New Regression Models for Predicting Noise Exposure in the Driver's Compartment of Malaysian Army Three-Tonne Trucks

Aziz, Shamsul Akmar Ab; Nuawi, Mohd. Zaki; Nor, Mohd Jailani Mohd; Daruis, Dian Darina Indah

doi:10.1155/2014/616093

Cited by 4 publications

(7 citation statements)

References 18 publications

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“…The noise level L Aeq while driving at a speed of 20 kmh -1 on the rough surface of the dirt road (71.2 dB(A)) was higher than that while driving on the smooth surface of the tarmac road (70.9 dB(A)). On the day of the test, the weather was sunny, with temperatures ranging from 32 to 34°C, and both road surfaces were dry 7) . The study by Aziz et al 13) on MA three-tonne trucks on paved roads that were not carrying loads found that the z-axis was the most dominant, with the highest RMS frequency-weighted vibration acceleration values compared with the x-and y-axes.…”

Section: Methodsmentioning

confidence: 99%

“…This dirt road was an unpaved road made from subgrade materials and had many random irregularities that produced excessive casual vibrations. In comparison, the tarmac road was a flat, smooth surface with occasional unevenness, which resulted in minimum disturbances 7) . The value of Z v ∞ depends on the test condition's parameters, which in the case of this study were vehicle speed and A (8) .…”

Section: Removedmentioning

confidence: 99%

“…Studies concerning the relationship between other I-kaz coefficients (Z ∞ ) with noise and vibration parameters from vehicles have been conducted by several researchers. Aziz et al 7) developed regression models for predicting noise exposure using linear polynomial regression, which was then used to plot graphs for the correlation between Z ∞ and the A-weighted equivalent sound pressure level (L Aeq ) for tarmac and dirt roads. Regression analyses between the I-kaz 3D coefficient (Z…”

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confidence: 99%

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

Aziz

Nuawi

Nor

2015

Journal of Occupational Health

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Shamsul Akmar Ab AZIZ, et al.: New regression model for predicting hand-arm vibration (HAV) oped regression model, we can easily predict HAV exposure from steering wheels for HAV exposure monitoring. ( Moving vehicles produce noise and vibrations that cause discomfort to drivers and passengers. There are many sources of this noise and vibrations, such as the engine, wind, chassis, and road-tire interaction. Vibration causes excitation of the chassis structure, which is transmitted via mechanical vibrations into the driver's compartment, where it can be felt as vehicle interior vibrations in the seat, steering wheel, and/or body floor 1) . Hand-arm vibration (HAV) is the vibration received by hands that are in direct contact with the surfaces of vibrating parts, such as steering wheels, and hand tools that produce vibrations, such as hand drills, chain saws, and grinders. Exposure to vibration from a vehicle depends on several factors, such as the type and design of the vehicle, speed, and environmental conditions. There are some physical variables relevant to the effects of hand-transmitted vibration, such as magnitude, frequency, direction of vibration, duration of exposure, area of contact with vibration, contact force (grip and push forces), hand posture, and environment 2) . HAV has been recognized as a significant hazard for the health and safety of workers. HAV syndrome (HAVS) is a general term embracing various kinds of health issues, including vascular disorders generally known as "vibration-induced white finger" (VWF). It causes impaired blood circulation and blanching of affected fingers and parts of the hand, neurological and musculoskeletal damage leading to numb- ∞ 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 devel-

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Section: Methodsmentioning

confidence: 99%

Section: Removedmentioning

confidence: 99%

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confidence: 99%

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

Aziz

Nuawi

Nor

2015

Journal of Occupational Health

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“…Engine idle vibration that occurs at the steering wheel can be simulated using the amplitude modulated acceleration time history, with the sinusoidal carrier reported to provide the best reproduction. 4,5 The process of detecting defects and problem solving in engineering has been performed using time and frequency domain analytical methods. Despite the frequency domain using dynamic response data source equal to the time domain, the amount of useful raw data is remarkably reduced through transformation and is more often used because it is easier to do the calculation and data processing.…”

Section: Selection Of Data For Analysis Of Hav In Steering Wheelsmentioning

confidence: 99%

“…Aziz et al 4 produced a regression model for predicting noise exposure in the driver's compartment, while Aziz et al 5 studied on whole body vibration (WBV) exposure from the vehicle seat. This paper aims to describe a new statistical analysis method, I-kaz Vibro, for monitoring hand-arm vibration (HAV) in MA three-tonne truck steering wheels.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Of Hand-Arm Vibration

Aziz

Nuawi²,

Nor

2017

IJAV

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This paper aims to describe the development of a statistical analysis method called the integrated kurtosis-based algorithm for a Z-notch filter (I-kaz) Vibro for monitoring hand-arm vibration (HAV) in Malaysian Army (MA) three-tonne truck steering wheels. HAV from the steering wheel was measured using a single axis piezoelectric accelerometer that was connected to a Brüel & Kjaer Type 3649 vibration analyser. The raw acceleration data was integrated to obtain velocity data and reintegrated to obtain displacement data. The data was analysed using I-kaz Vibro to determine the vibration values in relation to varying speeds of the truck and to determine the degree of data scattering for three-axial raw data signals for acceleration, velocity, and displacement in the x-, y-, and z-axes, respectively. Based on the results obtained, HAV experienced by the drivers can be presented using daily vibration exposure A(8), the I-kaz Vibro coefficient (Z −

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Development of Noise and Vibration Exposure Monitoring System for Malaysian Army (Ma) Three-Tonne Trucks

2016

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Exposure to noise and vibration in the driver’s cabin of Malaysian Army (MA) three-tonne trucks can cause discomfort to the drivers and passengers, and can be classified as hazardous if exposed to more than the standard 8 h time period. This study was conducted for two different road conditions often used by MA vehicles; tarmac and dirt roads. Noise exposure was measured using a DuO sound level meter, which is capable of recording raw sound pressure in Pa. Whole-body vibration (WBV) and hand-arm vibration (HAV) were measured using a Brüel&Kjær Type 3649 vibration analyser, which is capable to record WBV and HAV raw data from the driver’s seat and steering wheel. All the raw data was analysed using the Integrated Kurtosis-Based Algorithm for Z-Notch Filter (I-kazTM). Depending on the type of signal, an exposure model was developed for each measured parameter, with I-kazTM used for noise, I-kaz 3D for WBV and I-kaz Vibro for HAV. With reference to the limits from the respective international standards, a noise and vibration monitoring system for MA three-tonne trucks was then developed.

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New Regression Models for Predicting Noise Exposure in the Driver's Compartment of Malaysian Army Three-Tonne Trucks

Cited by 4 publications

References 18 publications

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

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

Monitoring Of Hand-Arm Vibration

Development of Noise and Vibration Exposure Monitoring System for Malaysian Army (Ma) Three-Tonne Trucks

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