Rolling Bearing Diagnosis using Cyclostationary tools and neural networks

Author

El-Samad, S. ; Raad, Ali

Author_Institution

Doctoral Sch. of Sci. & Technol., Lebanese Univ., Tripoli, Lebanon

fYear

2012

fDate

12-15 Dec. 2012

Firstpage

101

Lastpage

105

Abstract

Rotating machines are ubiquitous in the context of industrial control; early detection of their mechanical defects plays an important role for productivity and economic high gain. These defects are currently characterized by randomness and hidden periodicities. We propose in this regard is the study of the cyclical aspect of these defects by studying statistical tools in areas such as coherence spectrum. Applications on bearings are designed to show the impact of these tools.

Keywords

condition monitoring; fault diagnosis; mechanical engineering computing; neural nets; rolling bearings; coherence spectrum; cyclostationary tools; mechanical defects detection; neural networks; rolling bearing diagnosis; rotating machines; statistical tools; Coherence; Correlation; Educational institutions; Frequency conversion; Gears; Medical services; Neural networks; bearing diagnosis; coherence; coherence spectrum; correlation; correlation spectrum; cyclic frequency; default frequencies; neural network; second order cyclostationary;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Computational Tools for Engineering Applications (ACTEA), 2012 2nd International Conference on

Conference_Location

Beirut

Print_ISBN

978-1-4673-2488-5

Type

conf

DOI

10.1109/ICTEA.2012.6462844

Filename

6462844