Stack Autoencoder Transfer Learning Algorithm for Bearing Fault Diagnosis Based on Class Separation and Domain Fusion

In intelligent fault diagnosis, transfer learning can reduce the requirement of sufficient labeled data and the same data distribution. However, for the diagnosis of a new machine, there are still some limitations, such as low accuracy or the demand for some labeled data with fault information in th...

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Vydáno v:IEEE transactions on industrial electronics (1982) Ročník 69; číslo 3; s. 3047 - 3058
Hlavní autoři: Sun, Meidi, Wang, Hui, Liu, Ping, Huang, Shoudao, Wang, Pan, Meng, Jinhao
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
Algorithms
Class separation
Classification algorithms
Datasets
Decoding
deep learning
domain fusion
Domains
Fault diagnosis
Feature extraction
Machine learning
Machinery
Separation
Task analysis
Transfer learning
transfer learning (TL)
ISSN:0278-0046, 1557-9948
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Shrnutí:In intelligent fault diagnosis, transfer learning can reduce the requirement of sufficient labeled data and the same data distribution. However, for the diagnosis of a new machine, there are still some limitations, such as low accuracy or the demand for some labeled data with fault information in the new machine. In this article, we propose a stack autoencoder transfer learning algorithm based on the class separation and domain fusion (SAE-CSDF) to solve these problems. According to the characteristics of bearing faults, the proposed weighted domain fusion strategy can ensure the direction and balance in the transfer process. The proposed class separation degree can improve the accuracy of the target domain indirectly by extending the differences between the classes in the source domain. The effectiveness of the SAE-CSDF is verified via the mutual transfer of two public datasets and one laboratory dataset. The results show that the accuracy of the algorithm can reach 97% in the transfer between different machines, even if there is no labeled fault data in the new machine.
Bibliografie:ObjectType-Article-1
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2021.3066933