. "11"^^ . . . "Wear, Wear Debris, Classification, AdaBoost, CS-LBP, LBP"@en . "978-3-642-23686-0" . . "Machal\u00EDk, Stanislav" . "RIV/00216305:26230/11:PU96070!RIV13-MSM-26230___" . "S, Z(MSM0021630528)" . . "Jur\u00E1nek, Roman" . . . . "Proceedings of Advanced Concepts of Inteligent Vision Systems (ACIVS 2011)" . "[D5FD9B086567]" . . "Analysis Wear Debris Through Classification"@en . "Heidelberg" . "26230" . . "Analysis Wear Debris Through Classification"@en . . . "Zem\u010D\u00EDk, Pavel" . . "2011-08-22+02:00"^^ . "185863" . "Analysis Wear Debris Through Classification" . "This paper introduces a novel method of wear debris analysis through\u00A0classification of the particles based on machine learning. Wear debris consists\u00A0of particles of metal found in e.g. lubricant oils used in engineering\u00A0equipment. \u00A0Analytical ferrography is one of methods for wear debris analysis\u00A0and it is very important for early detection or even prevention of failures in\u00A0engineering equipment, such as combustion engines, gearboxes, etc. \u00A0The\u00A0proposed novel method relies on classification of wear debris particles into\u00A0several classes defined by the origin of such particles. Unlike the earlier\u00A0methods, the proposed classification approach is based on visual similarity of\u00A0the particles and supervised machine learning. The paper describes the method\u00A0itself, demonstrates its experimental results, and draws conclusions."@en . . "RIV/00216305:26230/11:PU96070" . . "Springer-Verlag" . "Het Pand, Ghent, Belgium" . . "3"^^ . . . . . . "3"^^ . "Analysis Wear Debris Through Classification" . "This paper introduces a novel method of wear debris analysis through\u00A0classification of the particles based on machine learning. Wear debris consists\u00A0of particles of metal found in e.g. lubricant oils used in engineering\u00A0equipment. \u00A0Analytical ferrography is one of methods for wear debris analysis\u00A0and it is very important for early detection or even prevention of failures in\u00A0engineering equipment, such as combustion engines, gearboxes, etc. \u00A0The\u00A0proposed novel method relies on classification of wear debris particles into\u00A0several classes defined by the origin of such particles. Unlike the earlier\u00A0methods, the proposed classification approach is based on visual similarity of\u00A0the particles and supervised machine learning. The paper describes the method\u00A0itself, demonstrates its experimental results, and draws conclusions." . . .