Broken Rotor Bar Detection by Image Texture Features and Fuzzy Logic

• Published in: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society Vol. 1; pp. 934 - 938
• Main Authors: Ferrucho-Alvarez, Edna R., Cabal-Yepez, Eduardo, Ledesma-Carrillo, Luis M., Martinez-Herrera, Ana L.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2019
• Subjects: Bars; Broken-rotor-bar detection; Feature extraction; Fuzzy logic; Fuzzy logic classifier; Image texture; Induction motors; Motor current signature analysis; Rotors; Steady-state; Texture image feature
• ISSN: 2577-1647
• DOI: 10.1109/IECON.2019.8927407

Induction motors (IM) are the most commonly used machines in industry; hence, early diagnosis of IM faults has become a subject of great interest for many researches, since if there is a fault and the IM keep working for long time, they and their associated systems might suffer catastrophic damages. Broken Rotor Bars (BRB) are among the most common and difficult to detect faults. Many methodologies based on Motor Current Signature Analysis (MCSA) for BRB detection have been proposed in recent years, which usually require long execution time and specialized hardware and software for their implementation. Therefore, in this work, a novel and straightforward methodology for BRB detection and classification during the induction motor steady state, based on MCSA, image texture feature (contrast), and a fuzzy logic classifier is proposed. Obtained results demonstrate the suitability of the proposed approach for identifying BRB with high effectiveness.