EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 336 (2022) E3S Web Conf., 336 (2022) 00051 References
Open Access
Issue
E3S Web Conf.
Volume 336, 2022
The International Conference on Energy and Green Computing (ICEGC’2021)
Article Number 00051
Number of page(s) 8
DOI https://doi.org/10.1051/e3sconf/202233600051
Published online 17 January 2022
  1. T. Jean-Claude, Diagnostic des machines électrique. Traité EGEM, série Génie électrique, pages 269–272 (2011). [Google Scholar]
  2. Motor Reliability Working Group IEEE Industry Applications Society. “Report of Large Motor Reliability Survey of Industrial and Commercial Installations, Part I”. IEEE Transactions on Industry Applications. Vol. IA-21, issue 4, p.853-864. July 1985. [Google Scholar]
  3. United States Industrial Electric Motor Systems Market Opportunities Assessment. U.S. Dept. of Energy, Washington, DC, USA, 1998. [Google Scholar]
  4. Mbo’o, C.P.; Hameyer, K. Fault diagnosis of bearing damage by means of the linear discriminant analysis of stator current features from the frequency selection. IEEE Trans. Ind. Appl. 2016, 52, 3861–3868. [Google Scholar]
  5. Sri, J.; Senanayaka, L.; Kandukuri, S.T.; Van Khang, H.; Robbersmyr, K.G. Early Detection and Classification of Bearing Faults Using Support Vector Machine Algorithm. In Proceedings of the 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD), Nottingham, UK, 20–21 April 2017. [Google Scholar]
  6. Li, Z.; Zhu, J.; Shen, X.; Zhang, C.; Guo, J. Fault diagnosis of motor bearing based on the Bayesian network. Procedia Eng. 2011, 16, 18–26. [CrossRef] [Google Scholar]
  7. Tian, J.; Azarian, M.H.; Pecht, M. Rolling Element Bearing Fault Detection Using Density-Based Clustering. In Proceedings of the 2014 International Conference on Prognostics and Health Management, Cheney, WA, USA, 22–25 June 2014. [Google Scholar]
  8. Xie, Y. A Fault Diagnosis Approach Using SVM with Data Dimension Reduction by PCA and LDA Method. In Proceedings of the 2015 Chinese Automation Congress (CAC), Wuhan, China, 27–29 November 2015. [Google Scholar]
  9. Deng, F. Ren, B. Fault Diagnosis of Rolling Bearing Using the Hermitian Wavelet Analysis, KPCA and SVM. In Proceedings of the 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Shanghai, China, 16–18 August 2017. [Google Scholar]
  10. Tu, D. Zheng, J. Jiang, Z. Pan, H. Multiscale Distribution Entropy and T-Distributed Stochastic Neighbor Embedding-Based Fault Diagnosis of Rolling Bearings. Entropy 2018, 20, 360. [CrossRef] [Google Scholar]
  11. A. Choudhary, D. Goyal, S.L. Shimi, A. kula, Condition Monitoring and Fault Diagnosis of Induction Motors: A Review. Arch. Comput. Methods Eng. 2018, 25, 1–18. [Google Scholar]
  12. P.A. Delgado-Arredondo, D. Morinigo-Sotelo, R.A. Osornio-Rios, J.G. Avina-Cervantes, H. Rostro-Gonzalez, R.D.J. Romero-Troncoso. Methodology for fault detection in induction motors via sound and vibration signals. Mech. Syst. Signal. Process. 2017, 83, 568–589. [CrossRef] [Google Scholar]
  13. Y. Gritli, A.O. Di Tommaso, F. Filippetti, R. Miceli, C. Rossi, A. Chatti. Investigation of motor current signature and vibration analysis for diagnosing rotor broken bars in double cage induction motors. In Proceedings of the International Symposium on Power Electronics Power Electronics, Electrical Drives, Automation and Motion, Sorrento, Italy, 20–22 June 2012; IEEE: Piscataway, NJ, USA, 2012; pp. 1360–1365. [Google Scholar]
  14. J. Antoni, G. Xin, N. Hamzaoui. Fast computation of the spectral correlation. Mech. Syst. Signal. Process. 2017, 92, 248–277. [Google Scholar]
  15. G. Singh, V. Naikan. Detection of half broken rotor bar fault in VFD driven induction motor drive using motor square current MUSIC analysis. Mech. Syst. Signal. Process. 2018, 110, 333–348. [Google Scholar]
  16. DAHER Homepage, https://www.daher.com/, last accessed 2021/05/10. [Google Scholar]
  17. Schneider Electric, https://www.se.com/ww/en/product/ATV11HU29 M2U, last accessed 2021/09/10 [Google Scholar]
  18. National Instruments, https://www.ni.com/en-lb/support/model.ni-9246.html, last accessed 2021/09/10. [Google Scholar]
  19. BRUEL & KjAER, https://www.bksv.com/fr/transducers/vibration/accelerometers/4384, last accessed 2021/05/10 [Google Scholar]
  20. National Instruments, https://www.ni.com/en-lb/support/model.crio-9040.html, last accessed 2021/09/10. [Google Scholar]
  21. P. Li. Robust logitboost and adaptive base class (abc) logitboost. arXiv preprint arXiv:1203.3491, 2012. [Google Scholar]
  22. M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16thinternational conference on World Wide Web, pages 521–530. ACM, 2007. [Google Scholar]
  23. Christopher JC Burges. From ranknet to lambdarank to lambdamart: An overview. Learning, 11(23-581):81, 2010. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.