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	Application of Predictive Maintenance Algorithms for State Monitoring of an Experimental Pneumatic Device Štastný, Petr ; Brablc, Martin (referee) ; Dobossy, Barnabás (advisor) This bachelor thesis deals with finding state indicators of pneumatic cylinder using algorithms of machine learning and data mining. The goal was to determine measurable quantity and algorithm of its evaluating, using which would be possible to identify state and sources of failures. The data of behavior of pneumatic cylinder were acquished on testing stand, which was equipped by sensors of 16 different quantities. Postprocessing and evaluating of the data took place in Matlab tools, particularly Diagnostic Feature Designer and Classification Learner. Detailed record
	Application of Algorithms of Predictive Maintanence for RUL Estimation Dvořák, Jan ; Brablc, Martin (referee) ; Dobossy, Barnabás (advisor) The aim of this thesis is to acquaint the reader with the areas of predictive maintenance and its algorithms within its prognostic part. The remaining useful life of the system will be determined on the data sets and the performed experiment using prognostic models in accordance with the algorithms described in the research section. MATLAB and its other applications described in the work were used for data processing and modeling. Detailed record
	Application of Algorithms of Predictive Maintanence for RUL Estimation Dvořák, Jan ; Brablc, Martin (referee) ; Dobossy, Barnabás (advisor) The aim of this thesis is to acquaint the reader with the areas of predictive maintenance and its algorithms within its prognostic part. The remaining useful life of the system will be determined on the data sets and the performed experiment using prognostic models in accordance with the algorithms described in the research section. MATLAB and its other applications described in the work were used for data processing and modeling. Detailed record
	Application of Predictive Maintenance Algorithms for State Monitoring of an Experimental Pneumatic Device Štastný, Petr ; Brablc, Martin (referee) ; Dobossy, Barnabás (advisor) This bachelor thesis deals with finding state indicators of pneumatic cylinder using algorithms of machine learning and data mining. The goal was to determine measurable quantity and algorithm of its evaluating, using which would be possible to identify state and sources of failures. The data of behavior of pneumatic cylinder were acquished on testing stand, which was equipped by sensors of 16 different quantities. Postprocessing and evaluating of the data took place in Matlab tools, particularly Diagnostic Feature Designer and Classification Learner. Detailed record

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