## 1 Introduction  
In the past few decades, structural health monitoring (SHM) technology has made remarkable advancements, leading to the widespread deployment of numerous monitoring systems and, consequently, resulting in a significant surge in monitoring data [1, 2, 3]. However, the harsh operational environments, sensor malfunctions, calibration, noise, and transmission errors inevitably introduce anomalies into the data, which can significantly affect the extraction of the structural feature information. Consequently, this may lead to inaccurate assessments and conclusions and even result in severe misjudgments and accidents. Currently, the issues of data quality and anomaly identification caused by massive data growth have garnered significant attention and have become an area of extensive research [4]. As mentioned earlier, anomalies in structural health monitoring data can be categorized into two types. The first type results from structural deterioration or damage, leading to abnormal variations in monitoring data. Such anomalies are primarily identified through the exploration of indicators representing structural characteristics [5, 6, 7, 8]. The second type arises due to data quality issues, causing monitoring data to exhibit features distinct from normal data. Anomaly detection for this category largely relies on data-driven approaches. One cost-effective solution to enhance the data-cleaning capability of existing traditional sensors is to implement data preprocessing on raw output data for data anomaly detection instead of integrating hardware modules and analysis software, which incurs high costs for self-validation capabilities [9]. For time-invariant structures, establishing input-output mapping models is one of the most common methods used to represent data anomalies. However, in civil engineering, the influence of environmental factors and noise makes it extremely challenging to construct accurate models, rendering data-driven methods the preferred choice. The research core of data-driven methods involves data preprocessing, feature extraction, anomaly detection, and classification. Traditional data anomaly detection relies on features such as statistical characteristics [10; 11]. In recent years, with the rapid development of machine learning and deep learning technologies, these techniques have found widespread applications in civil engineering [12; 13]. Notably, object detection technology has become a typical and practical solution in various scenarios, including vehicle trajectory recognition [14; 15], the dynamic weighing of bridges [16], and active collision prevention for ships [17]. In the domain of data anomaly detection, high-sampling-rate acceleration data generate a large volume of complex and diverse anomaly features. Furthermore, different facilities may exhibit varying types of equipment data anomalies, making it challenging to achieve uniformity with anomaly types [18; 19]. Researchers have conducted extensive studies to address the challenges posed by large datasets, diverse anomaly features, and numerous anomaly categories in data cleaning. Among various approaches, supervised learning algorithms have been widely applied. [PERSON] et al. [18] transformed acceleration time-series data into two-dimensional images and utilized deep neural networks for data anomaly detection and classification, which were validated using monitoring data from long-span, cable-stayed bridges. [PERSON] et al. [20] enhanced anomaly detection accuracy by incorporating the frequency domain characteristics of acceleration data alongside time series. They employed convolutional neural networks with a dual-channel input consisting of time series and spectrogram images. However, they encountered severe misclassification issues between certain categories, particularly for trend and drift types. [PERSON] et al. [21] addressed misclassification problems by introducing probability density function (PDF) features.