Yazar "Urkmez, Abdullah" seçeneğine göre listele

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    DESIGN OPTIMIZATION OF SUBMERSIBLE INDUCTION MOTORS by MULTIOBJECTIVE FUZZY GENETIC ALGORITHM
    (GAZI UNIV, FAC ENGINEERING ARCHITECTURE, 2008) Cunkas, Mehmet; Urkmez, Abdullah
    This paper presents multiobjective fuzzy genetic algorithm optimization approach to a submersible motor design. Utilizing the concept of fuzzy sets and convex fuzzy decision making, the motor design task is formulated as a multiobjective fuzzy optimization problem and solved using a genetic algorithm. The two-dimensional Finite Element Method (FEM) is then used to confirm the validity of the optimal design. The optimization results show the effectiveness and achievement of the proposed method.
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    The detection of rotor faults in the manufacturing of submersible induction motor
    (IEEE, 2007) Arabaci, Hayri; Bilgin, Osman; Urkmez, Abdullah
    In this study, rotor faults detection in submersible induction motors which is used at deep well submersible pumps is presented by analyzing stator current. In some production squirrel cage rotor bars are welded to end rings by argon welding. While the welding sometimes some bars are not connected to end rings ore bad connection have been occurred. This affects the motor performance. For not preventing the production speed motor tests should be made quickly. In this study practical results are taken from POLMOT factory which produce submersible induction motors. When the motor construction is finished its robustness is tested with no load test. Their stator current time frequency domain is made and its current spectrum is investigated. According to current spectrum analysis its fault and robustness is determined. For classification Artificial Neural Network (ANN) is used. A decision mechanism that uses ANN result matrixes is occurred to detect faulted rotors.
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    Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis
    (ELSEVIER SCI LTD, 2011) Tasdemir, Sakir; Urkmez, Abdullah; Inal, Seref
    In this study, the body measurements (BMs) of Holstein cows were determined using digital image analysis (IA) and these were used to estimate the live weight (LW) of each cow. For this purpose, an image capture arrangement was established in a dairy cattle farm. BMs including wither height (WH), hip height (HH), body length (BL), hip width (HW), plus the LWs of cows were first determined manually, by direct measurement. Then the digital photos of cows were taken from different directions synchronously and analyzed by IA software to calculate WH, HH, BL and HW of each cow. After comparing the BMs obtained by IA with the manual measurements, the accuracy was determined as 97.72% for WH, 98.00% for HH, 97.89% for BL and 95.25% for HW. The LW estimation using BMs was then performed by the aid of the regression equations, and the correlation coefficient between the estimated and real (manual) LW values obtained by weighing was calculated as 0.9787, which indicates the IA method is appropriate for LW estimation of Holstein cows. (C) 2011 Elsevier B.V. All rights reserved.
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    A fuzzy rule-based system for predicting the live weight of Holstein cows whose body dimensions were determined by image analysis
    (TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2011) Tasdemir, Sakir; Urkmez, Abdullah; Inal, Seref
    The aim of this study was to determine the body measurement of Holstein cows through image analysis (IA) and to estimate their live weight (LW) by means of a fuzzy rule-based model using the body measurements. For this purpose, a photography environment was established at a dairy cattle farm where a large number of cows were kept. First, digital photographs of each animal were synchronously taken from different directions with Canon EOS 400D cameras. At the same time, body dimensions, namely wither height (WH), hip height (HH), body length (BL), and hip width (NW), of the cows were manually measured using a laser meter and measuring stick. The LWs of the cows were found with a weighing scale and the data were automatically saved on a computer. In the second stage, the photos were analyzed by IA software developed in the Delphi programming language and body measurements were computed. Manually measured values were very close to IA results. Finally, a fuzzy system was developed by using these body measurements. This fuzzy system was developed by using MATLAB software. Weights that were estimated with the developed knowledge-based system were compared with those found by the platform scale. The correlation coefficient was calculated (r = 0.99). There was a statistically meaningful relationship between the compared data. The developed system can be used confidently, and the system on which the experiments were performed can be modeled successfully.
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    Rotor Bar Fault Diagnosis by Using Power Factor
    (INT ASSOC ENGINEERS-IAENG, 2011) Arabaci, Hayri; Bilgin, Osman; Urkmez, Abdullah
    The paper presents detection and classification of rotor bar faults at steady state operation in squirrel cage induction motor by using power factor. One phase current and voltage of the stator coils were used to calculate the power factor. To investigate effects of rotor faults on the power factor, its frequency spectrum was obtained by fast Fourier Transform (FFT). Significant picks in the spectrum were used to discern "healthy" and "faulty" motor conditions. The motor conditions were classified by Artificial Neural Network (ANN). In experiments three different rotor faults and healthy motor conditions were investigated by 30 HP, 8", with 18 bars, 380V, 2 poles and 50 Hz squirrel cage submersible induction motor. The proposed decision structure detects and classifies rotor bar faults with 100% accuracy.