A novel bearing fault diagnosis method using deep residual learning network

2022 ◽  
Author(s):  
Selen Ayas ◽  
Mustafa Sinasi Ayas
Keyword(s):  
Fault Diagnosis ◽  
Learning Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Residual Learning ◽  
Diagnosis Method

scholarly journals Rolling bearing fault diagnosis method based on VMD and LSSVM

2021 ◽  
Vol 1792 (1) ◽  
pp. 012035
Author(s):  
Xingtong Zhu ◽  
Zhiling Huang ◽  
Jinfeng Chen ◽  
Junhao Lu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

scholarly journals Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution

2022 ◽  
Vol 162 ◽  
pp. 108018
Author(s):  
Zhijian Wang ◽  
Jie Zhou ◽  
Wenhua Du ◽  
Yaguo Lei ◽  
Junyuan Wang
Keyword(s):  
Fault Diagnosis ◽  
Blind Deconvolution ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

Accuracy-Improved Bearing Fault Diagnosis Method Based on AVMD Theory and AWPSO-ELM Model

Measurement ◽  
2021 ◽  
pp. 109666
Author(s):  
Jinxi Wang ◽  
Yilan Zhang ◽  
Faye Zhang ◽  
Wei Li ◽  
Shanshan Lv ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

scholarly journals Improvement of Roller Bearing Diagnosis with Unlabeled Data Using Cut Edge Weight Confidence Based Tritraining

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Wei-Li Qin ◽  
Wen-Jin Zhang ◽  
Zhen-Ya Wang
Keyword(s):  
Fault Diagnosis ◽  
Roller Bearing ◽  
Unlabeled Data ◽  
Edge Weight ◽  
Ring Fault ◽  
Bearing Fault ◽  
Cut Edge ◽  
Bearing Fault Diagnosis ◽  
Roller Bearings ◽  
Diagnosis Method

Roller bearings are one of the most commonly used components in rotational machines. The fault diagnosis of roller bearings thus plays an important role in ensuring the safe functioning of the mechanical systems. However, in most cases of bearing fault diagnosis, there are limited number of labeled data to achieve a proper fault diagnosis. Therefore, exploiting unlabeled data plus few labeled data, this paper proposed a roller bearing fault diagnosis method based on tritraining to improve roller bearing diagnosis performance. To overcome the noise brought by wrong labeling into the classifiers training process, the cut edge weight confidence is introduced into the diagnosis framework. Besides a small trick called suspect principle is adopted to avoid overfitting problem. The proposed method is validated in two independent roller bearing fault experiment vibrational signals that both include three types of faults: inner-ring fault, outer-ring fault, and rolling element fault. The results demonstrate the desirable diagnostic performance improvement by the proposed method in the extreme situation where there is only limited number of labeled data.

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