Kernel Regression Residual Decomposition-Based Polynomial Frequency Modulation Integral Algorithm to Identify Physical Parameters of Time-Varying Systems under Random Excitation

The physical parameters (stiffness, damping) of time-varying (TV) systems under random excitation provide valuable information for their working condition but they are often overwhelmed by noise interference. To overcome this problem, paper presents a novel multi-level kernel regression residual decomposition method, which can not only effectively separate each modal component from the raw vibration acceleration signal, also eliminate Additionally, multiple degree-of-freedom (DOF) parameter identification problem is transformed into single DOF problem. Combined with derived polynomial frequency modulation integral algorithm and cross-correlation theory based on fractional Fourier ambiguity function, method proposed. provides new idea in modeling TV identifying excitation. demonstrate effectiveness proposed numerical simulations conducted three different cases variation (variation, quadratic variation, periodic variation) time. Moreover, its robustness evaluated adding signal-to-noise ratio levels (20 dB, 50 100 dB) to input signal. analysis results confirm performance