Reproduction of Equipment Wear Characteristics with Kernel Regression
Karol Koniuszewski , Paweł Domański
AbstractAbstract—Predictive maintenance task is of crucial role for any plant equipment supervision and scheduling of service activities. For this purpose it should be known what is current aging status of any equipment. Presented approach assumes that we know the nominal (starting) element curve and a damage one as well. It is also assumed that the aging course progresses according to some good practice aging Lorentz attrition (wear) curve. Kernel Regression algorithm is used to perform curve adaptation and then enabled to identify current element status and varying aging curve. The approach is tested on SISO and two-dimensional examples proving its ability to reproduce equipment aging characteristics.
|Publication size in sheets||0.5|
|Book||Proceedings of 21st IEEE Conference on Method and Models in Automation and Robotics, 2016, IEEE Institute of electrical and Electronics Engineers, ISBN 978-1-5090-1715-7, 1285 p., DOI:10.1109/MMAR.2016.7575223|
|project||Development of methodology of control, decision support and production management. Project leader: Zieliński Cezary,
, Phone: 5102, start date 19-05-2015, end date 31-12-2016, 504/02233/1031, Completed
|Score|| = 15.0, 27-03-2017, BookChapterMatConf|
= 15.0, 27-03-2017, BookChapterMatConf
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