Detection of Broken Rotor Bars in Induction Machines using Machine Learning Methods
Stefan Quabeck, Wenbo Shangguan, Daniel Scharfenstein, Rik W. De Doncker, 2020 23rd International Conference on Electrical Machines and Systems (ICEMS), 24-27 November 2020, doi: 10.23919/ICEMS50442.2020.9291033
Induction machines are used in a wide range of industrial applications due to their simplicity, ruggedness, and low cost. Despite their robustness, induction machines eventually fail at one point due to a variety of failure mechanisms. Many faults exhibit specific fault frequencies in the motor current spectrum, which allows for fault detection. Many classical fault detection methods have been developed for grid-connected machines with quasi-static 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 work applies a combination of classical fault detection approaches with different 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 5.5kW induction machine. The algorithm shows promising fault detection and classification capabilities and achieves an accuracy of 97.4%.