## 3 Experiment and Results
### Discrete Wavelet Transform--Based Metal Material Analysis Model  
The identification method and steps of the metal material analysis model are shown in Figure 3.  
In this paper, the characteristics of the signal in the time domain and frequency domain are analyzed, different eddy current characteristics are extracted from time and frequency domain, and the calculation model of the characteristic evaluation index is constructed to calculate the eddy current characteristic index. The consistency between the measured metal and the known metal materials is judged by comparing the metal eddy current characteristic database. Wavelet transform is an algorithm suitable for non-smooth signal analysis. It can decompose a given time-domain signal into several sets of wavelets carrying different frequency information. That means that the wavelet transform analysis method can be  
Figure 3: Identification method and steps of the eddy current method metal material analysis model. used to control the frequency components in the signal very well. It should be noticed that, when the wavelet reconstruction is performed, the reconstruction coefficients should be optimized for the specific length of the data. If too much low-frequency information is retained in the reconstructed signal, the jitter from signal acquisition cannot be removed. If the reconstructed signal retains too much high-frequency information, the effect of high frequency noise on the signal cannot be removed. However, if too little high-frequency or low-frequency information is retained, the signal will be severely distorted, affecting the feature extraction of the signal. The dbN wavelet has a better regularity: that is, the smooth error introduced by this wavelet is not easily detected as a sparse basis, making the signal reconstruction process relatively smooth. The characteristic of the dbN wavelet is that the order of vanishing moment increases with the increase of order (sequence N). The higher the vanishing moment, the better the smoothness, the stronger the localization ability of frequency domain, and the better the division effect of the frequency band [34]. In this paper, the wavelet base adopted in the experiment is db4; the approximate coefficient in level six is used for signal reconstruction with better signal-to-noise ratio (SNR). All the parameters used in the following analysis are based on the approximate coefficient at level six in db4. A comparison chart of the raw signal measured in air and the signal after wavelet transformed is show in Figure 4. It can be seen that high frequency noise has been filtered; this will affect classification accuracy. In this paper, the signals are evaluated in terms of peak frequency, peak time, zero crossing time, time of decay, main peak area and amplitude of attenuation, respectively. In selecting the characteristic parameters, 1000 independent measurements of the metal under test were performed in a random scan to obtain eddy current characteristic samples for the four standard metals (Cu, Fe, Al and St) and three standard metals of Fe (Fe-K162, Fe-K163 and Fe-K240) studied in this paper. Six extracted parameters of Fe samples are shown in Figure 5. It can be seen that Fe-K162, Fe-K163 and Fe-K240 can be well classified with parameters of zero crossing time, time of decay and main peak area. However, Fe-K162, Fe-K163 and Fe-K240 have a light  
Figure 4: Comparison of the signals before and after wavelet transformed. overlap in the feature of zero crossing time and time of decay. It is seen that, for each extracted feature, the tendency of the feature values varies with the material is different. This may be affected by the content of the elements in metallic alloys. From Table 1, it is seen that the iron element proportions of Fe-K162, Fe-K163 and Fe-K240 are 93.97%, 93.994% and 94.326%, respectively. The average values of the time of delay feature corresponding with iron element proportions are 318, 324 and 335, respectively; these have the same monotonicity compared to iron element proportions.