Aşırı Öğrenme Makinası Yöntemi ile Sürekli Mıknatıslı Senkron Motorun Moment Verileri Kullanılarak Sargı İzolasyonu Arızasının Tespiti

Yıl 2019, Cilt: 9 Sayı: 2, 7 - 15, 06.02.2020

Ferhat Çıra

Öz

Elektrik makinalarının arızalarının erken tespitinin yapılmaması, felaketle sonuçlanabilecek arızalara neden olduğu bilinmektedir. Endüstride en çok kullanılan motorların asenkron motorlar olması sebebiyle durum izleme ve arıza tespiti bu makinalar üzerine yoğunlaşmıştır. Oysa hassas hız ve konum kontrolü gerektiren uygulamalarda sürekli mıknatıslı motorların (SMSM) kullanılmaya başlanmasıyla birçok araştırmacı bu motorların arıza tespiti çalışmaları yaygınlaştı. SMSM’lerin hassas hız ve konum kontrolü kabiliyetleri tamamen sağlıklı çalışmalarına bağlıdır. En küçük bir arıza sonucu bu hassasiyet kaybolabileceğinden bu tür motorlarda arızanın erkenden tespit ve teşhis edilebilmesi çok önemlidir. Bu çalışmada SMSM’lerde sıkça meydana gelen stator izolasyon arızasının erken evrede arıza tespiti için bir boyutlu yerel ikili desenler (1b-YİÖ) tabanlı öznitelik çıkarım yöntemi kullanılmıştır. Bu amaçla sağlıklı ve farklı kısa devre arıza oranlarına sahip SMSM’lerdenlabview programı tabanlı veri toplama kartı ile moment verileri alınmıştır. Sağlıklı ve arızalı motorlardan alınan moment işaretlerine1b-YİÖ uygulanmış ve histogramları elde edilmiştir. Elde edilen histogramlar ile sağlıklı ve arızalı motorların öznitelikleri oluşturularak aşırı öğrenme makinesi (AÖM) yöntemi ile işaretler sınıflandırılmıştır. Arızanın tespitinin erken evrede yapılabilmesi için önerilen bu yaklaşım ile oldukça büyük bir başarı sağlandığı görülmüştür. Bu amaçla üretilen farklı arıza şiddetine sahip motorların farklı hız ve yüklenme koşulları altında yapılan deneyler ile yöntemin başarısı doğrulanmıştır. Böylece daha önce literatürde olmayan bir yöntem ile SMSM’nin stator izolasyon arızasının tespiti yüksek güvenirlikli ve başarıyla yapılmıştır. 

Anahtar Kelimeler

Tek boyutlu İkili örüntü yöntemi, Öznitelik çıkarma, Arıza tespiti, Sınıflandırma

Detection of Winding Insulation Fault by Using Moment Data of Permanent Magnet Synchronous Motor with Extreme Learning Machine Method

Öz

Failure in earlydetection of faults of electricalmachines is knowntocausecatastrophicmalfunction. As inductionmotorsaremostcommonmotors in industry, conditionmonitoringandfaultdetectionareconcentrated on thesemachines. However, withtheuse of permanentmagnetmotors (PMSM) in applicationsrequiringprecisespeedandpositioncontrol, manyresearchershavestudiedthefaultdetection of motors. Theprecisespeedandpositioncontrolcapabilities of thePMSMstotallydepend on theirhealthyoperation. Since thisprecision can be lostduetotheslightestfailure, it is veryimportanttodetectanddiagnosethefaultearly on suchmotors. Inthisstudy, a featureextractionmethodbased on onedimensionallocalbinarypattern (1D-LBP) methodwhich is a distinctivemethod, has beenusedforfeatureextraction. It has beenproposedforfaultdetection of earlystage stator insulationfaultoccurringfrequently in PMSMs.
Inthisstudy, torquedatawereobtainedfromPMSMswithhealthyanddifferentshortcircuitfaultrates. 1b-YİÖwasappliedtothesedata, andthehistograms of torquesignalswereobtained. Healthyandfaultymotorscould be classified at highsuccessratesapplyingone of theextremelearningmachine (ELM) technique, tohistograms. Using thesefeatures, ELM method is usedtoclassifythesignals. It has beenobservedthatgreatsuccess has beenachievedwiththisapproach in ordertodetectthefault in an earlystage. Thesuccess of themethod has beenconfirmedbyexperimentsperformedunderdifferentspeedandloadingconditions of motorswithdifferentfaultseverities. 

Anahtar Kelimeler

PermanentMagnetSynchronous Machine, LocalBinaryPattern, Texture Analysis

Kaynakça

• [1] Z. Q. Zhu and D. Howe, “Electrical Machines and Drives for Electric, Hybrid, and Fuel Cell Vehicles,” Proc. IEEE, vol. 95, no. 4, pp. 746–765, Apr. 2007.
• [2] Z. Yang, F. Shang, I. P. Brown, and M. Krishnamurthy, “Comparative Study of Interior Permanent Magnet, Induction, and Switched Reluctance Motor Drives for EV and HEV Applications,” IEEE Trans. Transp. Electrif., vol. 1, no. 3, pp. 245–254, Oct. 2015.
• [3] E. Bostanci, M. Moallem, A. Parsapour, and B. Fahimi, “Opportunities and Challenges of Switched Reluctance Motor Drives for Electric Propulsion: A Comparative Study,” IEEE Trans. Transp. Electrif., vol. 3, no. 1, pp. 58–75, Mar. 2017.
• [4] M. Zafarani, E. Bostanci, Y. Qi, T. Goktas, and B. Akin, “Interturn Short-Circuit Faults in Permanent Magnet Synchronous Machines: An Extended Review and Comprehensive Analysis,” IEEE J. Emerg. Sel. Top. Power Electron., vol. 6, no. 4, pp. 2173–2191, Dec. 2018.
• [5] F. Çıra, “Automatic determination of stator short circuit fault and fault severity of permanent magnet synchronous motor,” Inonu University, 2017.
• [6] B. L. Rajalakshmi Samaga and K. P. Vittal, “Comprehensive study of mixed eccentricity fault diagnosis in induction motors using signature analysis,” Int. J. Electr. Power Energy Syst., vol. 35, no. 1, pp. 180–185, Feb. 2012.
• [7] B. M. Ebrahimi, J. Faiz, S. Lotfi-fard, and P. Pillay, “Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform,” Mech. Syst. Signal Process., vol. 30, pp. 131–145, Jul. 2012.
• [8] A. Soualhi, G. Clerc, and H. Razik, “Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique,” IEEE Trans. Ind. Electron., vol. 60, no. 9, pp. 4053–4062, Sep. 2013.
• [9] Y. Nyanteh, C. Edrington, S. Srivastava, and D. Cartes, “Application of Artificial Intelligence to Real-Time Fault Detection in Permanent-Magnet Synchronous Machines,” IEEE Trans. Ind. Appl., vol. 49, no. 3, pp. 1205–1214, 2013.
• [10] J. C. Urresty, J. R. Riba, and L. Romeral, “A back-emf based method to detect magnet failures in PMSMs,” IEEE Trans. Magn., vol. 49, no. 1, pp. 591–598, 2013.
• [11] F. Cira, M. Arkan, B. Gumus, and T. Goktas, “Analysis of stator inter-turn short-circuit fault signatures for inverter-fed permanent magnet synchronous motors,” in IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, 2016, pp. 1453–1457.
• [12] A. Sarikhani and O. A. Mohammed, “Inter-turn fault detection in PM synchronous machines by physics-based EMF estimation,” in 2012 IEEE Energy Conversion Congress and Exposition (ECCE), 2012, pp. 1254–1261.
• [13] J. Ahmed Farooq, T. Raminosoa, A. Djerdir, and A. Miraoui, “Modelling and simulation of stator winding inter‐turn faults in permanent magnet synchronous motors,” COMPEL - Int. J. Comput. Math. Electr. Electron. Eng., vol. 27, no. 4, pp. 887–896, 2008.
• [14] M. Hadef, M. R. Mekideche, and A. Djerdir, “Vector controlled Permanent Magnet Synchronous Motor (PMSM) drive with stator turn fault,” in 19th International Conference on Electrical Machines, ICEM 2010, 2010.
• [15] “Report of Large Motor Reliability Survey of Industrial and Commercial Installations, Part II,” IEEE Trans. Ind. Appl., vol. IA-21, no. 4, pp. 865–872, 1985.
• [16] A. Gandhi, T. Corrigan, and L. Parsa, “Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors,” IEEE Trans. Ind. Electron., vol. 58, no. 5, 2011.
• [17] T. Hamiti, P. Arumugam, and C. Gerada, “Turn–turn short circuit fault management in permanent magnet machines,” IET Electr. Power Appl., vol. 9, no. 9, pp. 634–641, Nov. 2015.
• [18] P. Arumugam, C. Gerada, T. Hamiti, C. Hill, and S. Bozhko, “A review on turn-turn short circuit fault management,” in 2015 International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles (ESARS), 2015, pp. 1–5.
• [19] J. A. Rosero, L. Romeral, J. Cusido, A. Garcia, and J. A. Ortega, “On the short-circuiting Fault Detection in a PMSM by means of Stator Current Transformations,” in 2007 IEEE Power Electronics Specialists Conference, 2007, pp. 1936–1941.
• [20] Y. Lee and T. G. Habetler, “An On-Line Stator Turn Fault Detection Method for Interior PM Synchronous Motor Drives,” in APEC 07 - Twenty-Second Annual IEEE Applied Power Electronics Conference and Exposition, 2007, pp. 825–831.
• [21] B. M. Ebrahimi and J. Faiz, “Feature extraction for short-circuit fault detection in permanent-magnet synchronous motors using stator-current monitoring,” IEEE Trans. Power Electron., vol. 25, no. 10, pp. 2673–2682, 2010.
• [22] A. Stavrou, H. G. Sedding, and J. Penman, “Current monitoring for detecting inter-turn short circuits in induction motors,” IEEE Trans. Energy Convers., vol. 16, no. 1, pp. 32–37, 2001.
• [23] W. G. Zanardelli, E. G. Strangas, and S. Aviyente, “Identification of Intermittent Electrical and Mechanical Faults in Permanent-Magnet AC Drives Based on Time–Frequency Analysis,” IEEE Trans. Ind. Appl., vol. 43, no. 4, pp. 971–980, 2007.
• [24] O. A. Mohammed, Z. Liu, S. Liu, and N. Y. Abed, “Internal Short Circuit Fault Diagnosis for PM Machines Using FE-Based Phase Variable Model and Wavelets Analysis,” IEEE Trans. Magn., vol. 43, no. 4, 2007.
• [25] A. P. Zheng, J. Yang, and L. Wang, “Fault detection of stator winding interturn short circuit in PMSM based on wavelet packet analysis,” in Proceedings - 2013 5th Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2013, 2013, pp. 566–569.
• [26] M. A. Cash, T. G. Habetler, and G. B. Kliman, “Insulation failure prediction in AC machines using line-neutral voltages,” IEEE Trans. Ind. Appl., vol. 34, no. 6, pp. 1234–1239, 1998.
• [27] J. C. Urresty, J. R. Riba, M. Delgado, and L. Romeral, “Detection of demagnetization faults in surface-mounted permanent magnet synchronous motors by means of the zero-sequence voltage component,” IEEE Trans. Energy Convers., vol. 27, no. 1, pp. 42–51, 2012.
• [28] J.-C. Urresty, J.-R. Riba, H. Saavedra, and L. Romeral, “Detection of inter-turns short circuits in permanent magnet synchronous motors operating under transient conditions by means of the zero sequence voltage,” Proc. 2011 14th Eur. Conf. Power Electron. Appl., pp. 1–9, 2011.
• [29] O. Wallmark, L. Harnefors, and O. Carlson, “Control Algorithms for a Fault-Tolerant PMSM Drive,” IEEE Trans. Ind. Electron., vol. 54, no. 4, pp. 1973–1980, Aug. 2007.
• [30] J.-C. Urresty, J.-R. Riba, and L. Romeral, “Diagnosis of Interturn Faults in PMSMs Operating Under Nonstationary Conditions by Applying Order Tracking Filtering,” IEEE Trans. POWER Electron., vol. 28, no. 1, 2013.
• [31] J. Hang, J. Zhang, M. Cheng, and Z. Wang, “Fault diagnosis of mechanical unbalance for permanent magnet synchronous motor drive system under nonstationary condition,” in 2013 IEEE Energy Conversion Congress and Exposition, 2013, pp. 3556–3562.
• [32] F. Cira, M. Arkan, and B. Gumus, “Detection of Stator Winding Inter-Turn Short Circuit Faults in Permanent Magnet Synchronous Motors and Automatic Classification of Fault Severity via a Pattern Recognition System,” J. Electr. Eng. Technol., vol. 11, no. 2, pp. 416–424, 2016.
• [33] M. Khan and M. A. Rahman, “Development and Implementation of a Novel Fault Diagnostic and Protection Technique for IPM Motor Drives,” IEEE Trans. Ind. Electron., vol. 56, no. 1, pp. 85–92, Jan. 2009.
• [34] W. J. Staszewski, K. Worden, and G. R. Tomlinson, “TIME–FREQUENCY ANALYSIS IN GEARBOX FAULT DETECTION USING THE WIGNER–VILLE DISTRIBUTION AND PATTERN RECOGNITION,” Mech. Syst. Signal Process., vol. 11, no. 5, pp. 673–692, Sep. 1997.
• [35] J. a. Rosero, L. Romeral, J. a. Ortega, and E. Rosero, “Short-circuit detection by means of empirical mode decomposition and Wigner-Ville distribution for PMSM running under dynamic condition,” IEEE Trans. Ind. Electron., vol. 56, no. 11, pp. 4534–4547, 2009.
• [36] J. F. Martins, V. F. Pires, and A. J. Pires, “Unsupervised Neural-Network-Based Algorithm for an On-Line Diagnosis of Three-Phase Induction Motor Stator Fault,” IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 259–264, Feb. 2007.
• [37] C. Wang, X. Liu, and Z. Chen, “Incipient Stator Insulation Fault Detection of Permanent Magnet Synchronous Wind Generators Based on Hilbert–Huang Transformation,” IEEE Trans. Magn., vol. 50, no. 11, pp. 1–4, Nov. 2014.
• [38] J. Rosero, A. Garcia, J. Cusido, L. Romeral, and J. A. Ortega, “Fault detection by means of Hilbert Huang transform of the stator current in a PMSM with demagnetization,” 2007 IEEE Int. Symp. Intell. Signal Process. WISP, vol. 25, no. 2, pp. 312–318, 2007.
• [39] C. P. SALOMON et al., “Electrical Signature Analysis for Condition Monitoring of Permanent Magnet Synchronous Machine,” Adv. Electr. Comput. Eng., vol. 18, no. 4, pp. 91–98, Nov. 2018.
• [40] J. Wang and B. Sen, “Stator Inter-turn Fault Detection in SPM Machines Using PWM Ripple Current Measurement,” in 7th IET International Conference on Power Electronics, Machines and Drives (PEMD 2014), 2014, pp. 2.2.03-2.2.03.
• [41] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, Jul. 2002.
• [42] T. Ahonen, A. Hadid, and M. Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 12, pp. 2037–2041, Dec. 2006.
• [43] C. K. Tran, T. F. Lee, L. Chang, and P. J. Chao, “Face Description with Local Binary Patterns and Local Ternary Patterns: Improving Face Recognition Performance Using Similarity Feature-Based Selection and Classification Algorithm,” in 2014 International Symposium on Computer, Consumer and Control, 2014, pp. 520–524.
• [44] N. Chatlani and J. J. Soraghan, “Local binary patterns for 1-D signal processing,” in Proceedings of the 18th European Signal Processing Conference (EUSIPCO’10), 2010, pp. 95–99.
• [45] Q. Zhu, N. Chatlani and J. J. Soraghan “1-D Local binary patterns based VAD used INHMM-based improved speech recognition,” in Proceedings of the 20th European Signal Processing Conference (EUSIPCO ’12, pp. 1633–1637.
• [46] G.-B. Huang, D. H. Wang, and Y. Lan, “Extreme learning machines: a survey,” Int. J. Mach. Learn. Cybern., vol. 2, no. 2, pp. 107–122, Jun. 2011.
• [47] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006.
• [48] H.-J. Rong, Y.-S. Ong, A.-H. Tan, and Z. Zhu, “A fast pruned-extreme learning machine for classification problem,” Neurocomputing, vol. 72, no. 1–3, pp. 359–366, Dec. 2008.
• [49] S. Suresh, S. Saraswathi, and N. Sundararajan, “Performance enhancement of extreme learning machine for multi-category sparse data classification problems,” Eng. Appl. Artif. Intell., vol. 23, no. 7, pp. 1149–1157, Oct. 2010.