پیوندهای مفید
آمار
| تعداد نشریات | 7 |
| تعداد شماره‌ها | 409 |
| تعداد مقالات | 5,454 |
| تعداد مشاهده مقاله | 5,845,743 |
| تعداد دریافت فایل اصل مقاله | 5,217,694 |
Effective Approach to Use Artificial Intelligence for Detecting Different Faults in Working Electrical Machines | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 6، دوره 56، Issue 1 (Special Issue)، 2024، صفحه 57-78 اصل مقاله (1.57 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2023.22349.5534 | ||
| نویسندگان | ||
| Seyed Hamid Rafiei1؛ Mansoor Ojaghi* 1؛ Mahdi Sabouri2 | ||
| 1Department of Electrical Engineering, University of Zanjan, Zanjan, Iran | ||
| 2Department of Electrical and Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran. | ||
| چکیده | ||
| Artificial intelligence (AI) shows good potential for detecting and discriminating faults in electrical machines, however, they require initial training with sufficient data, which is almost impossible to collect for working electrical machines in the field. This paper proposes an effective approach to solve this problem by getting the required training data from exact simulation results. To evaluate this idea, the finite elements method is used to simulate a three-phase induction motor (IM) in the healthy state as well as the stator inter-turn fault, broken rotor bar fault, and mixed eccentricity fault conditions. Then, for every fault condition, some fault indices are extracted from the stator line current and used to arrange and train a suitable support vector machine (SVM) model to detect and discriminate the fault condition. A similar IM is prepared in the laboratory, where, its stator line currents are sampled and recorded under the healthy and the fault conditions, and the same fault indices are extracted from the stator currents. Some penalties, which are determined by comparing experimental test results and corresponding simulation results in the healthy state, are applied to the experimentally attained values of the indices. The modified indices are then applied to the trained SVM models, where, the attained results confirm the trained SVM models are equally able to detect and discriminate the faults in the real IMs. | ||
| کلیدواژه‌ها | ||
| Artificial intelligence؛ Electrical machines؛ Fault diagnosis؛ Finite elements method؛ Training data | ||
| مراجع | ||
|
[1] E. Solodkiy, D. Dadenkov, “Detection of Stator Inter-turn Short Circuit in Three- Phase Induction Motor Using Current Coordinate Transformation”, 2019 26th International Workshop on Electric Drives: Improvement in Efficiency of Electric Drives (IWED), IEEE, DOI: 10.1109/IWED.2019.8664353, Jan 30-Feb 02, 2019.
[2] H.B. Bostan Abad, M. Ojaghi, A. Taheri, “Efficient Index for Detecting the Stator Winding Interturn Fault in Six-Phase Squirrel Cage Induction Motors”, DOI: 10.1016/j.measurement.2021.109912 , 0263-2241/© 2021 Elsevier Ltd., 18 July 2021.
[3] M. Ojaghi, M. Sabouri, J. Faiz, “Performance Analysis of Squirrel-Cage Induction Motors Under Broken Rotor Bar and Stator Inter-Turn Fault Conditions Using Analytical Modeling”, IEEE Transactions on Magnetics, DOI:10.1109/TMAG.2018.2842240, vol. 54, no. 11, Nov. 2018.
[4] M. Y. Kaikaa, M. Hadjami, A. Khezzar, “Effects of the simultaneous presence of static eccentricity and broken rotor bars on the stator current of induction machine,” IEEE Trans. Industrial Electronics, DOI: 10.1109/TIE.2013.2270216, vol. 61, no. 5, pp. 2452-2463, May 2014.
[5] A. Toliyat, S. Nandi, S. Choi, and H. Meshgin-Kelk, “Electric machines modeling, condition monitoring, and fault diagnosis”, U.S.: Taylor & Francis Group, eBook Number-13: 978-1-4200-0628-5, Jan. 2012.
[6] M.B.K. Bouzid; G. Champenois, “Neural Network Based Method for the Automatic Detection of the Stator Faults of the Induction Motor”, 2013 Inter. Conference on Electrical Engineering and Software Applications, IEEE, DOI: 10.1109/ICEESA.2013.6578393, 15 Aug. 2013.
[7] S. Sridhar, K. Uma Rao, R. Umesh; K. S. Harish, “Condition Monitoring of Induction Motor using Statistical Processing”, 2016 IEEE Region 10 Conference (TENCON), DOI: 10.1109/TENCON.2016.7848597, 22-25 Nov. 2016.
[8] J. Seshadrinath, B. S. Fellow, B. K. Panigrahi, "Incipient Turn Fault Detection and Condition Monitoring of Induction Machine using Analytical Wavelet Transform", 2012 IEEE Industry Applications Society Annual Meeting, DOI: 10.1109/IAS.2012.6374026, 07-11 Oct. 2012.
[9] S. Das, C. Koley, P. Purkait, and S. Chakravorti, "Wavelet aided SVM Classifier for Stator InterTurn Fault Monitoring in Induction Motors", IEEE PES General Meeting, DOI: 10.1109/PES.2010.5589595, 25-29 July 2010.
[10] Dr. Jagadanand G, Fedora Lia Dias, “ARM Based Induction Motor Fault Detection Using Wavelet and Support Vector Machine”, 2015 IEEE International Conference on Signal Processing, Informatics, Communication, Energy Systems (SPICES), DOI: 10.1109/SPICES.2015.7091503, 19-21 Feb. 2015.
[11] S. Das, P. Purkait, C. Koley, and S. Chakravorti “Performance of a Load-immune Classifier for Robust Identification of Minor Faults in Induction Motor Stator Winding”, IEEE Trans. on Dielectrics and Electrical Insul., DOI 10.1109/TDEI.2013.003549, vol. 21, no. 1, Feb. 2014.
[12] M. Mohamed, E. Mohamed, A. Mohamed, and M.M. Hassan, “Detection of Inter Turn Short Circuit Faults in Induction Motor using Artificial Neural Network”, 2020 26th Conference of Open Innovations Ass. (FRUCT), DOI: 10.23919/FRUCT48808.2020.9087535, June 21,2020.
[13] K.J. Shih, M.F. Hsieh, B. J. Chen, and S.F. Huang, “Machine Learning for Inter-turn Short-circuit Fault Diagnosis in Permanent Magnet Synchronous Motors”, IEEE Transactions on Magnetics, DOI: 10.1109/TMAG.2022.3169173, 21 April 2022.
[14] A. Vinayak, R. Uddin, and Dr. Jagadanaand, "Inter phase fault detection in inverter fed Induction motor using wavelet transform",2017 3rd International Conference on Condition Assessment Tech. in Electrical Sys. (CATCON), IEEE, DOI: 10.1109/CATCON.2017.8280193, 16-18 Nov. 2017.
[15] C. Delpha, H. Chen, and D. Diallo, "SVM based diagnosis of inverter fed induction machine drive a new challenge", IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, DOI: 10.1109/IECON.2012.6389264, 20 Dec. 2012.
[16] F. Husari, and J. Seshadrinath, "Incipient Inter Turn Fault Detection and Severity Evaluation in Electric Drive System Using Hybrid HCNN-SVM Based Model", IEEE Transactions on Industrial Informatics, DOI: 10.1109/TII.2021.3067321, vol.18, Issue. 3, March 2022.
[17] Z. Liu, X. Yin, and others, “Online Rotor Mixed Fault Diagnosis Way Based on Spectrum Analysis of Instantaneous Power in Squirrel Cage Induction Motors”, IEEE Trans. on Energy Conversion, DOI: 10.1109/TEC.2004.832052, vol. 19, Issue. 3, pp. 485 - 490, 24 Aug. 2004.
[18] I. Ishkova, O. Vítek, “Diagnosis of eccentricity and broken rotor bar related faults of induction motor by means of motor current signature analysis”, 2015 16th International Scientific Conference on Electric Power Engineering (EPE), DOI: 10.1109/EPE.2015.7161130, 978-1-4673-6788-2/15/$31.00 ©2015 IEEE, 20-22 May 2015.
[19] V. Fireteanu, A. I. Constantin; A. Zorig, and A. Chouder, “Impact of the Stator Short-circuit, Rotor Broken Bar and Eccentricity Faults on Rotor Force for Loaded and No-load Induction Motors Operation,” 2018 Inter. Conference on Applied and Theoretical Electricity (ICATE), DOI: 10.1109/ICATE.2018.8551471, 978-1-5386-3806-4/18/$31.00 ©2018 IEEE, 29 Nov. 2018.
[20]. V. Fireteanu, A.I. Constantin, and M. Popa, “Influence of Single or Multiple Faults Short-circuit, Broken Rotor Bar and Eccentricity on the Torque and Rotor Force in Induction Motors”, 2018 XIII International Conference on Electrical Machines (ICEM), DOI: 10.1109/ICELMACH.2018.8507008, 978-1-5386-2477-7/18/$31.00 ©2018 IEEE, 25 Oct. 2018.
[21] A. Seghiour, T. Seghier, and B. Zegnini, “Diagnostic of the Simultaneous of Dynamic Eccentricity and Broken Rotor Bars Using the Magnetic Field Spectrum of the air-gap for an Induction Machine”, 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), DOI: 10.1109/CEIT.2015.7233158, 03 Sep. 2015.
[22] R. R. Schoen, and T.G. Habetler, “Effects of time-varying loads on rotor fault detection in induction machines,” Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting, in Con& Rec. 28th Annu. IAS Meeting, pp. 324-330, DOI: 10.1109/IAS.1993.298943, 02-08 Oct. 1993.
[23]. J. A. Daviu, A. Q. López, Others, “Evaluation of the detectability of rotor faults and eccentricities in induction motors via transient analysis of the stray flux”, 2017 IEEE Energy Conversion Congress and Exposition (ECCE), DOI: 10.1109/ECCE.2017.8096633, 978-1-5090-2998-3/17/$31.00 ©2017 IEEE, 07 Nov. 2017.
[24]. A. Ceban, R. Pusca, and R. Romary, “Eccentricity and Broken Rotor Bars Faults Effects on the External Axial Field,”, The XIX Inter. Conf. on Electrical Machines - ICEM 2010, DOI: 10.1109/ICELMACH.2010.5608009, 978-1-4244-4175-4/10/$25.00 ©2010 IEEE, 25 Oct. 2010.
[25] A. Pilloni, A. Pisano, E. Usai, and R. Panadero, “Detection of rotor broken bar and eccentricity faults in induction motors via second order sliding mode observer”, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC.2012.6426640, 978-1-4673-2064-1/12/$31.00 ©2012 IEEE, 10-13 Dec. 2012.
[26] G. Luo, T. G. Habetler, and J. Hurwitz, “A Multi-sensor Fusion Scheme for Broken Rotor Bar and Airgap Eccentricity Detection of Induction Machines”, 2019 IEEE Energy Conversion Congress and Exposition (ECCE), DOI: 10.1109/ECCE.2019.8912275, 978-1-7281-0395-2/19/$31.00 ©2019 IEEE, 28 Nov. 2019.
[27] M. G. Armaki, and R. Roshanfekr, “A New Approach for Fault Detection of Broken Rotor Bars in Induction Motor Based on Support Vector Machine,” 2010 18th Iranian Conference on Electrical Engineering, IEEE, DOI: 10.1109/IRANIANCEE.2010.5506976, 11-13 May 2010.
[28] H. Keskes, A. Braham, and Z. Lachiri, "On the use of Stationary Wavelet Packet Transform and Multiclass Wavelet SVM for Broken Rotor Bar detection", IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, DOI: 10.1109/IECON.2012.6389266, 978-1-4673-2421-2/12/$31.00 ©2012 IEEE, 20 Dec. 2012.
[29] D. Matić, F. Kulić, and others, "Design of Support Vector Machine Classifier for Broken Bar Detection," 2012 IEEE International Conference on Control Applications, DOI: 10.1109/CCA.2012.6402374, 978-1-4673-4505-7/12/$31.00 ©2012 IEEE, 03-05 Oct. 2012.
[30] D. Matić, F. Kulić, “SVM Broken Bar Detection Based on Analysis of Phase Current”, 2012 15th International Power Electronics and Motion Control Conference (EPE/PEMC), DOI: 10.1109/EPEPEMC.2012.6397456, 978-1-4673-1972-0/12/$31.00©2012 IEEE, 04-06 Sep. 2012.
[31] S. E. Zgarni, and A. Braham, "Intelligent Induction Motor Diagnosis System: A new challenge", 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), DOI: 10.1109/ATSIP.2016.7523125, 978-1-4673-8526-8/16/$31.00 ©2016 IEEE, 21-23 March 2016.
[32] H. Keskes, and A. Braham, “Recursive Undecimated Wavelet Packet Transform and DAG SVM for Induction Motor Diagnosis”, IEEE Transactions on industrial informatics, DOI: 10.1109/TII.2015.2462315. vol. 11, no. 5, PP: 1059 – 1066, Oct. 2015.
[33] C. M. Pezzani, J. M. Fontana, and Others, “SVM-Based System for Broken Rotor Bar Detection in Induction Motors”, 2018 IEEE ANDESCON, DOI: 10.1109/ANDESCON.2018.8564627, 06 Dec. 2018.
[34] H. O. Vishwakarma, K. Sajan, and Others, “Intelligent Bearing Fault Monitoring System Using Support Vector Machine and Wavelet Packet Decomposition for Induction Motors”, 2015 International Conference on Power and Advanced Control Engineering (ICPACE), DOI: 10.1109/ICPACE.2015.7274969, 12-14 Aug. 2015.
[35] J. Zarei, M. M. Arefi, and H. Hassani, "Bearing Fault Detection Based on Interval Type-2 Fuzzy Logic Systems for Support Vector Machines", 2015 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), DOI: 10.1109/ICMSAO.2015.7152214, 978-1-4673-6601-4/15$31.00 c 2015 IEEE, 27-29 May 2015.
[36] A. Choudhary, D. Goyal, and S. S. Letha, "Infrared Thermography based Fault Diagnosis of Induction Motor Bearings using Machine Learning", IEEE Sensors Journal, DOI: 10.1109/JSEN.2020.3015868, 1530-437X (c) 2020 IEEE, vol. 21, Issue. 2, 15 Jan. 2021.
[37] F. B. Abid, S. Zgarni, and A. Braham, “Bearing Fault Detection of Induction Motor Using SWPT and DAG Support Vector Machines”, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, DOI: 10.1109/IECON.2016.7793237, 978-1-5090-3474-1/16/$31.00 ©2016 IEEE, 23-26 Oct. 2016.
[38] S. Gunasekaran, S. Esakimuthu, and Others, “Condition Monitoring and Diagnosis of Outer Raceway Bearing Fault using Support Vector Machine”, 2018 Condition Monitoring and Diagnosis (CMD), DOI: 10.1109/CMD.2018.8535744, 23-26 Sep. 2018.
[39] F. B. Abid, S. Zgarni, and A. Braham, “Distinct Bearing Faults Detection in Induction Motor by a Hybrid Optimized SWPT and aiNet-DAG SVM”, IEEE Transactions on energy conversion, DOI: 10.1109/TEC.2018.2839083, vol. 33, no. 4, Dec. 2018.
[40] S. E. Pandarakone, K. Akahori, and Others, "Development of a Methodology for Bearing Fault Scrutiny and Diagnosis using SVM", 2017 IEEE International Conference on Industrial Technology (ICIT), DOI: 10.1109/ICIT.2017.7913097, 978-1-5090-5320-9/17/$31.00©2017 IEEE, 22-25 March 2017.
[41] H. Hassani, J. Zarei, and Others, "zSlices–Based General Type–2 Fuzzy Fusion of Support Vector Machines with Application to Bearing Fault Detection", IEEE transactions on industrial electronics, DOI: 10.1109/TIE.2017.2688963, vol. 64, Issue. 9, Sep. 2017.
[42] G. Georgoulas, V. Climente, and others, “Detection of Broken Bars and Mixed Eccentricity Faults Using the Start-up Transient”, 2016 IEEE 14th International Conference on Industrial Informatics (INDIN), DOI: 10.1109/INDIN.2016.7819198, 978-1-5090-2870-2/16/$31.00 ©2016 IEEE, 19-21 July 2016.
[43] M. Haji, and H.A. Toliyat, “Pattern Recognition – “A Technique for Induction Machines Rotor Fault Detection Eccentricity and Broken Bar Fault"”, Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. no.01CH37248), DOI: 10.1109/IAS.2001.955745, 0-7803-7116-X/01/$10.00 (C) 2001 IEEE, 30 Sep.- 04 Oct. 2001.
[44] H. Singh, M. Seera, and M. Z. Abdullah, “Detection and Diagnosis of Broken Rotor Bars and Eccentricity Faults in Induction Motors Using the Fuzzy Min-Max Neural Network”, The 2013 International Joint Conf. on Neural Networks (IJCNN), DOI: 10.1109/IJCNN.2013.6707003, 04-09 Aug. 2013.
[45] S. Hamdani, O. Touhami, and Others, “Neural Network technique for induction motor rotor faults classification –Dynamic eccentricity and broken bar faults”, 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives, DOI: 10.1109/DEMPED.2011.6063689, 978-1-4244-9303-6/11/$26.00 ©2011 IEEE, 05-08 Sep. 2011.
[46] G. Niu, T. Han, and Others, “Multi-agent decision fusion for motor fault diagnosis”, Mechanical Systems and Signal Processing, doi.org/10.1016/j.ymssp.2006.03.003, vol. 21, Issue. 3, pp. 1285-1299, April 2007.
[47] V. A. D. Silva, and R. Pederiva, “Fault detection in induction motors based on artificial intelligence”, Federal University of S˜ ao Jo˜ ao del Rei, Corpus ID: 16694089, 2013.
[48] I. Aydin, M. Karakose, and E. Akin, “Artificial Immune Based Support Vector Machine Algorithm for Fault Diagnosis of Induction Motors”, 2007 International Aegean Conf. on Electrical Machines and Power Elec., DOI: 10.1109/ACEMP.2007.4510505, 10-12 Sep. 2007.
[49] E. Smart, D. Brown , and L. Axel-Berg, “Comparing One and Two Class Classification Methods for Multiple Fault Detection on an Induction Motor” , 2013 IEEE Symposium on Industrial Electronics & Applications, DOI: 10.1109/ISIEA.2013.6738982, 22-25 Sep. 2013.
[50] J. B. Valencia, M. P. Sanchez, and Others, "Study of Performance of Several Techniques of Fault Diagnosis for Induction Motors in Steady-State with SVM Learning Algorithms", 2014 2nd International Conference on Artificial Intelligence, Modelling and Simulation, DOI 10.1109/AIMS.2014.47, 978-1-4799-7600-3/14 $31.00 © 2014 IEEE, 18-20 Nov. 2014.
[51] S. T. Kandukuri, S. L. Senanyaka, and Others, “A Two-Stage Fault Detection and Classification Scheme for Electrical Pitch Drives in Offshore Wind Farms using Support Vector Machine”, IEEE Transactions on Industry Applications, DOI 10.1109/TIA.2019.2924866, 0093-9994 (c) 2019 IEEE, vol. 55, Issue. 5, Sept.-Oct. 2019.
[52] D. G. pascual, “Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis”, Taylor & Francis Group, an informa businessno claim to original u.s. government, 20150225, Ebook – pdf: 978-1-4665-8406-8, April 22, 2015.
[53] Online available: https://www.analyticsvidhya.com/blog/2021/05/multiclass-classification-using-svm/
[54] Oskoyi Ballbering Co. “Oskoyi Ballbering catalogue,” https://www.1obc.com/ P.R.C., pp. 11-16, Mar. 2019. | ||
|
آمار تعداد مشاهده مقاله: 258 تعداد دریافت فایل اصل مقاله: 292 |
||