Health Monitoring System for Engine Dynamometer Shafts Based on Adaptive Parameter Identification.pdf (1.02 MB)
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Health monitoring system for transmission shafts based on adaptive parameter identification

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journal contribution
posted on 11.06.2018, 08:45 by Ioannis Souflas, Antonios PezouvanisAntonios Pezouvanis, Kambiz EbrahimiKambiz Ebrahimi
A health monitoring system for a transmission shaft is proposed. The solution is based on the real-time identification of the physical characteristics of the transmission shaft i.e. stiffness and damping coefficients, by using a physical oriented model and linear recursive identification. The efficacy of the suggested condition monitoring system is demonstrated on a prototype transient engine testing facility equipped with a transmission shaft capable of varying its physical properties. Simulation studies reveal that coupling shaft faults can be detected and isolated using the proposed condition monitoring system. Besides, the performance of various recursive identification algorithms is addressed. The results of this work recommend that the health status of engine dynamometer shafts can be monitored using a simple lumped-parameter shaft model and a linear recursive identification algorithm which makes the concept practically viable.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Mechanical Systems and Signal Processing

Volume

104

Pages

673 - 687

Citation

SOUFLAS, I., PEZOUVANIS, A. and EBRAHIMI, K.M., 2017. Health monitoring system for transmission shafts based on adaptive parameter identification. Mechanical Systems and Signal Processing, 104, pp.673-687.

Publisher

© Elsevier

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

15/11/2017

Publication date

2017

Notes

This paper was accepted for publication in the journal Mechanical Systems and Signal Processing and the definitive published version is available at https://doi.org/10.1016/j.ymssp.2017.11.023

ISSN

0888-3270

Language

en