{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T07:15:18Z","timestamp":1769584518335,"version":"3.49.0"},"reference-count":28,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Project of State Grid Jiangsu Electric Power Company","award":["J2023063"],"award-info":[{"award-number":["J2023063"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The efficiency of mechanical crushing is a key metric for evaluating machinery performance. However, traditional contact-based methods for measuring this efficiency are unable to provide real-time data monitoring and can potentially disrupt the production process. In this paper, we introduce a non-contact measurement technique for mechanical crushing efficiency based on deep learning algorithms. This technique utilizes close-range imaging equipment to capture images of crushed particles and employs deeply trained algorithmic programs rooted in symmetrical logical structures to extract statistical data on particle size. Additionally, we establish a relationship between particle size and crushing energy through experimental analysis, enabling the calculation of crushing efficiency data. Taking cement crushing equipment as an example, we apply this non-contact measurement technique to inspect cement particles of different sizes. Using deep learning algorithms, we automatically categorize and summarize the particle size ranges of cement particles. The results demonstrate that the crushing efficiencies of ore crushing particles, raw material crushing particles, and cement crushing particles can respectively reach 80.7%, 70.15%, and 80.27%, which exhibit a high degree of consistency with the rated value of the samples. The method proposed in this paper holds significant importance for energy efficiency monitoring in industries that require mechanical crushing.<\/jats:p>","DOI":"10.3390\/sym16070810","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T08:31:36Z","timestamp":1719563496000},"page":"810","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Energy Efficiency Measurement of Mechanical Crushing Based on Non-Contact Identification Method"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7414-7791","authenticated-orcid":false,"given":"Xiaoquan","family":"Lu","sequence":"first","affiliation":[{"name":"State Grid Jiangsu Electric Power Co., Ltd., Marketing Service Center, Nanjing 210019, China"}]},{"given":"Meimei","family":"Duan","sequence":"additional","affiliation":[{"name":"State Grid Jiangsu Electric Power Co., Ltd., Marketing Service Center, Nanjing 210019, China"}]},{"given":"Huiling","family":"Su","sequence":"additional","affiliation":[{"name":"State Grid Jiangsu Electric Power Co., Ltd., Marketing Service Center, Nanjing 210019, China"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"State Grid Jiangsu Electric Power Co., Ltd., Marketing Service Center, Nanjing 210019, China"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"State Grid Jiangsu Electric Power Co., Ltd., Marketing Service Center, Nanjing 210019, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"04020084","DOI":"10.1061\/(ASCE)GT.1943-5606.0002334","article-title":"New model for predicting permanent strain of granular materials in embankment subjected to low cyclic loadings","volume":"146","author":"Chen","year":"2020","journal-title":"J. Geotech. Geoenviron. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1139\/cgj-2018-0624","article-title":"Negative creep of soils","volume":"57","author":"Yao","year":"2020","journal-title":"Can. Geotech. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.geotexmem.2019.12.005","article-title":"Numerical study of creep effects on settlements and load transfer mechanisms of soft soil improved by deep cement mixed soil columns under embankment load","volume":"48","author":"Wu","year":"2020","journal-title":"Geotext. Geomembr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.cemconres.2018.05.002","article-title":"Concrete material science: Past; present; future innovations","volume":"112","year":"2018","journal-title":"Cem. Concr. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"117702","DOI":"10.1016\/j.powtec.2022.117702","article-title":"Packing density of limestone calcined clay binder","volume":"408","author":"Luzu","year":"2022","journal-title":"Powder Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.powtec.2020.08.091","article-title":"A stochastic particle replacement strategy for simulating breakage in DEM","volume":"377","author":"Tavares","year":"2021","journal-title":"Powder Technol."},{"key":"ref_7","first-page":"C115","article-title":"Determination of comminution characteristics from single particle breakage tests and its application to ball mill scale-up","volume":"97","author":"Narayanan","year":"1988","journal-title":"Trans. Inst. Min. Metall."},{"key":"ref_8","unstructured":"Banini, G.A. (2000). An Integrated Description of Rock Breakage in Comminution Machines. [Ph.D. Thesis, University of Queensland (JKMRC)]."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"101110","DOI":"10.1016\/S0032-5910(02)00217-6","article-title":"Breakage behaviour of different materials construction of a mastercurve for the breakage probability","volume":"129","author":"Vogel","year":"2003","journal-title":"Powder Technol"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.powtec.2008.08.011","article-title":"Analysis of particle fracture by repeated stressing as damage accumulation","volume":"190","author":"Tavares","year":"2009","journal-title":"Powder Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107921","DOI":"10.1016\/j.oceaneng.2020.107921","article-title":"Micro-mechanical analysis of caisson foundation in sand using DEM: Particle breakage effect","volume":"215","author":"Wang","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_12","first-page":"53","article-title":"Comparativestudy of ore particle size detection methods based on machine vision","volume":"45","author":"Lianming","year":"2017","journal-title":"Min. Mach."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105631","DOI":"10.1016\/j.compgeo.2023.105631","article-title":"DEM analysis of crushing evolution in cemented granular materials during pile penetration","volume":"161","author":"Benmebarek","year":"2023","journal-title":"Comput. Geotech."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"04019138","DOI":"10.1061\/(ASCE)EM.1943-7889.0001713","article-title":"DEM modeling of grain size effect in brittle granular soils","volume":"146","author":"Cil","year":"2020","journal-title":"J. Eng. Mech."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.powtec.2015.03.006","article-title":"A fractal model of contact force distribution and the unified coordination distribution for crushable granular materials under confined compression","volume":"279","author":"Yang","year":"2015","journal-title":"Powder Technol."},{"key":"ref_16","first-page":"21","article-title":"Dynamic Graph CNN for Learning on Point Clouds","volume":"38","author":"Wang","year":"2018","journal-title":"ACM Trans. Graph."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1111\/mice.12263","article-title":"Deep Learning-Based Crack Damage Detection Using Convolutional Neural Net-works","volume":"32","author":"Cha","year":"2017","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1109\/TDEI.2013.003585","article-title":"Partial discharge initiated by free moving metallic particles on GIS insulator surface: Severity diagnosis and assessment","volume":"21","author":"Qi","year":"2014","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.triboint.2018.01.015","article-title":"Prediction of wear trend of engines via on-line wear debris monitoring","volume":"120","author":"Cao","year":"2018","journal-title":"Tribol. Int."},{"key":"ref_21","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":"ref_22","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\u201373","author":"Yuan","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.wear.2017.09.022","article-title":"A hybrid search-tree discriminant technique for multivariate wear debris classification","volume":"392\u2013393","author":"Peng","year":"2017","journal-title":"Wear"},{"key":"ref_24","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"},{"key":"ref_25","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":"Lubr. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Distante, A., and Distante, C. (2020). Handbook of image Processing and Computer Vision, Springer International Publishing.","DOI":"10.1007\/978-3-030-42374-2"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.powtec.2009.03.027","article-title":"Some factors affecting sieving performance and efficiency","volume":"193","author":"Liu","year":"2009","journal-title":"Powder Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"187","DOI":"10.3103\/S0361521921030071","article-title":"Simulation of the Physical Characteristics of Dispersed Phase Particles Using the Results of Dynamic Light Scattering","volume":"55","author":"Kadiev","year":"2021","journal-title":"Solid Fuel Chem."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/7\/810\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:06:59Z","timestamp":1760108819000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/7\/810"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":28,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["sym16070810"],"URL":"https:\/\/doi.org\/10.3390\/sym16070810","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,28]]}}}