Yazar "Ozguven, Omer Faruk" seçeneğine göre listele

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    Detection of rotor fault in three-phase induction motor in case of low-frequency load oscillation
    (Springer, 2015) Goktas, Taner; Arkan, Muslum; Ozguven, Omer Faruk
    This paper proposes a method for the separation of broken rotor bar failure and low-frequency load fluctuation in line-fed three-phase induction motor. In practice, the presence of load fluctuation at has the same effect on a stator current of induction motor as a broken rotor bar fault. In such cases, the detection of broken rotor bar failure becomes difficult. To discern rotor fault and load oscillations, the analytical signal angular fluctuation (ASAF) method, which is a combination of Hilbert transform and the space vector angular fluctuation method, is used. The presented experimental results prove that low-frequency load oscillation and rotor fault can reliably be discriminated using the ASAF signal spectrum.
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    Fabric Defect Detection Methods for Circular Knitting Machines
    (Ieee, 2015) Hanbay, Kazim; Talu, Muhammed Fatih; Ozguven, Omer Faruk; Ozturk, Dursun
    In this paper, an online fabric defect detection system that can detect fabric defects which may occur during the fabric product in knitting machines is introduced. This system mainly includes three steps: 1) Construction of a defected/defect-free fabric database; 2) Obtaining and classification of the feature vectors; 3) Online working on embedded system. This study only contains information about the first two stages. In the first stage, 3242 'defected' and '5923' defect-free images were acquired by using a conveyor system which has line scan camera and linear light. In the second stage, filtering, feature extraction (wavelet transform, co-occurrence matrix and CoHOG) and classification (YSA) processes were carried out. As a result, obtaining the feature vectors through wavelet transform has reduced computation cost by 53% and also has successfully provided the classification of the defects by 90%.
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    Fabric defect detection systems and methods-A systematic literature review
    (Elsevier Gmbh, 2016) Hanbay, Kazim; Talu, Muhammed Fatih; Ozguven, Omer Faruk
    This paper presents a comprehensive literature review of fabric defect detection methods First, it briefly explains basic image acquisition system components such as camera and lens. Defect detection methods are categorized into seven classes as structural, statistical, spectral, model-based, learning, hybrid and comparison studies. These methods are evaluated according to such criteria as the accuracy, the computational cost, reliability, rotating/scaling invariant, online/offline ability to operate and noise sensitivity. Strengths and weaknesses of each approach are comparatively highlighted. In addition, the availability of utilizing methods for weaving and knitting in machines is investigated. The available review studies do not provide sufficient information about fabric defect detection systems for readers engaged in research in the area of textile and computer vision. A set of examination for efficient establishment of image acquisition system are added. In particular, lens and light source selection are mathematically expressed. (C) 2016 Elsevier GmbH. All rights reserved.
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    Real time fabric defect detection by using fourier transform
    (Gazi Univ, Fac Engineering Architecture, 2017) Hanbay, Kazim; Talu, Muhammed Fatih; Ozguven, Omer Faruk
    In fabric productions, real time defect detection systems are needed to detect the surface defects. There is currently no real time defect detection system in knitting fabric production. In this paper, a real time fabric defect detection system is developed and is tested on circular knitting machine. Textural features of fabric image are extracted based on Fourier transform. These textural features are seven and are calculated from the horizontal and vertical directions of Fourier frequency spectrum of the fabric image. The performance of the proposed method is firstly evaluated off-line through experiments based on comprehensive fabric database. The proposed method obtains superior performance, which also proves its utility in real-time inspection. Secondly, a real time machine-vision system has been designed for an efficient detection of the fabric defects under industrial conditions. Real time defect detection system is tested automatically by analyzing fabric images captured by a line scan camera. Experimental results show that the proposed detection model can successfully detect common circular knitting fabric defects.
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    Real-Time Detection of Knitting Fabric Defects Using Shearlet Transform
    (Ege Univ, 2019) Hanbay, Kazim; Talu, Muhammed Fatih; Ozguven, Omer Faruk; Ozturk, Dursun
    This paper proposes a vision-based fabric inspection system for the circular knitting machine. Firstly, a comprehensive fabric database called Fabric Defect Detection Database (FDDD) are constructed. To extract significant features of fabric images, shearlet transform is used. Means and variances are calculated from all subbands and combined into a high-dimensional feature vector. The proposed system is evaluated on a circular knitting machine in a textile factory. The real-time performance analysis is only carried out by inspecting single jersey knitted fabric. Our proposed system achieves the highest accuracy of 94.0% in the detection of single jersey knitting fabric defects.