Novel Methodology for Online Half-Broken-Bar Detection on Induction Motors

TL;DR: A novel methodology for half-broken-bar detection is presented, which combines current and vibration analysis by correlating the signal spectra to enhance detectability for mechanically loaded and unloaded operating conditions of the motor, which the other isolated techniques are unable to detect.

Abstract: The relevance of the development of monitoring systems for rotating machines is not only the ability to detect failures but is also how early these failures can be detected. Squirrel-cage induction motors are the most popular motors used in industry, consuming around 85% of the power in industrial plants. Broken rotor bars in induction motors are among the major failures that are desirable to detect at early stages because this failure significantly increases power consumption and is responsible for further damage to the machinery. Previously reported works base their analysis on current or vibration monitoring for broken-bar detection up to one broken bar under mechanically loaded motor conditions. The contribution of this paper presents a novel methodology for half-broken-bar detection, which combines current and vibration analysis by correlating the signal spectra to enhance detectability for mechanically loaded and unloaded operating conditions of the motor, which the other isolated techniques are unable to detect. The proposed methodology is implemented in a low-cost field-programmable gate array (FPGA), giving a special-purpose system-on-a-chip (SoC) solution for online operation, with the development of a complex postprocessing decision-making unit. Several cases of study are presented to demonstrate the performance of the implementation.