SHU-SUCG Research Centre of Building Information, Shanghai University, Shanghai 201400, China.
SILC Business School, Shanghai University, Shanghai 201800, China.
Sensors (Basel). 2023 Apr 12;23(8):3910. doi: 10.3390/s23083910.
The detection of anomalies in multivariate time-series data is becoming increasingly important in the automated and continuous monitoring of complex systems and devices due to the rapid increase in data volume and dimension. To address this challenge, we present a multivariate time-series anomaly detection model based on a dual-channel feature extraction module. The module focuses on the spatial and time features of the multivariate data using spatial short-time Fourier transform (STFT) and a graph attention network, respectively. The two features are then fused to significantly improve the model's anomaly detection performance. In addition, the model incorporates the Huber loss function to enhance its robustness. A comparative study of the proposed model with existing state-of-the-art ones was presented to prove the effectiveness of the proposed model on three public datasets. Furthermore, by using in shield tunneling applications, we verify the effectiveness and practicality of the model.
多元时间序列数据中的异常检测在复杂系统和设备的自动化和连续监测中变得越来越重要，这是由于数据量和维度的快速增加。针对这一挑战，我们提出了一种基于双通道特征提取模块的多元时间序列异常检测模型。该模块分别使用空间短时傅里叶变换（STFT）和图注意网络来关注多元数据的空间和时间特征。然后融合这两个特征，显著提高了模型的异常检测性能。此外，该模型还采用 Huber 损失函数来增强其鲁棒性。通过与现有最先进的模型进行比较研究，证明了所提出的模型在三个公共数据集上的有效性。此外，通过在盾构隧道应用中使用，验证了模型的有效性和实用性。
