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Liu et al. Intell Robot 2023;3(2):131-43 Intelligence & Robotics
DOI: 10.20517/ir.2023.07

Research Article Open Access

Semi-supervised joint adaptation transfer network with
conditional adversarial learning for rotary machine fault

diagnosis

2
3
1
Chun Liu 1,2 , Shaojie Li , Hongtian Chen , Xianchao Xiu , Chen Peng 1
1 School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
2 Institute of Artificial Intelligence, Shanghai University, Shanghai 200444, China.
3 Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China.

Correspondence to: Dr. Chun Liu, the School of Mechatronic Engineering and Automation, and also the School of Artificial Intelli-
gence, Shanghai University, Shanghai 200444, China. E-mail: Chun_Liu@shu.edu.cn

How to cite this article: Liu C, Li S, Chen H, Xiu X, Peng C. Fault diagnosis, joint adaptation transfer network, conditional adversarial
learning, rotary machine. Intell Robot 2023;3(2):7. http://dx.doi.org/10.20517/ir.2023.07
Received: 28 Feb 2023 First Decision: 17 Apr 2023 Revised: 27 Apr 2023 Accepted: 5 May 2023 Published: 20 May
2023
Academic Editor: Simon X. Yang Copy Editor: Yanbing Bai Production Editor: Yanbing Bai

Abstract
At present, artificial intelligence is booming and has made major breakthroughs in fault diagnosis scenarios. However,
the high diagnostic accuracy of most mainstream fault diagnosis methods must rely on sufficient data to train the di-
agnostic models. In addition, there is another assumption that needs to be satisfied: the consistency of training and
test data distribution. When these prerequisites are not available, the effectiveness of the diagnosis model declines
dramatically. To address this problem, we propose a semi-supervised joint adaptation transfer network with condi-
tional adversarial learning for rotary machine fault diagnosis. To fully utilize the fault features implied in unlabeled
data, pseudo-labels are generated through threshold filtering to obtain an initial pre-trained model. Then, a joint do-
main adaptation transfer network module based on conditional adversarial learning and distance metric is introduced
to ensure the consistency of the distribution in two different domains. Lastly, in three groups of experiments with dif-
ferent settings: a single fault with variable load, a single fault with variable speed, and a mixed fault with variable
speed and load, it was confirmed that our method can obtain competitive diagnostic performance.

Keywords: Fault diagnosis, joint adaptation transfer network, conditional adversarial learning, rotary machine

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, shar-
ing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate
if changes were made.

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