{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T06:08:21Z","timestamp":1772777301235,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T00:00:00Z","timestamp":1717804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National key R &amp; D project","award":["2021YFB3301802"],"award-info":[{"award-number":["2021YFB3301802"]}]},{"name":"National key R &amp; D project","award":["62302103"],"award-info":[{"award-number":["62302103"]}]},{"name":"National key R &amp; D project","award":["2022GH08"],"award-info":[{"award-number":["2022GH08"]}]},{"name":"National key R &amp; D project","award":["2020B1212060069"],"award-info":[{"award-number":["2020B1212060069"]}]},{"name":"National Natural Science Foundation of China","award":["2021YFB3301802"],"award-info":[{"award-number":["2021YFB3301802"]}]},{"name":"National Natural Science Foundation of China","award":["62302103"],"award-info":[{"award-number":["62302103"]}]},{"name":"National Natural Science Foundation of China","award":["2022GH08"],"award-info":[{"award-number":["2022GH08"]}]},{"name":"National Natural Science Foundation of China","award":["2020B1212060069"],"award-info":[{"award-number":["2020B1212060069"]}]},{"name":"International Science and Technology Cooperation Project in Huangpu District","award":["2021YFB3301802"],"award-info":[{"award-number":["2021YFB3301802"]}]},{"name":"International Science and Technology Cooperation Project in Huangpu District","award":["62302103"],"award-info":[{"award-number":["62302103"]}]},{"name":"International Science and Technology Cooperation Project in Huangpu District","award":["2022GH08"],"award-info":[{"award-number":["2022GH08"]}]},{"name":"International Science and Technology Cooperation Project in Huangpu District","award":["2020B1212060069"],"award-info":[{"award-number":["2020B1212060069"]}]},{"name":"Guangdong Provincial Key Laboratory of Cyber-Physical System","award":["2021YFB3301802"],"award-info":[{"award-number":["2021YFB3301802"]}]},{"name":"Guangdong Provincial Key Laboratory of Cyber-Physical System","award":["62302103"],"award-info":[{"award-number":["62302103"]}]},{"name":"Guangdong Provincial Key Laboratory of Cyber-Physical System","award":["2022GH08"],"award-info":[{"award-number":["2022GH08"]}]},{"name":"Guangdong Provincial Key Laboratory of Cyber-Physical System","award":["2020B1212060069"],"award-info":[{"award-number":["2020B1212060069"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The increasing deployment of industrial robots in manufacturing requires accurate fault diagnosis. Online monitoring data typically consist of a large volume of unlabeled data and a small quantity of labeled data. Conventional intelligent diagnosis methods heavily rely on supervised learning with abundant labeled data. To address this issue, this paper presents a semi-supervised Informer algorithm for fault diagnosis modeling, leveraging the Informer model\u2019s long- and short-term memory capabilities and the benefits of semi-supervised learning to handle the diagnosis of a small amount of labeled data alongside a substantial amount of unlabeled data. An experimental study is conducted using real-world industrial robot monitoring data to assess the proposed algorithm\u2019s effectiveness, demonstrating its ability to deliver accurate fault diagnosis despite limited labeled samples.<\/jats:p>","DOI":"10.3390\/s24123732","type":"journal-article","created":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T08:59:06Z","timestamp":1718009946000},"page":"3732","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Semi-Supervised Informer for the Compound Fault Diagnosis of Industrial Robots"],"prefix":"10.3390","volume":"24","author":[{"given":"Chuanhua","family":"Deng","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Cyber-Physical System, Guangdong University of Technology, Guangzhou 510006, China"}]},{"given":"Junjie","family":"Song","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Cyber-Physical System, Guangdong University of Technology, Guangzhou 510006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2800-4647","authenticated-orcid":false,"given":"Chong","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Cyber-Physical System, Guangdong University of Technology, Guangzhou 510006, China"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Cyber-Physical System, Guangdong University of Technology, Guangzhou 510006, China"}]},{"given":"Lianglun","family":"Cheng","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Cyber-Physical System, Guangdong University of Technology, Guangzhou 510006, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110936","DOI":"10.1016\/j.ymssp.2023.110936","article-title":"Bayesian Variational Transformer: A generalizable model for rotating machinery fault diagnosis","volume":"207","author":"Xiao","year":"2024","journal-title":"Mech. 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