{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T20:56:07Z","timestamp":1773348967539,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003399","name":"project of Shanghai Science and Technology Commission","doi-asserted-by":"publisher","award":["21ZR1423800"],"award-info":[{"award-number":["21ZR1423800"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"project of Shanghai Science and Technology Commission","doi-asserted-by":"publisher","award":["JT2021-KY-013"],"award-info":[{"award-number":["JT2021-KY-013"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"name":"project of Shanghai Municipal Transportation Commission","award":["21ZR1423800"],"award-info":[{"award-number":["21ZR1423800"]}]},{"name":"project of Shanghai Municipal Transportation Commission","award":["JT2021-KY-013"],"award-info":[{"award-number":["JT2021-KY-013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s23083910","type":"journal-article","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T02:57:08Z","timestamp":1681268228000},"page":"3910","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["DCFF-MTAD: A Multivariate Time-Series Anomaly Detection Model Based on Dual-Channel Feature Fusion"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7574-3894","authenticated-orcid":false,"given":"Zheng","family":"Xu","sequence":"first","affiliation":[{"name":"SHU-SUCG Research Centre of Building Information, Shanghai University, Shanghai 201400, China"},{"name":"SILC Business School, Shanghai University, Shanghai 201800, China"}]},{"given":"Yumeng","family":"Yang","sequence":"additional","affiliation":[{"name":"SHU-SUCG Research Centre of Building Information, Shanghai University, Shanghai 201400, China"},{"name":"SILC Business School, Shanghai University, Shanghai 201800, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2779-7027","authenticated-orcid":false,"given":"Xinwen","family":"Gao","sequence":"additional","affiliation":[{"name":"SHU-SUCG Research Centre of Building Information, Shanghai University, Shanghai 201400, China"},{"name":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China"}]},{"given":"Min","family":"Hu","sequence":"additional","affiliation":[{"name":"SHU-SUCG Research Centre of Building Information, Shanghai University, Shanghai 201400, China"},{"name":"SILC Business School, Shanghai University, Shanghai 201800, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep learning for time series classification: A review","volume":"33","author":"Forestier","year":"2019","journal-title":"Data Min. 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