A wavelet interpretation of vehicle-pavement interaction

Papagiannakis, A. T.; Zelelew, H. M.; Muhunthan, Balasingam

doi:10.1080/10298430701309378

Cited by 24 publications

(17 citation statements)

References 16 publications

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“…Wavelet Transform is a signal analysis technique that decomposes signals with localised wave-like functions, which represents signals in both the frequency domain and the spatial domain simultaneously and, as such, provides a good representation of localised signal features [37]. Wavelet Transform has been used extensively to analyse and process 2D roughness profiles, often for the detection of discontinuities in surfaces [38][39][40][41][42][43][44][45]. However, Wavelet Transform has not been used for the characterisation and measurement of wear.…”

Section: Frequency Analysis Of Surface Profiles and Wavelet Transformmentioning

confidence: 99%

A new methodology for measuring galling wear severity in high strength steels

Voss

Pereira

Rolfe

et al. 2017

Wear

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A new methodology for measuring galling wear severity in high strength steels, Wear, http://dx. AbstractWith the increased usage of Advanced High Strength Steels, galling wear has become a significant challenge for sheet metal stamping industries. Galling, in particular, can have a large economic impact due to the high costs and lost productivity associated with manual monitoring, refinishing/resurfacing damaged tooling and formed parts, and the need to apply expensive treatments or coatings to tool surfaces. This has led to a push for automated galling wear detection systems. However, developing such systems requires an accurate measurement of galling wear severity that can be easily implemented in industrial situations. Parameters used for measuring galling wear are often difficult to collect in large industrial style trials, and can be inaccurate as they are not targeted towards characterising the localised features associated with galling wear damage. In this study, a new galling wear characterisation and measurement methodology is introduced that accurately measures galling wear severity by targeting the localised features on sheet metal parts. This methodology involves calculating Discrete Wavelet Transform detail coefficients of 2D surface profiles. A case study on a series of deep drawn channel parts demonstrates the accuracy of the Discrete Wavelet Transform methodology when compared to visual assessment of galling wear severity. Based on comparison to visual assessment the presented Discrete Wavelet Transform galling wear measurement methodology outperforms other commonly used wear measures. The methodology provides a targeted, repeatable and non-subjective measure of galling wear severity. The specific outcome of this work provides an important tool for research into galling wear monitoring and detection systems in sheet metal forming, and the study of galling wear and its prevention in general.

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Section: Frequency Analysis Of Surface Profiles and Wavelet Transformmentioning

confidence: 99%

A new methodology for measuring galling wear severity in high strength steels

Voss

Pereira

Rolfe

et al. 2017

Wear

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“…This makes its use in pavement management more attractive than other signal processing tools such as Power Spectral Density analysis, where surface defects can be located along the length of a section. The energy measure in each waveband represents the extent of variation of pavement profile elevation in that waveband [6]. These outputs are generated separately for the profile data in the inner and outer wheel paths.…”

Section: Wavelet Analysismentioning

confidence: 99%

“…It decomposes the longitudinal road surface profile signal into a number of consecutive wavebands and has been used in a number of studies to evaluate road roughness characteristics [2][3][4][5][6]. The first step in performing DWO is to select a suitable mother wavelet for the decomposition.…”

Section: Wavelet Analysismentioning

confidence: 99%

Two applications of wavelet analysis for project level pavement management

Hassan¹

2015

Int. J. SDP

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Wavelet analysis is a signal processing technique that can be used to decompose longitudinal road surface profile signal into a number of wavebands. The outputs of the analysis include the signals (elevation vs. distance) and energies (a measure of elevation variation, i.e. surface roughness) in the different wavebands. The application of wavelet analysis in road pavement management at project level is described herein through two case studies. The first involves using wavelet analysis outputs in identifying and locating excitation sources of dynamic wheel loads (DWL) along a road section. The second case study involves using these outputs in assessing the effectiveness of rehabilitation treatment in reducing surface roughness in the different wavebands along the length of a road section. The outcomes of this research study indicate that the proposed applications are effective in addressing the intended purposes. Study findings also indicate that using HATI to highlight sections subject to high DWL at network level is promising. However, further testing is required to confirm its suitability at different speeds and operating environments.These assessment approaches help asset managers to identify and locate surface characteristics that increase pavement damage, propose suitable treatments and assess the quality of these treatments. In addition to achieving value for money, adopting such approach would ensure their assets' sustainability, mobility and comfort of all road users, in particular truck drivers. Long wavelength roughness with high contributions to DWL also has a detrimental effect on heavy vehicle ride.

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“…Discrete wavelet transform (DWT) decomposes the road profile signal into a number of wavebands and has been used in a number of studies to evaluate road roughness characteristics [11][12][13][14]. DWT was performed using a Matlab code to decompose the longitudinal profile data of the test sections into different subbands.…”

Section: Wavelet Analysismentioning

confidence: 99%

“…The low frequency range is 1.5-4 Hz, which corresponds to rigid body bounce and pitch modes. The high frequency range is [8][9][10][11][12][13][14][15] Hz and corresponds to rigid body hop mode, axle roll and load sharing suspension pitch modes [2,3]. At highway speeds along major freight routes (80-100 km/hr) in Victoria / Australia, longitudinal road profile wavelengths that cause excitations of DWL at low and high frequencies fall in the range of (5.6-18.5m) and (1.5-3.5m) respectively.…”

Section: Introductionmentioning

confidence: 99%

An approach for identifying and locating pavement sections subject to high dynamic wheel loads

Hassan

2013

WIT Transactions on the Built Environment

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A study was conducted to assess the effectiveness of the International Roughness Index (IRI) and Heavy Articulated Truck Index (HATI) to identify pavement sections subject to high dynamic wheel loads (DWL). HATI is a profile-based roughness index used by some Australian road agencies to identify road sections providing poor ride quality for heavy trucks. The longitudinal surface profile data of ten pavement sections were used to simulate DWL caused by a typical fully laden articulated truck and to calculate relevant IRI and HATI values. The results of this limited study showed that HATI could be a good indicator of road sections subject to high DWL. For the sections tested herein, it was found to have strong correlation with the Dynamic Aggregate Force Coefficient (DAFC), a measure of road damage due to DWL. The study also included using wavelet analysis to identify and locate the sources of DWL excitation for sections with different HATI values. Wavelet signals of longitudinal profile data in relevant roughness subbands proved to be effective in identifying and locating profile characteristics responsible for exciting truck body motions that induce high DWL. The approach proposed herein would help road agencies to adopt maintenance programs to eliminate or minimise the additional damage and ensure sustainability of their assets' and mobility of road users.

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A wavelet interpretation of vehicle-pavement interaction

Cited by 24 publications

References 16 publications

A new methodology for measuring galling wear severity in high strength steels

A new methodology for measuring galling wear severity in high strength steels

Two applications of wavelet analysis for project level pavement management

An approach for identifying and locating pavement sections subject to high dynamic wheel loads

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