Rolling element bearing fault diagnostics using acoustic emission technique and advanced signal processing

2015 ◽  
Vol 230 (1) ◽  
pp. 64-77 ◽  
Author(s):  
Farzad Hemmati ◽  
Mohammed Alqaradawi ◽  
Mohamed S Gadala
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Rolling Element Bearing ◽  
Fault Diagnostics ◽  
Acoustic Emission Technique ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing

Application of Acoustic Emission on Fault Diagnosis of Rolling Element Bearing

2011 ◽  
Vol 199-200 ◽  
pp. 895-898
Author(s):  
Hong Fang Yuan ◽  
Peng Wang ◽  
Hua Qing Wang
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Wide Band ◽  
Rolling Element Bearing ◽  
Detection Accuracy ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Recognition Experiment ◽  
Rolling Element ◽  
Element Bearing

Because AE (Acoustic Emission) signals in bearing fault monitoring unavoidably mixed various noise which lead to wide band characteristics, in this paper, the collected AE signals are pre-processed by EMD (Empirical Mode Decomposition) algorithm to extract useful information in the concerned frequency range, after that, power spectrum is used to locating analysis and pattern recognition. Experiment show that this method could improve the detection accuracy in rolling element bearing fault diagnosis.

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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