Health Monitoring System for Engine Dynamometer Shafts Based on Adaptive Parameter Identification.pdf (1.02 MB)
Health monitoring system for transmission shafts based on adaptive parameter identification
journal contribution
posted on 2018-06-11, 08:45 authored by Ioannis Souflas, Antonios PezouvanisAntonios Pezouvanis, Kambiz EbrahimiKambiz EbrahimiA 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 ProcessingVolume
104Pages
673 - 687Citation
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
© ElsevierVersion
- 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
2017-11-15Publication date
2017Notes
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.2017.11.023ISSN
0888-3270Publisher version
Language
- en