{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T05:02:52Z","timestamp":1775192572719,"version":"3.50.1"},"reference-count":21,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"vor","delay-in-days":255,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Benz Automotive Co. Ltd"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Recently, the development of the Industrial Internet of Things (IIoT) has led enterprises to re\u2010examine the research of the equipment\u2010state\u2010prediction models and intelligent manufacturing applications. Take industrial robots as typical example. Under the effect of scale, robot maintenance decision seriously affects the cost of spare parts and labor deployment. In this paper, an evaluation method is proposed to predict the state of robot lubricating oil based on support vector regression (SVR). It would be the proper model to avoid the structural risks and minimize the effect of small sample volume. IIoT technology is used to collect and store the valuable robot running data. The key features of the running state of the robot are extracted, and the machine learning model is applied according to the measured element contents of the lubricating oil. As a result, the cost of spare parts consumption can be saved for more than two million CNY per year.<\/jats:p>","DOI":"10.1155\/2021\/9441649","type":"journal-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T20:53:04Z","timestamp":1631566384000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["State Evaluation Method of Robot Lubricating Oil Based on Support Vector Regression"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6564-1955","authenticated-orcid":false,"given":"Dongdong","family":"Guo","sequence":"first","affiliation":[]},{"given":"Xiangqun","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Haitao","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Zimei","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1573-220X","authenticated-orcid":false,"given":"Zongrui","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2014.2349359"},{"key":"e_1_2_8_2_2","unstructured":"BeghiR. 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Asymptotic tracking control for nonaffine systems with disturbances Proceedings of the IEEE Transactions on Circuits and Systems II: Express Briefs August 2017 Piscataway NJ USA https:\/\/doi.org\/10.1109\/TCSII.2021.3080524.","DOI":"10.1109\/TCSII.2021.3080524"},{"key":"e_1_2_8_5_2","article-title":"Techniques and emerging trends for state of the art equipment maintenance systems\u2014a bibliometric analysis","volume":"8","author":"Burkhard H.","year":"2018","journal-title":"Applied Sciences"},{"key":"e_1_2_8_6_2","article-title":"Overview of predictive condition based maintenance research using bibliometric indicators","volume":"31","author":"Noman M. A.","year":"2018","journal-title":"Journal of King Saud University"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2018.07.011"},{"key":"e_1_2_8_8_2","doi-asserted-by":"crossref","unstructured":"ButteS. PrashanthA. R. andPatilS. Machine learning based predictive maintenance strategy: a super learning approach with deep neural networks Proceedings of the 2018 IEEE Workshop on Microelectronics and Electron Devices (WMED) April 2018 Boise ID USA IEEE.","DOI":"10.1109\/WMED.2018.8360836"},{"key":"e_1_2_8_9_2","doi-asserted-by":"crossref","unstructured":"BorgiT. HidriA. NeefB.et al. 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Lubricant Oil and Lubricating Oil Additive Concentrate Compositions US Patent Alexandria VI USA."},{"key":"e_1_2_8_17_2","doi-asserted-by":"publisher","DOI":"10.5958\/2322-0465.2014.01114.9"},{"key":"e_1_2_8_18_2","doi-asserted-by":"publisher","DOI":"10.4236\/jcc.2018.63004"},{"key":"e_1_2_8_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/en6041887"},{"key":"e_1_2_8_20_2","doi-asserted-by":"publisher","DOI":"10.3390\/en9020070"},{"key":"e_1_2_8_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11071-020-06111-6"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/9441649.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/9441649.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/9441649","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T11:58:27Z","timestamp":1722945507000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/9441649"}},"subtitle":[],"editor":[{"given":"Yu-Ting","family":"Bai","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":21,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/9441649"],"URL":"https:\/\/doi.org\/10.1155\/2021\/9441649","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-06-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"9441649"}}