Motor bearing fault diagnosis by a fundamental frequency amplitude based fuzzy decision system

2002 ◽  
Author(s):  
G. Goddu ◽  
Bo Li ◽  
Mo-Yuen Chow ◽  
J.C. Hung
Keyword(s):  
Fault Diagnosis ◽  
Fundamental Frequency ◽  
Fuzzy Decision ◽  
Decision System ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Motor Bearing

Touch screen-based motor bearing fault diagnosis

2012 ◽  
Author(s):  
Lijun Fu ◽  
Zhenhai Qian ◽  
Yan Tang ◽  
Meichen Zhu ◽  
Hongbin Liu
Keyword(s):  
Fault Diagnosis ◽  
Touch Screen ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Motor Bearing

IoT-Based Signal Enhancement and Compression Method for Efficient Motor Bearing Fault Diagnosis

2021 ◽  
Vol 21 (2) ◽  
pp. 1820-1828
Author(s):  
Huasong Tang ◽  
Siliang Lu ◽  
Gang Qian ◽  
Jianming Ding ◽  
Yongbin Liu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Signal Enhancement ◽  
Compression Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Motor Bearing

A Novel Contactless Angular Resampling Method for Motor Bearing Fault Diagnosis Under Variable Speed

2016 ◽  
Vol 65 (11) ◽  
pp. 2538-2550 ◽  
Author(s):  
Siliang Lu ◽  
Jie Guo ◽  
Qingbo He ◽  
Fang Liu ◽  
Yongbin Liu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Variable Speed ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Resampling Method ◽  
Motor Bearing

Motor Bearing Fault Diagnosis Based on MSICA-LSSVM

2011 ◽  
Vol 55-57 ◽  
pp. 747-752
Author(s):  
Zhong Hai Li ◽  
Hao Fei Mao ◽  
Jian Guo Cui ◽  
Yan Zhang
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Decomposition ◽  
Principal Component ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Fault Type ◽  
Diagnosis Method ◽  
Motor Bearing

The paper presents a motor bearing fault diagnosis method based on MSICA (Multi-scale Independent Principal Component Analysis) and LSSVM (Least Squares Support Vector Machine). MSICA is introduced into motor fault diagnosis. First, wavelet decomposition is used, and then ICA models are built by wavelet coefficients in each scale, which detect fault, and finally LSSVM is used to classify fault type. Conclusions are obtained from the analysis of the experimental data provided by Case Western Reserve University’s Bearing Data Website. And it indicates that the proposed method is simple and effective.

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