Bearing fault detection and diagnosis of a wind turbine

2011 ◽  
Author(s):  
Shenggang Yang ◽  
Xiaoli Li ◽  
Ming Liang
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Fault Detection And Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Detection And Diagnosis

scholarly journals Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique Under Constant and Variable Speed Conditions

2018 ◽  
Vol 8 (8) ◽  
pp. 1392 ◽  
Author(s):  
Moussa Hamadache ◽  
Dongik Lee ◽  
Emiliano Mucchi ◽  
Giorgio Dalpiaz
Keyword(s):  
Fault Detection ◽  
Image Recognition ◽  
Fault Detection And Diagnosis ◽  
Variable Speed ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Detection And Diagnosis ◽  
Recognition Technique

This paper addresses the application of an image recognition technique for the detection and diagnosis of ball bearing faults in rotating electrical machines (REMs). The conventional bearing fault detection and diagnosis (BFDD) methods rely on extracting different features from either waveforms or spectra of vibration signals to detect and diagnose bearing faults. In this paper, a novel vibration-based BFDD via a probability plot (ProbPlot) image recognition technique under constant and variable speed conditions is proposed. The proposed technique is based on the absolute value principal component analysis (AVPCA), namely, ProbPlot via image recognition using the AVPCA (ProbPlot via IR-AVPCA) technique. A comparison of the features (images) obtained: (1) directly in the time domain from the original raw data of the vibration signals; (2) by capturing the Fast Fourier Transformation (FFT) of the vibration signals; or (3) by generating the probability plot (ProbPlot) of the vibration signals as proposed in this paper, is considered. A set of realistic bearing faults (i.e., outer-race fault, inner-race fault, and balls fault) are experimentally considered to evaluate the performance and effectiveness of the proposed ProbPlot via the IR-AVPCA method.

EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

2016 ◽  
Vol 370 ◽  
pp. 394-423 ◽  
Author(s):  
Matej Žvokelj ◽  
Samo Zupan ◽  
Ivan Prebil
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Slewing Bearing ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Detection And Diagnosis
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