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

Zhi Chen,Ruiliang Zhang,Tie Wang,Fengshou Gu

doi:10.1155/2015/374083

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 7 Hz and 40 Hz 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.

Highlights

The chassis frame in a heavy-duty truck is often subjected to extreme loads because the vehicle operates in different development areas, such as mining and construction sites where the road condition is commonly very poor and with high risks for driving
It has been found that most of studies employ a combination of finite element (FE) analysis and an experimental verification [1,2,3] to determine the dynamic properties
In reality, the frame is assembled with different subsystems and can behave very differently because of the effect of added distribution masses and different nonstandard constraints which are difficult to be modelled in FE calculation and are measured through conventional controlled excitations

Summary

Introduction

The chassis frame in a heavy-duty truck is often subjected to extreme loads because the vehicle operates in different development areas, such as mining and construction sites where the road condition is commonly very poor and with high risks for driving. As the road excitations are not completely stationary and the response data can be heavily influenced by different noises such as secondary vibrations from the components connected to the frame and interfering excitations from power trains, it has been found that the direct use of measured signals, including their covariances as the input for the SSI/ref algorithm, can lead to numerous deceptive modes and it is difficult to obtain a consistent result for the frame dynamics analysis. In studies of [12, 13], the use of cross-correlation functions between response channels was proposed and showed effectiveness for both stationary and nonstationary white noise ambient excitation signals for modal parameters identification Based on these studies including the super performance of correlation function in extracting periodic signals in strong noisy data, an average correlation signal based SSI/ref is proposed to suppress the noise and nonstationary responses measured on the frame for identifying its dynamic properties.

Background and Methodology

Verification of the Average Correlation Signal Based SSI

Characterization of the Modal Responses to Different Roads

Findings

Conclusions