Open Access
Issue
E3S Web of Conf.
Volume 465, 2023
8th International Conference on Industrial, Mechanical, Electrical and Chemical Engineering (ICIMECE 2023)
Article Number 01027
Number of page(s) 9
Section Symposium on Mechanical, Chemical, and Advanced Materials Engineering
DOI https://doi.org/10.1051/e3sconf/202346501027
Published online 18 December 2023
  1. B. L. Widjiantoro, S. Munir, and K. Indriawati, “Fault Estimation on Induction Motor Based on Stator Inter-Turn Fault,” IPTEK J. Proc. Ser., no. 6, pp. 489–493, (2020). [Google Scholar]
  2. J. Pramudijanto, F. I. Adhim, L. P. Rahayu, and M. Z. Rusretin, “Disturbance Observer Design for Controlling the Speed of Three Phase Induction Motor,” JAREE-Journal Adv. Res. Electr. Eng., vol. 2, no. 1, pp. 52–57, (2018). [Google Scholar]
  3. P. Gangsar and R. Tiwari, “Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms,” Mech. Syst. Signal Process., vol. 94, pp. 464–481, (2017). [CrossRef] [Google Scholar]
  4. Priyanka, N. Turk, and R. Dahiya, “Condition monitoring of induction motors through simulation of bearing fault and air gap eccentricity fault,” Int. J. Recent Technol. Eng., vol. 8, no. 3, pp. 176– 193, (2019). [Google Scholar]
  5. A. Soualhi, K. Medjaher, and N. Zerhouni, “Bearing health monitoring based on hilbert-huang transform, support vector machine, and regression,” IEEE Trans. Instrum. Meas., vol. 64, no. 1, pp. 52–62, (2015). [CrossRef] [Google Scholar]
  6. F. Septianto, A. Widodo, and N. Sinaga, “Analisa Penurunan Efisiensi Motor Induksi Akibat Cacat pada Cage Ball Bantalan.,” Jurnal Teknik Mesin S-1, vol. 4, no. 4. pp. 397–407, (2015). [Google Scholar]
  7. M. Z. Ali, M. N. S. K. Shabbir, X. Liang, Y. Zhang, and T. Hu, “Machine Learning based Fault Diagnosis for Single- and Multi-Faults in Induction Motors Using Measured Stator Currents and Vibration Signals,” IEEE Trans. Ind. Appl., vol. 55, no. 3, pp. 2378–2391, (2019). [CrossRef] [Google Scholar]
  8. A. Siddique, G. S. Yadava, and B. Singh, “Applications of artificial intelligence techniques for induction machine stator fault diagnostics: Review,” in IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2003 - Proceedings, (2003), pp. 29–34. [Google Scholar]
  9. W. Wanto, R. Lulus. G. H., and D. Djoko Susilo, “Diagnosis Ketidaklurusan (Misalignment) Poros Menggunakan Metode Multiclass Support Vector Machine (Svm),” Mek. Maj. Ilm. Mek., vol. 18, no. 2, pp. 39–43, (2019). [Google Scholar]
  10. S. Tyagi and S. K. Panigrahi, “A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks,” J. Appl. Comput. Mech., vol. 3, no. 1, pp. 80–91, (2017), doi: 10.22055/jacm.2017.21576.1108. [Google Scholar]
  11. C. Wen-Lin, L. Chih-Jer, and K. Kai-Chun, “Fault Diagnosis of a Rotor and Ball-Bearing System Using DWT Integrated with SVM, GRNN, and Visual Dot Pattern,” (2019). [Google Scholar]
  12. H. Han, S. Cho, S. Kwon, and S. B. Cho, “Fault diagnosis using improved complete ensemble empirical mode decomposition with adaptive noise and power-based intrinsic mode function selection algorithm,” Electron., vol. 7, no. 2, (2018), doi: 10.3390/electronics7020016. [Google Scholar]
  13. empirical mode decomposition method,” Procedia CIRP, vol. 88, pp. 31–35, (2020), doi: 10.1016/j.procir.2020.05.006. [Google Scholar]
  14. K. Dragomiretskiy and D. Zosso, “Variational Mode Decomposition,” IEEE Trans. SIGNAL Process., vol. 62, no. 3, pp. 531–544, (2014). [CrossRef] [Google Scholar]
  15. M. F. H. Sianturi, Adiwijaya, and S. Al Faraby, “Klasifikasi Dokumen Menggunakan Kombinasi Algoritma Principal Component Analysis Dan Svm,” e-Proceeding Eng., vol. 4, no. 3, pp. 5141– 5143, (2017). [Google Scholar]
  16. L. Deng, A. Zhang, and R. Zhao, “Intelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM,” Adv. Mech. Eng., vol. 14, no. 1, pp. 1–18, (2022). [Google Scholar]

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