Abstract
The method of obtaining displacement through acceleration integration is widely used. To accurately obtain the vibration displacement of the vibration screening excitation platform, in this paper, the preprocessing method (EBKA-TVF-EMD) involving the use of enhanced black-winged kite algorithm (EBKA) to optimize the time-varying filtering based empirical mode decomposition (TVF-EMD) is proposed. First, the EBKA introduces the random number I and a Gompertz bacterial growth model to dynamically adjust the step size factor, enhancing global search capabilities and convergence stability, while avoiding local optima. Compared to traditional optimization algorithms, EBKA achieves superior performance and adaptability. Then, the EBKA is utilized to find the optimal combination of parameters of TVF-EMD (i.e., B-spline function and the bandwidth threshold). Simulation results confirm that EBKA-TVF-EMD effectively reconstructs acceleration signals, with significantly lower errors than conventional methods (TVF-EMD and PSO-TVF-EMD), even under varying noise and trend conditions. Finally, the vibration displacement signal is obtained by performing frequency-domain integration (FDI) on the reconstructed signal. Experimental validation on a vibration screening excitation platform further demonstrated the method's effectiveness: the reconstructed displacement signal yielded a double amplitude of 11.4 mm, with a relative error of only 1.75 % compared to the actual value (11.2 mm). This demonstrates that the proposed method not only enhances signal decomposition accuracy but also meets engineering requirements for precise displacement measurement.