{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:20:18Z","timestamp":1776385218903,"version":"3.51.2"},"reference-count":27,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T00:00:00Z","timestamp":1712793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Inner Mongolia Autonomous Region Military-civilian Integration Key Scientific Research Projects and Soft Scientific Research Projects","award":["JMZD202303"],"award-info":[{"award-number":["JMZD202303"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The real-time monitoring and fault diagnosis of modern machinery and equipment impose higher demands on equipment maintenance, with the extraction of morphological characteristics of wear debris in lubricating oil emerging as a critical approach for real-time monitoring of wear, holding significant importance in the field. The online visual ferrograph (OLVF) technique serves as a representative method in this study. Various semantic segmentation approaches, such as DeepLabV3+, PSPNet, Segformer, Unet, and other models, are employed to process the oil wear particle image for conducting comparative experiments. In order to accurately segment the minute wear debris in oil abrasive images and mitigate the influence of reflection and bubbles, we propose a multi-level feature reused Unet (MFR Unet) that enhances the residual link strategy of Unet for improved identification of tiny wear debris in ferrograms, leading to superior segmentation results.<\/jats:p>","DOI":"10.3390\/s24082444","type":"journal-article","created":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T07:09:28Z","timestamp":1712819368000},"page":"2444","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Real-Time Ferrogram Segmentation of Wear Debris Using Multi-Level Feature Reused Unet"],"prefix":"10.3390","volume":"24","author":[{"given":"Jie","family":"You","sequence":"first","affiliation":[{"name":"Ocean College, China University of Geosciences Beijing, Beijing 100083, China"}]},{"given":"Shibo","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China"}]},{"given":"Qinghai","family":"Yu","sequence":"additional","affiliation":[{"name":"Alumni and Social Cooperation Office, China University of Geosciences Beijing, Beijing 100083, China"}]},{"given":"Lianfu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China"}]},{"given":"Zhou","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China"}]},{"given":"Zheying","family":"Zong","sequence":"additional","affiliation":[{"name":"College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1016\/j.triboint.2006.01.019","article-title":"Towards the development of an automated wear particle classification system","volume":"39","author":"Stachowiak","year":"2006","journal-title":"Tribol. Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.ymssp.2008.03.016","article-title":"Vibration condition monitoring of planetary gearbox under varying external load","volume":"23","author":"Bartelmus","year":"2009","journal-title":"Mech. Syst. Signal Pract."},{"key":"ref_3","first-page":"420","article-title":"Shaft coupling model-based prognostics enhanced by vibration diagnostics","volume":"51","author":"Byington","year":"2009","journal-title":"Insight\u2014Non-Destr. Test. Cond. Monit."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1115\/1.2920897","article-title":"Wear debris formation and agglomeration","volume":"114","author":"Oktay","year":"1992","journal-title":"J. Tribol.-T Asme."},{"key":"ref_5","first-page":"4115","article-title":"Knowledge based wear particle analysis","volume":"1","author":"Laghari","year":"2007","journal-title":"Int. J. Comput. Inf. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/j.wear.2015.03.015","article-title":"Effect of abrasive particle size distribution on the wear rate and wear mode in micro-scale abrasive wear tests","volume":"328","author":"Gomez","year":"2015","journal-title":"Wear"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0301-679X(91)90039-C","article-title":"Key parameters for the reliable prediction of machine failure using wear particle analysis","volume":"24","author":"Hudnik","year":"1991","journal-title":"Tribol. Int."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.measurement.2018.09.012","article-title":"Size distribution analysis of wear debris generated in HEMM engine oil for reliability assessment: A statistical approach","volume":"131","author":"Kumar","year":"2019","journal-title":"Measurement"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ymssp.2013.09.010","article-title":"Diagnostics of bearings in presence of strong operating conditions non-stationarity\u2014A procedure of load-dependent features processing with application to wind turbine bearings","volume":"46","author":"Zimroz","year":"2014","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"037001","DOI":"10.1088\/0964-1726\/18\/3\/037001","article-title":"Capacitive Coulter counting: Detection of metal wear particles in lubricant using a microfluidic device","volume":"18","author":"Murali","year":"2009","journal-title":"Smart Mater. Struct."},{"key":"ref_11","unstructured":"Edmonds, J., Resner, M.S., and Shkarlet, K. (2000, January 25\u201325). Detection of precursor wear debris in lubrication systems. Proceedings of the 2000 IEEE Aerospace Conference, Big Sky, MT, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/0043-1648(83)90051-0","article-title":"Wear studies through particle size distribution I: Application of the Weibull distribution to ferrography","volume":"90","author":"Roylance","year":"1983","journal-title":"Wear"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1108\/00368791311292756","article-title":"Advancement and current status of wear debris analysis for machine condition monitoring: A review","volume":"65","author":"Kumar","year":"2013","journal-title":"Ind. Lubr. Tribol."},{"key":"ref_14","first-page":"470","article-title":"Wear debris: Basic features and machine health diagnostics","volume":"48","author":"Khan","year":"2006","journal-title":"Insight-Non-Destr. Test. Cond. Monit."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/j.triboint.2005.03.013","article-title":"Wear particle analysis\u2014Utilization of quantitative computer image analysis: A review","volume":"38","author":"Raadnui","year":"2005","journal-title":"Tribol. Int."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.triboint.2017.02.045","article-title":"Detection of gear pitting failure progression with on-line particle monitoring","volume":"118","author":"Kattelus","year":"2018","journal-title":"Tribol. Int."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"192","DOI":"10.3103\/S1068366608030070","article-title":"Morphology: Texture, shape, and color of friction surfaces and wear debris in tribodiagnostics problems","volume":"29","author":"Myshkin","year":"2008","journal-title":"J. Frict. Wear"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1080\/10402000902825762","article-title":"A new on-line visual ferrograph","volume":"52","author":"Wu","year":"2009","journal-title":"Tribol. T"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"065104","DOI":"10.1088\/1361-6501\/aab9fc","article-title":"A direct reflection OLVF debris detector based on dark-field imaging","volume":"29","author":"Li","year":"2018","journal-title":"Meas. Sci. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.ymssp.2005.09.015","article-title":"A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears","volume":"21","author":"Tan","year":"2007","journal-title":"Mech. Syst. Signal Pract."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Feng, S., Qiu, G., Luo, J., Han, L., Mao, J., and Zhang, Y. (2019). A Wear Debris Segmentation Method for Direct Reflection Online Visual Ferrography. Sensors, 19.","DOI":"10.3390\/s19030723"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1016\/j.ymssp.2017.08.014","article-title":"A non-reference evaluation method for edge detection of wear particles in ferrograph images","volume":"100","author":"Wang","year":"2018","journal-title":"Mech. Syst. Signal Pract."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1080\/10402004.2015.1091534","article-title":"A Hybrid Method for the Segmentation of a Ferrograph Image Using Marker-Controlled Watershed and Grey Clustering","volume":"59","author":"Wang","year":"2016","journal-title":"Tribol. T"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.wear.2014.01.004","article-title":"The segmentation of wear particles in ferrograph images based on an improved ant colony algorithm","volume":"311","author":"Wang","year":"2014","journal-title":"Wear"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.wear.2014.04.014","article-title":"Imaged wear debris separation for on-line monitoring using gray level and integrated morphological features","volume":"316","author":"Wu","year":"2014","journal-title":"Wear"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Feng, S., Qiu, G., Luo, J., and Mao, J. (2018, January 15\u201317). Binarization Method for On-Line Ferrograph Image Based on Uniform Curvelet Transformation. Proceedings of the 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Xi\u2019an, China.","DOI":"10.1109\/SDPC.2018.8664911"},{"key":"ref_27","first-page":"202968","article-title":"FECNN: A promising model for wear particle recognition","volume":"432\u2013433","author":"Peng","year":"2019","journal-title":"Wear Int. J. Sci. Technol. Frict. Lubr. Wear"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/8\/2444\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:26:17Z","timestamp":1760106377000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/8\/2444"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,11]]},"references-count":27,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24082444"],"URL":"https:\/\/doi.org\/10.3390\/s24082444","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,11]]}}}