New regression model for predicting hand-arm vibration (HAV) of Malaysian Army (MA) three-tonne truck steering wheels

Abstract

Objective: The objective of this study was to present a new method for determination of hand-arm vibration (HAV) in Malaysian Army (MA) three-tonne truck steering wheels based on changes in vehicle speed using regression model and the statistical analysis method known as Integrated Kurtosis-Based Algorithm for Z-Notch Filter Technique Vibro (I-kaz Vibro). Methodology: The test was conducted for two different road conditions, tarmac and dirt roads. HAV exposure was measured using a Brüel & Kjær Type 3649 vibration analyzer, which is capable of recording HAV exposures from steering wheels. The data was analyzed using I-kaz Vibro to determine the HAV values in relation to varying speeds of a truck and to determine the degree of data scattering for HAV data signals. Results: Based on the results obtained, HAV experienced by drivers can be determined using the daily vibration exposure A(8), I-kaz Vibro coefficient (Z_v^∞), and the I-kaz Vibro display. The I-kaz Vibro displays also showed greater scatterings, indicating that the values of Z_v^∞ and A(8) were increasing. Prediction of HAV exposure was done using the developed regression model and graphical representations of Z_v^∞. The results of the regression model showed that Z_v^∞ increased when the vehicle speed and HAV exposure increased. Discussion: For model validation, predicted and measured noise exposures were compared, and high coefficient of correlation (R²) values were obtained, indicating that good agreement was obtained between them. By using the developed regression model, we can easily predict HAV exposure from steering wheels for HAV exposure monitoring.