## 3 Experiment  
In this section, related experiments were carried out and the original Lamb wave data set was collected. A steel plate with dimensions 300 mm \(\times\) 300 mm \(\times\) 2 mm was considered as the specimen. The mechanical properties are shown in Table 1. As shown  
Figure 3: The structure of multi-task 2D-CNN. in Figure 4, 8 PZT sensors, namely, PZT1-PZT8, were pasted on the surface of the steel plate. Table 2 shows the parameters of the PZT sensor. The sensor network composed of 8 PZT sensors covered the entire impact area. The impact damage was conducted in a 160 mm square and divided into 16 subregions; each was a 40 mm square, numbered L1-L16, representing different damage locations. Damage degree is very important for structural health assessment and is an indispensable assessment index. Therefore, this paper used spring impact hammers with energies of 0.5, 1.0, and 1.5 J to apply impact loads to simulate impact damage at different severities. The SHM system (Nanjing Smart Monitoring Technology Co., Ltd., Nanjing, Jiangsu Province, China) was used to collect Lamb wave signals. The acquisition frequency was set to 100 kHz to sample 5000 data points. This signal length can cover most impact signals. The specific experimental settings are shown in Figure 5.  
Each subregion was subjected to 50 repeated impacts with the same energy level, resulting in a total of 50 \(\times\) 3 \(\times\) 8 \(\times\) 16 Lamb wave signals. Some of the original signals collected are shown in Figure 6. Figure 6a shows the raw signals collected by each sensor after impact with 0.5, 1.0, and 1.5 J impact hammers at position L1, and Figure 6b shows the raw signals collected by each sensor after impact with 0.5, 1.0, and 1.5 J impact hammers at position L16. Figure 6 shows that the changes in impact damage location and energy were not intuitive to the change in original signal. Using the original signal to estimate the location and energy of impact damage was difficult. In addition, the sensitivity of different PZT sensors to the impact changes was inconsistent. When the impact position was closer to a PZT sensor, the changes in received Lamb wave signals caused by impact energy were more serious than other sensors. Therefore, imaging processing was generally required to achieve multi-sensor data fusion. Figure 6: Lamb wave signals are received by sensor arrays at different impact energies. (**a**) Impact damage at location L1; (**b**) impact damage at location L16. In order to achieve multi-sensor data fusion, the signal processing method mentioned in Section 2.2 was used to convert the collected original signal into the gray image as the input dataset of the multi-task 2D-CNN. In the grayscale image, eight vertical dividing lines can be clearly observed, representing the impact signals received by the sensors numbered 1-8 in order from left to right. The grayscale values between each demarcation line were obtained by linear interpolation to generate image points during the stage of drawing the 3D surface map, followed by projection transformation and grayscale transformation. The magnitude of the grayscale value is affected by the impact energy and the impact location. The grayscale values on the dividing line represent the magnitude of the Lamb wave amplitude received by the sensor, and the grayscale values between the dividing lines preserve the effect of the change in impact location. The relative position relationship of the gray value contains the time domain information of the Lamb wave, such as the propagation time and arrival time, which contains the information about the impact location. Figure 7a-c shows the grayscale images obtained by processing the impacts of the three impact hammers of 0.5, 1.0, and 1.5 J at the L1 position, respectively. Since the PZT 1 is the closest to the L1 position, the amplitude of the signal received by the PZT 1 is the largest, and so the image points between the first and second dividing lines in the grayscale images have the darkest color.