Bir Asenkron Motorun Mekanik Titreşim Sinyallerinin Ölçülerek Arıza Analizinin Yapılması
Yıl 2020,
Ejosat Özel Sayı 2020 (HORA), 312 - 322, 15.08.2020
Süleyman Çeven
,
Raif Bayır
Öz
Asenkron motorlar, endüstride en yaygın kullanılan motor türüdür. Asenkron motorların verimlerinde meydana gelen bozulmalar, çalışma performanslarını ciddi şekilde etkilemektedir. Üretim sürecinin aksamaması için bu motorların bakımlarının periyodik bir şekilde yapılması ve durumlarının izlenmesi gerekmektedir. Bu çalışmada bir asenkron motorun farklı hız kademelerinde mekanik titreşimlerinin ölçülmesi için bir deney düzeneği tasarlanmış ve gerçekleştirilmiştir. Deney düzeneğinde asenkron motorun gövdesine "Z" ekseni yönünde bağlanan bir ivmeölçer yardımıyla titreşim sinyalleri elektrik sinyaline dönüştürülmüş ve bir veri alışveriş kartı üzerinden bilgisayar ortamına aktarılmıştır. Bu yöntem ile asenkron motorda gerçek zamanlı durum izleme gerçekleştirilmektedir. Motor üzerinde oluşan mekanik titreşimler ayrıca titreşim ölçümü yapabilen test cihazı ile ölçülmüş ve hafıza kartına kayıt edilmiştir. Elde edilen sensör verileri FFT dönüşümü kullanılarak analiz edilmiş ve sonuçlar yorumlanmıştır.
Teşekkür
Bu çalışma, Karabük Üniversitesi Lisansüstü Eğitim Enstitüsü doktora dersleri kapsamında üretilmiştir. Çalışmada ölçüm sonuçlarını doğrulamak amacıyla titreşim ölçüm cihazının kullanımında desteklerini esirgemeyen Düzce Üniversitesi öğretim üyesi Sayın Prof. Dr. Suat Sarıdemir ve Sayın Öğr. Gör. Zafer Cingiz’e teşekkürlerimizi sunarız.
Kaynakça
- Areias, I. A. dos S., da Silva, L. E., Bonaldi, E. L., de Lacerda de Oliveira, L. E., Lambert-Torres, G., & Bernardes, V. A. (2019). Evaluation of Current Signature in Bearing Defects by Envelope Analysis of the Vibration in Induction Motors. Energies, 12(21). doi: 10.3390/en12214029
- Ballal, M S, Khan, Z. J., Suryawanshi, H. M., & Sonolikar, R. L. (2006). Induction motor: fuzzy system for the detection of winding insulation condition and bearing wear. Electric Power Components and Systems, 34(2), 159–171.
- Ballal, Makarand S, Khan, Z. J., Suryawanshi, H. M., & Sonolikar, R. L. (2007). Adaptive neural fuzzy inference system for the detection of inter-turn insulation and bearing wear faults in induction motor. IEEE Transactions on Industrial Electronics, 54(1), 250–258.
- Bayir, R., & Bay, O. F. (2004). Serial wound starter motor faults diagnosis using artificial neural network. Proceedings of the IEEE International Conference on Mechatronics, 2004. ICM ’04., 194–199. doi: 10.1109/ICMECH.2004.1364436
- Benbouzid, M. E. (2000). A review of induction motors signature analysis as a medium for faults detection. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 47(5), 984–993. doi: 10.1109/41.873206
- Betta, G., Liguori, C., Paolillo, A., & Pietrosanto, A. (2002). A DSP-based FFT-analyzer for the fault diagnosis of rotating machine based on vibration analysis. IEEE Transactions on Instrumentation and Measurement, 51(6), 1316–1322.
- Bin, G. F., Gao, J. J., Li, X. J., & Dhillon, B. S. (2012). Early fault diagnosis of rotating machinery based on wavelet packets-Empirical mode decomposition feature extraction and neural network. Mechanical Systems and Signal Processing, 27, 696–711.
- Choudhary, A., Goyal, D., Shimi, S. L., & Akula, A. (2019). Condition Monitoring and Fault Diagnosis of Induction Motors: A Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 26(4), 1221–1238. doi: 10.1007/s11831-018-9286-z
- Cristalli, C., Paone, N., & Rodriguez, R. M. (2006). Mechanical fault detection of electric motors by laser vibrometer and accelerometer measurements. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 20(6), 1350–1361. doi: 10.1016/j.ymssp.2005.11.013
- Datta, S., & Sarkar, S. (2016). A review on different pipeline fault detection methods. Journal of Loss Prevention in the Process Industries, 41, 97–106. doi: https://doi.org/10.1016/j.jlp.2016.03.010
- de Jesus Rangel-Magdaleno, J., de Jesus Romero-Troncoso, R., Osornio-Rios, R. A., Cabal-Yepez, E., & Dominguez-Gonzalez, A. (2010). FPGA-based vibration analyzer for continuous CNC machinery monitoring with fused FFT-DWT signal processing. IEEE Transactions on Instrumentation and Measurement, 59(12), 3184–3194.
- Eftekhari, M., Moallem, M., Sadri, S., & Hsieh, M.-F. (2013). A novel indicator of stator winding inter-turn fault in induction motor using infrared thermal imaging. Infrared Physics & Technology, 61, 330–336.
- Glowacz, A., & Glowacz, Z. (2017). Diagnosis of stator faults of the single-phase induction motor using acoustic signals. Applied Acoustics, 117, 20–27.
- Goyal, D, & Pabla, B. S. (2016). The vibration monitoring methods and signal processing techniques for structural health monitoring: a review. Archives of Computational Methods in Engineering, 23(4), 585–594.
- Goyal, Deepam, Pabla, B. S., Dhami, S. S., & others. (2017). Condition monitoring parameters for fault diagnosis of fixed axis gearbox: a review. Archives of Computational Methods in Engineering, 24(3), 543–556.
- Henao, H., Capolino, G.-A., Fernandez-Cabanas, M., Filippetti, F., Bruzzese, C., Strangas, E., Pusca, R., Estima, J., Riera-Guasp, M., & Hedayati-Kia, S. (2014). Trends in fault diagnosis for electrical machines: A review of diagnostic techniques. IEEE Industrial Electronics Magazine, 8(2), 31–42.
- Hwang, Y.-R., Jen, K.-K., & Shen, Y.-T. (2009). Application of cepstrum and neural network to bearing fault detection. Journal of Mechanical Science and Technology, 23(10), 2730.
- Igba, J., Alemzadeh, K., Durugbo, C., & Eiriksson, E. T. (2016). Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes. Renewable Energy, 91, 90–106. doi: https://doi.org/10.1016/j.renene.2016.01.006
- Ilonen, J., Kamarainen, J.-K., Lindh, T., Ahola, J., Kalviainen, H., & Partanen, J. (2005). Diagnosis tool for motor condition monitoring. IEEE Transactions on Industry Applications, 41(4), 963–971.
- Jayakumar, K., & Thangavel, S. (2017). Industrial drive fault diagnosis through vibration analysis using wavelet transform. Journal of Vibration and Control, 23(12), 2003–2013. doi: 10.1177/1077546315606602
- Kochhar, A. K., Atkinson, J., Barrow, G., Burdekin, M., Hannam, R. G., Hinduja, S., Brunn, P., & Li, L. (1997). Proceedings of the 32nd International MATADOR Conference. Macmillan International Higher Education.
- Leung, F. H.-F., Lam, H.-K., Ling, S.-H., & Tam, P. K.-S. (2003). Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Transactions on Neural Networks, 14(1), 79–88.
- Liu, W. Y., Han, J. G., & Jiang, J. L. (2013). A novel ball bearing fault diagnosis approach based on auto term window method. Measurement, 46(10), 4032–4037. doi: https://doi.org/10.1016/j.measurement.2013.07.039
- Liu, X., Yang, Y., & Zhang, J. (2018). Resultant vibration signal model based fault diagnosis of a single stage planetary gear train with an incipient tooth crack on the sun gear. RENEWABLE ENERGY, 122, 65–79. doi: 10.1016/j.renene.2018.01.072
- Medoued, A., Metatla, A., Boukadoum, A., Bahi, T., & Hadjadj, I. (2009). Condition monitoring and diagnosis of faults in the electric induction motor. American Journal of Applied Sciences, 6(6), 1133.
- Mohammed, O. D., & Rantatalo, M. (2016). Dynamic response and time-frequency analysis for gear tooth crack detection. Mechanical Systems and Signal Processing, 66–67, 612–624. doi: https://doi.org/10.1016/j.ymssp.2015.05.015
- Mousavi, S., Kar, N. C., & Timusk, M. (2017). A novel parallel modelling-wavelet based mechanical fault detection using stator current signature of induction machine under variable load conditions. J Electr Eng Electr Technol, 6(2), 2–9.
- Nejjari, H., & Benbouzid, M. E. H. (1999). Application of fuzzy logic to induction motors condition monitoring. IEEE Power Engineering Review, 19(6), 52–54.
- Palmero, G. I. S., Santamaria, J. J., de la Torre, E. J. M., & González, J. R. P. (2005). Fault detection and fuzzy rule extraction in AC motors by a neuro-fuzzy ART-based system. Engineering Applications of Artificial Intelligence, 18(7), 867–874.
- Pang, J., Li, Y., Jin, X., & Xu, L. (n.d.). Detection and analysis of typical vibration load of grain harvester based on short-time Fourier method. The International Journal of Electrical Engineering & Education, 0(0), 0020720919884247. doi: 10.1177/0020720919884247
- Prudhom, A., Antonino-Daviu, J., Razik, H., & Climente-Alarcon, V. (2017). Time-frequency vibration analysis for the detection of motor damages caused by bearing currents. Mechanical Systems and Signal Processing, 84, 747–762. doi: https://doi.org/10.1016/j.ymssp.2015.12.008
- Raghavendra, K., & Karabasanagouda, B. N. (2014). Frequency response analysis of deep groove ball bearing. Int. J. Sci. Res, 3(8), 1920–1926.
- Rao, B. K. N., Pai, P. S., & Nagabhushana, T. N. (2012). Failure diagnosis and prognosis of rolling-element bearings using Artificial Neural Networks: A critical overview. Journal of Physics: Conference Series, 364(1), 12023.
- Sait, A. S., & Sharaf-Eldeen, Y. I. (2011). A Review of Gearbox Condition Monitoring Based on vibration Analysis Techniques Diagnostics and Prognostics. T. Proulx (Ed.), Rotating Machinery, Structural Health Monitoring, Shock and Vibration, Volume 5 (pp. 307–324). New York, NY: Springer New York.
- SaravanaKumar, R., Kumar, K. V., & Roy, K. (2009). Fuzzy Logic based fault detection in induction machines using Lab view. Int J Comput Sci Netw Secur, 9(9), 226–243.
- Saravanan, N., & Ramachandran, K. I. (2010). Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Systems with Applications, 37(6), 4168–4181.
- Saruhan, H., Saridemir, S., Qicek, A., & Uygur, I. (2014). Vibration Analysis of Rolling Element Bearings Defects. Journal of Applied Research and Technology, 12, 384–395. Retrieved from http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1665-64232014000300005&nrm=iso
- Shnibha, R., Albarbar, A., Abouhnik, A., & Ibrahim, G. (2012). A more reliable method for monitoring the condition of three-phase induction motors based on their vibrations. ISRN Mechanical Engineering, 2012.
- Siddiqui, K. M., Sahay, K., & Giri, V. K. (2015). Rotor broken bar fault detection in induction motor using transformative techniques. Journal of Electrical Engineering, 15(1), 135–141.
- Sudhakar, G. N. D. S., & Sekhar, A. S. (2009). Coupling Misalignment in Rotating Machines: Modelling, Effects and Monitoring. Noise & Vibration Worldwide, 40(1), 17–39. doi: 10.1260/0957-4565.40.1.17
- Sugumaran, V., & Ramachandran, K. I. (2011). Effect of number of features on classification of roller bearing faults using SVM and PSVM. Expert Systems with Applications, 38(4), 4088–4096.
- Talhaoui, H., Menacer, A., Kessal, A., & Kechida, R. (2014). Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis. ISA Transactions, 53(5), 1639–1649. doi: https://doi.org/10.1016/j.isatra.2014.06.003
- Tiwari, M., Gupta, K., & Prakash, O. (2000). Effect of radial internal clearance of a ball, bearing on the dynamics of a balanced horizontal rotor. JOURNAL OF SOUND AND VIBRATION, 238(5), 723–756. doi: 10.1006/jsvi.1999.3109
- Tsypkin, M. (2011). Induction motor condition monitoring: Vibration analysis technique - A practical implementation. 2011 IEEE International Electric Machines Drives Conference (IEMDC), 406–411. doi: 10.1109/IEMDC.2011.5994629
- Unal, M., Onat, M., Demetgul, M., & Kucuk, H. (2014). Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network. Measurement, 58, 187–196.
- Van Hecke, B., Yoon, J., & He, D. (2016). Low speed bearing fault diagnosis using acoustic emission sensors. Applied Acoustics, 105, 35–44.
- Wang, J., & Hu, H. (2006). Vibration-based fault diagnosis of pump using fuzzy technique. Measurement, 39(2), 176–185.
- Widodo, A., & Yang, B.-S. (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 21(6), 2560–2574.
- Wong, W.-K., Loo, C.-K., Lim, W.-S., & Tan, P.-N. (2010). Thermal condition monitoring system using log-polar mapping, quaternion correlation and max-product fuzzy neural network classification. Neurocomputing, 74(1–3), 164–177.
- Yamamoto, G. K., da Costa, C., & da Silva Sousa, J. S. (2016). A smart experimental setup for vibration measurement and imbalance fault detection in rotating machinery. Case Studies in Mechanical Systems and Signal Processing, 4, 8–18.
- Yang, B.-S., Oh, M.-S., Tan, A. C. C., & others. (2009). Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Systems with Applications, 36(2), 1840–1849.
- Zhang, W., Jia, M.-P., Zhu, L., & Yan, X.-A. (2017). Comprehensive overview on computational intelligence techniques for machinery condition monitoring and fault diagnosis. Chinese Journal of Mechanical Engineering, 30(4), 782–795.
- Zhen, D., Guo, J., Xu, Y., Zhang, H., & Gu, F. (2019). A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis. Sensors, 19(18). doi: 10.3390/s19183994
Fault Analysis of an Induction Motor by Measuring Mechanical Vibration Signals
Yıl 2020,
Ejosat Özel Sayı 2020 (HORA), 312 - 322, 15.08.2020
Süleyman Çeven
,
Raif Bayır
Öz
Induction motors are the most widely used motor type in the industry. The decrease in the efficiency and health of induction motors seriously affects their performance. In order for the production process not to fail, these motors should be maintained periodically and monitored for their condition. In this study, an experimental setup is designed and implemented to measure the mechanical vibrations of an induction motor at different speed levels. In the experimental setup, the vibration signals were converted into electrical signals with an accelerometer connected to the body of the iduction motor in the direction of the "Z" axis and transferred to the computer environment via a data acquisition card. With this method, real-time condition monitoring is provided on the induction motor. The mechanical vibrations that occur on the motor are also measured with a test device that can measure vibration and are recorded on the memory card. The sensor data obtained were analyzed using the FFT conversion and the results were interpreted.
Kaynakça
- Areias, I. A. dos S., da Silva, L. E., Bonaldi, E. L., de Lacerda de Oliveira, L. E., Lambert-Torres, G., & Bernardes, V. A. (2019). Evaluation of Current Signature in Bearing Defects by Envelope Analysis of the Vibration in Induction Motors. Energies, 12(21). doi: 10.3390/en12214029
- Ballal, M S, Khan, Z. J., Suryawanshi, H. M., & Sonolikar, R. L. (2006). Induction motor: fuzzy system for the detection of winding insulation condition and bearing wear. Electric Power Components and Systems, 34(2), 159–171.
- Ballal, Makarand S, Khan, Z. J., Suryawanshi, H. M., & Sonolikar, R. L. (2007). Adaptive neural fuzzy inference system for the detection of inter-turn insulation and bearing wear faults in induction motor. IEEE Transactions on Industrial Electronics, 54(1), 250–258.
- Bayir, R., & Bay, O. F. (2004). Serial wound starter motor faults diagnosis using artificial neural network. Proceedings of the IEEE International Conference on Mechatronics, 2004. ICM ’04., 194–199. doi: 10.1109/ICMECH.2004.1364436
- Benbouzid, M. E. (2000). A review of induction motors signature analysis as a medium for faults detection. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 47(5), 984–993. doi: 10.1109/41.873206
- Betta, G., Liguori, C., Paolillo, A., & Pietrosanto, A. (2002). A DSP-based FFT-analyzer for the fault diagnosis of rotating machine based on vibration analysis. IEEE Transactions on Instrumentation and Measurement, 51(6), 1316–1322.
- Bin, G. F., Gao, J. J., Li, X. J., & Dhillon, B. S. (2012). Early fault diagnosis of rotating machinery based on wavelet packets-Empirical mode decomposition feature extraction and neural network. Mechanical Systems and Signal Processing, 27, 696–711.
- Choudhary, A., Goyal, D., Shimi, S. L., & Akula, A. (2019). Condition Monitoring and Fault Diagnosis of Induction Motors: A Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 26(4), 1221–1238. doi: 10.1007/s11831-018-9286-z
- Cristalli, C., Paone, N., & Rodriguez, R. M. (2006). Mechanical fault detection of electric motors by laser vibrometer and accelerometer measurements. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 20(6), 1350–1361. doi: 10.1016/j.ymssp.2005.11.013
- Datta, S., & Sarkar, S. (2016). A review on different pipeline fault detection methods. Journal of Loss Prevention in the Process Industries, 41, 97–106. doi: https://doi.org/10.1016/j.jlp.2016.03.010
- de Jesus Rangel-Magdaleno, J., de Jesus Romero-Troncoso, R., Osornio-Rios, R. A., Cabal-Yepez, E., & Dominguez-Gonzalez, A. (2010). FPGA-based vibration analyzer for continuous CNC machinery monitoring with fused FFT-DWT signal processing. IEEE Transactions on Instrumentation and Measurement, 59(12), 3184–3194.
- Eftekhari, M., Moallem, M., Sadri, S., & Hsieh, M.-F. (2013). A novel indicator of stator winding inter-turn fault in induction motor using infrared thermal imaging. Infrared Physics & Technology, 61, 330–336.
- Glowacz, A., & Glowacz, Z. (2017). Diagnosis of stator faults of the single-phase induction motor using acoustic signals. Applied Acoustics, 117, 20–27.
- Goyal, D, & Pabla, B. S. (2016). The vibration monitoring methods and signal processing techniques for structural health monitoring: a review. Archives of Computational Methods in Engineering, 23(4), 585–594.
- Goyal, Deepam, Pabla, B. S., Dhami, S. S., & others. (2017). Condition monitoring parameters for fault diagnosis of fixed axis gearbox: a review. Archives of Computational Methods in Engineering, 24(3), 543–556.
- Henao, H., Capolino, G.-A., Fernandez-Cabanas, M., Filippetti, F., Bruzzese, C., Strangas, E., Pusca, R., Estima, J., Riera-Guasp, M., & Hedayati-Kia, S. (2014). Trends in fault diagnosis for electrical machines: A review of diagnostic techniques. IEEE Industrial Electronics Magazine, 8(2), 31–42.
- Hwang, Y.-R., Jen, K.-K., & Shen, Y.-T. (2009). Application of cepstrum and neural network to bearing fault detection. Journal of Mechanical Science and Technology, 23(10), 2730.
- Igba, J., Alemzadeh, K., Durugbo, C., & Eiriksson, E. T. (2016). Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes. Renewable Energy, 91, 90–106. doi: https://doi.org/10.1016/j.renene.2016.01.006
- Ilonen, J., Kamarainen, J.-K., Lindh, T., Ahola, J., Kalviainen, H., & Partanen, J. (2005). Diagnosis tool for motor condition monitoring. IEEE Transactions on Industry Applications, 41(4), 963–971.
- Jayakumar, K., & Thangavel, S. (2017). Industrial drive fault diagnosis through vibration analysis using wavelet transform. Journal of Vibration and Control, 23(12), 2003–2013. doi: 10.1177/1077546315606602
- Kochhar, A. K., Atkinson, J., Barrow, G., Burdekin, M., Hannam, R. G., Hinduja, S., Brunn, P., & Li, L. (1997). Proceedings of the 32nd International MATADOR Conference. Macmillan International Higher Education.
- Leung, F. H.-F., Lam, H.-K., Ling, S.-H., & Tam, P. K.-S. (2003). Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Transactions on Neural Networks, 14(1), 79–88.
- Liu, W. Y., Han, J. G., & Jiang, J. L. (2013). A novel ball bearing fault diagnosis approach based on auto term window method. Measurement, 46(10), 4032–4037. doi: https://doi.org/10.1016/j.measurement.2013.07.039
- Liu, X., Yang, Y., & Zhang, J. (2018). Resultant vibration signal model based fault diagnosis of a single stage planetary gear train with an incipient tooth crack on the sun gear. RENEWABLE ENERGY, 122, 65–79. doi: 10.1016/j.renene.2018.01.072
- Medoued, A., Metatla, A., Boukadoum, A., Bahi, T., & Hadjadj, I. (2009). Condition monitoring and diagnosis of faults in the electric induction motor. American Journal of Applied Sciences, 6(6), 1133.
- Mohammed, O. D., & Rantatalo, M. (2016). Dynamic response and time-frequency analysis for gear tooth crack detection. Mechanical Systems and Signal Processing, 66–67, 612–624. doi: https://doi.org/10.1016/j.ymssp.2015.05.015
- Mousavi, S., Kar, N. C., & Timusk, M. (2017). A novel parallel modelling-wavelet based mechanical fault detection using stator current signature of induction machine under variable load conditions. J Electr Eng Electr Technol, 6(2), 2–9.
- Nejjari, H., & Benbouzid, M. E. H. (1999). Application of fuzzy logic to induction motors condition monitoring. IEEE Power Engineering Review, 19(6), 52–54.
- Palmero, G. I. S., Santamaria, J. J., de la Torre, E. J. M., & González, J. R. P. (2005). Fault detection and fuzzy rule extraction in AC motors by a neuro-fuzzy ART-based system. Engineering Applications of Artificial Intelligence, 18(7), 867–874.
- Pang, J., Li, Y., Jin, X., & Xu, L. (n.d.). Detection and analysis of typical vibration load of grain harvester based on short-time Fourier method. The International Journal of Electrical Engineering & Education, 0(0), 0020720919884247. doi: 10.1177/0020720919884247
- Prudhom, A., Antonino-Daviu, J., Razik, H., & Climente-Alarcon, V. (2017). Time-frequency vibration analysis for the detection of motor damages caused by bearing currents. Mechanical Systems and Signal Processing, 84, 747–762. doi: https://doi.org/10.1016/j.ymssp.2015.12.008
- Raghavendra, K., & Karabasanagouda, B. N. (2014). Frequency response analysis of deep groove ball bearing. Int. J. Sci. Res, 3(8), 1920–1926.
- Rao, B. K. N., Pai, P. S., & Nagabhushana, T. N. (2012). Failure diagnosis and prognosis of rolling-element bearings using Artificial Neural Networks: A critical overview. Journal of Physics: Conference Series, 364(1), 12023.
- Sait, A. S., & Sharaf-Eldeen, Y. I. (2011). A Review of Gearbox Condition Monitoring Based on vibration Analysis Techniques Diagnostics and Prognostics. T. Proulx (Ed.), Rotating Machinery, Structural Health Monitoring, Shock and Vibration, Volume 5 (pp. 307–324). New York, NY: Springer New York.
- SaravanaKumar, R., Kumar, K. V., & Roy, K. (2009). Fuzzy Logic based fault detection in induction machines using Lab view. Int J Comput Sci Netw Secur, 9(9), 226–243.
- Saravanan, N., & Ramachandran, K. I. (2010). Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Systems with Applications, 37(6), 4168–4181.
- Saruhan, H., Saridemir, S., Qicek, A., & Uygur, I. (2014). Vibration Analysis of Rolling Element Bearings Defects. Journal of Applied Research and Technology, 12, 384–395. Retrieved from http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1665-64232014000300005&nrm=iso
- Shnibha, R., Albarbar, A., Abouhnik, A., & Ibrahim, G. (2012). A more reliable method for monitoring the condition of three-phase induction motors based on their vibrations. ISRN Mechanical Engineering, 2012.
- Siddiqui, K. M., Sahay, K., & Giri, V. K. (2015). Rotor broken bar fault detection in induction motor using transformative techniques. Journal of Electrical Engineering, 15(1), 135–141.
- Sudhakar, G. N. D. S., & Sekhar, A. S. (2009). Coupling Misalignment in Rotating Machines: Modelling, Effects and Monitoring. Noise & Vibration Worldwide, 40(1), 17–39. doi: 10.1260/0957-4565.40.1.17
- Sugumaran, V., & Ramachandran, K. I. (2011). Effect of number of features on classification of roller bearing faults using SVM and PSVM. Expert Systems with Applications, 38(4), 4088–4096.
- Talhaoui, H., Menacer, A., Kessal, A., & Kechida, R. (2014). Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis. ISA Transactions, 53(5), 1639–1649. doi: https://doi.org/10.1016/j.isatra.2014.06.003
- Tiwari, M., Gupta, K., & Prakash, O. (2000). Effect of radial internal clearance of a ball, bearing on the dynamics of a balanced horizontal rotor. JOURNAL OF SOUND AND VIBRATION, 238(5), 723–756. doi: 10.1006/jsvi.1999.3109
- Tsypkin, M. (2011). Induction motor condition monitoring: Vibration analysis technique - A practical implementation. 2011 IEEE International Electric Machines Drives Conference (IEMDC), 406–411. doi: 10.1109/IEMDC.2011.5994629
- Unal, M., Onat, M., Demetgul, M., & Kucuk, H. (2014). Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network. Measurement, 58, 187–196.
- Van Hecke, B., Yoon, J., & He, D. (2016). Low speed bearing fault diagnosis using acoustic emission sensors. Applied Acoustics, 105, 35–44.
- Wang, J., & Hu, H. (2006). Vibration-based fault diagnosis of pump using fuzzy technique. Measurement, 39(2), 176–185.
- Widodo, A., & Yang, B.-S. (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 21(6), 2560–2574.
- Wong, W.-K., Loo, C.-K., Lim, W.-S., & Tan, P.-N. (2010). Thermal condition monitoring system using log-polar mapping, quaternion correlation and max-product fuzzy neural network classification. Neurocomputing, 74(1–3), 164–177.
- Yamamoto, G. K., da Costa, C., & da Silva Sousa, J. S. (2016). A smart experimental setup for vibration measurement and imbalance fault detection in rotating machinery. Case Studies in Mechanical Systems and Signal Processing, 4, 8–18.
- Yang, B.-S., Oh, M.-S., Tan, A. C. C., & others. (2009). Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Systems with Applications, 36(2), 1840–1849.
- Zhang, W., Jia, M.-P., Zhu, L., & Yan, X.-A. (2017). Comprehensive overview on computational intelligence techniques for machinery condition monitoring and fault diagnosis. Chinese Journal of Mechanical Engineering, 30(4), 782–795.
- Zhen, D., Guo, J., Xu, Y., Zhang, H., & Gu, F. (2019). A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis. Sensors, 19(18). doi: 10.3390/s19183994