An intelligent diagnosing method to compressors' faults based on fuzzy RBFNN

Keyong Wang; Hong Cao; Yan Shang; Chengtian Song

doi:10.1117/12.819749

31 December 2008 An intelligent diagnosing method to compressors' faults based on fuzzy RBFNN

Keyong Wang, Hong Cao, Yan Shang, Chengtian Song

Proceedings Volume 7130, Fourth International Symposium on Precision Mechanical Measurements; 713059 (2008) https://doi.org/10.1117/12.819749
Event: Fourth International Symposium on Precision Mechanical Measurements, 2008, Anhui, China

Abstract

In order to diagnose the compressors' fault on line, an intelligent checking method is presented in the paper. A vibration sensor was put on the compressors that should be detected. The vibration signals obtained by the sensor contain a great deal information, which reflects the compressors' qualities and their type of faults. It has been proved that, the vibration signals obtained from compressors with different faults have different time domain features and frequency domain features. We extract those features, and then get a feature vector which is sent to an intelligent information processor. In order to improve the generalization and robustness of the processor, we adopt a fuzzy clustering radial basis function (RBF) neural networks as the information processor. A method of fuzzy C-means clustering based on minimized mean square error rule is used to determine the RBF layer, and the shape factors of RBF neurons are determined by the grades of membership. The experimental results show that, fuzzy clustering RBF neural networks neural networks have powerful ability of pattern recognition, and the faults diagnosis method is feasible to diagnose the fault of the revolving machinery.

Citation Download Citation

Keyong Wang, Hong Cao, Yan Shang, and Chengtian Song "An intelligent diagnosing method to compressors' faults based on fuzzy RBFNN", Proc. SPIE 7130, Fourth International Symposium on Precision Mechanical Measurements, 713059 (31 December 2008); https://doi.org/10.1117/12.819749

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