  We introduce an approach for damage detection in gearboxes based on the
analysis of sensor data with the multi-resolution dynamic mode decomposition
(mrDMD). The application focus is the condition monitoring of wind turbine
gearboxes under varying load conditions, in particular irregular and stochastic
wind fluctuations. We analyze data stemming from a simulated vibration response
of a simple nonlinear gearbox model in a healthy and damaged scenario and under
different wind conditions. With mrDMD applied on time-delay snapshots of the
sensor data, we can extract components in these vibration signals that
highlight features related to damage and enable its identification. A
comparison with Fourier analysis, Time Synchronous Averaging and Empirical Mode
Decomposition shows the advantages of the proposed mrDMD-based data analysis
approach for damage detection.
