{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T16:37:17Z","timestamp":1770136637687,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2016,1,12]],"date-time":"2016-01-12T00:00:00Z","timestamp":1452556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2015M581879"],"award-info":[{"award-number":["2015M581879"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Basic Research Program of China","award":["2014CB046301"],"award-info":[{"award-number":["2014CB046301"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51475454"],"award-info":[{"award-number":["51475454"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National High Technology Research and Development Program of China","award":["2013AA06A411"],"award-info":[{"award-number":["2013AA06A411"]}]},{"name":"Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM) has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved fly optimization algorithm (IFOA) to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM) was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD) and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system.<\/jats:p>","DOI":"10.3390\/s16010090","type":"journal-article","created":{"date-parts":[[2016,1,12]],"date-time":"2016-01-12T04:42:26Z","timestamp":1452573746000},"page":"90","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Identification of Shearer Cutting Patterns Using Vibration Signals Based on a Least Squares Support Vector Machine with an Improved Fruit Fly Optimization Algorithm"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4087-9430","authenticated-orcid":false,"given":"Lei","family":"Si","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining &amp; Technology, No. 1 Daxue Road, Xuzhou 221116, China"},{"name":"School of Information and Electrical Engineering, China University of Mining &amp; Technology, No. 1 Daxue Road, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongbin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining &amp; Technology, No. 1 Daxue Road, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8632-6532","authenticated-orcid":false,"given":"Xinhua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining &amp; Technology, No. 1 Daxue Road, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining &amp; Technology, No. 1 Daxue Road, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ze","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining &amp; Technology, No. 1 Daxue Road, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining &amp; Technology, No. 1 Daxue Road, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.measurement.2014.04.015","article-title":"A novel approach for coal seam terrain prediction through information fusion of improved D\u2013S evidence theory and neural network","volume":"54","author":"Si","year":"2014","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1109\/28.222427","article-title":"Remnant roof coal thickness measurement with passive gamma ray instruments in coal mine","volume":"29","author":"Bessinger","year":"1993","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/28.245703","article-title":"A radar coal thickness sensor","volume":"29","author":"Chufo","year":"1993","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3700","DOI":"10.1063\/1.1141538","article-title":"An FT-IR based instrument for measuring spectral emittance of material at high temperature","volume":"61","author":"Markham","year":"1990","journal-title":"Rev. Sci. Instrum."},{"key":"ref_5","first-page":"1900","article-title":"Wavelet-based coal-rock image feature extraction and recognition","volume":"38","author":"Sun","year":"2013","journal-title":"J. China Coal Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1016\/j.ymssp.2008.06.009","article-title":"Rotating machinery prognostics: State of the art, challenges and opportunities","volume":"23","author":"Heng","year":"2009","journal-title":"Mech. Syst. Signal Pr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1016\/j.engappai.2012.03.017","article-title":"Multi-BP expert system for fault diagnosis of power system","volume":"26","author":"Ma","year":"2013","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.5370\/JEET.2014.9.6.2058","article-title":"Intuitionistic fuzzy expert system based fault diagnosis using dissolved gas analysis for power transformer","volume":"9","author":"Mani","year":"2014","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1109\/TSG.2014.2386305","article-title":"An artificial neural network approach for early fault detection of gearbox bearings","volume":"6","author":"Bangalore","year":"2015","journal-title":"IEEE TRANS. Smart Grid"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.3390\/s110201721","article-title":"Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals","volume":"11","author":"Barshan","year":"2011","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.5370\/JEET.2013.8.5.1049","article-title":"Single parameter fault identification technique for DC motor through wavelet analysis and fuzzy logic","volume":"8","author":"Winston","year":"2013","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/s00521-014-1731-9","article-title":"Hybrid intelligent system-based rough set and ensemble classifier for breast cancer diagnosis","volume":"26","year":"2015","journal-title":"Neural. Comput. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1109\/TNNLS.2013.2280280","article-title":"Online motor fault detection and diagnosis using a hybrid FMM-CART model","volume":"25","author":"Seera","year":"2014","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5919","DOI":"10.3390\/s120505919","article-title":"An intelligent diagnosis method for rotating machinery using least squares mapping and a fuzzy neural network","volume":"12","author":"Li","year":"2012","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"17130","DOI":"10.3390\/s131217130","article-title":"Online least squares one-class support vector machines-based abnormal visual event detection","volume":"13","author":"Wang","year":"2013","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1016\/j.neucom.2015.05.034","article-title":"Novel approaches using evolutionary computation for sparse least square support vector machines","volume":"168","author":"Silva","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.engfailanal.2015.05.010","article-title":"Prediction of stress concentration factor of corrosion pits on buried pipes by least squares support vector machine","volume":"55","author":"Ji","year":"2015","journal-title":"Eng. Fail. Anal."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5253","DOI":"10.1016\/j.apm.2015.03.032","article-title":"Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine","volume":"39","author":"Li","year":"2015","journal-title":"Appl. Math. Model"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.asoc.2014.10.041","article-title":"Particle Swarm Optimization based support vector machine for damage level prediction of non-reshaped berm breakwater","volume":"27","author":"Harish","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"892","DOI":"10.2112\/JCOASTRES-D-13-00087.1","article-title":"Sea wave parameters prediction by support vector machine using a genetic algorithm","volume":"31","author":"Elbisy","year":"2015","journal-title":"J. Coast. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1080\/09540091.2014.924902","article-title":"Imbalanced data classification via support vector machines and genetic algorithms","volume":"26","author":"Cervantes","year":"2014","journal-title":"Connect. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.neucom.2015.04.069","article-title":"Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization","volume":"167","author":"Zhang","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.asoc.2009.11.003","article-title":"A multi-objective artificial immune algorithm for parameter optimization in support vector machine","volume":"11","author":"Aydin","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.knosys.2011.07.001","article-title":"A new fruit fly optimization algorithm: Taking the financial distress model as an example","volume":"26","author":"Pan","year":"2012","journal-title":"Knowl.-Based Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.knosys.2012.08.015","article-title":"A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm","volume":"37","author":"Li","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"459","DOI":"10.12785\/amis\/072L12","article-title":"Using fruit fly optimization algorithm optimized grey model neural network to perform satisfaction analysis for e-business service","volume":"7","author":"Chen","year":"2013","journal-title":"Appl. Math. Inform. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s11071-013-0814-y","article-title":"Fruit fly optimization algorithm based fractional order fuzzy-PID controller for electronic throttle","volume":"73","author":"Sheng","year":"2013","journal-title":"Nonlinear Dynam."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1108\/03684921311323699","article-title":"Design and optimization of key control characteristics based on improved fruit fly optimization algorithm","volume":"42","author":"Xing","year":"2013","journal-title":"Kybernetes"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.4028\/www.scientific.net\/KEM.474-476.1103","article-title":"Application of wavelet packet energy spectrum in coal-rock interface recognition","volume":"474","author":"Wang","year":"2011","journal-title":"Key Eng. Mater."},{"key":"ref_30","first-page":"77","article-title":"Study on identified method of coal and rock interface based on image identification","volume":"39","author":"Sun","year":"2011","journal-title":"Coal Sci. Technol."},{"key":"ref_31","first-page":"94","article-title":"Application study on the torsional vibration test in coal-rock interface recognition","volume":"41","author":"Ren","year":"2010","journal-title":"J. Taiyuan Univ. Technol."},{"key":"ref_32","first-page":"100","article-title":"Acoustic modeling of coal-rock interface identification","volume":"43","author":"Yang","year":"2015","journal-title":"Coal Sci. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sahoo, R., and Mazid, A.M. (2009, January 10\u201313). Application of opto-tactile sensor in shearer machine design to recognize rock surfaces in underground coal mining. Proceedings of the IEEE International Conference on Industrial Technology, Churchill, Australia.","DOI":"10.1109\/ICIT.2009.4939645"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bausov, I.Y., Stolarczyk, G.L., Stolarczyk, L.G., and Koppenjan, S.D.S. (2007, January 27\u201329). Look-ahead radar and horizon sensing for coal cutting drums. Proceedings of the 4th International Workshop on Advanced Ground Penetrating Radar, Naples, Italy.","DOI":"10.1109\/AGPR.2007.386553"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least squares support vector machine classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1162\/089976603321891855","article-title":"Asymptotic behaviors of support vector machines with Gaussian kernel","volume":"15","author":"Keerthi","year":"2003","journal-title":"Neural Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S1793536909000047","article-title":"Ensemble empirical mode decomposition: A noise-assisted data analysis method","volume":"1","author":"Wu","year":"2009","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_39","first-page":"223","article-title":"Signal denoising based on EEMD for non-stationary signals and its application in fault diagnosis","volume":"47","author":"Lv","year":"2011","journal-title":"Comput. Eng. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"16950","DOI":"10.3390\/s131216950","article-title":"Fault diagnosis of rotating machinery based on an adaptive ensemble empirical mode decomposition","volume":"13","author":"Lei","year":"2013","journal-title":"Sensors"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Lon. A"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.mechmachtheory.2015.03.013","article-title":"Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm","volume":"90","author":"Yang","year":"2015","journal-title":"Mech. Mach. Theory"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/1\/90\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:17:35Z","timestamp":1760210255000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/1\/90"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,1,12]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2016,1]]}},"alternative-id":["s16010090"],"URL":"https:\/\/doi.org\/10.3390\/s16010090","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,1,12]]}}}