Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive

• Autorzy: Jankowska Kamila , Ewert Pawel

Identyfikatory

DOI

10.2478/pead-2021-0008

• Języki publikacji: EN

Abstrakty

EN

Due to their many advantages, permanent magnet synchronous motors (PMSMs) are increasingly used in not only industrial drive systems but also electric and hybrid vehicle drives, aviation and other applications. Unfortunately, PMSMs are not free from damage that occurs during their operation. It is assumed that about 40% of the damage that occurs is related to rolling bearing damage. This article focuses on the use of Kohonen neural network (KNN) for rolling bearing damage detection in a PMSM drive system. The symptoms from the fast Fourier transform (FFT) and Envelope (ENV) Analysis of the mechanical vibration acceleration signal were analysed. The signal ENV was obtained by applying the Hilbert transform (HT). Two neural network functions are discussed: a detector and a classifier. The detector detected the damage and the classifier determined the type of damage to the rolling bearing (undamaged bearing, damaged rolling element, outer or inner race). The effectiveness of the analysed networks from the point of view of the applied signal processing method, map size, type of neighbourhood radius, distance function and the influence of input data normalisation are presented. The results are presented in the form of a confusion matrix, together with 2D and 3D maps of active neurons.

Słowa kluczowe

EN

PMSM; rolling bearing; electric drive diagnostics; self-organising map; shallow neural network

• Wydawca: De Gruyter
• Czasopismo: Power Electronics and Drives
• Rocznik: 2021
• Tom: Vol. 6 (41)
• Strony: 100--112
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Jankowska Kamila

• Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland

• https://orcid.org/0000-0003-0727-4816

autor

Ewert Pawel

• Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland

• https://orcid.org/0000-0001-9760-7092

Bibliografia

• Akar, M., Hekim, M. and Orhan, U. (2015). Mechanical Fault Detection in Permanent Magnet Synchronous Motors Using Equal Width Discretization-Based Probability Distribution and a Neural Network Model. Turkish Journal of Electrical Engineering and Computer Sciences, 23(3), pp. 813–823.
• Breard, G. T. (2017). Evaluating Self-Organizing Map Quality Measures as Convergence Criteria. University of Rhode Island: Open Access Master’s Theses, Paper 1033.
• Ewert, P., Kowalski, C. T. and Orlowska-Kowalska, T. (2020). Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach. Electronics, 9(9), pp. 1334.
• Ewert, P., Orlowska-Kowalska, T. and Jankowska, K. (2021). Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks. Energies, 14(3), pp. 712.
• Faiz, J., Takbash, A. M. and Mazaheri-Tehrani, E. (2017). A Review of Application of Signal Processing Techniques for Fault Diagnosis of Induction Motors—Part I. AUT Journal of Electrical Engineering, 49(2), pp. 109–122.
• Frosini, L., Harlişca, C. and Szabó, L. (2015). Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement. IEEE Transactions on Industrial Electronics, 62(3), pp. 1846–1854.
• Germen E., Başaran M. and Fidan M. (2014). Sound Based Induction Motor Fault Diagnosis Using Kohonen Self-Organizing Map. Mechanical Systems and Signal Processing, 46(1), pp. 45–58.
• He, J., Somogyi, C., Strandt, A. and Demerdash, N. A. (2014). Diagnosis of Stator Winding Short-Circuit Faults in an Interior Permanent Magnet Synchronous Machine. In: Proceedings of the 2014 IEEE Energy Conversion Congress and Exposition (ECCE), USA: Pittsburgh, PA.
• Henao, H., Capolino, G. A., Fernandez-Cabanas, M., Filippetti, F., Bruzzese, C., Strangas, E., Pusca, R., Estima, J., Riera-Guasp, M. and Hedayati-Kia, S. (2014). Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques. IEEE Industrial Electronics Magazine, 8(2), pp. 31–42.
• Immovilli, F., Bellini, A., Rubini, R. and Tassoni, C. (2010). Diagnosis of Bearing Faults in Induction Machines by Vibration or Current Signals: A Critical Comparison. IEEE Transactions on Industry Applications. 46(4), pp. 1350–1359.
• Jaganathan, B., Venkatesh, S., Bhardwaj, Y. and Prakash, C. A. (2011). Kohonen’s Self Organizing Map Method of Estimation of Optimal Parameters of a Permanent Magnet Synchronous Motor drive. In: Proceedings of the India International Conference on Power Electronics 2010 (IICPE2010), New Delhi, India.
• Kohonen, T. (2001). Self-Organizing Maps. Berlin, Germany: Springer.
• Liu, H., Li, D., Yuan, Y., Zhang, S., Zhao, H. and Deng, W. (2019). Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT. Applied Sciences, 9(7), pp. 1439.
• Lu, S., He, Q. and Zhao, J. (2018). Bearing Fault Diagnosis of a Permanent Magnet Synchronous Motor via a Fast and Online Order Analysis Method in an Embedded System. Mechanical Systems and Signal Processing, 113, pp. 36–49.
• Nkuna, J. S. R. (2006). Vibration Condition Monitoring and Fault Classification of Rolling Element Bearings Utilising Kohonen’s Self-organising Maps. Theses and Dissertations (Mechanical Engineering). Ph.D. Thesis, Vaal University of Technology: Vanderbijlpark, South Africa.
• Picot, A., Obeid, Z., Régnier, J., Poignant, S., Darnis, O. and Maussion, P. (2014). Statistic-Based Spectral Indicator for Bearing Fault Detection in Permanent-Magnet Synchronous Machines Using the Stator Current. Mechanical Systems and Signal Processing, 46(2), pp. 424–441.
• Rosero, J., Romeral, L., Rosero, E. and Urresty, J. (2009). Fault Detection in Dynamic Conditions by means of Discrete Wavelet Decomposition for PMSM Running Under Bearing Damage. In: Proceedings of the 2009 Twenty-Fourth Annual IEEE Applied Power Electronics Conference and Exposition, Washington, DC, USA.
• Skora, M., Ewert, P. and Kowalski, C. T. (2019). Selected Rolling Bearing Fault Diagnostic Methods in Wheel Embedded Permanent Magnet Brushless Direct Current Motors. Energies, 12(21), pp. 4212.
• Skowron, M., Wolkiewicz, M., Orlowska-Kowalska, T. and Kowalski, C. T. (2019). Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors. Energies, 12(12), pp. 2392.
• Ullah, Z., Lodhi, B. A. and Hur, J. (2020). Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG. Energies, 13(15), pp. 3834.
• Zhang, J., Wu, J., Hu, B. and Tang, J. (2020). Intelligent Fault Diagnosis of Rolling Bearings Using Variational Mode Decomposition and Self-Organizing Feature Map. Journal of Vibration and Control, 26(21–22), pp. 1886–1897.