Morphological characterisation of engineered surfaces by wavelet transform

Lee, S.-H.; Zahouani, H.; Caterini, R; Mathia, T.

doi:10.1016/s0890-6955(97)00105-3

Cited by 73 publications

(37 citation statements)

References 8 publications

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“…Waveletsψ(t) are small waves that are oscillatory and limited in range [7,8]. Independent variable t is defined as "time" or sometimes "a spatial variable".…”

Section: Wavelet Transform and Its Application For The Analysis Of A mentioning

confidence: 99%

Investigating Advantages and Disadvantages of the Analysis of a Geometrical Surface Structure with the Use of Fourier and Wavelet Transform

Adamczak¹,

Makiela²,

Stępień³

2010

Metrology and Measurement Systems

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Nowadays a geometrical surface structure is usually evaluated with the use of Fourier transform. This type of transform allows for accurate analysis of harmonic components of surface profiles. Due to its fundamentals, Fourier transform is particularly efficient when evaluating periodic signals. Wavelets are the small waves that are oscillatory and limited in the range. Wavelets are special type of sets of basis functions that are useful in the description of function spaces. They are particularly useful for the description of non-continuous and irregular functions that appear most often as responses of real physical systems. Bases of wavelet functions are usually well located in the frequency and in the time domain. In the case of periodic signals, the Fourier transform is still extremely useful. It allows to obtain accurate information on the analyzed surface. Wavelet analysis does not provide as accurate information about the measured surface as the Fourier transform, but it is a useful tool for detection of irregularities of the profile. Therefore, wavelet analysis is the better way to detect scratches or cracks that sometimes occur on the surface. The paper presents the fundamentals of both types of transform. It presents also the comparison of an evaluation of the roundness profile by Fourier and wavelet transforms.

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“…Waveletsψ(t) are small waves that are oscillatory and limited in range [7,8]. Independent variable t is defined as "time" or sometimes "a spatial variable".…”

Section: Wavelet Transform and Its Application For The Analysis Of A mentioning

confidence: 99%

Investigating Advantages and Disadvantages of the Analysis of a Geometrical Surface Structure with the Use of Fourier and Wavelet Transform

Adamczak¹,

Makiela²,

Stępień³

2010

Metrology and Measurement Systems

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“…Some models based on fractals [22] and wavelets [23] give solutions on local simple geometry (roughness to waviness on rectangular shapes). In the case of cylindrical shapes, Fourier Tchebychef models used by Gouskov [24] defines specific form errors such as eccentric or conical.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling of 2D and 3D Assemblies Taking Into Account Form Errors of Plane Surfaces

Samper

Adragna

Favrelière

et al. 2009

Journal of Computing and Information Science in Engineering

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“…The pattern recognition of tribological surfaces has shown to be useful in the areas of failure analysis and machine condition monitoring [1][2][3][4][5][6]. Fully computerised pattern recognition systems are becoming common since they often outperform human inspections with respect to speed, cost and reliability.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of methods for reduction of surface texture features

Stachowiak

Podsiadło

2006

Tribol Lett

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The diagnosis of worn and damaged surfaces is an important issue in machine failure analysis and condition monitoring. Of many approaches used, image classification based on feature parameters has often proven to be particularly useful. However, large image databases can be computationally costly to analyse, and the datasets are susceptible to noise. Hence, it is essential to determine which feature parameters hold the most useful information, in order to improve the classification rate and computation time. This paper presents a performance evaluation of dimension reduction techniques currently used in pattern recognition. A comparison of three methods is conducted, in order to determine which is able to produce the best results over a large range of image datasets. The methods analysed are: Non-Linear Fisher, Principal Component Analysis and kernel Principal Component Analysis. These are then tested against four different classifiers to obtain the best combination. These classifiers are: Linear Discriminant Classifier, Quadratic Discriminant Classifier, k-Nearest Neighbour and Support Vector Machine Classifiers. For further analysis, two combined dimension reduction and classification methods are tested: Minimum Classification Error and reduced feature space Support Vectors. For the comparison, four datasets of images with different scales and rotations are used, i.e. Brodatz textures, artificially generated isotropic fractal images and Talysurf images of sandblasted and abraded steel surfaces. The results showed that a combination of the Non-Linear Fisher dimension reduction technique and a Linear Support Vector Machine Classifier gave the best performance overall and are the most promising for the application in automated machine condition monitoring and expert free failure analysis. Further improvement can be achieved by performing a step-wise dimension reduction by first reducing the features using the Principal Component Analysis method, then further reduction with the Non-Linear Fisher technique.

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Morphological characterisation of engineered surfaces by wavelet transform

Cited by 73 publications

References 8 publications

Investigating Advantages and Disadvantages of the Analysis of a Geometrical Surface Structure with the Use of Fourier and Wavelet Transform

Investigating Advantages and Disadvantages of the Analysis of a Geometrical Surface Structure with the Use of Fourier and Wavelet Transform

Modeling of 2D and 3D Assemblies Taking Into Account Form Errors of Plane Surfaces

Evaluation of methods for reduction of surface texture features

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