Eş zamanlı Arıza Teşhisi için FPGA Tabanlı Akıllı Durum İzleme Yöntemlerinin Geliştirilmesi / Development of FPGA Based Intelligent Condition Monitoring Methods for Synchronously Fault Diagnosis

Yıl 2012, Cilt: 2 Sayı: 3, 13 - 22, 09.10.2012

İlhan Aydın , Mehmet Karaköse , Erhan Akın

Öz

 

Özet

Çağdaş endüstriyel sistemlerde bütün sistem başarımının iyi bir düzeyde tutulması gerekir. Bu sistemlerde asenkron motor önemli bileşenlerden biridir ve iş gücünün büyük bir kısmını karşılarlar. Bu motorlarda oluşan arızalar sistem çalışmasını önemli bir ölçüde etkiler. Bu motorlar genellikle çevrimdışı olarak belirli zamanlarda izlenir. Fakat bu yöntem hem maliyetli hem de fabrikada üretimin durmasına neden olur. Bu çalışmada asenkron motorlarda oluşan stator, rotor ve sonlandırıcı halka arızalarının gerçek zamanlı teşhisi için bir akıllı durum izleme yaklaşımı sunulmuştur. Stator arızalarının teşhisi için önerilen bulanık sistem üç faz akım sinyalinin büyüklüğünden faydalanmaktadır. Rotor ve sonlandırıcı halka arızaları ise negatif seçim tabanlı bağışık sistem algoritması ile teşhis edilmektedir. Donanımsal tasarım Altera Cyclone III FPGA (Sahada Programlanabilir Kapı Dizileri) kartı üzerinde gerçekleştirilmiştir.

 

 

Abstract

In modern industrial systems, the overall system performance should be hold at a good level. In these systems, induction motor is one of major components and it constitutes a big part of work-power. The faults occurred in induction motors dramatically affect the system performance. These motors are generally monitored offline in a scheduled time. However, this method is both cost and it causes breakdown of the production in a factory. In this study, an intelligent condition monitoring approach is proposed to diagnose stator and rotor faults in real time. Fuzzy system which proposed to diagnose stator faults utilizes the magnitudes of three phase currents. Rotor and end-ring faults are diagnosed by negative selection based immune system algorithm. Hardware design was implemented on Altera Cyclone III FPGA.

Anahtar Kelimeler

FPGA, bulanık mantık, negatif seçim algoritması, arıza teşhisi, asenkron motor, Fuzzy logic, negative selection algorithm, fault diagnosis, induction motor

Kaynakça

• Chow, M.Y., “Methodologies of Using Artificial Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detection”, World Scientific Publishing, Singapore, 1998.
• Troncoso, R.J., Gallaga, R. S., Yepez, E.C., Perez, A.G., Rios, R. A. O., Salas, R.A., Vidales, H. M., Huber, N., “FPGA-Based Online Detection of Multiple Combined Faults in Induction Motors through Information Entropy and Fuzzy Inference”, IEEE Trans. On Industrial Electronics, Vol. 58, No. 11, pp. 5263 – 5270, 2011.
• Bellini, A., Filippetti, F., Tassoni, C., Capolino, G., "Advances in Diagnostic Techniques for Induction Machines", IEEE Trans. Indus. Electr., Vol. 55, No. 12, pp. 4109-4126, 2008.
• Panadero, R. P., Sanchez, M. P., Guasp, M. R., Folch, J. R., Perez, E. H., Cruz, J. P., “Improved Resolution of the MCSA Method via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip”, IEEE Trans. on Energy Conv., Vol. 24, No. 1, pp. 52-59, 2009.
• Briz, F., Degner, M. W., Garcia, P., Bragado, D., “Broken Rotor Bar Detection in Line-Fed Induction Machines using Complex Wavelet Analysis of Startup Transients”, IEEE Trans. on Industry Applications, Vol. 44, No. 3, pp. 760-768, 2008.
• Bouzida, A., Touhami, O., Ibtiouen, R., Belouchrani, A., Fadel, M., Rezzoug, A., “Fault Diagnosis in Industrial Induction Machines through Discrete Wavelet Transform”, IEEE Trans. On Industrial Electronics, Vol. 58, No. 9, pp. 4385 – 4395, 2011.
• Tsoumas, I. P., Georgoulas, G., Mitronikas, E. D., Safacas, A. N., “Asynchronous Machine Rotor Fault Diagnosis Technique Using Complex Wavelets”, IEEE Trans. on Energy Conversion, Vol. 23, No. 2, pp. 444- 459, 2008.
• Pires, V. F., Martins, J. F., Pires, A. J., “Eigenvector/eigenvalue Analysis of a 3D Current Referential Fault Detection and Diagnosis of an Induction Motor”, Energy Conversion and Management, Vol. 51, No. 5, pp. 901-907, 2010.
• Chilengue, Z., Dente, J. A., Branco, P. J. C., “An Artificial Immune System Approach for Fault Detection in the Stator and Rotor Circuits of Induction Machines”, Electric Power Systems Research, Vol. 81, No. 1, pp. 158-169, 2011.
• Aydin, I., Karakose, M., Akin, E., “A Multi-objective Artificial Immune Algorithm for Parameter Optimization in Support Vector Machine”, Applied Soft Computing, Vol. 11, No.1, pp. 120-129, 2011.
• Ayhan, B., “Linguistic Rule Generation for Broken Rotor Bar Detection in Squirrel-Cage Induction Motors”, PhD Thesis, North Carolina State University, Raleigh, NC, 2005.
• Su, H., Chong, K. T., “Induction Machine Condition Monitoring using Neural Network Modeling”, IEEE Trans. on Industrial Electronics, Vol. 54, No. 1, pp. 241–249, 2007.
• Zidani, F., Benbouzid, M. E. H., Diallo, D. Nait-Said, M. S., “Induction Motor Stator Faults Diagnosis by a Current Concordia Pattern-Based Fuzzy Decision System”, IEEE Trans. Energy Conversion, Vol. 18, No. 4, pp. 469–475, 2003.
• Rodriguez, P. V. J., Arkkio, A., “Detection of stator winding fault in induction motor using fuzzy logic”, Applied Soft Computing, Vol. 8, pp. 1112–1120, 2008.
• da Silva, A. M., Povinelli, R. J., Demerdash, N.A.O., “Induction machine broken bar and stator short-circuit fault diagnostics based on three phase stator current envelopes”, IEEE Trans. on Industrial Electronics, Vol. 55, No. 3, pp. 1310-1318, 2008.
• Moreno, A. O., Troncoso, R. J. R., Frias, J. A. V., Gillen, J. R., Perez, A. G., “Automatic Online Diagnosis Algorithm for Broken-Bar Detection on Induction Motors Based on Discrete Wavelet Transform for FPGA Implementation”, IEEE Trans. on Industrial Electronics, Vol. 55, No. 5, pp. 2193- 2202, 2008.
• Akin, E., Aydin, I., Karakose, M., “FPGA Based Intelligent Condition Monitoring of Induction Motors: Detection, Diagnosis, and Prognosis”, IEEE International Conference on Industrial Electronics (ICIT) & Southeastern Symposium on System Theory (SSST), March 14-17, Auburn, Alabama, USA, pp. 373-378, 2011.
• Aydin, I., Karakose, M., Akin, E., “Arıza Teşhisi için Gerçek Zamanlı Bağışık Sistem Uygulaması”, Otomatik Kontrol Türk Milli Komitesi 2011 Ulusal Toplantısı, 13-14 Eylül, İzmir, Türkiye, 2011.
• Monmasson, E., Idkhajine, L., Cirstea, M. N., Bahri, I., Tisan, A., Naouar, M. W., “FPGAs in Industrial Control Applications”, IEEE Trans. on Industrial Informatics, Vol. 7, No. 2, pp. 224-243, 2011.
• Xilinx Staff, “Celebrating 20 Years of Innovation”, Xcell Journal, 48, 2004.
• de Castro, L.N., Zuben, F. J. V., “Learning and Optimization using the Clonal Selection Principle”, IEEE Trans. on Evolutionary Computation, Vol. 6, No. 3, pp. 239-251, 2002.
• Forrest, S., Perelson, A. S., Allen, L., Cherukuri, R., “Self-non-self-discrimination in a Computer”, Proceedings of IEEE symposium on research in security and privacy, May 16-18, Oakland, CA , USA, pp. 202–212, 1994.
• Aydin, I., Karaköse, M., Akin, E., “Genetik Algoritma Kullanan Yapay Bağışık Sistem Tabanlı Arıza Teşhis Modeli”, Dokuz Eylül Üniversitesi Fen ve Mühendislik Dergisi, Cilt: 11, No: 31, s:57-72, 2009.
• Barriga, A., Sanchez-Solano, S., Brox, P., Cabrera, A., Baturone, I., “Modeling and Implementation of Fuzzy Systems based on VHDL”, International J. of Approximate Reasoning, Vol. 41, pp. 164-178, 2006.
• Altera Data Book,