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Characterizing the Dynamic Response of a Chassis Frame in a Heavy-Duty Dump Vehicle based on an Improved Stochastic System Identification

Chen, Zhi, Wang, Tie, Gu, Fengshou and Jing, Peng (2015) Characterizing the Dynamic Response of a Chassis Frame in a Heavy-Duty Dump Vehicle based on an Improved Stochastic System Identification. Shock and Vibration. ISSN 1875-9203

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Abstract

This paper presents an online method for the assessment of the dynamic performance of the chassis frame in a heavy-duty dump truck based on a novel stochastic subspace identification (SSI) method. It introduces the use of an average correlation signal as the input data to conventional SSI methods in order to reduce the noisy and nonstationary contents in the vibration signals from the frame, allowing accurate modal properties to be attained for realistically assessing the dynamic behaviour of the frame when the vehicle travels on both bumped and unpaved roads under different operating conditions. The modal results show that the modal properties obtained online are significantly different from the offline ones in that the identifiable modes are less because of the integration of different vehicle systems onto the frame. Moreover, the modal shapes between 7Hz and 40Hz clearly indicate the weak section of the structure where earlier fatigues and unsafe operations may occur due to the high relative changes in the modal shapes. In addition, the loaded operations show more modes which cause high deformation on the weak section. These results have verified the performance of the proposed SSI method and provide reliable references for optimizing the construction of the frame.

Item Type: Article
Subjects: T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
School of Computing and Engineering > Automotive Engineering Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
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Depositing User: Cherry Edmunds
Date Deposited: 24 Jul 2015 10:28
Last Modified: 01 Dec 2015 18:21
URI: http://eprints.hud.ac.uk/id/eprint/25290

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