Feature extraction on machined surface texture image of tool wear based on fractional brown motion

Peng, Chao; Zheng, Jianmin; Li, Xubo; Song, Young-Chae; Shi, Jiao-Jiao

doi:10.1142/9789814730518_0084

Cited by 4 publications

(6 citation statements)

References 7 publications

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“…The relation between misclassifications and classes can be seen in Table II. 7 As shown on Table II, the classifier has problems when it 7 On this table permutations have been considered as equal cases, so for example, Aluminum Foil being misclassified as Cracker has been considered equivalent to Cracker being misclassified as Aluminum Foil. When the Naive Bayes classifier is trained with 40% of the original textures, using all 104 features proposed in section 2 and section 3, without the PCA process, using only the canny-filtered images the mean success of the classifier in a total of 10,000 tests 6 is 52.07%, with a maximum value of 58.44% and a standard deviation of 1.63%.…”

Section: Classification Resultsmentioning

confidence: 99%

“…Now, assuming that 3-gray levels 2 -which here have been called "A", "B" and "C"-are defined a new image I K can be described by (6). Thus, the original image can be rewritten, considering (6), as shown in (7).…”

Section: Haralick Texture Featuresmentioning

confidence: 99%

“…The average number of misclassifications in this case is 77. The relation between misclassifications and classes can be seen in Table XIII 7 . When the Naive Bayes classifier is trained with 40% of the original textures, using all 104 features proposed in section 2 and section 3, processing the features with the PCA algorithm, using only the canny and mean-filtered images, the mean success of the classifier in a total of 10,000 tests 6 is 81.91%, with a maximum value of 88.68% and a standard deviation of 1.73%.…”

Section: Bb Cy Ct Cr LI Op Sd Sp Symentioning

confidence: 99%

“…The average number of misclassifications in this case is 88. The relation between misclassifications and classes can be seen in Table XVI 7 . Finally, Table XV compares the classification results for all cases while Fig.…”

Section: Bb Cy Ct Cr LI Op Sd Sp Symentioning

confidence: 99%

“…The extraction of texture information has shown positive results when used for characterizing and estimate parameters of any image [1], [2], [3], [4], [5], [6] and [7]. Each parameter will depend strongly on the type of image to be processed (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Texture Classification based on Spectral Analysis and Haralick Features

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et al. 2016

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In this work we discuss a method to classify a set of texturized images based on the extraction of their Haralick Features. This kind of Classification is capable of providing texture-based measurements (such as contrast or correlation) and use them as main parameters to classify the same type of patterns in other images. In order to improve the classification success ratio a spectral analysis of the textures and, therefore, the use of filters, before the classification step, is proposed here. In this work the classification success has been evaluated using Mean and Canny filters. On the other hand, the Principal Component Analysis is used to optimize the features extracted for the patterns on each image, before introduced into the classifier. With this method the classifying success ratio for the KTH-TIPS subset and 10,000 different permutations was increased-in averagefrom 72.28% to 84.25%.

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Section: Classification Resultsmentioning

confidence: 99%

Section: Haralick Texture Featuresmentioning

confidence: 99%

Section: Bb Cy Ct Cr LI Op Sd Sp Symentioning

confidence: 99%

Section: Bb Cy Ct Cr LI Op Sd Sp Symentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Texture Classification based on Spectral Analysis and Haralick Features

Valentin

Albuquerque

et al. 2016

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Remaining useful life prediction for multivariable stochastic degradation systems with non‐Markovian diffusion processes

Zhou

Chen

et al. 2020

Quality & Reliability Eng

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Multivariable stochastic degradation system (MSDS) is quite common in indus-tries such as blast furnace ironmaking, vehicle transportation, and aerospace manufacturing. Large-scale complex equipments may be affected by multiple factors, resulting in not just a single deteriorating performance characteristic. It is difficult to handle unknown failure structures of practical systems by using traditional univariate degradation modeling methods. A novel health index (HI) is constructed to quantitatively analyze the health state for the overall system. Considering the interaction between internal reactions and external environments, the fractional Brownian motion (FBM), a typical non-Markovian diffusion process, is added for the purpose of reflecting stochastic uncertainties and memory effects. Based on the wavelet estimators and the maximum likelihood estimation (MLE) algorithm, multi-sensor observations of degradation variables are analyzed simultaneously to identify model parameters. A closed-form distribution of system-level remaining useful life (RUL) is obtained with a mild two-layer approximation. Relevant case studies are then handled that adequately demonstrate the effectiveness and the practical utility of the proposed method.

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Correlation statistics of a Fourier transform feature with flank wear on different sections of turned surface images for real time monitoring applications

Küllaç

Çuvalcı

2023

Measurement

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Feature extraction on machined surface texture image of tool wear based on fractional brown motion

Cited by 4 publications

References 7 publications

Texture Classification based on Spectral Analysis and Haralick Features

Texture Classification based on Spectral Analysis and Haralick Features

Remaining useful life prediction for multivariable stochastic degradation systems with non‐Markovian diffusion processes

Correlation statistics of a Fourier transform feature with flank wear on different sections of turned surface images for real time monitoring applications

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