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All issues Volume 355 (2026) EPJ Web Conf., 355 (2026) 01004 References
Open Access
Issue
EPJ Web Conf.
Volume 355, 2026
4th International Conference on Sustainable Technologies and Advances in Automation, Aerospace and Robotics (STAAAR 2025)
Article Number 01004
Number of page(s) 16
Section Robotics, Exoskeletons and AI Modeling
DOI https://doi.org/10.1051/epjconf/202635501004
Published online 03 March 2026
  1. Lai, Lianyou, Weijian Xu, and Zhongzhe Song. “A Novel Fault Diagnosis Method for Rolling Bearings Based on Spectral Kurtosis and LS-SVM.” Electronics 14, no. 14 (2025): 2790. https://doi.org/10.3390/electronics14142790. [Google Scholar]
  2. Imane, Moussaoui, Chemseddine Rahmoune, Moahmed Zair, and Djamel Benazzouz. “Multi-Fault Bearing Diagnosis under Time-Varying Conditions Using Empirical Wavelet Transform, Gaussian Mixture Model, and Random Forest Classifier.” Advances in Mechanical Engineering 16, no. 8 (2024): 16878132241275787. https://doi.org/10.1177/16878132241275787. [Google Scholar]
  3. Salunkhe, Vishal G., and R. G. Desavale. “An Intelligent Prediction for Detecting Bearing Vibration Characteristics Using a Machine Learning Model.” Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems 4, no. 3 (2021): 031004. https://doi.org/10.1115/1.4049938 [Google Scholar]
  4. Jamil et al. (2021) evaluated the performance of SVM and KNN classifiers in diagnosing faults in rolling element bearings using feature-based approaches. Their study, published in Vibroengineering Procedia, demonstrated how these classifiers can effectively distinguish fault conditions based on extracted features. https://doi.org/10.21595/vp.2021.22307. [Google Scholar]
  5. Shen, Weijie, Maohua Xiao, Zhenyu Wang, and Xinmin Song. “Rolling Bearing Fault Diagnosis Based on Support Vector Machine Optimized by Improved Grey Wolf Algorithm.” Sensors 23, no. 14 (2023): 6645. https://doi.org/10.3390/s23146645. [Google Scholar]
  6. Wang, Tianhao, Hongying Meng, Fan Zhang, and Rui Qin. “Fault Detection of Wheelset Bearings through Vibration-Sound Fusion Data Based on Grey Wolf Optimizer and Support Vector Machine.” Technologies 12, no. 9 (2024): 144. https://doi.org/10.3390/technologies12090144. [Google Scholar]
  7. Zheng, Hong, and Lei Zhou. “Rolling Element Bearing Fault Diagnosis Based on Support Vector Machine.” 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), IEEE, April 2012, 544–47. https://doi.org/10.1109/CECNet.2012.6201982. [Google Scholar]
  8. Fasana, Alessandro, Stefano Marchesiello, Miriam Pirra, Luigi Garibaldi, and Alessandra Torri. “Spectral Kurtosis against SVM for Best Frequency Selection in Bearing Diagnostics.” Mécanique& Industries 11, no. 6 (2010): 489–94. https://doi.org/10.1051/meca/2010056. [Google Scholar]
  9. Zhu, Ke Heng, Xi Geng Song, and Dong Xin Xue. “Roller Bearing Fault Diagnosis Based on IMF Kurtosis and SVM.” Advanced Materials Research 694–697 (May 2013): 1160–66. https://doi.org/10.4028/www.scientific.net/AMR.694-697.1160 [Google Scholar]
  10. Gangsar, Purushottam, and Rajiv Tiwari. “Online Diagnostics of Mechanical and Electrical Faults in Induction Motor Using Multiclass Support Vector Machine Algorithms Based on Frequency Domain Vibration and Current Signals.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering 5, no. 3 (2019): 031001. https://doi.org/10.1115/1.4043268. [Google Scholar]
  11. Pule, Mushabi, Oduetse Matsebe, and Ravi Samikannu. “Application of PCA and SVM in Fault Detection and Diagnosis of Bearings with Varying Speed.” Mathematical Problems in Engineering 2022 (April 2022): 1–12. https://doi.org/10.1155/2022/5266054. [Google Scholar]
  12. Kannan, Vigneshwar, Tieling Zhang, and Huaizhong Li. “A Review of the Intelligent Condition Monitoring of Rolling Element Bearings.” Machines 12, no. 7 (2024): 484. https://doi.org/10.3390/machines12070484. [Google Scholar]

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