Application of Predictive Maintenance Algorithms for State Monitoring of an Experimental Pneumatic Device

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