Predictive Modeling of a Two-Stage Gearbox towards Fault Detection

This paper presents a systematic approach to the modeling and analysis of a benchmark two-stage gearbox test bed to characterize gear fault signatures when processed with harmonic wavelet transform (HWT) analysis. The eventual goal of condition monitoring is to be able to interpret vibration signals from nonstationary machinery in order to identify the type and severity of gear damage. To advance towards this goal, a lumped-parameter model that can be analyzed efficiently is developed which characterizes the gearbox vibratory response at the system level. The model parameters are identified through correlated numerical and experimental investigations. The model fidelity is validated first by spectrum analysis, using constant speed experimental data, and secondly by HWT analysis, using nonstationary experimental data. Model prediction and experimental data are compared for healthy gear operation and a seeded fault gear with a missing tooth. The comparison confirms that both the frequency content and the predicted, relative response magnitudes match with physical measurements. The research demonstrates that the modeling method in combination with the HWT data analysis has the potential for facilitating successful fault detection and diagnosis for gearbox systems.