Yazar "Ertunc, H. Metin" seçeneğine göre listele

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    A new automatic bearing fault size diagnosis using time-frequency images of CWT and deep transfer learning methods
    (Tubitak Scientific & Technological Research Council Turkey, 2022) Kaya, Yilmaz; Kuncan, Fatma; Ertunc, H. Metin
    Bearings are generally used as bearings or turning elements. Bearings are subjected to high loads and rapid speeds. Furthermore, metal-to-metal contact within the bearing makes it sensitive. In today's machines, bearing failures disrupt the operation of the system or completely stop the system. Bearing failures that can occur can cause enormous damage to the entire system. Therefore, it is necessary to anticipate bearing failures and to carry out a regular diagnostic examination. Various systems have been developed for fault diagnosis. In recent years, deep transfer learning (DTL) methods are often preferred in current bearing diagnosis models, as they provide time savings and high success rates. Deep transfer learning models also improve diagnosis accuracy under certain conditions by greatly reducing human intervention. Diagnosis at the right time is very important for the sustainability and efficiency of industrial production. A technique based on continuous wavelet transform (CWT) and two dimensional (2D) convolutional neural networks (CNN) is presented in this paper to detect fault size from vibration data of various bearing failure types. Time-frequency (TF) color scalogram images for bearing vibration signals were obtained using the CWT method. Using AlexNet, GoogleNet, Resnet, VGG16, and VGG19 deep transfer learning methods with scalogram images, fault size prediction from vibration signals was performed. Five different transfer deep learning models were used for three different data sets. It was observed that the success rates obtained varied between 96.67% and 100%.
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    A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification
    (Taylor & Francis Ltd, 2021) Kaya, Yilmaz; Kuncan, Melih; Kaplan, Kaplan; Minaz, Mehmet Recep; Ertunc, H. Metin
    Recently, precise and deterministic feature extraction is one of the current research topics for bearing fault diagnosis. For this aim, an experimental bearing test setup was created in this study. In this setup, vibration signals were obtained from the bearings on which artificial faults were generated in specific sizes. A new feature extraction method based on co-occurrence matrices for bearing vibration signals was proposed instead of the conventional feature extraction methods, as in the literature. The One (1) Dimensional-Local Binary Patterns (1D-LBP) method was first applied to bearing vibration signals, and a new signal whose values ranged between 0-255 was obtained. Then, co-occurrence matrices were obtained from these signals. The correlation, energy, homogeneity, and contrast features were extracted from these matrices. Different machine learning methods were employed with these features to carry out the classification process. Three different data sets were used to test the proposed approach. As a result of analysing the signals with the proposed model, the success rate is 87.50% for dataset1 (different speed), 96.5% for dataset2 (fault size (mm)) and 99.30% for dataset3 (fault type - inner ring, outer ring, ball) was found, respectively.
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    A novel feature extraction method for bearing fault classification with one dimensional ternary patterns
    (Elsevier Science Inc, 2020) Kuncan, Melih; Kaplan, Kaplan; Minaz, Mehmet Recep; Kaya, Yilmaz; Ertunc, H. Metin
    Bearing is one of the most critical parts used in rotary machines. Bearing faults break down the mechanism where it is located. Moreover, the faults may cause to malfunction by spreading to the entire system. Thus this may result in catastrophic failure eventually. Precise and decisive feature extraction from the raw vibration signal maintains to be one of the current topics explored for fault diagnosis in bearings. In this study, vibration signals are obtained from bearings which are formed with artificial faults of specific dimensions from a bearing test setup. Instead of employing traditional feature extraction methods found in the literature, a novel feature extraction method for bearing faults called one-dimensional ternary pattern (1D-TP) is applied. The proposed approach is a statistical method that uses patterns obtained from comparisons between neighbors of each value on vibration signals. The study aims to identify the size (mm) of the fault by determining the bearing part (inner ring, outer ring, ball) from which the faults in the bearings are caused. Several classification techniques were performed by using ternary patterns with RF (Random Forest), k-NN (1<-nearest neighbor), SVM (Support Vector Machine), BayesNet, ANN (Artificial Neural Networks) models. As a result of analyzing the signals obtained from the experimental setup with the proposed model, 91.25% for dataset_1 (different speed), 100% for dataset_2 (fault type - inner ring, outer ring, ball) and 100% for dataset_3 (fault size (mm)) success rates are determined. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.
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    An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis
    (Elsevier, 2020) Kaplan, Kaplan; Kaya, Yilmaz; Kuncan, Melih; Minaz, Mehmet Recep; Ertunc, H. Metin
    Bearings are one of the most widespread components used for energy transformation in machines. Mechanical wear and faulty bearings reduce the efficiency of rotating machines and thus increase energy consumption. The feature extraction process is an essential part of fault diagnosis in bearings. In order to diagnose the fault caused by the bearing correctly, it is necessary to determine an effective feature extraction method that best describes the fault. In this study, a new approach based on texture analysis is proposed for diagnosing bearing vibration signals. Bearing vibration signals were first converted to gray scale images. It can be understood from the images that the signals of different bearing failures form different textures. Then, using these images, LBP (Local Binary Pattern) and texture features were obtained. Using these features, different machine learning models and bearing vibration signals are classified. Three different data sets were created to test the proposed approach. For the first data set, the signals composed of very close velocities were classified. 95.9% success rate was observed for the first data set. The second data set consists of faulty signals at different parts of the bearing (inner ring, outer ring and ball) measured in the same RPM. The type of fault has been determined, and a 100% success rate was obtained for this data set. The final data set is composed of the fault size dimensions (mm) of different ratios. With the proposed approach, a 100% success rate was obtained in the classification of these signals. As a result, it was observed that the obtained feature had promising results for three different data types and was more successful than the traditional methods. (C) 2019 Elsevier B.V. All rights reserved.
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    Brain tumor classification using modified local binary patterns (LBP) feature extraction methods
    (Churchill Livingstone, 2020) Kaplan, Kaplan; Kaya, Yilmaz; Kuncan, Melih; Ertunc, H. Metin
    Automatic classification of brain tumor types is very important for accelerating the treatment process, planning and increasing the patient's survival rate. Today, MR images are used to determine the type of brain tumor. Manual diagnosis of brain tumor type depends on the experience and sensitivity of radiologists. Therefore, researchers have developed many brain tumor classification models to minimize the human factor. In this study, two different feature extraction (nLBP and alpha LBP) approaches were used to classify the most common brain tumor types; Glioma, Meningioma, and Pituitary brain tumors. nLBP is formed based on the relationship for each pixel around the neighbors. The nLBP method has a d parameter that specifies the distance between consecutive neighbors for comparison. Different patterns are obtained for different d parameter values. The alpha LBP operator calculates the value of each pixel based on an angle value. The angle values used for calculation are 0, 45, 90 and 135. To test the proposed methods, it was applied to images obtained from the brain tumor database collected from Nanfang Hospital, Guangzhou, China, and Tianjin Medical University General Hospital between the years of 2005 and 2010. The classification process was performed by using K-Nearest Neighbor (Knn) and Artificial Neural Networks (ANN), Random Forest (RF), A1DE, Linear Discriminant Analysis (LDA) classification methods, with the feature matrices obtained with nLBP, alpha LBP and classical LBP from the images in the data set. The highest success rate in brain tumor classification was 95.56% with the nLBPd = 1 feature extraction method and Knn model.
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    Classification of bearing vibration speeds under 1D-LBP based on eight local directional filters
    (Springer, 2020) Kaya, Yilmaz; Kuncan, Melih; Kaplan, Kaplan; Minaz, Mehmet Recep; Ertunc, H. Metin
    Bearings are the most commonly used machine element in order to reduce rotational friction in machines and to compensate radial and axial loads. It is very important to determine the faults in the bearings in terms of the machine health. In order to accurately diagnose bearing-related faults with traditional machine learning methods, it is necessary to identify the features that characterize bearing fault most accurately. Therefore, a new feature extraction procedure has been proposed to determine the vibration signal velocities of different fault sizes and types in this study. The new approach has been employed to obtain features from the vibration signals for different scenarios. After different filtering based on 1D-LBP method, the F-1D-LBP method was used to construct feature vectors. The filters reduce the noise in the signals and provide different feature groups. In other words, it is aimed to generate filters in order to extract different patterns that can separate signals. For each filter applied, different patterns can be obtained for the same local point on signals. Thus, the signals can be represented by different feature vectors. Then, by using these feature groups with various machine learning methods, vibration velocities were separated from each other. As a result, it was observed that the obtained feature had promising results for classification of bearing vibrations.
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    Classification of CNC Vibration Speeds by Heralick Features
    (Graz Univ Technolgoy, Inst Information Systems Computer Media-Iicm, 2024) Kuncan, Melih; Kaplan, Kaplan; Kaya, Yilmaz; Minaz, Mehmet Recep; Ertunc, H. Metin
    In the contemporary landscape of industrial manufacturing, the concept of computer numerical control (CNC) has emerged due to the optimization of conventional machinery, distinguished by its remarkable precision and expeditious processing capabilities. These inherent advantages have seamlessly paved the way for the pervasive integration of CNC machines across a myriad of industrial manufacturing sectors. The present study embarks upon a comprehensive inquiry, delving into the intricate analysis of a specialized prototype CNC molding machine, encompassing a meticulous assessment of its structural rigidity, robustness, and propensity for vibrational occurrences. Moreover, an insightful exploration is undertaken to discern the intricate interplay between vibrational signals and intricate machining processes, particularly under diverse conditions such as the presence or absence of the cutting tool, and at varying rotational speeds denoted in revolutions per minute (RPM). The trajectory of this research voyage encompasses an extensive array of empirical experiments meticulously conducted on the prototype CNC machine, with synchronous real-time acquisition of vibrational data. This empirical journey starts by generating two distinct datasets, each meticulously designed to encompass an assemblage of seven distinct rotational speeds, spanning the spectrum from 18000 to 30000 RPM, thereby facilitating enhanced diversity within the dataset. In parallel, a secondary dataset is meticulously derived from the CNC machine operating in the absence of the cutting tool, thereby encapsulating an exhaustive range of 20 discrete RPM values. The extraction of pivotal features aimed at discerning between the vibrational signals arising from distinct conditions (i.e., those emanating from situations involving the presence or absence of the cutting tool) and the associated variance in CNC machine speeds is facilitated through an innovative framework grounded in co -occurrence matrices. The culmination of this methodological framework is the identification of discernible co -occurrence matrices, thereby facilitating the subsequent computation of Heralick features. The classification effort was performed systematically using 10 -fold cross -validation analysis, covering a number of different machine learning models. The outcomes emanating from this intricate sequence of systematic methodologies underscore remarkable achievements. Specifically, the classification of vibrational signals corresponding to varying CNC machine speeds, contingent upon the presence or absence of the cutting tool, yields commendable accuracy rates of 94.27% and 94.16%, respectively. Notably, an exemplary accuracy rate of 100% is attained when classifying differing conditions (i.e., situations involving the presence or absence of the cutting tool) across specific RPM settings, prominently at 22000 24000 26000 28000 and 30000 RPM.
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    Design, production and novel NC tool path generation of CNC tire mold processing machine
    (Gazi Univ, Fac Engineering Architecture, 2018) Kuncan, Melih; Kaplan, Kaplan; Ertunc, H. Metin; Kucukates, Selim
    In this study, a mechanical design, mathematical modeling and software algorithm have been realized for CNC prototype machine which is mainly used in tire sector and in the others (shoe sole plate and medical prosthetic manufacturing, aviation, automotive, jewelry sector etc.). For this aim, a mechanical design and manufacturing of CNC tire mold machine was firstly performed. The most feasible design model has been determined as a result of the research and analysis carried out for the mechanical design stage. Thus, it is aimed to provide the designed machine to be used in other areas besides tire mold processing sector. The main contribution of the study is the development of an original and mathematical transformation algorithm that transfer the texts and patterns to 3D complex surfaces with CNC machines. Moreover, an interface for the users is developed based on C # compiler using the outputs from the software algorithm. The NC codes of the algorithm output were tested on the designed prototype machine. Then, the prototype machine was tested with different materials (wood, iron, steel etc.) and the test results were observed. Based on the test results, pattern and character processing with the desired precision is carried out successfully.