Article | Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017 | Rotating Machinery Library for Diagnosis Linköping University Electronic Press Conference Proceedings
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Title:
Rotating Machinery Library for Diagnosis
Author:
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
DOI:
10.3384/ecp17132381
Download:
Full text (pdf)
Year:
2017
Conference:
Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017
Issue:
132
Article no.:
043
Pages:
381-387
No. of pages:
7
Publication type:
Abstract and Fulltext
Published:
2017-07-04
ISBN:
978-91-7685-575-1
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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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.

Keywords: Rotating Machinery, Vibration, Diagnosis

Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017

Author:
Tatsuro Ishibashi, Bing Han, Tadao Kawai
Title:
Rotating Machinery Library for Diagnosis
DOI:
http://dx.doi.org/10.3384/ecp17132381
References:

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Matthew Klenk, Johan de Kleer, Daniel G. Bobrow and Bill Janssen Using Modelica Models for Qualitative Reasoning. Proceedings of the 10th International Modelica Conference pp. 205-211, Lund, Sweden. doi: https://doi.org/10.3384/ecp14096205

Raj Minhas, Johan de Kleer, Ion Matei, Bhaskar Saha, Bill Janssen, Daniel G. Bobrow and Tolga Kurtoglu. Using Fault Augmented Modelica Models for Diagnostics. Proceedings of the 10th International Modelica Conference 2014, pp. 437-445, Lund, Sweden. doi: https://doi.org/10.3384/ecp14096437

Osami Matsushita, Masato Tanaka, Hiroshi Kanki, Masao Kobayashi and Patrick Keogh Vibrations of Rotating Machinery Springer Japan, 2017. doi: https://doi.org/10.1007/978-4-431-55456-1

Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017

Author:
Tatsuro Ishibashi, Bing Han, Tadao Kawai
Title:
Rotating Machinery Library for Diagnosis
DOI:
https://doi.org10.3384/ecp17132381
Note: the following are taken directly from CrossRef
Citations:
  • Aneesh G. Nath, Sandeep S. Udmal & Sanjay Kumar Singh (2020). Role of artificial intelligence in rotor fault diagnosis: a comprehensive review. Artificial Intelligence Review, : . DOI: 10.1007/s10462-020-09910-w


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