Abstract
A new approach to detect and classify fault conditions of induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of experiments using real data measurements of motor currents from healthy and faulty induction motors is conducted. FMM-CART is able to detect and classify the associated inductor motor faults with good accuracy rates. Useful rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.
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Seera, M., Lim, C., Ishak, D. (2011). A Hybrid FMM-CART Model for Fault Detection and Diagnosis of Induction Motors. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_82
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DOI: https://doi.org/10.1007/978-3-642-24965-5_82
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