###### Abstract
[PERSON] [28] made use of the traditional Fourier transform to analyze the characteristics of the original signal in the frequency domain, and then constructed a 2D-CNN to locate the damage to CFRP structures. [PERSON] [29] used a 1D-CNN for the damage detection in thin aluminum plates, and the damage location error was about 7%. [PERSON] [30] used two independent CNNs to determine the existence and location of the damage, which adds a lot of work. However, none of the above studies can simultaneously identify the damage location and severity of the damage. In the field of structural health monitoring, accurate damage severity identification is the foundation of structural integrity assessment and preventive maintenance [31]. The current damage diagnosis models all perform a single damage diagnosis task, and different damage diagnosis tasks require different model structures. Although this can realize damage diagnosis, it ignores the connection between different damage diagnosis tasks, which will occupy a large amount of hardware and software resources in practical applications, resulting in low computational efficiency. In multi-task learning, it is mainly considered that different tasks actually have certain correlations [32]. Multi-task learning trains the model parameters of multiple tasks at the same time, forcing the model to learn the features and complementary information shared among the tasks, and the tasks play the role of regularization, thus reducing the risk of overfitting and improving the generalization ability [33]. Generally multi-task learning outperforms single-task learning due to the presence of implicit data augmentation mechanisms in multi-task learning, and multi-task learning effectively increases the number of training instances. The actual collection of Lamb wave signals is inevitably affected by changes in the surrounding environment and sensor acquisition errors, resulting in a certain degree of noise in the collected Lamb wave signals. When we train the model only on the impact damage location task, our goal is to obtain good accuracy on the impact damage location task, and it is easy to ignore the noise and generalization performance associated with the data and assume the risk of overfitting on the impact damage location task. Similarly, when we train the model only on the impact damage severity task, our goal is to obtain good accuracy on the impact damage severity task, easily ignoring the noise and generalization performance associated with the data and taking the risk of overfitting on the impact damage severity task. The impact damage location task and the impact damage severity task have different noise patterns, and learning both tasks at the same time averages out the noise patterns, reduces the risk of overfitting, and results in better model performance. Therefore, an impact damage monitoring method based on a multi-task 2D-CNN is proposed in this paper. The interaction mechanism of Lamb waves with different impact locations and severities is studied on the test platform. To realize multi-sensor data fusion, the original impact damage data are transformed into a grayscale image by a novel image processing method. A monitoring frame capable of simultaneously detecting impact position and energy is built through a multi-task 2D-CNN. This network structure has multiple impact damage detection tasks, part of the network structure and parameters are shared, and the commonness and characteristics of each task can be learned. This network structure considers the link between the impact damage location identification task and the impact damage energy identification task by extracting features common to both and suppressing features that are not relevant to task identification. Subsequently, each task-specific network utilizes the common features before continuing to learn, resulting in improved recognition accuracy for each task. In addition, the prior knowledge of wave velocity and the expert knowledge of feature extraction are no longer necessary information.