A new rotating machinery fault diagnosis method based on local oscillatory-characteristic decomposition

2018 ◽  
Vol 78 ◽  
pp. 98-107 ◽  
Author(s):  
Kang Zhang ◽  
Xiangmin Chen ◽  
Lida Liao ◽  
Mingzhu Tang ◽  
Jiateng Wu
Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yongbo Li ◽  
Xianzhi Wang ◽  
Shubin Si ◽  
Xiaoqiang Du

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.


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 12348-12359 ◽  
Author(s):  
Zhen Jia ◽  
Zhenbao Liu ◽  
Chi-Man Vong ◽  
Michael Pecht

2015 ◽  
Vol 46 ◽  
pp. 201-214 ◽  
Author(s):  
Yongbo Li ◽  
Minqiang Xu ◽  
Zhao Haiyang ◽  
Yu Wei ◽  
Wenhu Huang

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