###### Abstract  
Structural health inspection systems are widely used to manage and maintain infrastructure that involves massive sensor devices. However, these sensors receive the natural environment or external factors in the long-term exposure to the outdoor environment, resulting in the failure of the sensors, which causes multiple categories of abnormal data in the collected data. The data often is unbalanced due to the random nature of failures. This unbalanced anomaly data poses a major challenge to existing anomaly detection methods and will affect the effectiveness of the information provided by the structural health monitoring system. In the paper, a data migration method is proposed to migrate bridge data to the target bridge dataset for expansion so that the number of images of different categories in the target bridge dataset increases. This method can be divided into three steps: firstly, to classify the data; secondly, to determine the suitability of the data and to construct the dataset; and finally, to train the data. The comparative validation is used to compare the training performance of the dataset using data migration with the dataset only using the target bridge to analyze the abnormal data identification in each category. In the experiment, the recall of some categories of data reached a significant increase of more than 30%, achieving better identification of various categories of abnormal data. Adopting the method of data migration between different bridges can solve the impact of imbalanced data and improve the recognition performance of categories with fewer images. structural health monitoring; anomaly detection; transfer learning; imbalanced dataset; data migration +
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in the research and evaluation that buildings are broken and damaged [12; 13]. Structural health inspection technology can make intelligent assessments and decisions on the health condition of civil infrastructure, such as damage destruction, performance degradation, and operation efficiency [14; 15]. It provides a scientific basis for the maintenance and management of engineering structures, life prediction, and security usage [16; 17]. The sensors installed on the structure collect each physical quantity to be sensed in real time, timed, triggered, or mixed mode, gathering a large amount of information over a long period of time. However, there is a lot of abnormal information contained in them due to their leakage in the outdoor environment during the structural health detection systems processes, which are easily affected by the surrounding environment, external forces, or its own factors such as sensor failure and abnormal transmission [18; 19]. For example, the signals collected by the sensors may be mixed with various noises due to the interference of the surrounding environmental factors, which makes it difficult to extract and analyze their valid information accurately. This cluttered information can interfere with normal analysis and may lead to incorrect judgments about the health of the engineering structure, which causes a lot of waste of human and material resources [20]. Anomaly detection (AD) techniques are required so that only high-quality data is available for analysis of the state of the structure [21; 22]. AD is an important process for SHM to cleanse data before implementing any diagnostic algorithms, which can analyze and clean the data to obtain high-quality data for effective analysis of structural health conditions. AD method based on convolutional neural network (CNN) is developed with the development of machine learning and deep learning. The CNN is a type of neural network designed to process data with a grid-like structure [23; 24]. It enables feature extraction of things through the role of CNN species convolutional layer, pooling layer, and fully connected layer [25]. Additionally, then the features are used to classify, identify, predict, or make decisions about the thing [26].