Detection of broken rotor bars in induction machines using machine learning methods

Abstract

Induction machines are used in a wide range of industrial applications due to their simplicity, ruggedness, and low price. Despite their robustness, induction machines eventually fail due to a variety of mechanisms. Most faults exhibit specific frequency components in the motor current spectrum, which allows for fault detection. Many classical fault detection methods have been developed for grid-connected machines with relatively fixed operating points. In inverter-driven machines with a wide operating range, these methods cannot reliably detect and classify faults. Machine learning methods have been successfully used for various classification tasks. This study therefore combines classical fault detection approaches with various fault classification algorithms to reliably detect induction machine faults over a wide operating range. The developed fault classification method is evaluated using steady-state measurements on an inverter-fed 5.5kWinduction machine. The algorithm shows promising fault detection and classification capabilities, achieving an accuracy of 97.4% over a wide load range.