Research Article
BibTex RIS Cite

Durum İzleme ve İstatistiksel Süreç Kontrolü Kullanarak Şebeke Kalkışlı Daimi Mıknatıslı Senkron Motorda Rulman Arızası Tespiti

Year 2020, Volume: 7 Issue: 2, 781 - 794, 31.05.2020
https://doi.org/10.31202/ecjse.696229

Abstract

Şebeke Kalkışlı Daimi Mıknatıslı Senkron (ŞKDMSM) yüksek verim, yüksek güç faktörü ve self starting üstün özelliklerinden dolayı bant sistemleri, fan sistemleri gibi endüstriyel ortamlardaki birçok uygulamada kullanılmaktadır. Bu motorların arızalarının erken tespiti, üretim kayıplarının yanısıra yüksek bakım ve onarım masraflarını da ortadan kaldıracaktır. Bu çalışmada ŞKDMSM’nin rulman arızalarının tespiti için SCADA tabanlı gerçek zamanlı durum izleme ve arıza tespit yöntemi önerilmiştir. Bu amaçla öncelikle motor akım ve gerilim verilerinin izlenmesi amacıyla SCADA tabanlı durum izleme otomasyonu gerçekleştirilmiştir. Sağlam bir ŞKDMSM’den farklı devir ve yük koşulları altında izlenen akım sinyallerinin üstel ağırlıklı hareketli ortalama (ÜAHO) tabanlı bir istatistiksel proses kontrol yöntemi ile analiz edilerek motorun normal çalışma limitleri belirlenmiştir. Daha sonra arıza durumundaki bir ŞKDMSM’nin akım sinyallerine ait ÜAHO verileri kullanılarak bu limitlerin aşımlarına göre arıza tespiti yapılmıştır. Elde edilen sonuçlar tasarlanan SCADA otomasyonunun güvenli veri toplama ve kaydetme özelliğine sahip olduğunu ve önerilen arıza tespit yönteminin ise ŞKDMSM’nin rulman arızalarının tespiti için başarılı bir araç olduğunu göstermiştir.

Supporting Institution

Tokat Gaziosmanpaşa Üniversitesi Bilimsel Araştırma Projeleri Birimi

Project Number

2018/59

Thanks

Bu çalışmanın ortaya çıkmasında verdiği destekten ötürü Bilimsel Araştırma Projeleri Birimine teşekkür ederiz

References

  • [1] Yu M., Tao X., Xin D., Xin Z., “Fault diagnosis and numerical simulation of broken rotor bars for small cage induction motors”, in 2017 Chinese Automation Congress (CAC), pp. 5355-5359, 2017.
  • [2] İlten E., Demirtaş M., "Sınırlı Hafızalı Sistem için Kesirli PIλ Kontrolör Uygulaması". El-Cezeri Journal of Science and Engineering, 2018, 5: 237-242.
  • [3] Kim K., Kim H. J., Lee J., “Demagnetization Analysis of Permanent Magnets According to Rotor Types of Interior Permanent Magnet Synchronous Motor”, IEEE Transactions on Magnetics, 2009, 45: 2799-2802.
  • [4] Wang X., Wang Z., Xu Z., He J., Zhao W., “Diagnosis and Tolerance of Common Electrical Faults in T-Type Three-Level Inverters Fed Dual Three-Phase PMSM Drives.” IEEE Transactions on Power Electronics, 2020, 35(2): 1753–1769.
  • [5] Behbahanifard H. Sadoughi A., “Line start permanent magnet synchronous motor performance and design; a Review”. Journal of World's Electrical Engineering and Technology, 2015, 4(2):58-66.
  • [6] Maraaba L., Al-Hamouz Z., Abido M., “An Accurate Tool for Detecting Stator Inter-Turn Fault in LSPMSM”, IEEE Access, 2019, 7: 88622-88634.
  • [7] Nandi S., Toliyat H. A., Li X., “Condition Monitoring and Fault Diagnosis of Electrical Motors-A Review”, IEEE Transactions on Energy Conversion, 2005, 20: 719-729.
  • [8] Proakis J. G., Manolakis D. G., Digital Signal Processing: Principles, Algorithms and Applications, Pentice Hall, 2007, 20-33.
  • [9] Boashash, B., "Time-Frequency Signal Analysis. Advances in Spectrum Estimation and Array Processing," S. Haykin, Ed., ed: Prentice- Hall, 1990, pp. 418-517.
  • [10] Alağaş H. M., Pınarbaşı M., “Ewma Kontrol Grafikleri İle Düz Dişli Çarklarında İstatiksel Süreç Kontrolü”, 13th International Conference on Econometris, Operations Research, and Statics, 2012, Kırıkkale.
  • [11] Djmal I. B., Mansouri M., Nounou M., Nounou H., Hamida A. B., “Fault detection using UKF-based optimized EWMA method in wastewater treatment plant”, in 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 1-6, 2018.
  • [12] Bostan H., "İstatistiksel proses kontrol tekniklerinin motor yenileştirme sürecinde kullanımı," Yüksek Lisans Tezi, Balıkesir Üniversitesi Fen Bilimleri Enstitüsü, 2010, Balıkesir.
  • [13] Eser, E., "Asenkron Motorlarda İstatistiksel Proses Kontrol Yöntemi İle Arıza Tespiti," Yüksek Lisans Tezi, Gaziosmanpaşa Üniversitesi, 2018, Tokat.
  • [14] He Y., Yu Z., Li J., Ma G., “Weld seam profile extraction using top-down visual attention and fault detection and diagnosis via EWMA for the stable robotic welding process”, International Journal of Advanced Manufacturing Technology, 2019, 104:3883-3897.
  • [15] Mirimani S. M., Vahedi A., Marignetti F., De Santis E., “Static eccentricity fault detection in single-stator–single-rotor axial-flux permanent-magnet machines”, IEEE Transactions on Industry Applications, 2012, 48: 1838-1845.
  • [16] Soreshjani M. H., Haghparast M., “Classical Direct Torque Control performance of Line Start PM Synchronous Motor for different conditions”, International Transactions on Electrical Energy Systems, 2015, 25:2595-2620.
  • [17] Gozuoglu A., Ozgonenel O., “Training Set Design for Smart Grids and Scada Co-Simulations”, in 2019 7th International Istanbul Smart Grids and Cities Congress and Fair (ICSG), 2019, pp. 124-128.
  • [18] Stapenhurst T., Mastering statistical process control, ed Routledge, 2013.
  • [19] Niezgoda J., “The use of statistical process control tools for analysing financial statements”, Folia Oeconomica Stetinensia, 2017, 17: 129-137.
  • [20] Marvuglia A., Antonio M., “Monitoring of wind farms’ power curves using machine learning techniques”,Applied Energy, 2012, 98: 574-583.
  • [21] Yang H-H, Huang M-L., Lai C-M., Jin J-R., “An approach combining data mining and control charts-based model for fault detection in wind turbines”, Renewable Energy, 2018, 115:808-816.
  • [22] Fugate M. L., Sohn H., Farrar C. R., “Vibration-based damage detection using statistical process control”, Mechanical Systems and Signal Processing, 2001,15: 707-721.
  • [23] Zerbato C., Furlani C. E., Silva R. P., Voltarelli M. A., Santos A. F. D., “Statistical control of processes aplied for peanut mechanical digging in soil textural classes”, Engenharia Agrícola, 2017, 37: 315-322.
  • [24] Üstünsoy, F. ve Sayan, H. H.,. PLC Destekli SCADA ile Enerji Yönetimi İçin Örnek Laboratuvar Çalışması. Politeknik Dergisi, 2018, 21(4):1007-1014.

Bearing Fault Detection by Using Condition Monitoring and Statistical Process Control in Line Start Permanent Magnet Synchronous Motor

Year 2020, Volume: 7 Issue: 2, 781 - 794, 31.05.2020
https://doi.org/10.31202/ecjse.696229

Abstract

Line start permanent magnet synchronous motor (LSPMSM) is used in many applications in industrial environments such as belt systems and fan systems due to its high efficiency, high power factor and self-starting features. Early detection of LSPMSM failures will eliminate production losses and high maintenance and repair costs. In this study, SCADA-based online condition monitoring and fault detection method is proposed for detecting bearing failures of LSPMSM. For this purpose, SCADA based condition monitoring automation was carried out primarily to monitor motor current and voltage data. The normal operating limits of the motor were determined by analyzing the current signals monitored under different speed and load conditions from a healthy LSPMSM with an exponential weighted moving average (EWMA) based statistical process control method. Then, fault detection was made according to the exceeding of these limits by using EWMA data of the current signals of a the LSPMSM in case of faulthy. The obtained results showed that the designed SCADA automation has the ability to collect and save data safely, and the proposed fault detection method is a successful tool for the detection of bearing failures of LSPMSM. 

Project Number

2018/59

References

  • [1] Yu M., Tao X., Xin D., Xin Z., “Fault diagnosis and numerical simulation of broken rotor bars for small cage induction motors”, in 2017 Chinese Automation Congress (CAC), pp. 5355-5359, 2017.
  • [2] İlten E., Demirtaş M., "Sınırlı Hafızalı Sistem için Kesirli PIλ Kontrolör Uygulaması". El-Cezeri Journal of Science and Engineering, 2018, 5: 237-242.
  • [3] Kim K., Kim H. J., Lee J., “Demagnetization Analysis of Permanent Magnets According to Rotor Types of Interior Permanent Magnet Synchronous Motor”, IEEE Transactions on Magnetics, 2009, 45: 2799-2802.
  • [4] Wang X., Wang Z., Xu Z., He J., Zhao W., “Diagnosis and Tolerance of Common Electrical Faults in T-Type Three-Level Inverters Fed Dual Three-Phase PMSM Drives.” IEEE Transactions on Power Electronics, 2020, 35(2): 1753–1769.
  • [5] Behbahanifard H. Sadoughi A., “Line start permanent magnet synchronous motor performance and design; a Review”. Journal of World's Electrical Engineering and Technology, 2015, 4(2):58-66.
  • [6] Maraaba L., Al-Hamouz Z., Abido M., “An Accurate Tool for Detecting Stator Inter-Turn Fault in LSPMSM”, IEEE Access, 2019, 7: 88622-88634.
  • [7] Nandi S., Toliyat H. A., Li X., “Condition Monitoring and Fault Diagnosis of Electrical Motors-A Review”, IEEE Transactions on Energy Conversion, 2005, 20: 719-729.
  • [8] Proakis J. G., Manolakis D. G., Digital Signal Processing: Principles, Algorithms and Applications, Pentice Hall, 2007, 20-33.
  • [9] Boashash, B., "Time-Frequency Signal Analysis. Advances in Spectrum Estimation and Array Processing," S. Haykin, Ed., ed: Prentice- Hall, 1990, pp. 418-517.
  • [10] Alağaş H. M., Pınarbaşı M., “Ewma Kontrol Grafikleri İle Düz Dişli Çarklarında İstatiksel Süreç Kontrolü”, 13th International Conference on Econometris, Operations Research, and Statics, 2012, Kırıkkale.
  • [11] Djmal I. B., Mansouri M., Nounou M., Nounou H., Hamida A. B., “Fault detection using UKF-based optimized EWMA method in wastewater treatment plant”, in 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 1-6, 2018.
  • [12] Bostan H., "İstatistiksel proses kontrol tekniklerinin motor yenileştirme sürecinde kullanımı," Yüksek Lisans Tezi, Balıkesir Üniversitesi Fen Bilimleri Enstitüsü, 2010, Balıkesir.
  • [13] Eser, E., "Asenkron Motorlarda İstatistiksel Proses Kontrol Yöntemi İle Arıza Tespiti," Yüksek Lisans Tezi, Gaziosmanpaşa Üniversitesi, 2018, Tokat.
  • [14] He Y., Yu Z., Li J., Ma G., “Weld seam profile extraction using top-down visual attention and fault detection and diagnosis via EWMA for the stable robotic welding process”, International Journal of Advanced Manufacturing Technology, 2019, 104:3883-3897.
  • [15] Mirimani S. M., Vahedi A., Marignetti F., De Santis E., “Static eccentricity fault detection in single-stator–single-rotor axial-flux permanent-magnet machines”, IEEE Transactions on Industry Applications, 2012, 48: 1838-1845.
  • [16] Soreshjani M. H., Haghparast M., “Classical Direct Torque Control performance of Line Start PM Synchronous Motor for different conditions”, International Transactions on Electrical Energy Systems, 2015, 25:2595-2620.
  • [17] Gozuoglu A., Ozgonenel O., “Training Set Design for Smart Grids and Scada Co-Simulations”, in 2019 7th International Istanbul Smart Grids and Cities Congress and Fair (ICSG), 2019, pp. 124-128.
  • [18] Stapenhurst T., Mastering statistical process control, ed Routledge, 2013.
  • [19] Niezgoda J., “The use of statistical process control tools for analysing financial statements”, Folia Oeconomica Stetinensia, 2017, 17: 129-137.
  • [20] Marvuglia A., Antonio M., “Monitoring of wind farms’ power curves using machine learning techniques”,Applied Energy, 2012, 98: 574-583.
  • [21] Yang H-H, Huang M-L., Lai C-M., Jin J-R., “An approach combining data mining and control charts-based model for fault detection in wind turbines”, Renewable Energy, 2018, 115:808-816.
  • [22] Fugate M. L., Sohn H., Farrar C. R., “Vibration-based damage detection using statistical process control”, Mechanical Systems and Signal Processing, 2001,15: 707-721.
  • [23] Zerbato C., Furlani C. E., Silva R. P., Voltarelli M. A., Santos A. F. D., “Statistical control of processes aplied for peanut mechanical digging in soil textural classes”, Engenharia Agrícola, 2017, 37: 315-322.
  • [24] Üstünsoy, F. ve Sayan, H. H.,. PLC Destekli SCADA ile Enerji Yönetimi İçin Örnek Laboratuvar Çalışması. Politeknik Dergisi, 2018, 21(4):1007-1014.
There are 24 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Saadet Gülsüm Gözüoğlu This is me 0000-0002-4564-207X

Zafer Doğan 0000-0002-7953-0578

Project Number 2018/59
Publication Date May 31, 2020
Submission Date February 28, 2020
Acceptance Date April 21, 2020
Published in Issue Year 2020 Volume: 7 Issue: 2

Cite

IEEE S. G. Gözüoğlu and Z. Doğan, “Durum İzleme ve İstatistiksel Süreç Kontrolü Kullanarak Şebeke Kalkışlı Daimi Mıknatıslı Senkron Motorda Rulman Arızası Tespiti”, El-Cezeri Journal of Science and Engineering, vol. 7, no. 2, pp. 781–794, 2020, doi: 10.31202/ecjse.696229.
Creative Commons License El-Cezeri is licensed to the public under a Creative Commons Attribution 4.0 license.
88x31.png