ANALYSIS OF THE INFLUENCE OF LOOSE TERMINATION LEVEL WITH INCREASING TERMINATION TEMPERATURE OF ELECTRICAL EQUIPMENT USING INFRARED THERMOGRAPHY

##plugins.themes.academic_pro.article.sidebar##

Published Mar 2, 2022
https://doi.org/10.31943/teknokom.v5i1.68
Download
PDF
Statistic

Downloads

Download data is not yet available.
Vol. 5 No. 1 (2022): TEKNOKOM

##plugins.themes.academic_pro.article.main##

Pranoto Budi Laksono
Universitas Pamulang

Abstract

The use of electrical energy in the industrial is very important to drive production machines. Machine maintenance is carried out so that the lifetime of the machine becomes longer so that the production process continues. Temperature is one of the most common indicators of the structural health of equipment and components. This means that the main symptoms of damage to machines and equipment can be indicated by increasing the temperature of the equipment. One of the symptoms of an increase in equipment temperature is due to a loose cable termination. The use of infrared thermography to measure the temperature rise due to loose termination is one of the methods in electrical machine maintenance. Previous studies using infrared thermography to measure the temperature rise due to loose termination have been carried out by many researchers, but these studies did not show a correlation between the level of loose termination and the increase in temperature but only stated that the loose termination caused an increase in temperature. This study focuses on finding the relationship between the level of loose termination compared to standard torque and the increase in temperature at the termination point. This is very useful as a quick overview in determining the level of urgency in maintenance activity, that percentage of loose termination of a certain value below the standard will give an increase in temperature of a certain value as well. This study resulted, for the torque setting condition 67% below the standard (loose) the temperature increase at the terminal was 13.4% - 15.6%, for the torque setting condition 33% below the standard temperature increase at the terminal of 12.3% -13.8% which mean that the increase in temperature is directly proportional to the level of slack termination and is also directly proportional to the increase in the motor speed. If the termination torque level is lower and the motor speed is increased, the terminal temperature rise will be drastically rise.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Author Biography

Pranoto Budi Laksono, Universitas Pamulang

Department of Electrical Engineering, Universitas Pamulang, Indonesia

How to Cite
Laksono, P. B. . (2022). ANALYSIS OF THE INFLUENCE OF LOOSE TERMINATION LEVEL WITH INCREASING TERMINATION TEMPERATURE OF ELECTRICAL EQUIPMENT USING INFRARED THERMOGRAPHY. TEKNOKOM, 5(1), 78–87. https://doi.org/10.31943/teknokom.v5i1.68

References

  1. J. A. Roque A. Osornio-Rios, RJose A. Antonino-Daviu and R. De Jesus Romero-Troncoso, “Recent industrial applications of infrared thermography: A review,” IEEE Trans. Ind. Informatics, vol. 15, no. 2, pp. 615–625, 2019, doi: 10.1109/TII.2018.2884738.
  2. P. K. Gore and P. Gore, “Failure due to poor termination & loose connections in electrical systems,” Water Energy Int., vol. 61RNI, no. 1, pp. 48–51, 2018.
  3. C. Morales-Perez, J. Rangel-Magdaleno, H. Peregrina-Barreto, J. Ramirez-Cortes, and E. Vazquez-Pacheco, “Bearing fault detection technique by using thermal images: A case of study,” I2MTC 2019 - 2019 IEEE Int. Instrum. Meas. Technol. Conf. Proc., vol. 2019-May, pp. 1–6, 2019, doi: 10.1109/I2MTC.2019.8826953.
  4. A. Gugaliya, G. Singh, and V. N. A. Naikan, “Effective combination of motor fault diagnosis techniques,” Proc. 2018 IEEE Int. Conf. Power, Instrumentation, Control Comput. PICC 2018, pp. 1–5, 2018, doi: 10.1109/PICC.2018.8384812.
  5. D. Lopez-Perez and J. Antonino-Daviu, “Application of Infrared Thermography to Failure Detection in Industrial Induction Motors: Case Stories,” IEEE Trans. Ind. Appl., vol. 53, no. 3, pp. 1901–1908, 2017, doi: 10.1109/TIA.2017.2655008.
  6. M. W. Hoffmann et al., “Integration of novel sensors and machine learning for predictive maintenance in medium voltage switchgear to enable the energy and mobility revolutions,” Sensors (Switzerland), vol. 20, no. 7, pp. 1–24, 2020, doi: 10.3390/s20072099.
  7. V. Vavilov and D. Burleigh, Infrared Thermography and Thermal Nondestructive Testing. Cham, Switzerland: Springer, 2020.
  8. S. Doshvarpassand, C. Wu, and X. Wang, “An overview of corrosion defect characterization using active infrared thermography,” Infrared Phys. Technol., vol. 96, pp. 366–389, 2019, doi: https://doi.org/10.1016/j.infrared.2018.12.006.
  9. A. Ghahramani, G. Castro, S. A. Karvigh, and B. Becerik-Gerber, “Towards unsupervised learning of thermal comfort using infrared thermography,” Appl. Energy, vol. 211, pp. 41–49, 2018, doi: https://doi.org/10.1016/j.apenergy.2017.11.021.
  10. A. Choudhary, D. Goyal, and S. S. Letha, “Infrared Thermography-Based Fault Diagnosis of Induction Motor Bearings Using Machine Learning,” IEEE Sens. J., vol. 21, no. 2, pp. 1727–1734, 2021, doi: 10.1109/JSEN.2020.3015868.
  11. A. W. Kandeal et al., “Infrared thermography-based condition monitoring of solar photovoltaic systems: A mini review of recent advances,” Sol. Energy, vol. 223, pp. 33–43, 2021, doi: https://doi.org/10.1016/j.solener.2021.05.032.
  12. M. Ortega, E. Ivorra, A. Juan, P. Venegas, J. Martínez, and M. Alcañiz, “MANTRA: An Effective System Based on Augmented Reality and Infrared Thermography for Industrial Maintenance,” Appl. Sci., vol. 11, no. 1, 2021, doi: 10.3390/app11010385.
  13. E. Resendiz-Ochoa, R. A. Osornio-Rios, J. P. Benitez-Rangel, R. D. J. Romero-Troncoso, and L. A. Morales-Hernandez, “Induction Motor Failure Analysis: An Automatic Methodology Based on Infrared Imaging,” IEEE Access, vol. 6, no. c, pp. 76993–77003, 2018, doi: 10.1109/ACCESS.2018.2883988.
  14. F. Ciampa, P. Mahmoodi, F. Pinto, and M. Meo, “Recent advances in active infrared thermography for non-destructive testing of aerospace components,” Sensors (Switzerland), vol. 18, no. 2, 2018, doi: 10.3390/s18020609.
  15. Q. Fang, B. D. Nguyen, C. I. Castanedo, Y. Duan, and X. M. II, “Automatic defect detection in infrared thermography by deep learning algorithm,” in Thermosense: Thermal Infrared Applications XLII, 2020, vol. 11409, pp. 180–195, [Online]. Available: https://doi.org/10.1117/12.2555553.
  16. A. Kirsten Vidal de Oliveira, M. Aghaei, and R. Rüther, “Aerial infrared thermography for low-cost and fast fault detection in utility-scale PV power plants,” Sol. Energy, vol. 211, pp. 712–724, 2020, doi: https://doi.org/10.1016/j.solener.2020.09.066.
  17. A. Glowacz and Z. Glowacz, “Diagnosis of the three-phase induction motor using thermal imaging,” Infrared Phys. Technol., vol. 81, no. 11, pp. 7–16, 2017, doi: 10.1016/j.infrared.2016.12.003.