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Krill herd optimization based fault diagnosis for hybrid mechatronic system

Yu, M., Liu, X., Xiao, C., Jin, X., Wang, H.ORCID: 0000-0003-2789-9530 and Jiang, C. (2019) Krill herd optimization based fault diagnosis for hybrid mechatronic system. In: Chinese Control Conference (CCC) 2019, 27 - 30 July 2019, Guangzhou, China

Link to Published Version: https://doi.org/10.23919/ChiCC.2019.8865670
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Abstract

This paper deals with the fault diagnosis of hybrid mechatronic system by using bond graph and krill herd optimization algorithm. The hybrid mechatronic system consists of a DC motor, transmission shaft, gearbox and a load while the different gear ratios are considered for mode switching. The fault diagnosis is based on the analytical redundancy relations (ARR) and the fault signature matrix (FSM) which are derived from the hybrid bond graph (HBG) with the linear friction considered. Furtherly, for the purpose of the fault estimation, the krill herd optimization is utilized to identify the fault parameters from the suspected fault candidates (SFC) and obtain the exact values of the fault parameters. A simulation example in MATLAB is conducted to verify the effectiveness of the proposed method.

Item Type: Conference Paper
URI: http://researchrepository.murdoch.edu.au/id/eprint/52818
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