## 2 Literature Review  
### Anomaly Detection  
Anomaly detection methods can usually be divided into supervised learning and unsupervised learning according to the availability of sample labels in the dataset. Supervised anomaly detection methods refer to model training using a labeled dataset, where the target output labels indicate whether a sample is normal or abnormal. [PERSON] et al. [19] used a convolutional neural network (CNN) to extract features from normal data and the data of known fault types in both temporal and spatial dimensions. This model was employed to address anomaly detection problems in chemical production processes. [PERSON] et al. [20] transformed time-series data into two-dimensional grayscale images, extracted features of four known faults, and implemented fault identification for wind turbines based on a CNN. [PERSON] et al. [21] introduced a hierarchical clustering approach to the random forest model and constructed a multi-class anomaly classification model using a new random forest model that can combine decision tree selection and weighted voting fusion strategy. In industrial fields where the types of anomalies are relatively limited, supervised-based anomaly detection methods usually work better and can make better use of labeled data for modeling. However, the amount of data generated during the shield tunneling process is relatively large, the proportion of abnormal data is small, and the types of anomalies are diverse, resulting in sample imbalance in the dataset. Therefore, using supervised learning for anomaly detection in shield tunneling faces greater challenges. Unsupervised learning typically only requires normal data as input without the need for annotated abnormal samples, mainly including proximity-based and reconstruction-based methods [22]. Proximity-based methods usually rely on distance or similarity calculations between data points and assume that normal and abnormal data reside in different regions. They define anomaly scores according to the degree to which the data are close to the normal and abnormal regions. One Class SVM (OCSVM) only needs to learn the decision boundary of the normal data and detect any data falling outside the decision boundary as an anomaly [23]. [PERSON] et al. [24] proposed an anomaly detection algorithm based on the Local Outlier Factor (LOF), which calculates the local density between each data point and its neighboring points. If a data point has a low local density while its neighboring points have high local densities, it is classified as an anomaly. Reconstruction-based methods include autoencoders (AEs), generative adversarial networks (GANs), and their variations. Among them, AE-based methods have made significant progress in anomaly detection in recent years and have been widely applied due to their advantages in representation learning. When using these methods to solve anomaly detection problems, they use normal data for training and learn the distribution characteristics of normal data so that the model can reconstruct normal data with a small reconstruction error. However, when the input data contains anomalies, the model cannot reconstruct it effectively, resulting in more reconstruction errors [25]. [PERSON] et al. [26] proposed an improved unsupervised anomaly detection method based on autoencoders by reconstructing the loss function. They trained the model using fault-free data to learn the features of normal samples and constructed a model capable of accurately identifying anomalies of cutting tools. Compared with an ordinary AE structure, the VAE (Variational Autoencoder) model has better noise robustness through latent variable modeling, sample generation ability, and latent space continuity assumption. Therefore, it is more suitable for anomaly detection in industrial fields [27]. [PERSON] et al. [28] proposed an anomaly detection method based on VAE and validated its effectiveness on three different engineering datasets, showing significant improvement compared to traditional autoencoders.