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Author keywords
induction motors, fault diagnosis, rotors, digital signal processing, spectral analysis
References keywords
induction(27), motors(20), detection(15), fault(13), rotor(11), industrial(10), diagnosis(10), broken(10), electronics(9), systems(8)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2019-05-31
Volume 19, Issue 2, Year 2019, On page(s): 61 - 68
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.02008
Web of Science Accession Number: 000475806300008
SCOPUS ID: 85066316496
Abstract
Over the years induction motors have established an uncanny knack for providing a plethora of utilities in the industry, where the fault monitoring and detection has become necessary. Several techniques could be applied for the monitoring and identification of broken rotor bars when the motor is fed by a variable speed drive (VSD). Nevertheless, many of these methodologies detect this fault and other failures in the steady state condition, but this monitoring grow into more complicated analysis during the startup transient condition owing to the large number of harmonics, which the VSD insert to the current signal. The novelty of the proposed methodology is the application of the reassignment during the startup transient and the steady state conditions to identify one broken rotor bar in the induction motor. The proposed methodology is experimented with both, real and synthetic signals. The problems that Short Time Fourier Transform (STFT), shows for the identification of broken rotor bars are exhibited. The proposed methodology includes an automatic diagnosis (K-means algorithm), where the signal energy is used. The results show that the Reassigned Short Time Fourier Transform (RSTFT) technique and K-means methods are appropriated for the effective monitoring and diagnosis of one broken rotor bar in the induction motor during the startup and steady state conditions of operation. |
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[1] V. Ghorbanian and J. Faiz, "A survey on time and frequency characteristics of induction motors with broken rotor bars in line-start and inverter-fed modes", Mechanical Systems and Signal Processing, vol. 54-55, pp. 427-456, 2015. [CrossRef] [Web of Science Times Cited 59] [SCOPUS Times Cited 61] [2] K. Kim and A.G. Parlos, "Induction Motor Fault Diagnosis Based on Neuropredictors and Wavelet Signal Processing," IEEE/ASME Trans. Mechatronics, vol. 7, no. 2 pp. 201-219, 2002. [CrossRef] [Web of Science Times Cited 140] [SCOPUS Times Cited 177] [3] F. Filippetti, G. Franceschini, C. Tassoni and P. Vas, "Recent developments of induction motor drives fault diagnosis using AI techniques," IEEE Transactions on Industrial Electronics, vol.47, pp. 1966-1973, 2000. [CrossRef] [Web of Science Times Cited 339] [SCOPUS Times Cited 444] [4] M. El Hachemi Benbouzid, "A review of induction motors signature analysis as a medium for faults detection", IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp. 984-993, 2000. [CrossRef] [Web of Science Times Cited 983] [SCOPUS Times Cited 1379] [5] J. Milimonfared, H.M.Kelk,S.Nandi,A.D.Minassians, and H. A. Toliyat, "A novel approach for broken-rotor-bar detection in cage induction motors," IEEE Trans. Ind. Appl., vol. 35, no. 5, pp.1000-1006, 1999. [CrossRef] [Web of Science Times Cited 166] [SCOPUS Times Cited 198] [6] J. de Jesus Rangel-Magdaleno, H. Peregrina-Barreto, J. Ramirez-Cortes, P. Gomez-Gil and R. Morales-Caporal, "FPGA-Based Broken Bars Detection on Induction Motors Under Different Load Using Motor Current Signature Analysis and Mathematical Morphology", IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 5, pp. 1032-1040, 2014. [CrossRef] [Web of Science Times Cited 61] [SCOPUS Times Cited 69] [7] A. Garcia-Perez, R. Romero-Troncoso, E. Cabal-Yepez and R. Osornio-Rios, "The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors", IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp. 2002-2010, 2011. [CrossRef] [Web of Science Times Cited 185] [SCOPUS Times Cited 204] [8] A. Bellini, A. Yazidi, F. Filippetti, C. Rossi and G. Capolino, "High Frequency Resolution Techniques for Rotor Fault Detection of Induction Machines", IEEE Transactions on Industrial Electronics, vol. 55, no. 12, pp. 4200-4209, 2008. [CrossRef] [Web of Science Times Cited 129] [SCOPUS Times Cited 153] [9] J. Antonino-Daviu, S. Aviyente, E. G. Strangas, M. Riera-Guasp, J. Roger-Folch, and R.B. Pérez, "An EMD-based invariant feature extraction algorithm for rotor bar condition monitoring", in Proc. IEEE 2011 International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives (SDEMPED), Bologna, Italy, Sept. 2011, pp. 669-675. [CrossRef] [SCOPUS Times Cited 31] [10] R. Valles-Novo, J. de Jesus Rangel-Magdaleno, J. Ramirez-Cortes, H. Peregrina-Barreto and R. Morales-Caporal, "Empirical Mode Decomposition Analysis for Broken-Bar Detection on Squirrel Cage Induction Motors", IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 5, pp. 1118-1128, 2015. [CrossRef] [Web of Science Times Cited 83] [SCOPUS Times Cited 100] [11] R. Romero-Troncoso, D. Morinigo-Sotelo, O. Duque-Perez, R. Osornio-Rios, M. Ibarra-Manzano and A. Garcia-Perez, "Broken rotor bar detection in VSD-fed induction motors at startup by high-resolution spectral analysis", in Proc. 2014 International Conference on Electrical Machines (ICEM), Berlin Sept. 2014, pp. 1848-1854. [CrossRef] [SCOPUS Times Cited 19] [12] A. Garcia-Ramirez, R. Osornio-Rios, D. Granados-Lieberman, A. Garcia-Perez and R. Romero-Troncoso, "Smart Sensor for Online Detection of Multiple-Combined Faults in VSD-Fed Induction Motors", Sensors, vol. 12, no. 9, pp. 11989-12005, 2012. [CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 23] [13] R. Romero-Troncoso, D. Morinigo-Sotelo, O. Duque-Perez, P. Gardel-Sotomayor, R. Osornio-Rios and A. Garcia-Perez "Early broken rotor bar detection techniques in VSD-fed induction motors at steady-state", in Proc. IEEE 2013 International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives (SDEMPED), Valencia, Spain, August 2013, pp. 105-113. [CrossRef] [SCOPUS Times Cited 16] [14] M. Dlamini, P. Barendse and A. Khan, "Detecting faults in inverter-fed induction motors during startup transient conditions", in Proc. IEEE 2014 Energy Conversion Congress and Exposition (ECCE), Pittsburg, Sept. 2014, pp. 3131-3138. [CrossRef] [SCOPUS Times Cited 6] [15] J. Pons-Llinares, D. Morinigo-Sotelo, O. Duque-Perez, J. Antonino-Daviu and M. Perez-Alonso, "Transient detection of close components through the chirplet transform: Rotor faults in inverter-fed induction motors", in Proc. IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society, Dallas, October 2014, pp. 3386-3392. [CrossRef] [SCOPUS Times Cited 31] [16] J. Pons-Llinares, J. Antonino-Daviu, J. Roger-Folch, D. Morinigo-Sotelo and O. Duque-Perez, "Mixed eccentricity diagnosis in Inverter-Fed Induction Motors via the Adaptive Slope Transform of transient stator currents", Mechanical Systems and Signal Processing, vol. 48, no. 1-2, pp. 423-435, 2014. [CrossRef] [Web of Science Times Cited 35] [SCOPUS Times Cited 40] [17] E. Cabal-Yepez, A. Fernandez-Jaramillo, A. Garcia-Perez, R. Romero-Troncoso and J. Lozano-Garcia, "Real-time condition monitoring on VSD-fed induction motors through statistical analysis and synchronous speed observation". International Transactions on Electrical Energy Systems, 2014. [CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 22] [18] J. Faiz, V. Ghorbanian and B. Ebrahimi, "EMD-Based Analysis of Industrial Induction Motors with Broken Rotor Bars for Identification of Operating Point at Different Supply Modes", IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 957-966, 2014. [CrossRef] [Web of Science Times Cited 66] [SCOPUS Times Cited 80] [19] A. Papandreou-Suppappola, "Applications in time-frequency signal processing", vol. 10. pp. 179-203 CRC press, 2002. [CrossRef] [20] Chassande-Motin, Eric, François Auger, and Patrick Flandrin. "Reassignment." In Time-Frequency Analysis: Concepts and Methods. Edited by Franz Hlawatsch and François Auger. pp. 249-277, London: ISTE/John Wiley and Sons, 2008. [CrossRef] [SCOPUS Times Cited 7] [21] B. Akin, U. Orguner, H. A. Toliyat and M. Rayner, "Low order PWM inverter harmonics contributions to the inverter-fed induction machine fault diagnosis," IEEE Transactions on Industrial Electronics, vol. 55, no. 2, pp. 610-619, 2008. [CrossRef] [Web of Science Times Cited 102] [SCOPUS Times Cited 128] [22] B. Ayhan, H. Joel Trusell, M.-Y. Chow, and M.-H. Song, "On the use of a lower sampling rate for broken rotor bar detection width DTFT and AR-based spectrum methods," IEEE Transactions on Industrial Electronics, vol. 55, no. 3, pp. 1421-1434, Mar. 2008. [CrossRef] [Web of Science Times Cited 91] [SCOPUS Times Cited 108] [23] S. Pan, T. Han, A. C. C. Tan, and T. R. Lin, "Fault diagnosis system of induction motors based on multiscale entropy and support vector machine with mutual information algorithm," Shock and Vibration, vol. 2016, Article ID 5836 717, 12 pages, 2016. [CrossRef] [Web of Science Times Cited 26] [SCOPUS Times Cited 27] [24] A. Guerra de Araujo Cruz, R. Delgado Gomes, F. Antonio Belo and A. Cavalcante Lima Filho, "A Hybrid System Based on Fuzzy Logic to Failure Diagnosis in Induction Motors", IEEE Latin America Transactions, vol. 15, no. 8, pp. 1480-1489, 2017. [CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 32] [25] J. Amezquita-Sanchez, M. Valtierra-Rodriguez, C. Perez-Ramirez, D. Camarena-Martinez, A. Garcia-Perez and R. Romero-Troncoso, "Fractal dimension and fuzzy logic systems for broken rotor bar detection in induction motors at start-up and steady-state regimes", Measurement Science and Technology, vol. 28, no. 7, p. 075001, 2017. [CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 35] [26] L. Maraaba, Z. Al-Hamouz and M. Abido, "An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors", energies, vol.11, no.3, March 2018. [CrossRef] [Web of Science Times Cited 56] [SCOPUS Times Cited 67] [27] F. Khater, M. Abu El-Sebah and M. Osama, "Fault diagnostics in an inverter feeding an induction motor using fuzzy logic", Journal of Electrical Systems and Information Technology, vol. 4, no. 1, pp. 10-17, 2017. [CrossRef] [28] L. Maraaba, Z. Al-Hamouz and M. Abido, "An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors", Energies, vol. 11, no. 3, p. 653, 2018. [CrossRef] [Web of Science Times Cited 56] [SCOPUS Times Cited 67] [29] A. Glowacz, "Acoustic based fault diagnosis of three-phase induction motor", Applied Acoustics, vol. 137, pp. 82-89, 2018. [CrossRef] [Web of Science Times Cited 144] [SCOPUS Times Cited 167] [30] D. Chen andW. J.Wang, "Classification of wavelet map patterns using multi-layer neural networks for gear fault detection," Mechanical Systems and Signal Processing, vol. 16,no. 4, pp. 695- 704, 2002. [CrossRef] [Web of Science Times Cited 46] [SCOPUS Times Cited 52] [31] V. N. Ghate and S. V. Dudul, "Optimal MLP neural network classifier for fault detection of three phase induction motor," Expert Systems with Applications, vol. 37, no. 4, pp. 3468-3481, 2010. [CrossRef] [Web of Science Times Cited 103] [SCOPUS Times Cited 132] [32] Z. Liu, H. Cao, X. Chen, Z. He and Z. Shen, "Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings", Neurocomputing, vol. 99, pp. 399-410, 2013. [CrossRef] [Web of Science Times Cited 225] [SCOPUS Times Cited 282] [33] P. Chauhan and M. Shukla, "A review on outlier detection techniques on data stream by using different approaches of K-means algorithm", in Proc. International Conference on Advances in Computer Engineering and Applications (ICACEA), Ghaziabad, India, March 2015, pp. 580 - 585. [CrossRef] [SCOPUS Times Cited 26] [34] D. Camarena-Martinez, M. Valtierra-Rodriguez, J. Amezquita-Sanchez, D. Granados-Lieberman, R. Romero-Troncoso and A. Garcia-Perez, "Shannon Entropy and K-means Method for Automatic Diagnosis of Broken Rotor Bars in Induction Motors Using Vibration Signals". Shock and vibrations, 2016. [CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 29] [35] C. T. Yiakopoulos, K. C. Gryllias, and I. A. Antoniadis, "Rolling element bearing fault detection in industrial environments based on a K-means clustering approach", Expert Systems with Applications, vol. 38, no. 3, pp. 2888-2911, 2011. [CrossRef] [Web of Science Times Cited 170] [SCOPUS Times Cited 210] Web of Science® Citations for all references: 3,379 TCR SCOPUS® Citations for all references: 4,422 TCR Web of Science® Average Citations per reference: 94 ACR SCOPUS® Average Citations per reference: 123 ACR TCR = Total Citations for References / ACR = Average Citations per Reference We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... 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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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