{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T20:23:54Z","timestamp":1761164634123,"version":"build-2065373602"},"reference-count":16,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The advent of 6G networks has revolutionized power system monitoring by enabling ultra\u2010fast, low\u2010latency communication, which is essential for real\u2010time fault prediction and diagnosis in power measurement equipment. However, conventional fault diagnostic methods often rely on centralized data processing, which raises significant concerns about data privacy threats, latency, and inefficiencies in real\u2010time problem identification. We provide a Big Data\u2010Driven Predictive Analytics with Federated Learning (BD\u2010PA\u2010FL) platform to address these issues. Without sending sensitive raw data, this novel method enables decentralized, privacy\u2010preserving model training across numerous edge devices. By utilizing distributed big data and safeguarding data privacy, BD\u2010PA\u2010FL enables decentralized predictive analytics through FL. It avoids centralized data pooling, which lowers latency and improves real\u2010time, privacy\u2010aware fault detection in contrast to traditional fault diagnosis. To enable effective and intelligent fault prediction at the network edge, the proposed framework incorporates several essential elements. First, vital operating metrics from power equipment are captured by real\u2010time sensor data collection. After that, insightful feature extraction methods are employed to identify significant patterns in the unprocessed data, enabling the detection of anomalies at an early stage. FL algorithms allow the system to collaboratively train predictive models across distributed nodes without sharing sensitive data, preserving privacy. By leveraging a cloud\u2010edge AI architecture, the system ensures scalability, low latency, and effective resource utilization for predictive maintenance. Experimental results confirm that the BD\u2010PA\u2010FL framework significantly improves fault detection accuracy, reduces downtime, and enhances overall grid reliability in a secure, 6G\u2010enabled environment.<\/jats:p>","DOI":"10.1002\/itl2.70107","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T14:56:20Z","timestamp":1757343380000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Safety Fault Prediction and Diagnosis of Power Measurement Equipment Based on\n                    <scp>6G<\/scp>\n                    Big Data Analysis"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5921-1287","authenticated-orcid":false,"given":"Yin","family":"Gao","sequence":"first","affiliation":[{"name":"State Grid Anhui Marketing Service Center  Hefei China"}]}],"member":"311","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23135970"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.etran.2023.100254"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.108855"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10111309"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104168"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09934-2"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.anucene.2022.109577"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISR50024.2021.9419561"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2021.230058"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42835-022-01032-3"},{"issue":"2","key":"e_1_2_7_12_1","first-page":"1","article-title":"Sensing as the Key to the Safety and Sustainability of New Energy Storage Devices","volume":"8","author":"Yi Z.","year":"2023","journal-title":"Protection and Control of Modern Power Systems"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2022.3174238"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3241588"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.118866"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22082822"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.joule.2020.11.018"}],"container-title":["Internet Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70107","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T18:25:18Z","timestamp":1761071118000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/itl2.70107"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":16,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1002\/itl2.70107"],"URL":"https:\/\/doi.org\/10.1002\/itl2.70107","archive":["Portico"],"relation":{},"ISSN":["2476-1508","2476-1508"],"issn-type":[{"type":"print","value":"2476-1508"},{"type":"electronic","value":"2476-1508"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"2025-03-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-26","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70107"}}