Automated Condition Monitoring for Helical Gears based on measuring Instantaneous Angular Speed with Magnetoresistive Sensors

Abstract

In this work, a novel approach is presented to extract physically motivated features for damage detection of gears in single-stage gearboxes by an automated order analysis of the instantaneous angular speed. This extraction method was applied to measurement data of various magnetoresistive sensors installed in a gearbox test bed and the obtained characteristics were examined in validation scenarios for their susceptibility to external disturbances. The classification results were compared to results obtained with an automatic Machine Learning (ML) method both on the data of the magneto-resistive sensors and an accelerometer. The new method has major advantages, especially with respect to transferability to other rotational speeds.