Simulating Torsional Vibrations of Faulty Bevel Gears Using the Polygonal Contact Method

Abstract

Gears are an integral component of electromechanical applications, but accurate condition monitoring methods, including data-driven predictive maintenance, are strongly dependent on high-quality data, especially from faulty components. To address the scarcity of data, we proposed a multibody simulation using an advanced polygonal contact method to replicate torsional vibrations from an experimental azimuth thruster test rig. The key novelty is the ability to simulate both healthy and faulty gears with arbitrary fault geometries. The simulated signals closely matched the measurements in both time and frequency domains. In the time domain, average torque levels and periodic fluctuations aligned well, although measured signals exhibited higher peak-to-peak amplitudes and greater noise, particularly in healthy conditions at lower rotational speeds. In the frequency domain, the simulations accurately reproduced expected fault frequencies and corresponding sidebands, with larger faults producing higher amplitudes. While the simulations tended to overestimate peak amplitudes and underestimate external noise, the results were highly comparable to measurements and consistent with the physical expectations. These findings provide a robust foundation for enhancing data-driven condition monitoring methods, particularly those employing machine learning or deep learning.