## 1 Introduction
Although this improvement alleviated the misclassification of trend and drift categories, the classification performance for minor and outlier categories remained limited. [PERSON] et al. [22] addressed data imbalance issues by utilizing unsupervised deep learning algorithms. They stacked time-series, power spectral density, and Gramian Angular Summation Field (GASF) features as a three-channel input, trained normal data using Generative Adversarial Networks (GAN), and used the output as input for convolutional neural networks to achieve data anomaly feature extraction and classification. Converting one-dimensional signals and their features into two-dimensional images to transform the data anomaly detection problem into an image feature recognition task has been a promising approach. However, due to the influence of image resolution on feature extraction and the lack of additional information in the process, employing 1D convolutional neural networks (CNNs) and statistical features for multi-class anomaly discrimination has proven to be an effective solution [19]. This method not only achieves high accuracy but also incurs lower training costs. Moreover, if the specific application scenario does not require categorizing data anomalies, the data anomaly detection task can be simplified into a binary classification problem [23; 24; 25]. Researchers [26] have effectively addressed the challenges of data imbalance and time-consuming label creation using unsupervised deep learning algorithms. By combining Gramian Angular Field (GAF) features with Generative Adversarial Networks (GANs) and autoencoders, they achieved data normality discrimination. The above studies demonstrate that utilizing general data features and dedicated models is an effective approach to enhance the generalization performance of data cleaning. [PERSON] et al. [10] found that by extracting appropriate data features and merging labels based on these features, higher accuracy in data anomaly detection can be achieved while effectively reducing input feature dimensions, which enhances computational speed. Similarly, [PERSON] et al. [11] employed statistical features of monitoring data to train random forest classifiers within decision trees, resulting in the transformation of raw data into statistical features and the subsequent reduction of input dimensions. By employing a hierarchical decision approach, an average classification accuracy of 98% was achieved. A multi-stage classification concept was introduced and validated in the anomaly classification of real bridge monitoring data [27; 28]. These studies indicate that establishing multi-stage classification models for specific anomalies can effectively enhance the accuracy of classification. Building upon these research foundations, this research establishes a two-stage framework for data anomaly classification. In the first stage, the model is based on the symmetry of acceleration signal amplitudes and frequency-domain characteristics, effectively identifying preliminary categories of data anomalies. In the second stage, a further classification is applied to an outlier category and a normal category, making the proposed framework capable of perceiving both macroscopic and detailed features. The structure of this paper is organized as follows: Section 2 introduces the data source, dataset composition, and data preprocessing methods employed. Section 3 outlines the framework and model training process of the proposed method. Section 4 presents the classification performance of the model. Section 5 concludes the entire study.