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Image texture feature fusion enhancement for bearing fault diagnosis based on maximum gradient

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  • Sun, Yongjian
  • Yu, Gang
  • Wang, Wei

Abstract

In modern manufacturing industry, mechanical equipment plays a crucial role. In order to address the difficulty of signal feature extraction in mechanical equipment, this paper proposes a image Texture Feature Fusion Enhancement (TFFE) method based on maximum gradient. A mathematical transformation method is used to convert one-dimensional time series into two forms of images: symmetrized dot pattern and scalogram. The texture features are obtained by calculating the maximum gradient of the two types of images. The proposed image Texture Feature Fusion Enhancement (TFFE) method is used to combine different images and enhance the texture features. Finally, the Darknet53 network is used as the image classification method to conduct intelligent classification of rolling bearing faults. The classification effect of the method is verified by a series of experiments, in which the validity of the images used in different image conditions is verified, and the network used in different network conditions show better classification performance. The method’s ability to resist noise is also validated in experiments under different noise conditions. The experimental results show that the proposed image enhancement method can improve fault features in the image and maintain good diagnostic performance.

Suggested Citation

  • Sun, Yongjian & Yu, Gang & Wang, Wei, 2025. "Image texture feature fusion enhancement for bearing fault diagnosis based on maximum gradient," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:reensy:v:260:y:2025:i:c:s0951832025002108
    DOI: 10.1016/j.ress.2025.111009
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    References listed on IDEAS

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    1. Xu, Yadong & Yan, Xiaoan & Feng, Ke & Sheng, Xin & Sun, Beibei & Liu, Zheng, 2022. "Attention-based multiscale denoising residual convolutional neural networks for fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    2. Miao, Mengqi & Yu, Jianbo, 2024. "Deep feature interactive network for machinery fault diagnosis using multi-source heterogeneous data," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. Hei, Zhendong & Sun, Weifang & Yang, Haiyang & Zhong, Meipeng & Li, Yanling & Kumar, Anil & Xiang, Jiawei & Zhou, Yuqing, 2025. "Novel domain-adaptive Wasserstein generative adversarial networks for early bearing fault diagnosis under various conditions," Reliability Engineering and System Safety, Elsevier, vol. 257(PA).
    4. Yu, Tian & Li, Chaoshun & Huang, Jie & Xiao, Xiangqu & Zhang, Xiaoyuan & Li, Yuhong & Fu, Bitao, 2024. "ReF-DDPM: A novel DDPM-based data augmentation method for imbalanced rolling bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    5. Zhou, Liang & Wang, Huawei & Xu, Shanshan, 2023. "Aero-engine prognosis strategy based on multi-scale feature fusion and multi-task parallel learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    6. Zhang, Zhongwei & Jiao, Zonghao & Li, Youjia & Shao, Mingyu & Dai, Xiangjun, 2024. "Intelligent fault diagnosis of bearings driven by double-level data fusion based on multichannel sample fusion and feature fusion under time-varying speed conditions," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    7. Yang, Miaorui & Zhang, Kun & Sheng, Zhipeng & Zhang, Xiangfeng & Xu, Yonggang, 2024. "The amplitude modulation bispectrum: A weak modulation features extracting method for bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    8. Li, Xin & Li, Yong & Yan, Ke & Shao, Haidong & (Jing) Lin, Janet, 2023. "Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Rui Zhang & Na Zhao & Liuhu Fu & Xiaolu Bai & Jianghui Cai, 2023. "Recognizing defects in stainless steel welds based on multi-domain feature expression and self-optimization," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1293-1309, March.
    10. Park, Chan Hee & Kim, Hyeongmin & Suh, Chaehyun & Chae, Minseok & Yoon, Heonjun & Youn, Byeng D., 2022. "A health image for deep learning-based fault diagnosis of a permanent magnet synchronous motor under variable operating conditions: Instantaneous current residual map," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    11. Chen, Chuanhai & Li, Bowen & Guo, Jinyan & Liu, Zhifeng & Qi, Baobao & Hua, Chunlei, 2022. "Bearing life prediction method based on the improved FIDES reliability model," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    12. Bai, Ruxue & Meng, Zong & Xu, Quansheng & Fan, Fengjie, 2023. "Fractional Fourier and time domain recurrence plot fusion combining convolutional neural network for bearing fault diagnosis under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
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