Open Access
E3S Web Conf.
Volume 43, 2018
ASTECHNOVA 2017 International Energy Conference
Article Number 01004
Number of page(s) 5
Published online 29 June 2018
  1. M. Rana, S. Sahabuddin, and S. Mondol, “Design and Implementation of a Digital Tachometer,” Int. J. Sci. Eng. Technol., vol. 5, no. 1, pp. 85–87, (2016). [Google Scholar]
  2. G. Sachdev, A. Goyal, A. Jain, and J. Surana, “A Study on Vehicle and Counter Measure,” Int. J. Eng. Dev. Res., vol. 5, no. 2, pp. 909–921, (2017). [Google Scholar]
  3. K. Iwata, T. Yamamichi, Y. Sugiyama, and T. Kitano, “Train Position Detection System by Means of Inertial Sensors together with a Tachometer Generator,” QR RTRI, vol. 57, no. 4, pp. 293–299, (2016). [CrossRef] [Google Scholar]
  4. R. Jia, J. O. Yang, H. Lin, and J. Chen, “A Real-Time Differential Anti Strong Interference Tachometer based on Arduino,” Int. Conf. Appl. Mech. Electron. Mechatron. Eng. AMEME, pp. 1–6, (2016). [Google Scholar]
  5. S. Nashit, S. Adhikari, S. Farhan, S. Avinash, and A. Gambhire, “Design, Fabrication and Testing of Regenerative Braking Test Rig for BLDC Motor,” Int. Res. J. Eng. Technol. IRJET, vol. 3, no. 5, pp. 1881–1884, (2016). [Google Scholar]
  6. N. Mane, P. Mane, K. Sabale, and M. Mokashi, “Design of Multirotor Wind Turbine with Solar Hybrid System,” Int. J. Curr. Eng. Technol., no. 6, pp. 1–4, (2016). [Google Scholar]
  7. M. Muchtar, S. Manjang, D. A. Suriamiharja, and M. A. Thaha, “Physical Model Performance of Wave Energy Converter based on Water Mass Gravity Force under Container Shape Variation,” Int. J. Sci. Res. IJSR, vol. 5, no. 9, pp. 1627–1630, (2016). [Google Scholar]
  8. A. Bhadani, D. Koladiya, J. Devani, and A. Tahiliani, “Modelling and Controlling of BLDC Motor,” Int. J. Adv. Eng. Res. Dev., vol. 3, no. 3, pp. 139–144, (2016). [Google Scholar]
  9. S. Das, P. B. Deb, S. Chakraborty, N. Nag, M. Laha, and P. S. Majumdar, “Contactless Speed Monitoring and Displaying,” Int. Res. J. Eng. Technol., vol. 3, no. 12, pp. 876–878, (2016). [Google Scholar]
  10. I. Khelf, L. Laouar, A. M. Bouchelaghem, D. Remond, and S. Saad, “Adaptive fault diagnosis in rotating machines using indicators selection,” Mech. Syst. Signal Process., vol. 40, pp. 452–468, (2013). [CrossRef] [Google Scholar]
  11. K. Alameh, N. Cite, G. Hoblos, and G. Barakat, “Vibration-based Fault Diagnosis Approach for Permanent Magnet Synchronous Motors,” IFAC - Pap. OnLine, vol. 48–21, pp. 1444–1450, (2015). [CrossRef] [Google Scholar]
  12. P. M. Papadopoulos, V. Reppa, M. M. Polycarpou, and C. G. Panayiotou, “Distributed Diagnosis of Actuator and Sensor Faults in HVAC Systems,” IFAC - Pap. OnLine, vol. 50–1, pp. 4209–4215, (2017). [CrossRef] [Google Scholar]
  13. M. Saied, B. Lussier, I. Fantoni, H. Shraim, and C. Francis, “Fault Diagnosis and Fault-Tolerant Control of an Octorotor UAV using motors speeds,” IFAC - Pap. OnLine, vol. 50–1, pp. 5263–5268, (2017). [CrossRef] [Google Scholar]
  14. F. L., A. M., and D. A., “A real-time algorithm for fault identification in machining centres,” 18th World Congr. Int. Fed. Autom. Control, vol. 44, no. 1, pp. 5201–5206, (2011). [Google Scholar]
  15. Y. Zhi-Ling, W. Bin, D. Xing-Hui, and L. Hao, “Expert System of Fault Diagnosis for Gear Box in Wind Turbine,” Syst. Eng. Procedia, vol. 4, pp. 189–195, (2012). [CrossRef] [Google Scholar]
  16. W. Li, S. Zhang, and S. Rakheja, “Feature Denoising and Nearest-Farthest Distance Preserving Projection for Machine Fault Diagnosis,” IEEE Trans. Ind. Inform., vol. 12, pp. 393–404, (2016). [CrossRef] [Google Scholar]
  17. T. N. Kruglova, “Wavelet analysis for fault diagnosis of electrical machines using current signals,” 2nd Int. Conf. Ind. Eng. Appl. Manuf. ICIEAM, pp. 1–5, (2016). [Google Scholar]
  18. N. Hamad, K. F. Brethee, F. Gu, and A. D. Ball, “An investigation of electrical motor parameters in a sensorless variable speed drive for machine fault diagnosis,” 22nd Int. Conf. Autom. Comput. ICAC, pp. 329–335, (2016). [Google Scholar]
  19. Z. Du, X. Chen, H. Zhang, H. Miao, Y. Guo, and B. Yang, “Feature Identification with Comprehensive Measurements for Machine Fault Diagnosis,” IEEE Trans. Instrum. Meas., vol. 65, no. 5, pp. 977–987, (2016). [CrossRef] [Google Scholar]
  20. K. T. Sreekumar et al., “Locality constrained linear coding for fault diagnosis of rotating machines using vibration analysis,” Annu. IEEE India Conf. INDICON, pp. 1–6, (2016). [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.