An Intelligent Fault Diagnosis Method Based on Domain Adaptation and Its Application for Bearings Under Polytropic Working Conditions

2021 ◽  
Vol 70 ◽  
pp. 1-14
Author(s):  
Zihao Lei ◽  
Guangrui Wen ◽  
Shuzhi Dong ◽  
Xin Huang ◽  
Haoxuan Zhou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Intelligent Fault Diagnosis ◽  
Diagnosis Method

scholarly journals Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis

2021 ◽  
Vol 11 (17) ◽  
pp. 7983
Author(s):  
Kun Xu ◽  
Shunming Li ◽  
Ranran Li ◽  
Jiantao Lu ◽  
Xianglian Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Wasserstein Distance ◽  
Diagnostic Methods ◽  
Mechanical Equipment ◽  
Intelligent Fault Diagnosis ◽  
Sample Distribution ◽  
Feature Extractor ◽  
Long Time ◽  
Diagnosis Method

Due to the mechanical equipment working under variable speed and load for a long time, the distribution of samples is different (domain shift). The general intelligent fault diagnosis method has a good diagnostic effect only on samples with the same sample distribution, but cannot correctly predict the faults of samples with domain shift in a real situation. To settle this problem, a new intelligent fault diagnosis method, domain adaptation network with double adversarial mechanism (DAN-DAM), is proposed. The DAN-DAM model is mainly composed of a feature extractor, two label classifiers and a domain discriminator. The feature extractor and the two label classifiers form the first adversarial mechanism to achieve class-level alignment. Moreover, the discrepancy between the two classifiers is measured by Wasserstein distance. Meanwhile, the feature extractor and the domain discriminator form the second adversarial mechanism to realize domain-level alignment. In addition, maximum mean discrepancy (MMD) is used to reduce the distance between the extracted features of two domains. The DAN-DAM model is verified by multiple transfer experiments on some datasets. According to the transfer experiment results, the DAN-DAM model has a good diagnosis effect for the domain shift samples. Moreover, the diagnostic accuracy is generally higher than other mainstream diagnostic methods.

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