Abstract
In this paper, we introduce a design methodology for prototype-based classifiers, more specifically the well-known LVQ family, aiming at improving their accuracy in fault detection/classification tasks. A laboratory testbed is constructed to generate the datasets which are comprised of short-circuit faults of different impedance levels, in addition to samples of the normal functioning of the motor. The generated data samples are difficult to classify as normal or faulty ones, especially if the faults are of high impedance (usually misinterpreted as non-faulty samples). Aiming at reducing misclassification, we use K-means and cluster validation techniques for finding an adequate number of labeled prototypes and their correct initialization for the efficient design of LVQ classifiers. By means of comprehensive computer simulations, we compare the performances of several LVQ classifiers in the aforementioned engineering application, showing that the proposed methodology eventually leads to high classification rates.
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Notes
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Mean squared quantization error: \(MSQE = \frac{1}{n_k} \sum _{\forall \mathbf {x} \in c_k} \Vert \mathbf {x} - \mathbf {m}_{c}^k\Vert ^2\), where \(n_k\) is the number of data samples of the class \(c_k\) and \(\mathbf {m}_c^k\) is the nearest prototype belonging to class \(c_k\).
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Acknowledgments
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. The authors also thank CNPq (grant 309451/2015-9) for the financial support and IFCE for the infrastructure of the Laboratory of Energy Processing.
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Sousa, D.P., Barreto, G.A., Cavalcante, C.C., Medeiros, C.M.S. (2020). LVQ-type Classifiers for Condition Monitoring of Induction Motors: A Performance Comparison. In: Vellido, A., Gibert, K., Angulo, C., Martín Guerrero, J. (eds) Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019. Advances in Intelligent Systems and Computing, vol 976. Springer, Cham. https://doi.org/10.1007/978-3-030-19642-4_13
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DOI: https://doi.org/10.1007/978-3-030-19642-4_13
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