Deep Domain Generalization Combining A Priori Diagnosis Knowledge Toward Cross-Domain Fault Diagnosis of Rolling Bearing

2021 ◽  
Vol 70 ◽  
pp. 1-11
Author(s):  
Huailiang Zheng ◽  
Yuantao Yang ◽  
Jiancheng Yin ◽  
Yuqing Li ◽  
Rixin Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
A Priori ◽  
Rolling Bearing ◽  
Cross Domain

scholarly journals A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3382
Author(s):  
Zhongwei Zhang ◽  
Mingyu Shao ◽  
Liping Wang ◽  
Sujuan Shao ◽  
Chicheng Ma
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Class Imbalance ◽  
Rolling Bearing ◽  
Industrial Applications ◽  
Mechanical Equipment ◽  
Reliable Operation ◽  
Cross Domain ◽  
Geometric Difference ◽  
Model Generalization

As the key component to transmit power and torque, the fault diagnosis of rotating machinery is crucial to guarantee the reliable operation of mechanical equipment. Regrettably, sample class imbalance is a common phenomenon in industrial applications, which causes large cross-domain distribution discrepancies for domain adaptation (DA) and results in performance degradation for most of the existing mechanical fault diagnosis approaches. To address this issue, a novel DA approach that simultaneously reduces the cross-domain distribution difference and the geometric difference is proposed, which is defined as MRMI. This work contains three parts to improve the sample class imbalance issue: (1) A novel distance metric method (MVD) is proposed and applied to improve the performance of marginal distribution adaptation. (2) Manifold regularization is combined with instance reweighting to simultaneously explore the intrinsic manifold structure and remove irrelevant source-domain samples adaptively. (3) The ℓ2-norm regularization is applied as the data preprocessing tool to improve the model generalization performance. The gear and rolling bearing datasets with class imbalanced samples are applied to validate the reliability of MRMI. According to the fault diagnosis results, MRMI can significantly outperform competitive approaches under the condition of sample class imbalance.

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
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