{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T14:31:25Z","timestamp":1777386685877,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key RD Project of Anhui Science and Technology Department","award":["202004b11020026"],"award-info":[{"award-number":["202004b11020026"]}]},{"name":"Nanjing International Joint Research and Development Program","award":["202112003"],"award-info":[{"award-number":["202112003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents bearing fault diagnosis using the image classification of different fault patterns. Feature extraction for image classification is carried out using a novel approach of Color recurrence plots, which is presented for the first time. Color recurrence plots are created using non-linear embedding of the vibration signals into delay coordinate space with variable time lags. Deep learning-based image classification is then performed by building the database of the extracted features of the bearing vibration signals in the form of Color recurrence plots. A Series of computational experiments are performed to compare the accuracy of bearing fault classification using Color recurrence plots. The standard bearing vibration dataset of Case Western Reserve University is used for those purposes. The paper demonstrates the efficacy and the accuracy of a new and unique approach of scalar time series extraction into two-dimensional Color recurrence plots for bearing fault diagnosis.<\/jats:p>","DOI":"10.3390\/s22228870","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T06:24:42Z","timestamp":1668666282000},"page":"8870","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Color Recurrence Plots for Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"22","author":[{"given":"Vilma","family":"Petrauskiene","sequence":"first","affiliation":[{"name":"Department of Mathematical Modelling, Kaunas University of Technology, Studentu 50-146, LT 51368 Kaunas, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1387-4124","authenticated-orcid":false,"given":"Mayur","family":"Pal","sequence":"additional","affiliation":[{"name":"Department of Mathematical Modelling, Kaunas University of Technology, Studentu 50-146, LT 51368 Kaunas, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6640-1905","authenticated-orcid":false,"given":"Maosen","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Engineering Mechanics, Hohai University, Hohai 210098, China"},{"name":"College of Civil and Architecture Engineering, Chuzhou University, Chuzhou 239000, China"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"Intelligent Transportation and Intelligent Construction Engineering Research Center, Jiangsu Dongjiao Intelligent Control Technology Group Co., Nanjing 211161, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3348-9717","authenticated-orcid":false,"given":"Minvydas","family":"Ragulskis","sequence":"additional","affiliation":[{"name":"Department of Mathematical Modelling, Kaunas University of Technology, Studentu 50-146, LT 51368 Kaunas, Lithuania"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.1016\/j.eswa.2010.07.119","article-title":"Fault diagnosis of ball bearings using machine learning methods","volume":"38","author":"Kankar","year":"2011","journal-title":"Expert Syst. 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