Adversarial multi-domain adaptation for machine fault diagnosis with variable working conditions
Autor: | Changqing Shen, Bingru Yang, Qi Li, Liang Chen, Yiyun Xu, Shuangjie Liu |
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Rok vydání: | 2020 |
Předmět: |
Computer science
020208 electrical & electronic engineering 010401 analytical chemistry Feature extraction 02 engineering and technology computer.software_genre Fault (power engineering) 01 natural sciences Field (computer science) 0104 chemical sciences Visualization Domain (software engineering) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Data mining Transfer of learning computer Classifier (UML) |
Zdroj: | INDIN |
Popis: | Due to the complexity of industrial intelligent diagnosis, transfer learning-based fault diagnosis has become an evolving focus of the research field. Transfer learning uses knowledge of the source domain to identify faults in the target domain, which is a powerful tool to solve the problem of fault signal domain shift. However, existing methods have a limitation on multiple target domains. In other words, for different domains, respective transfer tasks are necessary. To seek a breakthrough, a adversarial multi-domain adaptation (AMDA) fault diagnosis method is proposed, realizing the fault diagnosis of multiple target domains by using the knowledge of a single source domain. AMDA is divided into three parts, namely, feature extractor, fault classifier and domain classifier. Through multi-domain adversarial learning, feature extractor and domain classifier mine the knowledge shared by multiple domains, and fault classifier can identify fault features distributed in different domains. The proposed AMDA method can surpass some traditional transfer learning fault diagnosis methods. Furthermore, as feature visualization result revealed, AMDA has significant advantages in multi-domain and broad research prospects. |
Databáze: | OpenAIRE |
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