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Liu et al. Intell Robot 2023;3(2):131-43 I http://dx.doi.org/10.20517/ir.2023.07 Page 141

frequency domain representation mainly provides information about the frequency components and relative
strengths of the signal but may not be able to fully reflect all the information about the signal, especially if the
signal is very complex or contains multiple frequencies. In addition, the interpretation of frequency domain
representation may be more difficult to understand and may require higher levels of professional knowledge
foranalysis. Althoughfrequencydomainrepresentationcanprovidevaluableinformationaboutthefrequency
components of the signal, it may not be as effective in capturing the complex time characteristics of the signal.
Therefore, time domain representation is more prominent in terms of intuitiveness and practicality. Through
these comparative tests, it is demonstrated that the transfer effects of our proposed method are practical for
different speed scenarios.

In summary, the improvement in diagnostic performance achieved by our method can be attributed to the
combination of JMMD and adversarial domain training modules, which effectively address the challenges of
domain adaptation in multimodal conditions. Additionally, the use of the time domain signal as input also
contributes to the improvement in diagnostic performance.

5. CONCLUSION
This paper proposes a novel semi-supervised joint adaptation transfer network with conditional adversarial
learningforfaultdiagnosisoftherotarymachine,whichcaneffectivelysolvetheproblemofpoordiagnosisdue
to insufficient data in the target domain. The proposed fault diagnosis method first incorporates information
from unlabeled target domain data by introducing a pre-trained model. Two domain adaptation modules
are then used to close the distance between the distributions of different domains, thereby improving the
effectiveness of the diagnostics of mutual migrations in the two different domains. Ultimately, our approach
is validated to achieve reliable results for variable loads, variable speeds, and mixed fault-type diagnostic tasks
in three different experimental settings. However, the method we proposed has not been validated using fault
data obtained from real scenarios, where the fault patterns are typically more complex, and the data often
contains a significant amount of noise. As a result, there is a possibility that the performance of this method
could be affected.

In this work, we focus more on domain adaptation between data in different domains so that pseudo-labels
use only empirical thresholds to filter reliable labels. In future investigations, we will focus on how to filter for
more reliable pseudo-labels in order to make the best possible use of unlabeled data and further improve the
diagnosis of tasks with insufficient labeling data.

DECLARATIONS
Authors’ contributions
Made substantial contributions to the conception and design of the study and performed data analysis and
interpretation: Liu C, Li S
Performeddataacquisitionandprovidedadministrative, technical, andmaterialsupport: ChenH,XiuX,Peng
C

Availability of data and materials
CWRU: [25] JNU: [26] SEU: [27]

Financial support and sponsorship
This work was supported by the National Natural Science Foundation of China (62103250, 62273223, and
62173218); Shanghai Sailing Program (21YF1414000); Project of Science and Technology Commission of
Shanghai Municipality, China (22JC1401401).