Asynchronous Motor Bearing Fault Detection Methods

2011 ◽  
Vol 383-390 ◽  
pp. 5055-5058
Author(s):  
Li Ling Sun ◽  
Bao Long Zhang
Keyword(s):  
Fault Detection ◽  
Failure Probability ◽  
Asynchronous Motor ◽  
Detection Methods ◽  
Ball Bearings ◽  
Bearing Failure ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Deep Groove ◽  
Motor Bearing

Bearing, asynchronous deep groove ball bearings are widely used in induction motor field. Motor bearing failure probability is as high as 40% in asynchronous motor. It accounts for the largest proportion of failures in the motor. Therefore, people have been on studying motor bearing fault detection methods for further research. So far, people have studied a variety of modern detection methods.

A novel LDA-based approach for motor bearing fault detection

2015 ◽  
Author(s):  
Lucio Ciabattoni ◽  
Gionata Cimini ◽  
Francesco Ferracuti ◽  
Alessandro Freddi ◽  
Gianluca Ippoliti ◽  
...  
Keyword(s):  
Fault Detection ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Motor Bearing

scholarly journals Wheel Bearing Fault Isolation and Prognosis Using Acoustic Based Approach

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Jianshe Feng ◽  
Xinyu Du ◽  
Mutasim Salman
Keyword(s):  
Fault Detection ◽  
Vibration Signal ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Installation Space ◽  
Bearing Failure ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Failure Prognosis ◽  
Wheel Bearing

Wheel bearing fault detection, isolation and failure prognosis are critical to improve perceived quality and customer experience for retail vehicles, and to reduce the repair cost and down time for fleet vehicles. Currently, most of the research in bearing failure and degradation diagnosis focus on vibration signal analytics. However, these techniques are rarely applied in automotive industry due to the high sensor cost, installation space limitation, and limited communication bandwidth. In this work, an acoustic based approach for wheel bearing fault detection and isolation is developed to overcome these limitations. Since the bearing noise is a precursor of bearing failure, the proposed method is a prognosis solution. The whole solution is verified using the data collected from a production vehicle. The results show that the proposed method can predict the wheel bearing failure with promising accuracy and robustness.

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