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DCFF-MTAD: A Multivariate Time-Series Anomaly Detection Model Based on Dual-Channel Feature Fusion.
Xu, Zheng; Yang, Yumeng; Gao, Xinwen; Hu, Min.
Affiliation
  • Xu Z; SHU-SUCG Research Centre of Building Information, Shanghai University, Shanghai 201400, China.
  • Yang Y; SILC Business School, Shanghai University, Shanghai 201800, China.
  • Gao X; SHU-SUCG Research Centre of Building Information, Shanghai University, Shanghai 201400, China.
  • Hu M; SILC Business School, Shanghai University, Shanghai 201800, China.
Sensors (Basel) ; 23(8)2023 Apr 12.
Article in En | MEDLINE | ID: mdl-37112251
ABSTRACT
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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies Language: En Journal: Sensors (Basel) Year: 2023 Type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies Language: En Journal: Sensors (Basel) Year: 2023 Type: Article Affiliation country: China