{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T17:44:10Z","timestamp":1785174250750,"version":"3.55.0"},"reference-count":29,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:00:00Z","timestamp":1693785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Liaoning Province","award":["2023-MS-241"],"award-info":[{"award-number":["2023-MS-241"]}]},{"name":"Natural Science Foundation of Liaoning Province","award":["61901283"],"award-info":[{"award-number":["61901283"]}]},{"name":"National Natural Science Foundation of China","award":["2023-MS-241"],"award-info":[{"award-number":["2023-MS-241"]}]},{"name":"National Natural Science Foundation of China","award":["61901283"],"award-info":[{"award-number":["61901283"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In low-voltage distribution systems, the load types are complex, so traditional detection methods cannot effectively identify series arc faults. To address this problem, this paper proposes an arc fault detection method based on multimodal feature fusion. Firstly, the different mode features of the current signal are extracted by mathematical statistics, Fourier transform, wavelet packet transform, and continuous wavelet transform. The different modal features include one-dimensional features, such as time-domain features, frequency-domain features, and wavelet packet energy features, and two-dimensional features of time-spectrum images. Secondly, the extracted features are preprocessed and prioritized for importance based on different machine learning algorithms to improve the feature data quality. The features of higher importance are input into an arc fault detection model. Finally, an arc fault detection model is constructed based on a one-dimensional convolutional network and a deep residual shrinkage network to achieve high accuracy. The proposed detection method has higher detection accuracy and better performance compared with the arc fault detection method based on single-mode features.<\/jats:p>","DOI":"10.3390\/s23177646","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T10:24:30Z","timestamp":1693823070000},"page":"7646","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Series Arc Fault Detection Based on Multimodal Feature Fusion"],"prefix":"10.3390","volume":"23","author":[{"given":"Na","family":"Qu","sequence":"first","affiliation":[{"name":"School of Safety Engineering, Shenyang Aerospace University, Shenyang 110136, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenlong","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Safety Engineering, Shenyang Aerospace University, Shenyang 110136, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congqiang","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Safety Engineering, Shenyang Aerospace University, Shenyang 110136, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,4]]},"reference":[{"key":"ref_1","unstructured":"Fire Department of the Ministry of Public Security (2018). 2018 China Fire Yearbook, Yunnan People\u2019s Publishing House."},{"key":"ref_2","unstructured":"Bureau of Fire and Rescue, Ministry of Emergency Management (2022, February 18). 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