{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T01:56:27Z","timestamp":1775181387234,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,5]],"date-time":"2018-04-05T00:00:00Z","timestamp":1522886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 51375485"],"award-info":[{"award-number":["No. 51375485"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61503398"],"award-info":[{"award-number":["No. 61503398"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["No. 2017JJ2300"],"award-info":[{"award-number":["No. 2017JJ2300"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As bearings are critical components of a mechanical system, it is important to characterize their wear states and evaluate health conditions. In this paper, a novel approach for analyzing the relationship between online oil multi-parameter monitoring samples and bearing wear states has been proposed based on an improved gray k-means clustering model (G-KCM). First, an online monitoring system with multiple sensors for bearings is established, obtaining oil multi-parameter data and vibration signals for bearings through the whole lifetime. Secondly, a gray correlation degree distance matrix is generated using a gray correlation model (GCM) to express the relationship of oil monitoring samples at different times and then a KCM is applied to cluster the matrix. Analysis and experimental results show that there is an obvious correspondence that state changing coincides basically in time between the lubricants\u2019 multi-parameters and the bearings\u2019 wear states. It also has shown that online oil samples with multi-parameters have early wear failure prediction ability for bearings superior to vibration signals. It is expected to realize online oil monitoring and evaluation for bearing health condition and to provide a novel approach for early identification of bearing-related failure modes.<\/jats:p>","DOI":"10.3390\/s18041111","type":"journal-article","created":{"date-parts":[[2018,4,5]],"date-time":"2018-04-05T16:50:58Z","timestamp":1522947058000},"page":"1111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Evaluation for Bearing Wear States Based on Online Oil Multi-Parameters Monitoring"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5147-4079","authenticated-orcid":false,"given":"Si-Yuan","family":"Wang","sequence":"first","affiliation":[{"name":"Science and Technology on Integrated Logistics Support Laboratory, National University of Defense Technology, Changsha 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7289-8062","authenticated-orcid":false,"given":"Ding-Xin","family":"Yang","sequence":"additional","affiliation":[{"name":"Science and Technology on Integrated Logistics Support Laboratory, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Hai-Feng","family":"Hu","sequence":"additional","affiliation":[{"name":"Science and Technology on Integrated Logistics Support Laboratory, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"29363","DOI":"10.3390\/s151129363","article-title":"Early fault diagnosis of bearings using an improved spectral kurtosis by maximum correlated kurtosis deconvolution","volume":"15","author":"Jia","year":"2015","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1109\/TFUZZ.2016.2593921","article-title":"Fuzzy Fault Detection Filter Design for T-S Fuzzy Systems in the Finite-Frequency Domain","volume":"25","author":"Chibani","year":"2017","journal-title":"IEEE Trans. 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