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Open AccessArticle
MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions
by
Jinghua Li
Jinghua Li ,
Yonghe Wei
Yonghe Wei and
Xiaojiao Gu
Xiaojiao Gu *
College of Mechanical Engineering, Shenyang Ligong University, Nanping Middle Road 6, Shenyang 110159, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(19), 8791; https://doi.org/10.3390/app14198791 (registering DOI)
Submission received: 17 August 2024
/
Revised: 17 September 2024
/
Accepted: 25 September 2024
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Published: 29 September 2024
Abstract
In response to the challenges of bearing fault diagnosis under small sample sizes and variable operating conditions, this paper proposes a novel method based on the two-dimensional analysis of vibration acceleration signals and a Multi-Task Conditional Generative Adversarial Network (MTC-GAN). This method first constructs two-dimensional images of vibration signals by leveraging the physical properties of the bearing acceleration signals and employs Local Binary Patterns (LBP) to extract subtle texture features from these images, thereby generating fault feature signatures with high discriminative power across different operating conditions. Subsequently, MTC-GAN is utilized for data augmentation, and the trained discriminator is used to perform fault classification tasks, improving classification accuracy under conditions with small sample sizes. Experimental results demonstrate that the proposed method achieves excellent fault diagnosis accuracy and robustness under both small sample sizes and varying operating conditions. Compared to traditional methods, this approach exhibits higher efficiency and reliability in handling complex operating conditions and data scarcity.
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MDPI and ACS Style
Li, J.; Wei, Y.; Gu, X.
MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions. Appl. Sci. 2024, 14, 8791.
https://doi.org/10.3390/app14198791
AMA Style
Li J, Wei Y, Gu X.
MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions. Applied Sciences. 2024; 14(19):8791.
https://doi.org/10.3390/app14198791
Chicago/Turabian Style
Li, Jinghua, Yonghe Wei, and Xiaojiao Gu.
2024. "MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions" Applied Sciences 14, no. 19: 8791.
https://doi.org/10.3390/app14198791
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