Pertanika Journal of Science & Technology
Pertanika Journal Home Facebook
Pertanika · Universiti Putra Malaysia Press

Pertanika Journal of Science & Technology

Official journal of Universiti Putra Malaysia for scholarly work across science, engineering and related technologies.

e-ISSN 2231-8526 ISSN 0128-7680
Browse latest issue Author guidelines Submit Here
Research article

Sound Sensor Placement Strategy for Condition Monitoring of Induction Motor Bearing

Iradiratu Diah Prahmana Karyatanti, Istiyo Winarno, Ardik Wijayanto, Dwisetiono, Nuddin Harahab, Ratno Bagus Edy Wibowo and Agus Budiarto

Volume 31, Issue 6 October 2023 Published 12 October 2023
https://doi.org/10.47836/pjst.31.6.25
KeywordsBearing, condition monitoring, placement strategy, sound signal, spectrum analysis
Article content

Abstract

Damage to the bearing elements will affect the rotation of the rotor and lead to the cessation of motor operation. Therefore, it is imperative to monitor the condition of the bearings to provide information on timely maintenance actions, improve reliability, and prevent serious damage. One of the important keys to an effective and accurate monitoring system is the placement of sensors and proper signal processing. Sound signal issued by the motor during operation capable of describing its elements’ condition. Therefore, this study aims to develop a sound sensor placement strategy appropriate for monitoring the condition of induction motor bearing components. This study was carried out on three-phase induction motors’ outer-race, inner-race, and ball-bearing sections with the signal processing method using the spectrum analysis. Furthermore, the effect of sound sensor placement on condition monitoring accuracy was determined using the One-Way Analysis of Variance (One-Way ANOVA) approach. This process tests the null hypothesis and determines whether the average of all groups is the same (H0) or different (H1). Furthermore, Tukey’s test was applied to obtain effective sound sensor placement, with voice-based condition monitoring used for effective identification. The test found that the accuracy of monitoring the bearing condition was 92.66% by placing the sound sensor at 100 cm from the motor body.
Supporting literature

References

  1. AlShorman, O., Alkahatni, F., Masadeh, M., Irfan, M., Glowacz, A., Althobiani, F., Kozik, J. & Glowacz, W. (2021). Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study. Advances in Mechanical Engineering, 13(2), 1-19. https://doi.org/10.1177/1687814021996915
  2. Barusu, M. R., & Deivasigamani, M. (2020). Non-invasive vibration measurement for diagnosis of bearing faults in 3-phase squirrel cage induction motor using microwave sensor. IEEE Sensors Journal, 21(2), 1026-1039. https://doi.org/10.1109/JSEN.2020.3004515
  3. Bhogal, S. S., Sindhu, C., Dhami, S. S., & Pabla, B. S. (2015). Minimization of surface roughness and tool vibration in CNC milling operation. Journal of Optimization, 2015, Article 192030. https://doi.org/10.1155/2015/192030
  4. Chatterjee, S., Barman, R., Roy, S. & Dey, S. (2020, December 16-19). Bearing fault detection in induction motors employing difference visibility graph. [Paper presentation]. 2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), Rajasthan, India. https://doi.org/10.1109/PEDES49360.2020.9379635
  5. Ewert, P., Kowalski, C. T., & Orlowska-Kowalska, T. (2020). Low-cost monitoring and diagnosis system for rolling bearing faults of the induction motor based on neural network approach. Electronics, 9(9), Article 1334. https://doi.org/10.3390/electronics9091334
  6. Glowacz, A., Glowacz, W., Glowacz, Z., & Kozik, J. (2018). Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement, 113, 1-9. https://doi.org/10.1016/j.measurement.2017.08.036
  7. Goyal, D., Mongia, C., & Sehgal, S. (2021). Applications of digital signal processing in monitoring machining processes and rotary components: A review. IEEE Sensors Journal, 21(7), 8780-8804. https://doi.org/10.1109/JSEN.2021.3050718
  8. Goyal, D., Pabla, B. S., & Dhami, S. S. (2019). Non-contact sensor placement strategy for condition monitoring of rotating machine-elements. Engineering Science and Technology, an International Journal, 22(2), 489-501. https://doi.org/10.1016/j.jestch.2018.12.006
  9. Gundewar, S. K., & Kane, P. V. (2021). Condition monitoring and fault diagnosis of induction motor. Journal of Vibration Engineering & Technologies, 9(4), 643-674. https://doi.org/10.1007/s42417-020-00253-y
  10. Lee, W. J., Xia, K., Denton, N. L., Ribeiro, B., & Sutherland, J. W. (2021). Development of a speed invariant deep learning model with application to condition monitoring of rotating machinery. Journal of Intelligent Manufacturing, 32(2), 393-406. https://doi.org/10.1007/s10845-020-01578-x
  11. Nakamura, H., Asano, K., Usuda, S., & Mizuno, Y. (2021). A diagnosis method of bearing and stator fault in motor using rotating sound based on deep learning. Energies, 14(5), Article 1319. https://doi.org/10.3390/en14051319
  12. Nirwan, N. W., & Ramani, H. B. (2022). Condition monitoring and fault detection in roller bearing used in rolling mill by acoustic emission and vibration analysis. Proceedings of Materials Today, 51, 344-354. https://doi.org/10.1016/j.matpr.2021.05.447
  13. Qiao, M., Yan, S., Tang, X., & Xu, C. (2020). Deep convolutional and LSTM recurrent neural networks for rolling bearing fault diagnosis under strong noises and variable loads. IEEE Access, 8, 66257-66269. https://doi.org/doi:10.1109/access.2020.2985617
  14. Shabbir, M. N. S. K., Liang, X., & Chakrabarti, S. (2020). An ANOVA-based fault diagnosis approach for variable frequency drive-fed induction motors. IEEE Transactions on Energy Conversion, 36(1), 500-512. https://doi.org/10.1109/TEC.2020.3003838
  15. Toma, R. N., Prosvirin, A. E., & Kim, J. M. (2020). Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors, 20(7), Article 1884. https://doi.org/10.3390/s20071884
  16. Vamsi, I., Sabareesh, G. R., & Penumakala, P. K. (2019). Comparison of condition monitoring techniques in assessing fault severity for a wind turbine gearbox under non-stationary loading. Mechanical Systems and Signal Processing, 124, 1-20. https://doi.org/10.1016/j.ymssp.2019.01.038
  17. Vanraj, Dhami, S. S., & Pabla, B. S. (2017). Optimization of sound sensor placement for condition monitoring of fixed-axis gearbox. Cogent Engineering, 4(1), Article 1345673. https://doi.org/10.1080/23311916.2017.1345673
  18. Wang, T., Lu, G., & Yan, P. (2019). Multi-sensors based condition monitoring of rotary machines: An approach of multidimensional time-series analysis. Measurement, 134, 326-335. https://doi.org/10.1016/j.measurement.2018.10.089
  19. Zhang, S., Wang, B., Kanemaru, M., Lin, C., Liu, D., Miyoshi, M., Teo, K. H., & Habetler, T. G. (2020). Model-based analysis and quantification of bearing faults in induction machines. IEEE Transactions on Industry Applications, 56(3), pp.2158-2170. https://doi.org/10.1109/tia.2020.2979383
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .