Research Article
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Year 2023, Volume: 11 Issue: 4, 380 - 386, 22.12.2023
https://doi.org/10.17694/bajece.1283336

Abstract

References

  • [1] Koca, Y. B., & Ünsal, A. (2017). Asenkron motor arızalarının değerlendirilmesi. Teknik Bilimler Dergisi, 7(2), 37-46.
  • [2] Yi, L., Sun, T., Yu, W., Xu, X., Zhang, G., & Jiang, G. (2022). Induction motor fault detection by a new sliding mode observer based on backstepping. Journal of Ambient Intelligence and Humanized Computing, 1-14.
  • [3] Miljković, D. (2011, May). Fault detection methods: A literature survey. In 2011 Proceedings of the 34th international convention MIPRO (pp. 750-755). IEEE.
  • [4] Liu, W., Chen, Z., & Zheng, M. (2020, July). An audio-based fault diagnosis method for quadrotors using convolutional neural network and transfer learning. In 2020 American Control Conference (ACC) (pp. 1367-1372). IEEE.
  • [5] Eibe Frank, Mark A. Hall, and Ian H. Witten (2016). The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.
  • [6] Boltežar, M., Simonovski, I., & Furlan, M. (2003). Fault detection in DC electro motors using the continuous wavelet transform. Meccanica, 38, 251-264.
  • [7] Matzka, S., Pilz, J., & Franke, A. (2021, September). Structure-borne and Air-borne Sound Data for Condition Monitoring Applications. In 2021 4th International Conference on Artificial Intelligence for Industries (AI4I) (pp. 1-4). IEEE.
  • [8] Yang, B. S., Di, X., & Han, T. (2008). Random forests classifier for machine fault diagnosis. Journal of mechanical science and technology, 22(9), 1716-1725.
  • [9] Dos Santos, T., Ferreira, F. J., Pires, J. M., & Damásio, C. (2017, May). Stator winding short-circuit fault diagnosis in induction motors using random forest. In 2017 IEEE International Electric Machines and Drives Conference (IEMDC) (pp. 1-8). IEEE.
  • [10] Vamsi, I. V., Abhinav, N., Verma, A. K., & Radhika, S. (2018, December). Random forest based real time fault monitoring system for industries. In 2018 4th International Conference on Computing Communication and Automation (ICCCA) (pp. 1-6). IEEE.
  • [11] Sonje, M. D., Kundu, P., & Chowdhury, A. (2017, August). A novel approach for multi class fault diagnosis in induction machine based on statistical time features and random forest classifier. In IOP Conference Series: Materials Science and Engineering (Vol. 225, No. 1, p. 012141). IOP Publishing.
  • [12] Saberi, A. N., Sandirasegaram, S., Belahcen, A., Vaimann, T., & Sobra, J. (2020, August). Multi-Sensor fault diagnosis of induction motors using random forests and support vector machine. In 2020 International Conference on Electrical Machines (ICEM) (Vol. 1, pp. 1404-1410). IEEE.
  • [13] Quiroz, J. C., Mariun, N., Mehrjou, M. R., Izadi, M., Misron, N., & Radzi, M. A. M. (2018). Fault detection of broken rotor bar in LS-PMSM using random forests. Measurement, 116, 273-280.
  • [14] Harach, T., Simonik, P., Vrtkova, A., Mrovec, T., Klein, T., Ligori, J. J., & Koreny, M. (2023). Novel Method for Determining Internal Combustion Engine Dysfunctions on Platform as a Service. Sensors, 23(1), 477.
  • [15] Elhaija, W. A., & Al-Haija, Q. A. (2023). A novel dataset and lightweight detection system for broken bars induction motors using optimizable neural networks. Intelligent Systems with Applications, 17, 200167.
  • [16] Reyes-Malanche, J. A., Villalobos-Pina, F. J., Ramırez-Velasco, E., Cabal-Yepez, E., Hernandez-Gomez, G., & Lopez-Ramirez, M. (2023). Short-Circuit Fault Diagnosis on Induction Motors through Electric Current Phasor Analysis and Fuzzy Logic. Energies, 16(1), 516.
  • [17] Roy, S. S., Dey, S., & Chatterjee, S. (2020). Autocorrelation aided random forest classifier-based bearing fault detection framework. IEEE Sensors Journal, 20(18), 10792-10800.
  • [18] Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
  • [19] Wang, J., Rao, C., Goh, M., & Xiao, X. (2023). Risk assessment of coronary heart disease based on cloud-random forest. Artificial Intelligence Review, 56(1), 203-232.

Different Induction Motor Faults by New Proposed Random Forest Method

Year 2023, Volume: 11 Issue: 4, 380 - 386, 22.12.2023
https://doi.org/10.17694/bajece.1283336

Abstract

Induction motors (IM) are widely used in industry. Failures in asynchronous motors cause disruptions and interruptions in production processes. Due to this situation, economic losses are experienced. Monitoring the induction motor status and monitoring the symptoms before the failure occurs is a matter of great importance in the industry. In this study, 8 different situations that may occur in the motor were monitored through the acceleration and sound data obtained from the induction motor. The feature vector was created with the Short-Term Fourier Transform (STFT) method on the acceleration and sound data obtained from the engine. The feature vectors were classified using the Random Forest (RF) method. The feature vectors created from the acceleration and sound data were also analyzed separately and the classification performance was examined. In addition, a new RF algorithm based on weight values using the Gini algorithm has been proposed. With this algorithm, the traditional RF algorithm has been developed and the success rates have been increased. In classical RF classification based on acceleration and sound data, 89.9% accuracy was achieved. The success rate of the proposed model was 95.7%. This shows that the proposed model successfully detects all types of faults in asynchronous motors. In addition, when we compared in terms of time, it was observed that the proposed model produced faster and more accurate results both in fault detection and in the production maintenance phase.

References

  • [1] Koca, Y. B., & Ünsal, A. (2017). Asenkron motor arızalarının değerlendirilmesi. Teknik Bilimler Dergisi, 7(2), 37-46.
  • [2] Yi, L., Sun, T., Yu, W., Xu, X., Zhang, G., & Jiang, G. (2022). Induction motor fault detection by a new sliding mode observer based on backstepping. Journal of Ambient Intelligence and Humanized Computing, 1-14.
  • [3] Miljković, D. (2011, May). Fault detection methods: A literature survey. In 2011 Proceedings of the 34th international convention MIPRO (pp. 750-755). IEEE.
  • [4] Liu, W., Chen, Z., & Zheng, M. (2020, July). An audio-based fault diagnosis method for quadrotors using convolutional neural network and transfer learning. In 2020 American Control Conference (ACC) (pp. 1367-1372). IEEE.
  • [5] Eibe Frank, Mark A. Hall, and Ian H. Witten (2016). The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.
  • [6] Boltežar, M., Simonovski, I., & Furlan, M. (2003). Fault detection in DC electro motors using the continuous wavelet transform. Meccanica, 38, 251-264.
  • [7] Matzka, S., Pilz, J., & Franke, A. (2021, September). Structure-borne and Air-borne Sound Data for Condition Monitoring Applications. In 2021 4th International Conference on Artificial Intelligence for Industries (AI4I) (pp. 1-4). IEEE.
  • [8] Yang, B. S., Di, X., & Han, T. (2008). Random forests classifier for machine fault diagnosis. Journal of mechanical science and technology, 22(9), 1716-1725.
  • [9] Dos Santos, T., Ferreira, F. J., Pires, J. M., & Damásio, C. (2017, May). Stator winding short-circuit fault diagnosis in induction motors using random forest. In 2017 IEEE International Electric Machines and Drives Conference (IEMDC) (pp. 1-8). IEEE.
  • [10] Vamsi, I. V., Abhinav, N., Verma, A. K., & Radhika, S. (2018, December). Random forest based real time fault monitoring system for industries. In 2018 4th International Conference on Computing Communication and Automation (ICCCA) (pp. 1-6). IEEE.
  • [11] Sonje, M. D., Kundu, P., & Chowdhury, A. (2017, August). A novel approach for multi class fault diagnosis in induction machine based on statistical time features and random forest classifier. In IOP Conference Series: Materials Science and Engineering (Vol. 225, No. 1, p. 012141). IOP Publishing.
  • [12] Saberi, A. N., Sandirasegaram, S., Belahcen, A., Vaimann, T., & Sobra, J. (2020, August). Multi-Sensor fault diagnosis of induction motors using random forests and support vector machine. In 2020 International Conference on Electrical Machines (ICEM) (Vol. 1, pp. 1404-1410). IEEE.
  • [13] Quiroz, J. C., Mariun, N., Mehrjou, M. R., Izadi, M., Misron, N., & Radzi, M. A. M. (2018). Fault detection of broken rotor bar in LS-PMSM using random forests. Measurement, 116, 273-280.
  • [14] Harach, T., Simonik, P., Vrtkova, A., Mrovec, T., Klein, T., Ligori, J. J., & Koreny, M. (2023). Novel Method for Determining Internal Combustion Engine Dysfunctions on Platform as a Service. Sensors, 23(1), 477.
  • [15] Elhaija, W. A., & Al-Haija, Q. A. (2023). A novel dataset and lightweight detection system for broken bars induction motors using optimizable neural networks. Intelligent Systems with Applications, 17, 200167.
  • [16] Reyes-Malanche, J. A., Villalobos-Pina, F. J., Ramırez-Velasco, E., Cabal-Yepez, E., Hernandez-Gomez, G., & Lopez-Ramirez, M. (2023). Short-Circuit Fault Diagnosis on Induction Motors through Electric Current Phasor Analysis and Fuzzy Logic. Energies, 16(1), 516.
  • [17] Roy, S. S., Dey, S., & Chatterjee, S. (2020). Autocorrelation aided random forest classifier-based bearing fault detection framework. IEEE Sensors Journal, 20(18), 10792-10800.
  • [18] Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
  • [19] Wang, J., Rao, C., Goh, M., & Xiao, X. (2023). Risk assessment of coronary heart disease based on cloud-random forest. Artificial Intelligence Review, 56(1), 203-232.
There are 19 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Araştırma Articlessi
Authors

Çiğdem Bakır 0000-0001-8482-2412

Early Pub Date January 25, 2024
Publication Date December 22, 2023
Published in Issue Year 2023 Volume: 11 Issue: 4

Cite

APA Bakır, Ç. (2023). Different Induction Motor Faults by New Proposed Random Forest Method. Balkan Journal of Electrical and Computer Engineering, 11(4), 380-386. https://doi.org/10.17694/bajece.1283336

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