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Subdomain Adaptation Transfer Learning Network for Fault Diagnosis of Roller Bearings
- Source :
- IEEE Transactions on Industrial Electronics. 69:8430-8439
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Due to the data distribution discrepancy, fault diagnosis models, trained with labeled data in one scene, likely fails in classifying by unlabeled data acquired from the other scenes. Transfer learning is capable to generalize successful application trained in one scene to the fault diagnosis in the other scenes. However, the existing transfer methods do not pay much attention to reduce adaptively marginal and conditional distribution biases, and also ignore the degree of contribution between both biases and among network layers, which limit classification performance and generalization in reality. To overcome these weaknesses, we established a new fault diagnosis model, called subdomain adaptation transfer learning network (SATLN). Firstly, two convolutional building blocks were stacked to extract transferable features from raw data. Then, the pseudo label learning was amended to construct target subdomain of each class. Furthermore, a sub-domain adaptation was combined with domain adaptation to reduce both marginal and conditional distribution biases simultaneously. Finally, a dynamic weight term was applied for adaptive adjustment of the contributions from both discrepancies and each network layers. The SATLN method was tested with six transfer tasks. The results demonstrate the effectiveness and superiority of the SATLN in the cross-domain fault diagnosis field.
- Subjects :
- business.industry
Generalization
Computer science
Pattern recognition
Conditional probability distribution
Fault (power engineering)
Field (computer science)
Term (time)
Control and Systems Engineering
Artificial intelligence
Limit (mathematics)
Electrical and Electronic Engineering
business
Transfer of learning
Adaptation (computer science)
Subjects
Details
- ISSN :
- 15579948 and 02780046
- Volume :
- 69
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Industrial Electronics
- Accession number :
- edsair.doi...........2493fe291a9e519c94fe15d7dd2fd920