###### Abstract  
High-speed trains may be subjected to various forms of physical impacts during long-term operation, causing structural damage and endangering driving safety. Therefore, impact damage monitoring remains a daunting challenge for the stable operation of high-speed train structures. The existing methods cannot simultaneously detect the location and severity of impact damage, which poses challenges to structural integrity assessment and preventive maintenance. This article proposes an impact damage monitoring method based on multi-task 2D-CNN. Sensor data fusion is performed using a 2D image processing method to convert a 1D impact damage signal into a 2D grayscale image. The fused grayscale image contains information related to the location and severity of impact damage. A damage detection framework was established using multi-task 2D-CNN for feature extraction, impact location classification, and impact energy quantification. This model can learn the commonalities and characteristics of each task by sharing network structure and parameters and can effectively improve the accuracy of each task. Compared with single-task learning, multi-task learning performs better on the metrics of the impact location task recognizing the impact energy task and reduces the training time by 30.83%. With a reduced number of samples, the performance of multi-task learning is more stable and can still effectively identify the location and severity of impact damage. structural health monitoring; Lamb wave; CNN; multi-task learning; impact damage +
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pointed out the limitation that only when the sensor network has sufficient density can the imaging algorithm ensure the impact damage location accuracy. Although the TDOA method is widely used, the positioning accuracy depends on the prior information on Lamb wave propagation speed in the structure [11]. However, the propagation speed of Lamb waves in the structure is usually unknown and changes rapidly with changes in the environment, such as temperature or applied stress [12; 13], which are difficult to estimate. In addition, these methods need to extract damage characteristics from complex signals and then establish a physical formula with clear physical significance to determine the damage location. Commonly used damage indicators are time of flight (TOF) [14], normalized amplitude [15], and energy ratio [16]. Frequently, the extraction of these features requires mastering the traditional signal processing methods and acquiring the professional knowledge of experts in relevant fields. However, Lamb wave signals are formed by coupling direct waves, various reflected waves, and noise through a nonlinear system, which makes it challenging to extract appropriate features based on experience. So, we certainly need to create a data-driven impact damage monitoring method without prior knowledge. Deep learning is widely used in SHM due to its ability to construct complex nonlinear mapping relationships [17; 18; 19; 20; 21]. The convolutional neural network (CNN) is a kind of deep neural network that can learn the relevant characteristics from massive historical data to reduce the workload of damage feature selection and solve the disadvantages of the conventional means of relying on expert experience [22; 23; 24; 25]. In the field of impact monitoring, by stacking the network layer of the CNN, it is possible to realize the automatic extraction of deep-level features based on massive historical data without the need for a priori physical information such as wave speed and empirical feature knowledge to identify the location of the impact damage and the impact damage energy. [PERSON] [26] used continuous wavelet transform to transform the original data into a two-dimensional time-frequency image and applied a 2D-CNN to identify the damage location. [PERSON] [27] put forward a model based on time-varying damage characteristics combined with a 1D-CNN to locate the damage.