Evaluating Signal Processing Methods for Instantaneous Frequency Analysis in Time-Varying Mass Structures
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Time-varying mass (TVM) structures exhibit complex dynamic phenomena but remain insufficiently investigated, particularly in the frequency domain. For example, granular discharge in silos generates vibrations due to rapid mass reduction, leading to nonlinear and non-stationary responses. This study aims to evaluate the capability of three common signal processing algorithms-Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), and Empirical Wavelet Transform (EWT)-for analysing instantaneous frequency variations in TVM structures. The signals are first decomposed into mono-component modes and subsequently analyzed using the Hilbert transform to extract instantaneous frequency. The investigation is conducted in two stages: (i) numerical validation using an artificial nonlinear signal and a time-varying parameter SDOF system with known frequency histories, and (ii) application to experimental acceleration data obtained from sand discharge in a polycarbonate silo under noisy conditions. The findings show that EMD provides the most accurate frequency estimation for clean signals, whereas VMD and EWT demonstrate improved stability for experimental data with significant noise. The study provides a systematic comparison of decomposition-based instantaneous frequency methods in TVM structures and highlights the importance of appropriate method selection for safer and more reliable frequency-domain structural design.
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