Broken Rotor Bar Detection in Induction Motors using Digital Taylor-Fourier Transform

Abstract

Bar damage is one of the most frequent faults in induction machines. The bar can be partially or fully broken, and the damage can appear in more than one bar. This type of dam-age may cause adverse effects as temperature rise, mechanical stress, frequency variation, increase in electricity consumption, and increase in motor vibrations, among others. Therefore, to schedule maintenance operations and accelerate repair processes, an opportune detection and classification of faults are imperative. This goes in concordance with the philosophy of electrical systems in the world, which consists of guiding them towards the concept of intelligent systems based on algorithms to track the dynamics of the systems accurately. This paper focuses on a motor current signature analysis through the Digital Taylor-Fourier transform, aiming to apply the digital Taylor-Fourier filters in the spurious frequencies, with the final purpose to reconstruct the filtered signal to obtain its frequency spectrum and, through statistical methods, identify in a precise way the type of bar damage. The proposed methodology is conducted in Matlab and evaluated for a group of data corresponding to a motor with one broken bar under 3/4 and 1/2 load conditions.