{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T19:55:42Z","timestamp":1776110142525,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T00:00:00Z","timestamp":1630627200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hyundai Robotics","award":["K2020120"],"award-info":[{"award-number":["K2020120"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We introduce a new approach for online and offline soft fault diagnosis in motor power cables, utilizing periodic burst injection and nonintrusive capacitive coupling. We focus on diagnosing soft faults because local cable modifications or soft faults that occur without any indication while the cable is still operational can eventually develop into hard faults; furthermore, advance diagnosis of soft faults is more beneficial than the later diagnosis of hard faults, with respect to preventing catastrophic production stoppages. Both online and offline diagnoses with on-site diagnostic ability are needed because the equipment in the automated lines operates for 24 h per day, except during scheduled maintenance. A 1D CNN model was utilized to learn high-level features. The advantages of the proposed method are that (1) it is suitable for wiring harness cables in automated factories, where the installed cables are extremely short; (2) it can be simply and identically applied for both online and offline diagnoses and to a variety of cable types; and (3) the diagnosis model can be directly established from the raw signal, without manual feature extraction and prior domain knowledge. Experiments conducted with various fault scenarios demonstrate that this method can be applied to practical cable faults.<\/jats:p>","DOI":"10.3390\/s21175936","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T13:18:26Z","timestamp":1630934306000},"page":"5936","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Online and Offline Diagnosis of Motor Power Cables Based on 1D CNN and Periodic Burst Signal Injection"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9230-4970","authenticated-orcid":false,"given":"Heonkook","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 37673, Korea"},{"name":"Hyundai Robotics Co., Ltd., 50, Techno Sunhwan-ro 3-gil, Yuga, Dalseong-gun, Daegu 43022, Korea"}]},{"given":"Hyeyun","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 37673, Korea"}]},{"given":"Hojin","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 37673, Korea"}]},{"given":"Sang Woo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 37673, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2606","DOI":"10.1109\/TIM.2017.2700178","article-title":"Multiple chirp reflectometry for determination of fault direction and localization in live branched network cables","volume":"66","author":"Chang","year":"2017","journal-title":"IEEE Trans. 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