveal weather an interesting pattern is emerging. The feature extraction technique is needed for preliminary processing of recorded time-series vibrations over a long period of time to obtain suitable parameters. The neural network parameters are determined by means of the recursive orthogonal least squares algorithm. In network training procedure, much simulation and practical samples are utilized to verify and test the network performance. And according to the output result, the fault pattern can be recognized. The actual applications show that the method is effective for detection and diagnosis of rotating machine fault and the experiment result is correct.

Citation Download Citation

Shanlin Kang, Yuzhe Kang, and Jingwei Chen "Performance of wavelet analysis and neural network for detection and diagnosis of rotating machine fault", Proc. SPIE 7128, Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment, 71280B (13 October 2008); https://doi.org/10.1117/12.806444

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available