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Open AccessArticle
A Cross-Working Condition-Bearing Diagnosis Method Based on Image Fusion and a Residual Network Incorporating the Kolmogorov–Arnold Representation Theorem
by
Ziyi Tang
Ziyi Tang 1,3
,
Xinhao Hou
Xinhao Hou 1,3,
Xin Wang
Xin Wang 2,3,*
and
Jifeng Zou
Jifeng Zou 1,3
1
School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China
2
Engineering Training Center, Tianjin University of Technology, Tianjin 300384, China
3
Institute of Intelligent Control and Fault Diagnosis, Tianjin University of Technology, Tianjin 300384, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(16), 7254; https://doi.org/10.3390/app14167254 (registering DOI)
Submission received: 16 June 2024
/
Revised: 16 August 2024
/
Accepted: 16 August 2024
/
Published: 17 August 2024
Abstract
With the optimization and advancement of industrial production and manufacturing, the application scenarios of bearings have become increasingly diverse and highly coupled. This complexity poses significant challenges for the extraction of bearing fault features, consequently affecting the accuracy of cross-condition fault diagnosis methods. To improve the extraction and recognition of fault features and enhance the diagnostic accuracy of models across different conditions, this paper proposes a cross-condition bearing diagnosis method. This method, named MCR-KAResNet-TLDAF, is based on image fusion and a residual network that incorporates the Kolmogorov–Arnold representation theorem. Firstly, the one-dimensional vibration signals of the bearing are processed using Markov transition field (MTF), continuous wavelet transform (CWT), and recurrence plot (RP) methods, converting the resulting images to grayscale. These grayscale images are then multiplied by corresponding coefficients and fed into the R, G, and B channels for image fusion. Subsequently, fault features are extracted using a residual network enhanced by the Kolmogorov–Arnold representation theorem. Additionally, a domain adaptation algorithm combining multiple kernel maximum mean discrepancy (MK-MMD ) and conditional domain adversarial network with entropy conditioning (CDAN+E ) is employed to align the source and target domains, thereby enhancing the model’s cross-condition diagnostic accuracy. The proposed method was experimentally validated on the Case Western Reserve University (CWRU) dataset and the Jiangnan University (JUN) dataset, which include the 6205-2RS JEM SKF, N205, and NU205 bearing models. The method achieved accuracy rates of 99.36% and 99.889% on the two datasets, respectively. Comparative experiments from various perspectives further confirm the superiority and effectiveness of the proposed model.
Share and Cite
MDPI and ACS Style
Tang, Z.; Hou, X.; Wang, X.; Zou, J.
A Cross-Working Condition-Bearing Diagnosis Method Based on Image Fusion and a Residual Network Incorporating the Kolmogorov–Arnold Representation Theorem. Appl. Sci. 2024, 14, 7254.
https://doi.org/10.3390/app14167254
AMA Style
Tang Z, Hou X, Wang X, Zou J.
A Cross-Working Condition-Bearing Diagnosis Method Based on Image Fusion and a Residual Network Incorporating the Kolmogorov–Arnold Representation Theorem. Applied Sciences. 2024; 14(16):7254.
https://doi.org/10.3390/app14167254
Chicago/Turabian Style
Tang, Ziyi, Xinhao Hou, Xin Wang, and Jifeng Zou.
2024. "A Cross-Working Condition-Bearing Diagnosis Method Based on Image Fusion and a Residual Network Incorporating the Kolmogorov–Arnold Representation Theorem" Applied Sciences 14, no. 16: 7254.
https://doi.org/10.3390/app14167254
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