Fig 3 - uploaded by Enrique V. Carrera
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(a) Learning capacity vs different numbers of clusters with k-means. (b) Sensitivity and specificity vs the parameter M .
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Induction motors are widely used around the world in many industrial and commercial applications. Early detection of faults in these devices is important to avoid service disruption and increase their useful life. Thus, many non-invasive schemes have been proposed to detect failures in induction motors using machine learning techniques mainly. Many...
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... 3(a) shows the learning capacity of the non-supervised algorithm for different number of centroids. We can see that 13 centroids maximize the performance of the model. On the other hand, Fig. 3(b) present the sensitivity and specificity of the k-means algorithm for different M values, when the number of centroids is 13. We can see that the sensitivity and specificity are simultaneously maximized when M = 1.49. In similar way to the neural network, this non-supervised algorithm is evaluated with a k-fold cross-validation (k = ...
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