Title:	Specific Data Sampling and Filtering Helps to Detect and Isolate Periodic Disturbances
Authors:	Ettler, Pavel Puchr, Ivan
Citation:	ETTLER, P. PUCHR, I. Specific Data Sampling and Filtering Helps to Detect and Isolate Periodic Disturbances. In Proceedings of the 30th Mediterranean Conference on Control and Automation (MED 2022). Vouliagmeni, Greece: IEEE, 2022. s. 755-760. ISBN: 978-1-66540-673-4 , ISSN: neuvedeno
Issue Date:	2022
Publisher:	IEEE
Document type:	konferenční příspěvek ConferenceObject
URI:	2-s2.0-85136250945 http://hdl.handle.net/11025/50801
ISBN:	978-1-66540-673-4
ISSN:	neuvedeno
Keywords in different language:	digital filtering;Fault detection and isolation;FFT;modelling;predictive maintenance;rolling mills
Abstract in different language:	The Fast Fourier Transform is the unique tool enabling to engage the frequency domain analysis in detection and isolation of periodic disturbances in industrial processes containing rotating elements. Nevertheless, there exist particular problems where the time-domain examination of oscillations can provide equally or more accurate results with less effort. Specific data sampling, filtering and process modelling are introduced in the paper with the aim to classify sources of oscillations in the process of cold rolling. Although the FFT may not be part of the method itself, its use allows to illustrate the presented research.
Rights:	Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © IEEE
Appears in Collections:	Konferenční příspěvky / Conference papers (NTIS) OBD

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.