Open AccessNew regression model for predicting hand‐arm vibration (HAV) of Malaysian Army (MA) three‐tonne truck steering wheels
Author(s) -
Aziz Shamsul Akmar Ab,
Nuawi Mohd Zaki,
Nor Mohd Jailani Mohd
Publication year - 2015
Publication title -
journal of occupational health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.664
H-Index - 59
ISSN - 1348-9585
DOI - 10.1539/joh.14-0206-oa
Subject(s) - truck , tonne , automotive engineering , vibration , regression analysis , metric (unit) , engineering , environmental science , aeronautics , computer science , statistics , mathematics , waste management , operations management , acoustics , physics
New regression model for predicting hand‐arm vibration (HAV) of Malaysian Army (MA) three‐tonne truck steering wheels: Shamsul Akmar Ab A ziz , et al . Department of Mechanical and Material Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), MalaysiaObjective 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 Ikaz 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 ( Ƶ v ∞ ), and the I‐kaz Vibro display. The I‐kaz Vibro displays also showed greater scatterings, indicating that the values of Ƶ v ∞ and A(8) were increasing. Prediction of HAV exposure was done using the developed regression model and graphical representations of Ƶ v ∞ . The results of the regression model showed that Ƶ ∞ 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 ( R 2 ) 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.