Bearing and gear monitoring under variable speeds and loads using regime normalization

In practice, rotating machines operate under real-time variable speed and load conditions, particularly in railways, wind turbines, robotics, etc. In the presence of faults, the degradation of critical components is significantly accelerated, making the developments of robust monitoring algorithms crucial to be able to identify these faults with regard to regime variation. For this purpose, a signal processing methodology is proposed. It is based on regime normalization that allows the grouping of different regimes belonging to the same health state. It consists of a thresholding method that detect condition variation and uses distance metrics for normalization. This, allows reducing dispersion between the class observations while separating the classes representing different health states. To verify the performance of the methodology, current, voltage and vibration data from a gearbox system under variable speeds and loads are used.