## 1 Introduction
However, this method is related to plate structure and material, and thus it has poor universality. There are advantages to studying signal characteristic change from a frequency domain, such as anti-interference and correlation between characteristic change and frequency. However, there have been few reports on extracting damage information directly from the frequency domain of single sensing data because the Lamb wave carrying damage information is a typical nonstationary signal, and signal processing based on Fourier transform cannot truly reflect the change characteristics of a damage signal. However, time-frequency analysis methods, such as Wigner Ville distribution, short-time Fourier transform, and wavelet transform, have limitations in theory and make it difficult to support in-depth analysis of the changes of the active Lamb wave monitoring signal [18; 19]. Therefore, in the research of active Lamb wave monitoring technology, finding an advanced signal processing method to extract damage information directly from a single sensing signal has been a hot spot of structural health monitoring research. Hilbert-Huang transform (HHT) represents a signal processing method that uses a signal extreme point to decompose the signal adaptively and then analyzes instantaneous signal characteristics using Hilbert transform (HT) [20]. Moreover, it is a common method for nonstationary signal processing. [PERSON] et al. [18] stated that HHT has been a promising technique for extracting the properties of nonlinear and nonstationary signals. They used HHT to process the AE data of a spindle box and found that the marginal spectrum of acoustic emission had a better correspondence with the variation of spindle load, bearing, and base geometry with the temperature. [PERSON] et al. [19] suggested using the smoothed pseudo-Wigner-Ville distribution (SPWVD) and HHT for the analysis of body joint motion vibration signals. The time-frequency representation of the suggested methods was considered as a time-frequency image from which the statistical features based on the image pixels were extracted. The results showed that the highest classification accuracy of 88.76% was obtained when the features were extracted by the HHT. [PERSON] et al. [21] used HHT to process the sensing signals emitted by the mechanical looseness signals. The loosening signal with strong background noise was first decomposed by empirical mode decomposition (EMD), and then the instantaneous frequency of the first intrinsic mode function (IMF) was calculated to accurately determine the loose position. [PERSON] et al. [22] believed that HHT was a new method for time-frequency analysis. They used HHT to determine the variation in the normalized instantaneous frequency as an index vector of composite wing damage. While studying the simulated vibration signal of a composite wing, even when the effect of noise was considered, the index vector could still detect small damage (0.03% of the entire wing area) that caused a decrease in the elastic parameters. [PERSON] et al. (2019) compared the inherent strengths and weaknesses of CWT and HHT in quantifying the scale-and location-specific soil spatial variation. The HHT method showed superior performance in identifying the scales and locations of soil spatial variability over the CWT because HHT used a more flexible basis for interpreting real data and could deal with nonlinear problems. [PERSON] et al. (2019) applied HHT to analyze and process milling signals. After the signals were decomposed into IMFs by EMD, it was found that each one of them contained a unique vibration mode caused by machining, and thus every cutting phenomenon was detected easily in the Hilbert spectrum. [PERSON] et al. (2019) introduced HHT signal processing technology into the active Lamb wave based monitoring of plate structural health. In their method, the marginal spectrum of the monitoring signal was obtained and compared with the spectrum.