Predicting whole-body vibration (WBV) exposure of Malaysian Army three-tonne truck drivers using Integrated Kurtosis-Based Algorithm for Z-Notch Filter Technique 3D (I-kaz 3D)

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Abstract

The objective of this study is to present a new method for determination of whole-body vibration (WBV) exposure in the driver's seat of Malaysian Army (MA) three-tonne trucks based on changing vehicle speed using regression models and the statistical analysis method known as Integrated Kurtosis-Based Algorithm for Z-Notch Filter Technique 3D (I-kaz 3D). The study was conducted on two different road conditions; tarmac and dirt roads. WBV exposure was measured using a Brüel & Kjær Type 3649 vibration analyser, which is capable to record WBV exposures from the driver seat and vibration from the truck, and comparisons were made between the two types of roads. The data was analysed using I-kaz 3D to determine the WBV values in relation to varying speeds of the truck and to determine the degree of data scattering for WBV data signals. Based on the results obtained, WBV exposure levels can be presented using frequency weighted root mean square (RMS) accelerations (a w ), vibration dose value equivalent to 8h (VDV(8)), I-kaz 3D coefficient (Z3D∞) and the I-kaz 3D display. The I-kaz 3D displays showed greater scatterings, indicating that the values of Z3D∞ and VDV(8) were getting higher. The prediction of WBV exposure was done using the developed regression models and graphical representations of Z3D∞. The results of the regression models showed that Z3D∞ increased when vehicle speed and WBV exposure increased. For model validation, predicted and measured noise exposures were compared, with high coefficient of correlation (R 2 ) values obtained, indicating that a good agreement was obtained between them. By using the developed regression models, we can easily predict WBV exposure in the driver's seat for WBV exposure monitoring.

Original languageEnglish
JournalInternational Journal of Industrial Ergonomics
DOIs
Publication statusAccepted/In press - 19 Aug 2014

Fingerprint

Truck drivers
Notch filters
Motor Vehicles
Vibration
military
driver
Seats
Trucks
Display devices
Scattering
regression
road
Values
Statistical methods
Monitoring
statistical analysis
Statistical Models

Keywords

  • Integrated Kurtosis-Based Algorithm for Z-Notch Filter Technique 3D (I-kaz 3D)
  • Malaysian Army (MA) three-tonne trucks
  • Regression models
  • Vibration dose values (VDV)
  • Whole-body vibration (WBV)

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Public Health, Environmental and Occupational Health

Cite this

@article{3368f131b7dc4838b61219a90249def2,
title = "Predicting whole-body vibration (WBV) exposure of Malaysian Army three-tonne truck drivers using Integrated Kurtosis-Based Algorithm for Z-Notch Filter Technique 3D (I-kaz 3D)",
abstract = "The objective of this study is to present a new method for determination of whole-body vibration (WBV) exposure in the driver's seat of Malaysian Army (MA) three-tonne trucks based on changing vehicle speed using regression models and the statistical analysis method known as Integrated Kurtosis-Based Algorithm for Z-Notch Filter Technique 3D (I-kaz 3D). The study was conducted on two different road conditions; tarmac and dirt roads. WBV exposure was measured using a Br{\"u}el & Kj{\ae}r Type 3649 vibration analyser, which is capable to record WBV exposures from the driver seat and vibration from the truck, and comparisons were made between the two types of roads. The data was analysed using I-kaz 3D to determine the WBV values in relation to varying speeds of the truck and to determine the degree of data scattering for WBV data signals. Based on the results obtained, WBV exposure levels can be presented using frequency weighted root mean square (RMS) accelerations (a w ), vibration dose value equivalent to 8h (VDV(8)), I-kaz 3D coefficient (Z3D∞) and the I-kaz 3D display. The I-kaz 3D displays showed greater scatterings, indicating that the values of Z3D∞ and VDV(8) were getting higher. The prediction of WBV exposure was done using the developed regression models and graphical representations of Z3D∞. The results of the regression models showed that Z3D∞ increased when vehicle speed and WBV exposure increased. For model validation, predicted and measured noise exposures were compared, with high coefficient of correlation (R 2 ) values obtained, indicating that a good agreement was obtained between them. By using the developed regression models, we can easily predict WBV exposure in the driver's seat for WBV exposure monitoring.",
keywords = "Integrated Kurtosis-Based Algorithm for Z-Notch Filter Technique 3D (I-kaz 3D), Malaysian Army (MA) three-tonne trucks, Regression models, Vibration dose values (VDV), Whole-body vibration (WBV)",
author = "Aziz, {Shamsul Akmar Ab} and Nuawi, {Mohd. Zaki} and {Mohd Nor}, {Mohd. Jailani}",
year = "2014",
month = "8",
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doi = "10.1016/j.ergon.2015.08.008",
language = "English",
journal = "International Journal of Industrial Ergonomics",
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AU - Aziz, Shamsul Akmar Ab

AU - Nuawi, Mohd. Zaki

AU - Mohd Nor, Mohd. Jailani

PY - 2014/8/19

Y1 - 2014/8/19

N2 - The objective of this study is to present a new method for determination of whole-body vibration (WBV) exposure in the driver's seat of Malaysian Army (MA) three-tonne trucks based on changing vehicle speed using regression models and the statistical analysis method known as Integrated Kurtosis-Based Algorithm for Z-Notch Filter Technique 3D (I-kaz 3D). The study was conducted on two different road conditions; tarmac and dirt roads. WBV exposure was measured using a Brüel & Kjær Type 3649 vibration analyser, which is capable to record WBV exposures from the driver seat and vibration from the truck, and comparisons were made between the two types of roads. The data was analysed using I-kaz 3D to determine the WBV values in relation to varying speeds of the truck and to determine the degree of data scattering for WBV data signals. Based on the results obtained, WBV exposure levels can be presented using frequency weighted root mean square (RMS) accelerations (a w ), vibration dose value equivalent to 8h (VDV(8)), I-kaz 3D coefficient (Z3D∞) and the I-kaz 3D display. The I-kaz 3D displays showed greater scatterings, indicating that the values of Z3D∞ and VDV(8) were getting higher. The prediction of WBV exposure was done using the developed regression models and graphical representations of Z3D∞. The results of the regression models showed that Z3D∞ increased when vehicle speed and WBV exposure increased. For model validation, predicted and measured noise exposures were compared, with high coefficient of correlation (R 2 ) values obtained, indicating that a good agreement was obtained between them. By using the developed regression models, we can easily predict WBV exposure in the driver's seat for WBV exposure monitoring.

AB - The objective of this study is to present a new method for determination of whole-body vibration (WBV) exposure in the driver's seat of Malaysian Army (MA) three-tonne trucks based on changing vehicle speed using regression models and the statistical analysis method known as Integrated Kurtosis-Based Algorithm for Z-Notch Filter Technique 3D (I-kaz 3D). The study was conducted on two different road conditions; tarmac and dirt roads. WBV exposure was measured using a Brüel & Kjær Type 3649 vibration analyser, which is capable to record WBV exposures from the driver seat and vibration from the truck, and comparisons were made between the two types of roads. The data was analysed using I-kaz 3D to determine the WBV values in relation to varying speeds of the truck and to determine the degree of data scattering for WBV data signals. Based on the results obtained, WBV exposure levels can be presented using frequency weighted root mean square (RMS) accelerations (a w ), vibration dose value equivalent to 8h (VDV(8)), I-kaz 3D coefficient (Z3D∞) and the I-kaz 3D display. The I-kaz 3D displays showed greater scatterings, indicating that the values of Z3D∞ and VDV(8) were getting higher. The prediction of WBV exposure was done using the developed regression models and graphical representations of Z3D∞. The results of the regression models showed that Z3D∞ increased when vehicle speed and WBV exposure increased. For model validation, predicted and measured noise exposures were compared, with high coefficient of correlation (R 2 ) values obtained, indicating that a good agreement was obtained between them. By using the developed regression models, we can easily predict WBV exposure in the driver's seat for WBV exposure monitoring.

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KW - Malaysian Army (MA) three-tonne trucks

KW - Regression models

KW - Vibration dose values (VDV)

KW - Whole-body vibration (WBV)

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