Induction motor (IM) is commonly used in various industrial applications. Reliable online IM health condition monitoring systems are critically needed in industries to improve operational accuracy and safety of the IMs and the machinery. A new evolving algorithm is proposed to provide more decision-making transparency, as well as better classification and processing efficiency. The effectiveness of the developed intelligent classifier is examined by simulation and experimental tests.
References
[1]
Zeraoulia, M., Benbouzid, M.E.H. and Diallo, D. (2006) Electric Motor Drive Selection Issues for HEV Propulsion Systems: A Comparative Study. IEEE Transactions on Vehicular Technology, 55, 1756-1764. https://doi.org/10.1109/TVT.2006.878719
[2]
Saidur, R. (2010) A Review on Electrical Motors Energy Use and Energy Savings. Renewable and Sustainable Energy Reviews, 14, 877-898.
https://doi.org/10.1016/j.rser.2009.10.018
[3]
Randall, R. B. (2011) Vibration-Based Condition Monitoring: Industrial, Automotive and Aerospace Applications. John Wiley & Sons Ltd., Chichester.
https://doi.org/10.1002/9780470977668
[4]
Matic, D., Kulic, F., Pineda-Sánchez, M. and Kamenko, I. (2012) Support Vector Machine Classifier for Diagnosis in Electrical Machines: Application to Broken Bar. Expert Systems with Applications, 39, 8681-8689.
https://doi.org/10.1016/j.eswa.2012.01.214
[5]
Esfahani, E.T., Wang, S. and Sundararajan, V. (2014) Multisensor Wireless System for Eccentricity and Bearing Fault Detection in Induction Motors. IEEE/ASME Transactions on Mechatronics, 19, 818-826.
https://doi.org/10.1109/TMECH.2013.2260865
[6]
Moosavian, A., Ahmadi, H., Tabatabaeefar, A. and Khazaee, M. (2013) Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing. Shock and Vibration, 20, 263-272.
https://doi.org/10.1155/2013/360236
[7]
Jang, J.R. (1993) ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics, 23, 665-685.
https://doi.org/10.1109/21.256541
[8]
Karray, F.O. and De Silva, C. (2004) Soft Computing and Intelligent Systems Design: Theory, Tools, and Applications. Pearson/Addison Wesley, Harlow, England.
[9]
Angelov, P. (2004) An Approach for Fuzzy Rule-Base Adaptation Using On-Line Clustering. International Journal of Approximate Reasoning, 35, 275-289.
https://doi.org/10.1016/j.ijar.2003.08.006
[10]
Angelov, P. and Zhou, X. (2008) Evolving Fuzzy-Rule-Based Classifiers from Data Streams. IEEE Transactions on Fuzzy Systems, 16, 1462-1475.
https://doi.org/10.1109/TFUZZ.2008.925904
[11]
Hadrien, J. (2018) Deep Learning Book Series 2.9 the Moore Penrose Pseudoinverse. http://archive.ics.uci.edu/ml
[12]
Dua, D. and Graff, C. (2019) UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA.
http://archive.ics.uci.edu/ml
[13]
Filippetti, F., Franceschini, G., Tassoni, C. and Vas, P. (1998) AI Techniques in Induction Machines Diagnosis Including the Speed Ripple Effect. IEEE Transactions on Industry Applications, 34, 98-108. https://doi.org/10.1109/28.658729
[14]
Kliman, G.B. and Stein, J. (1992) Methods of Motor Current Signature Analysis. Electric Machines & Power Systems, 20, 463-474.
https://doi.org/10.1080/07313569208909609