## 1 Introduction
These methods do not require data with abnormal labels and can achieve anomaly detection only by learning the features of normal data. There is an obvious imbalance between abnormal and normal data in the shield tunneling process, and the proportion of normal data is more significant. Therefore, compared with supervised-based anomaly detection algorithms, unsupervised-based anomaly detection algorithms are more suitable. The purpose of anomaly identification is to further analyze the nature or causes of anomaly, which involves identifying the specific abnormal scenario. Current research primarily relies on engineering experience and rule-based judgment to realize anomaly identification. Relevant studies record the occurrence process of anomalies in shield tunneling projects and describe the specific characteristics of parameters before and after the occurrence of anomalies. This provides directional guidance for anomaly identification in shield tunneling. [PERSON] [12] summarized the principles of hydraulic cylinder leakage, reversing valve leakage, and overflow valve leakage in the propulsion system of the shield machine based on expert experience. He then detected these anomalies based on if-then rules and a fuzzy inference engine. However, the shield tunneling process contains many parameters, and various abnormal scenarios involve complex correlations among multiple parameters. Traditional anomaly identification methods based on empirical rules may make it difficult to accurately describe the characteristics of different abnormal scenarios. The occurrence of anomalies is often accompanied by some changes in the characteristics of energy consumption. This point of view has been proved by many theories. Energy dissipation theory suggests that in normal operating conditions, the transformation and dissipation of energy in the system follow a stable process, while abnormal behavior may result in abnormal energy flow and dissipation [13, 14, 15]. Catastrophe Theory also demonstrates that anomalies can lead to the accumulation or release of energy, thereby causing a sudden change in energy consumption [15]. These theories provide a new perspective for anomaly detection. Existing studies have demonstrated the feasibility of using energy consumption to provide feedback on the operational state of the system. By monitoring the energy consumption in various stages of construction, different anomalies can be detected in time. [PERSON] et al. [16] used the energy consumption of the spindle as an indicator for the real-time monitoring of the CFRP drilling process and detected multiple types of anomalies in the drilling process. [PERSON] et al. [17] and [PERSON] et al. [18] collected and extracted energy consumption data features from systems operating in different states. They then constructed detection models using these features to differentiate between normal and abnormal operation modes of the systems, validating the effectiveness of using energy consumption data as the monitoring parameter. Therefore, the energy consumption in a system is often closely related to its stability. Using energy consumption as the detection target provides an effective solution to the selection of monitoring parameters for anomaly detection. In addition, since energy consumption can directly reflect the system's operational state, anomaly detection methods based on energy consumption can capture different abnormal scenarios, thereby enhancing the generalization ability of the anomaly detection methods. Inspired by this, energy consumption during shield tunneling process can serve as an indicator of anomalies. Compared with other operational data, it can make the anomaly detection model more suitable for detecting various abnormal scenarios. Therefore, herein, we analyze and monitor the shield tunneling process from the perspective of energy consumption. Based on the characteristics of the energy consumption data of shield tunneling, we design an anomaly detection model that can improve the accuracy of anomaly detection.