Lvq-type classifiers for condition monitoring of induction motors: a performance comparison

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.