Tatsuro Ishibashi
Meidensha Corporation, Japan
Bing Han
Department of Mechanical & Physical Engineering, Osaka City University, Japan
Tadao Kawai
Department of Mechanical & Physical Engineering, Osaka City University, Japan
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http://dx.doi.org/10.3384/ecp17132381Published in: Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017
Linköping Electronic Conference Proceedings 132:43, p. 381-387
Published: 2017-07-04
ISBN: 978-91-7685-575-1
ISSN: 1650-3686 (print), 1650-3740 (online)
This paper presents our new rotating machinery library. Diagnosing the complex system accurately based on stochastic method requires an enormous amount of data, both with and without faults. Acquiring operation data with all kinds of faults for each components is very hard and costly. To generate data for rotating machinery diagnosis, we developed rotating machinery library using Modelica. It provides the basic components such as rotor, shaft, bearing, coupling, housing and support. Its component models are implemented on basis of rotor dynamics theory. This library makes it possible accessing rotating machinery operation data with various faults such as unbalanced rotor, shaft bending and ball bearing faults. To validate our models, we compared both Modelica simulation and experiment with a rotor kit as a test case.
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