{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T07:00:44Z","timestamp":1763535644351,"version":"3.41.2"},"reference-count":31,"publisher":"ASME International","issue":"2","license":[{"start":{"date-parts":[[2019,12,11]],"date-time":"2019-12-11T00:00:00Z","timestamp":1576022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51675403","51975455"],"award-info":[{"award-number":["51675403","51975455"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Ferrograph analysis has been adopted over decades for determining the root causes of on-going wear faults. After decades of manual operation, this traditional technique is being driven by intelligent algorithms for automatic identification of wear debris. However, the accuracy and robustness of this algorithm remain marginalized when applied in industries due to various types and color blurry of particles. To address this issue, this paper introduces an automatic ferrograph analysis model with a segmentation method and a two-level classification strategy. In order to obtain wear particles from the color ferrograph image, an adaptive Otsu threshold is adopted in three channel images to solve the color blurry in particle segmentation. By grouping particle parameters into shape and morphology ones, a two-level identification strategy is proposed. The first one is to classify rubbing, cutting, and spherical particles, referring to the fuzzy approach degree of shape parameters. In the second level, the shape-close particles are classified with imperceptible textures and back propagation neural network (BPNN). These objective parameters are constructed by applying the principal component analysis into seven texture features and inputted into a BPNN-based model to classify fatigue and severe sliding particles. In order to train the BPNN, more than 100 ferrograph images are sampled together, whereby standard ferrograph analysis is performed on the particle identification. The performance of the identification exhibits an accuracy exceeding 90% for rubbing, cutting, and spherical particles, whereas about 80% accuracy has been registered for both severe sliding and fatigue particles.<\/jats:p>","DOI":"10.1115\/1.4045291","type":"journal-article","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T07:02:07Z","timestamp":1571986927000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":16,"title":["Ferrograph Analysis With Improved Particle Segmentation and Classification Methods"],"prefix":"10.1115","volume":"20","author":[{"given":"Shuo","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Tonghai","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Kunpeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Thompson","family":"Sarkodie-Gyan","sequence":"additional","affiliation":[{"name":"Laboratory for Industrial Metrology and Automation, Department of Electrical and Computer Engineering, University of Texas at El Paso, El Paso, TX 79968"}]}],"member":"33","published-online":{"date-parts":[[2019,12,11]]},"reference":[{"issue":"1","key":"2021022706141482800_CIT0001","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. Lubrication Tribol."},{"key":"2021022706141482800_CIT0002","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.measurement.2018.10.032","article-title":"Three-Dimensional Reconstruction of Wear Particle Surface Based on Photometric Stereo","volume":"133","author":"Wang","year":"2019","journal-title":"Measurement"},{"key":"2021022706141482800_CIT0003","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.wear.2019.05.005","article-title":"Online Wear Characterisation of Rolling Element Bearing Using Wear Particle Morphological Features","volume":"430","author":"Peng","year":"2019","journal-title":"Wear"},{"key":"2021022706141482800_CIT0004","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.triboint.2019.05.029","article-title":"A Hybrid Convolutional Neural Network for Intelligent Wear Particle Classification","volume":"138","author":"Peng","year":"2019","journal-title":"Tribol. Int."},{"issue":"12","key":"2021022706141482800_CIT0005","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."},{"issue":"3","key":"2021022706141482800_CIT0006","doi-asserted-by":"crossref","first-page":"723","DOI":"10.3390\/s19030723","article-title":"A Wear Debris Segmentation Method for Direct Reflection Online Visual Ferrography","volume":"19","author":"Feng","year":"2019","journal-title":"Sensors"},{"issue":"3","key":"2021022706141482800_CIT0007","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. Trans."},{"key":"2021022706141482800_CIT0008","doi-asserted-by":"crossref","DOI":"10.1109\/IST.2014.6958477","article-title":"Research on Image Processing Technology for Online Oil Monitoring System","author":"Ma","year":"2014"},{"issue":"1\u20132","key":"2021022706141482800_CIT0009","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":"2021022706141482800_CIT0010","first-page":"122","article-title":"Self Organizing Analysis Platform for Wear Particle","volume":"6","author":"Memon","year":"2005","journal-title":"Trans. Eng. Comput. Technol."},{"key":"2021022706141482800_CIT0011","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.wear.2019.01.060","article-title":"Wear Particle Classification Considering Particle Overlapping","volume":"422","author":"Peng","year":"2019","journal-title":"Wear"},{"key":"2021022706141482800_CIT0012","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 Process."},{"key":"2021022706141482800_CIT0013","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.ymssp.2015.10.013","article-title":"Shape Classification of Wear Particles by Image Boundary Analysis Using Machine Learning Algorithms","volume":"72","author":"Yuan","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"issue":"2","key":"2021022706141482800_CIT0014","doi-asserted-by":"crossref","first-page":"411","DOI":"10.3901\/CJME.2014.02.411","article-title":"Intelligent Identification of Wear Mechanism Via On-Line Ferrograph Images","volume":"27","author":"Wu","year":"2014","journal-title":"Chin. J. Mech. Eng."},{"issue":"1\u20132","key":"2021022706141482800_CIT0015","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.wear.2013.04.021","article-title":"A Wear Particle Identification Method by Combining Principal Component Analysis and Grey Relational Analysis","volume":"304","author":"Wang","year":"2013","journal-title":"Wear"},{"issue":"5","key":"2021022706141482800_CIT0016","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1002\/ls.1411","article-title":"Wear Particle Classification Using Genetic Programming Evolved Features","volume":"30","author":"Xu","year":"2018","journal-title":"Lubrication Sci."},{"key":"2021022706141482800_CIT0017","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1016\/j.wear.2018.12.087","article-title":"Integrated Model of BP Neural Network and Cnn Algorithm for Automatic Wear Debris Classification","volume":"426","author":"Wang","year":"2019","journal-title":"Wear"},{"key":"2021022706141482800_CIT0018","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.wear.2012.02.002","article-title":"A New Approach to Numerical Characterisation of Wear Particle Surfaces in Three-Dimensions for Wear Study","volume":"282","author":"Tian","year":"2012","journal-title":"Wear"},{"issue":"1","key":"2021022706141482800_CIT0019","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.triboint.2007.04.004","article-title":"Automated Classification of Wear Particles Based on Their Surface Texture and Shape Features","volume":"41","author":"Stachowiak","year":"2008","journal-title":"Tribol. Int."},{"issue":"1\u20132","key":"2021022706141482800_CIT0020","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/S0043-1648(98)00280-4","article-title":"Computer Image Analysis of Wear Particles in Three-dimensions for Machine Condition Monitoring","volume":"223","author":"Peng","year":"1998","journal-title":"Wear"},{"issue":"1","key":"2021022706141482800_CIT0021","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method From Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybernet."},{"key":"2021022706141482800_CIT0022","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1016\/j.wear.2014.12.047","article-title":"Oxidation Wear Monitoring Based on the Color Extraction of On-Line Wear Debris","volume":"332","author":"Peng","year":"2015","journal-title":"Wear"},{"issue":"4","key":"2021022706141482800_CIT0023","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1016\/j.asoc.2012.12.022","article-title":"Novel Initialization Scheme for Fuzzy C-means Algorithm on Color Image Segmentation","volume":"13","author":"Tan","year":"2013","journal-title":"Appl. Soft Comput."},{"issue":"4","key":"2021022706141482800_CIT0024","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1023\/A:1019126732337","article-title":"Automatic Wear-Particle Classification Using Neural Networks","volume":"5","author":"Peng","year":"1998","journal-title":"Tribol. Lett."},{"issue":"24","key":"2021022706141482800_CIT0025","doi-asserted-by":"crossref","first-page":"6581","DOI":"10.1016\/j.disc.2007.12.038","article-title":"Lower Bounding the Boundary of a Graph in Terms of Its Maximum Or Minimum Degree","volume":"308","author":"M\u00fcller","year":"2008","journal-title":"Discrete Math."},{"issue":"1","key":"2021022706141482800_CIT0026","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.chemolab.2004.02.005","article-title":"Image Texture Analysis: Methods and Comparisons","volume":"72","author":"Bharati","year":"2004","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"6","key":"2021022706141482800_CIT0027","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1109\/TSMC.1978.4309999","article-title":"Textural Features Corresponding to Visual Perception","volume":"8","author":"Tamura","year":"1978","journal-title":"IEEE Trans. Syst. Man Cybernet."},{"issue":"3","key":"2021022706141482800_CIT0028","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.jmatprotec.2005.06.040","article-title":"Multi-Response Optimization in Friction Properties of PBT Composites Using Taguchi Method and Principle Component Analysis","volume":"170","author":"Fung","year":"2005","journal-title":"J. Mater. Process. Technol."},{"issue":"1\u20133","key":"2021022706141482800_CIT0029","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.jmatprotec.2006.07.015","article-title":"Optimizing Multiple Quality Characteristics Via Taguchi Method-Based Grey Analysis","volume":"182","author":"Pan","year":"2007","journal-title":"J. Mater. Process. Technol."},{"issue":"2","key":"2021022706141482800_CIT0030","doi-asserted-by":"crossref","first-page":"3845","DOI":"10.1016\/j.eswa.2008.02.066","article-title":"Stitching Defect Detection and Classification Using Wavelet Transform and BP Neural Network","volume":"36","author":"Wong","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"2021022706141482800_CIT0031","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.neucom.2018.09.048","article-title":"A CNN-RNN architecture for multi-label weather recognition","volume":"322","author":"Zhao","year":"2018","journal-title":"Neurocomputing"}],"container-title":["Journal of Computing and Information Science in Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/doi\/10.1115\/1.4045291\/6648142\/jcise_20_2_021001.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/doi\/10.1115\/1.4045291\/6648142\/jcise_20_2_021001.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T06:14:36Z","timestamp":1614406476000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article\/doi\/10.1115\/1.4045291\/1066028\/Ferrograph-Analysis-With-Improved-Particle"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,11]]},"references-count":31,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,4,1]]}},"URL":"https:\/\/doi.org\/10.1115\/1.4045291","relation":{},"ISSN":["1530-9827","1944-7078"],"issn-type":[{"type":"print","value":"1530-9827"},{"type":"electronic","value":"1944-7078"}],"subject":[],"published":{"date-parts":[[2019,12,11]]},"article-number":"021001"}}