Nonlinear damping identification using extended continuous wavelet transform and long-short term memory: Application to a spur gear pair system

Damping significantly affects the dynamic analysis of spur gear pair systems. The identified damping ratios may suffer from instability owing to many reasons, such as time-varying conditions and nonlinear effects. Long-short-term memory (LSTM) has been developed for damping model identification in systems with nonlinear behavior. However, owing to the poor quality of the input data, the identification results may not be reliable. In this regard, this study proposes a hybrid technique based on the Continuous Wavelet Transform (CWT) and LSTM methods. The major novelty of this study is the utilization of the time-frequency information provided by the CWT as the input data. Therefore, the LSTM network was fed these extracted features to enhance noise attenuation and improve the robustness and stability of nonlinear damping identification. Thus, the CWT technique is used as a preprocessing tool for the observed signals, enhancing the data quality by reducing the influence of noise. To verify the effectiveness of the proposed CWT–LSTM approach for damping identification, CWT representations of the simulated spur gear pair system response were used in a series of analyses. The numerical results indicate that the combined CWT–LSTM approach provides more reliable and accurate nonlinear damping estimation than the conventional LSTM model. This methodology has a strong potential for the accurate identification of damping in gear transmission systems.