{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:42:24Z","timestamp":1774539744456,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:00:00Z","timestamp":1672963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Collaborative Innovation Center of Key Power Energy-Saving Technologies in Beijing","award":["PXM2018_014212_000015_4_4"],"award-info":[{"award-number":["PXM2018_014212_000015_4_4"]}]},{"name":"Collaborative Innovation Center of Key Power Energy-Saving Technologies in Beijing","award":["5205C02000GL"],"award-info":[{"award-number":["5205C02000GL"]}]},{"name":"Collaborative Innovation Center of Key Power Energy-Saving Technologies in Beijing","award":["110052972027\/067"],"award-info":[{"award-number":["110052972027\/067"]}]},{"name":"Technology Project of State Grid Shanxi Electric Power Company","award":["PXM2018_014212_000015_4_4"],"award-info":[{"award-number":["PXM2018_014212_000015_4_4"]}]},{"name":"Technology Project of State Grid Shanxi Electric Power Company","award":["5205C02000GL"],"award-info":[{"award-number":["5205C02000GL"]}]},{"name":"Technology Project of State Grid Shanxi Electric Power Company","award":["110052972027\/067"],"award-info":[{"award-number":["110052972027\/067"]}]},{"name":"Beijing Education Commission: Key Technology Research on Intelligent Operation and Maintenance of Big Data for Power Distribution","award":["PXM2018_014212_000015_4_4"],"award-info":[{"award-number":["PXM2018_014212_000015_4_4"]}]},{"name":"Beijing Education Commission: Key Technology Research on Intelligent Operation and Maintenance of Big Data for Power Distribution","award":["5205C02000GL"],"award-info":[{"award-number":["5205C02000GL"]}]},{"name":"Beijing Education Commission: Key Technology Research on Intelligent Operation and Maintenance of Big Data for Power Distribution","award":["110052972027\/067"],"award-info":[{"award-number":["110052972027\/067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time\u2212frequency domain joint impedance spectrum is proposed for cable fault identification and localization based on a deep belief network (DBN). Firstly, based on the distribution parameter model of power cables, we model and analyze the cables under normal operation and different fault types, and we obtain the headend input impedance spectrum and the headend input time\u2212frequency domain impedance spectrum of cables under various operating conditions. The headend input impedance amplitude and phase of normal operation and different fault cables are extracted as the original input samples of the cable fault type identification model; the real part of the headend input time\u2013frequency domain impedance of the fault cables is extracted as the original input samples of the cable fault location model. Then, the unsupervised pre\u2212training and supervised inverse fine\u2212tuning methods are used for automatically learning, training, and extracting the cable fault state features from the original input samples, and the DBN\u2212based cable fault type recognition model and location model are constructed and used to realize the type recognition and location of cable faults. Finally, the proposed method is validated by simulation, and the results show that the method has good fault feature extraction capability and high fault type recognition and localization accuracy.<\/jats:p>","DOI":"10.3390\/s23020684","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T06:38:27Z","timestamp":1673246307000},"page":"684","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Fault Identification and Localization of a Time\u2212Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks"],"prefix":"10.3390","volume":"23","author":[{"given":"Qingzhu","family":"Wan","sequence":"first","affiliation":[{"name":"School of Electric and Control Engineering, North China University of Technology, Beijing100144, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7620-6943","authenticated-orcid":false,"given":"Yimeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electric and Control Engineering, North China University of Technology, Beijing100144, China"}]},{"given":"Runjiao","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Electric and Control Engineering, North China University of Technology, Beijing100144, China"}]},{"given":"Qinghai","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Electric and Control Engineering, North China University of Technology, Beijing100144, China"}]},{"given":"Xiaoxue","family":"Li","sequence":"additional","affiliation":[{"name":"Key Account Division, Beijing Aerospace Data Stock Company National Big-Data Application Technology, Beijing100044, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7174","DOI":"10.3390\/s22197174","article-title":"A Comprehensive Operation Status Evaluation Method for Mining XLPE Cables","volume":"22","author":"Yanwen","year":"2022","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, M., Bu, J., Song, Y., Pu, Z., Wang, Y., and Xie, C. 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