Identification of the mass inertia moment in an electromechanical system based on wavelet-neural method
Authors:
- Marcin Tomczyk,
- Barbara Borowik,
- Bohdan Borowik
Abstract
This paper presents the results of testing of a complex electromechanical system model. These results have been obtained for accepted in simu-lations the method of identifying an inertia moment of reduced masses on shaftof induction motor drive during the changes of a backlash zone width. The effectiveness of correct diagnostic conclusions enables coefficients anal-ysisof testing signals wavelet expansion as well as weights of a supervised learning neural network. The earlier fault detection of five important state variables, which describe physical quantities of chosen complex electro-mechanical system has been verified forits correctness during the backlash zonewidth monitoring in the early stage of its gradual rise. The proposed here algorithm with mass inertia moment changes has proved to be an effectivediagnostic method in the area of system changeable dynamic conditions and this has been shown in the resulting changes of backlash zone width.
- Record ID
- CUT5e3ec3911dc444a8b892e8c16ba1793d
- Publication categories
- ;
- Author
- Journal series
- APPLIED COMPUTER SCIENCE, ISSN 1895-3735, e-ISSN 2353-6977
- Issue year
- 2018
- Vol
- 14
- No
- 2
- Pages
- 96-111
- Other elements of collation
- schem.; tab.; Bibliografia (na s.) - 110; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - vol. 14, no. 2
- Keywords in English
- induction motor, wavelet transformation, backlash zone, neural networks
- DOI
- DOI:10.23743/acs-2018-16 Opening in a new tab
- URL
- http://www.acs.pollub.pl/index.php?option=com_content&view=article&id=383:-medical-imaging-and-3d-reconstruction-for-obtaining-the-geometrical-and-physical-model-of-a-congenital-bilateral-radio-ulnar-synostosis&catid=77:vol-14-no-22018&Itemid=147 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 11
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT5e3ec3911dc444a8b892e8c16ba1793d/
- URN
urn:pkr-prod:CUT5e3ec3911dc444a8b892e8c16ba1793d
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