A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests

2020 ◽  
Vol 139 ◽  
pp. 106609 ◽  
Author(s):  
Qin Hu ◽  
Xiao-Sheng Si ◽  
Qing-Hua Zhang ◽  
Ai-Song Qin
Keyword(s):  
Fault Diagnosis ◽  
Random Forests ◽  
Rotating Machinery ◽  
Multi Scale ◽  
Diagnosis Method ◽  
Machinery Fault Diagnosis

scholarly journals A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying-Speed Conditions Using Infrared Thermography

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yongbo Li ◽  
Xianzhi Wang ◽  
Shubin Si ◽  
Xiaoqiang Du
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Infrared Thermography ◽  
Rotating Machinery ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Fault Features ◽  
Diagnosis Method ◽  
Systematic Framework ◽  
Machinery Fault Diagnosis

A novel systematic framework, infrared thermography- (IRT-) based method, for rotating machinery fault diagnosis under nonstationary running conditions is presented in this paper. In this framework, IRT technique is first applied to obtain the thermograph. Then, the fault features are extracted using bag-of-visual-word (BoVW) from the IRT images. In the end, support vector machine (SVM) is utilized to automatically identify the fault patterns of rotating machinery. The effectiveness of proposed method is evaluated using lab experimental signal of rotating machinery. The diagnosis results show that the IRT-based method has certain advantages in classification rotating machinery faults under nonstationary running conditions compared with the traditional vibration-based method.

scholarly journals A Rotating Machinery Fault Diagnosis Method Based on Feature Learning of Thermal Images

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 12348-12359 ◽  
Author(s):  
Zhen Jia ◽  
Zhenbao Liu ◽  
Chi-Man Vong ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Rotating Machinery ◽  
Thermal Images ◽  
Diagnosis Method ◽  
Machinery Fault Diagnosis
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