Application of vibration signature analysis techniques for rolling element bearing fault identification

2011 ◽  
Vol 8 (1) ◽  
pp. 21-36
Author(s):  
P Jayaswal ◽  
S N Verma
Keyword(s):  
Rolling Element Bearing ◽  
Fault Identification ◽  
Signature Analysis ◽  
Analysis Techniques ◽  
Bearing Fault ◽  
Rolling Element ◽  
Vibration Signature ◽  
Element Bearing

scholarly journals Machine Fault Signature Analysis

2008 ◽  
Vol 2008 ◽  
pp. 1-10 ◽  
Author(s):  
Pratesh Jayaswal ◽  
A. K. Wadhwani ◽  
K. B. Mulchandani
Keyword(s):  
Rolling Contact ◽  
Rolling Element Bearing ◽  
Electrical Machine ◽  
Signature Analysis ◽  
Bearing Fault ◽  
Rolling Element ◽  
Gear Fault ◽  
Recent Developments ◽  
Vibration Signature ◽  
Machine Fault

The objective of this paper is to present recent developments in the field of machine fault signature analysis with particular regard to vibration analysis. The different types of faults that can be identified from the vibration signature analysis are, for example, gear fault, rolling contact bearing fault, journal bearing fault, flexible coupling faults, and electrical machine fault. It is not the intention of the authors to attempt to provide a detailed coverage of all the faults while detailed consideration is given to the subject of the rolling element bearing fault signature analysis.

Cyclic Spectrum Analysis on Rolling-Element Bearing with Inner-Race Point Defect

2011 ◽  
Vol 291-294 ◽  
pp. 1469-1473
Author(s):  
Wei Ke ◽  
Yong Xiang Zhang ◽  
Lin Li
Keyword(s):  
Point Defect ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing ◽  
Simulation Signal ◽  
Inner Race ◽  
Cyclic Spectrum

Vibration signal of rolling-element bearing is random cyclostationarity when a fault develops, the proper analysis of which can be used for condition monitor. Cyclic spectrum is a common cyclostationary analysis method and has a great many algorithms which have distinct efficiency in different application circumstance, two common algorithms (SSCA and FAM) are compared in the paper. The FAM is recommended to be used in diagnosing rolling-element bearing fault via calculation of simulation signal in different signal to noise ratio. The cyclic spectrum of practice signal of rolling-element bearing with inner-race point defect is analyzed and a new characteristic extraction method is put forward. The preferable result is acquired verify the correctness of the analysis and indicate that the cyclic spectrum is a robust method in diagnosing rolling-element bearing fault.

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