Recognition of texture types of wear particles

Laghari, Mohammad Shakeel

doi:10.1007/s00521-003-0367-y

Cited by 20 publications

(9 citation statements)

References 10 publications

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“…The grey level co-occurrence matrix (GLCM) estimates image properties related to second-order statistics and is usually utilized for analysis on texture features of wear debris. 14,21 Haralick et al 22 proposed 14 statistical features extracted from the GLCM for image classification. Several of these features are widely used in the literature, e.g.…”

Section: Number Of Wear-debris Chains Cwmentioning

confidence: 99%

Characterization of wear-debris group in on-line visual ferrographic images

Mao

Cao

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part J:

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Wear-debris characterization using ferrography, which is of importance in machine condition monitoring and fault diagnosis, remains a challenge. The newly developed on-line visual ferrograph can provide digital wear-debris images and perform on-line analysis during wear monitoring. In this article, image projection transformation was utilized for extracting the overall characteristics of the wear-debris chains according to the fundamental feature of wear-debris arraying along the horizontal direction. Moreover, Full Binary Tree Based Image Division was also proposed to analyse the regional features on different scales in on-line visual ferrographic images. Several descriptive parameters including thinning ratio, chain length and chain width were proposed. In the experiments, four types of images with different wear-debris groups were compared and a group of time-sequence on-line visual ferrographic images of an inline four-cylinder gasoline engine was studied. It is found that more comprehensive wear information can be acquired through the proposed multi-parameter description method. Meanwhile, the image projection transformation method extracts macrocharacteristics of images rapidly and efficiently and the thinning ratio measures wear-debris width in an intuitive way.

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Section: Number Of Wear-debris Chains Cwmentioning

confidence: 99%

Characterization of wear-debris group in on-line visual ferrographic images

Mao

Cao

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part J:

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show abstract

“…Several pattern recognition techniques have been used in an attempt to classify engineering surfaces without the need of human experts. These techniques include fractal analysis [14], a hybrid wavelet-fractal method [15], co-occurrence matrices and artificial neural networks [16,17]. In some works surface texture was classified according to wear mechanisms [18,19].…”

Section: Introductionmentioning

confidence: 99%

Shape and texture features in the automated classification of adhesive and abrasive wear particles

Stachowiak

Podsiadło

2006

Tribol Lett

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In this study the automated classification system, developed previously by the authors, was used to classify wear particles. Two kinds of wear particles, adhesive and abrasive, were classified. The wear particles were generated using a pin-on-disk tribometer. Various operating conditions of load, sliding time and abrasive grit size were applied to simulate adhesive and abrasive wear of different severity. SEM images of wear particles were acquired, forming a database for further analysis. The particle images were divided into eight groups or classes, each class representing different wear test conditions. All eight particle classes were first examined visually. Next, area, perimeter and elongation parameters were determined for each class and the parameters were statistically analysed. The automated classification system, based on particle surface texture, was then applied to all particle classes. The results of the automated particle classification were compared to those based on either the visual assessment of particle morphology or numerical parameter values. It was shown that the texture-based classification system was a more efficient and accurate way of distinguishing between various wear particles than classification based on size and shape of wear particles. It seems that the texture-based classification method developed has great potential to become a very useful tool in the machine condition monitoring industry.

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“…Although Neural Network-based classifiers were used with some success in the classification of Brodatz textures and the TILDA dataset [29], defective items [30], wear particle textures [31][32][33], and tool wear [34][35][36][37][38][39], they are not considered in this study. The main reason is that neural networks require a large number of simulations before a close-to-optimum structure of the network is built.…”

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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Recognition of texture types of wear particles

Cited by 20 publications

References 10 publications

Characterization of wear-debris group in on-line visual ferrographic images

Characterization of wear-debris group in on-line visual ferrographic images

Shape and texture features in the automated classification of adhesive and abrasive wear particles

Evaluation of methods for reduction of surface texture features

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