Fault Diagnosis of Rotating System Based ICA-SVM

Author

Li, Na ; Li, Hong ; Fang, Yanjun

Author_Institution

Electr. & Autom. Eng., Nanjing Normal Univ.

Volume

2

fYear

0

fDate

0-0 0

Firstpage

5590

Lastpage

5594

Abstract

ICA (independent component analysis) is used to the fault diagnosis of rotating system. The architecture of diagnosis system based on denoising and blind source separation is proposed. SVM (support vector machine) network is adopted for fault study training and recognition. Using the signal preprocessed by ICA based different contrast function in denoising and blind source separation, the constraint knowledge newly can be easily added to the diagnosis system. The vibration source signal is got by ICA with constraints, and the kernel function of SVM is radical base function (RBF). Simulation results show that the method in this paper has good performance

Keywords

blind source separation; fault diagnosis; independent component analysis; signal denoising; support vector machines; blind source separation; denoising; diagnosis system; fault diagnosis; independent component analysis; kernel function; radical base function; rotating system; support vector machine; vibration source signal; Automation; Blind source separation; Educational institutions; Fault diagnosis; Independent component analysis; Mechanical engineering; Noise reduction; Petroleum; Power engineering and energy; Support vector machines; ICA (Independent Component Analysis); SVM (Support Vector Machine; fault diagnosis;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714144

Filename

1714144