Supervised and unsupervised learning process in damage classification of rolling element bearings
Piotr Czop 1  
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AGH University of Science and Technology, Department of Robotics and Mechatronics, al. Mickiewicza 30, 30-059 Kraków
CORRESPONDING AUTHOR
Marcin Strączkiewicz   

AGH University of Science and Technology, Department of Robotics and Mechatronics, al. Mickiewicza 30, 30-059 Kraków, Al. Mickiewicza 30, Budynek D-1, 30-059 Kraków, Polska
Publication date: 2016-06-04
Submission date: 2016-03-31
Acceptance date: 2016-03-31
 
Diagnostyka 2016;17(2):71–80
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ABSTRACT
Damage classification plays a crucial role in the process of management in nearly every branch of industry. In fact, is becomes equally important as damage detection, since it can provide information of malfunction severity and hence lead to improvement of a production or manufacturing process. Within this paper selected supervised and unsupervised pattern recognition methods are employed for this purpose. The attention of the authors is given to assessment of selection, performance benchmarking and applicability of selected pattern recognition methods. The investigation is performed on the data collected using an experimental test grid and rolling element bearing with deteriorating condition of an outer race.
eISSN:2449-5220
ISSN:1641-6414