Abstract
Although having robust construction, the three-phase induction motor is frequently exposed to electrical, mechanical, and thermal stress, which, over time, may result in failure. This study addresses the application of signal processing tools and artificial neural networks to perform a multiple-fault diagnosis, considering different coupled load levels. The strategy consists of converting abc-referenced stator currents to dq rotating coordinate system by Clarke and Park transforms. After that, wavelet transform is employed to decompose the projected signals and the standard deviation values of the detail coefficients are calculated. Subsequently, we compare the performances of self-organizing maps, multilayer perceptrons, and radial basis function classifiers using four result metrics: accuracy, precision, sensitivity, and specificity rates. The conclusion is that all classifiers performed well for low levels of coupled load. However, for levels close to the nominal motor value, only the perceptron was able to discriminate the tested conditions.
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Acknowledgements
We would like to thank the Federal University of Technology - Paraná, the Araucária Foundation for the Support of the Scientific and Technological Development of the State of Paraná (process no. 06/56093-3), and the National Council for Technological and Scientific Development (CNPq) (processes no. 474290/ 2008-5, 473576/2011-2, 552269/2011-5, 201902/2015-0, and 405760/2021-3). Also, this study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.
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Vitor, A.L.O., Scalassara, P.R., Goedtel, A. et al. Patterns Based on Clarke and Park Transforms of Wavelet Coefficients for Classification of Electrical Machine Faults. J Control Autom Electr Syst 34, 230–245 (2023). https://doi.org/10.1007/s40313-022-00946-7
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DOI: https://doi.org/10.1007/s40313-022-00946-7