Chen Honglei, Liu Zenghua, Wu Bin, He Cunfu
Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China; Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Beijing 100124, China.
Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China; Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Beijing 100124, China.
Ultrasonics. 2021 Mar;111:106333. doi: 10.1016/j.ultras.2020.106333. Epub 2020 Dec 16.
Parameter estimation techniques based on chirplet models and intelligent algorithms can realize the simultaneous multi-information extraction of signals. They have attracted considerable attention for the processing of Lamb wave signals to detect defects and evaluate the material properties. Influenced by their dispersive nature, Lamb wave signals possess nonlinear instantaneous frequencies and asymmetric envelopes. However, the classical chirplet models are established with either Gaussian windows or linear chirps. They are inadequate for characterizing the dispersion features of ultrasonic signals whose excitations are modulated by Hanning windows. In our previous work, a nonlinear Hanning-windowed chirplet (NHWC) model with nine parameters was proposed to realize the full characterization of waveforms. However, the large number of parameters limits its application. A simple NHWC model with seven parameters was designed by submitting the same nonlinear phase modulation term into the Hanning-windowed sine function in this paper. Furthermore, a real-coded multi-objective genetic algorithm was developed to realize the parameter estimation of signals by combining a clustering algorithm and the NHWC model. Different strategies were adopted to ensure the convergence of the algorithm. The maximum extreme values were adopted to realize adaptive discretization of the search space and the updating of parameters. The parameters in the NHWC models were divided into implicit and explicit parts, and different strategies were applied to update them. The clustering algorithm and a sorting combination method were employed to generate a Pareto set. Experimental results showed that the parameter estimation with the simplified NHWC model exhibited a more robust performance than that of the model that contained nine parameters for characterizing the Lamb wave signals with or without the dispersion features. The arrival time, amplitude, and instantaneous frequencies of wave packets were identified with the parameter estimation technique.
