Autocorrelation Based Feature Extraction for Bearing Fault Detection in Induction Motors

2019 ◽  
Author(s):  
Sayantan Dey ◽  
Sayanjit Singha Roy ◽  
Kaniska Samanta ◽  
Sudip Modak ◽  
Soumya Chatterjee
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Induction Motors ◽  
Bearing Fault ◽  
Bearing Fault Detection

Fractal dimension theory-based approach for bearing fault detection in induction motors

2016 ◽  
Author(s):  
Carlos A. Perez-Ramirez ◽  
Juan P. Amezquita-Sanchez ◽  
Martin Valtierra-Rodriguez ◽  
Aurelio Dominguez-Gonzalez ◽  
David Camarena-Martinez ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Fault Detection ◽  
Induction Motors ◽  
Dimension Theory ◽  
Bearing Fault ◽  
Bearing Fault Detection

Vibration Analysis Based Feature Extraction for Bearing Fault Detection

2012 ◽  
Vol 197 ◽  
pp. 124-128
Author(s):  
Jie Liu ◽  
Chun Sheng Yang ◽  
Qing Feng Lou
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Rolling Element Bearings ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Rotary Machine ◽  
Catastrophic Failures ◽  
Rolling Element ◽  
Significant Research ◽  
Recent Developments

Rolling element bearings are widely used in various rotary machines. Most rotary machine failures are attributed to unexpected bearing faults. Accordingly, reliable bearing fault detection is critically needed in industries to prevent these machines’ performance degradation, malfunction, or even catastrophic failures. Feature extraction plays an important role in bearing fault detection and significant research efforts have thus far been devoted to this subject from both academia and industry. This paper intends to provide a brief review of the recent developments in feature extraction for bearing fault detection, and the focus will be placed on the advances in methods for dealing with the nonstationary characteristics of bearing fault signatures.

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