Sürdürülebilir Kalkınma Amaçları

• 2

• MakaleYayıncıda açık erişim2024Scopus SJRQ2/r/munzur/2237
  Mechanical and electrical faults detection in induction motor across multiple sensors with CNN-LSTM deep learning model

  Ertargin, Merve; Yildirim, Ozal; Orhan, Ahmet

  The utilization of monitoring sensors in machinery has led to the mainstream adoption of fault detection and diagnosis in time series data across various industrial applications. Deep learning techniques, specifically in constructing fault diagnosis models by extracting insights from historical equipment fault data, are receiving widespread attention as crucial tools in ensuring the safety and reliability of motor systems. In this study, a CNN-LSTM-based deep learning model is proposed for the detection of electric motor faults. Three distinct sets of accelerometer sensor data are provided as input to the model, enabling a comprehensive evaluation of its performance across various sensor configurations. The model demonstrated a remarkable capacity for generalization, achieving impressive accuracy rates of 99.96% for Accelerometer-1, 98.88% for Accelerometer-2, and 99.37% for Accelerometer-3. This underscores the robustness and adaptability of the proposed CNN-LSTM model in effectively detecting electric motor faults regardless of the specific accelerometer sensor employed.

  AtıfScopus47·OpenAlex38

  İncele
• MakaleYalnız künye2022Scopus SJRQ2/r/munzur/2415
  Fault location method on two-terminal transmission line using synchronized time information of traveling waves

  Akmaz, Düzgün; Mamiş, Mehmet Salih

  In this study, approximate derivative (AD) signal processing method using synchronous voltages data, based on the traveling wave theory, was developed to determine fault locations in transmission lines. The first aerial mode voltage signals were obtained by applying the Clarke transformation to the voltage signals that occur after a short circuit fault. Then, by taking the first AD of the aerial mode voltage signals, the traveling waves were obtained. Finally, the location of the fault was found by using the time difference information of the traveling waves, wave velocity value and transmission line length. The proposed signal processing method was found to be effective in determining the location of faults. It was tested and compared to the discrete wavelet transform (DWT) for various fault conditions. Based on the simulation results, the proposed method was found to have a slightly higher success rate than DWT, in general. Moreover, it can work with low sampling frequencies, and it provides a better resolution than DWT to display the traveling waves in noise conditions.

  • 
  AtıfScopus9·WoS8·OpenAlex7

  İncele
• MakaleAçık Erişim2018Scopus SJRQ3/r/munzur/3824
  Design, analysis, and control of in-wheel switched reluctance motor for electric vehicles

  Omaç, Z.; Polat, M.; Öksüztepe, E.; Yıldırım, M.; Yakut, O.; Eren, H.; Kaya, M.; Kürüm, H.

  Estimation of dimension parameters for an electric machine has great importance before assembling on production line. As a matter of fact, researchers should find optimum solution once they decide to perform analytical design of an electric machine. In this study, we have tried to find dimensional and electrical parameters via derived mathematical equations for in-wheel switched reluctance motor (IW-SRM), and the motor has been manufactured. Moreover, an experimental setup is designed, and the speed and torque control of IW-SRM is carried out. The motor tests including both standstill and running test are performed by using the experimental setup. Initial size parameters are intuitively provided as motor analysis is conducted by software package. Then, numerous trials are examined to get optimum results. In fact, this motor is employed by an electric vehicle whose design is ongoing. Therefore, optimum motor parameters for required base speed and torque have been estimated by solving generated equations for IW-SRM with 18/12 poles via MATLAB. Considering parameters estimated, analysis of IW-SRM has been performed by Ansoft Maxwell 15.0 Software Package based on 3D finite element method (3D-FEM). Consequently, the estimated parameters have been validated by the results of Maxwell 3D FEM. Experimental results of the motor manufactured are obtained via the motor driver designed; also have been validated by Maxwell 3D.

  • 
  AtıfScopus14·WoS15·OpenAlex24

  İncele
• MakaleAçık Erişim2016Scopus SJRQ3/r/munzur/4145
  Switched reluctance motor control without position sensor by using data obtained from finite element method in artificial neural network

  Polat, Mehmet; Oksuztepe, Eyyup; Kurum, Hasan

  Switched reluctance motors (SRMs) require sensitive rotor information in order to work sensitively and properly and especially for the commutation of phase currents. Position information is generally acquired from a sensor which is generally placed into the stator or connected to the rotor. However, sensors have disadvantages in terms of cost, safety, complexity and volume. In order to remove these disadvantages, recent studies concentrate on SRMs working without a position sensor. In this study control methods working without a position sensor are examined, the applied method is presented comprehensively. The education and test data of the artificial neural network is not obtained from simulation or experimental results; it is obtained from magnetic analyses carried out in ANSYS 10 and the results are used in position speed and current sensorless control of SRM. The validity of proposed method is demonstrated by using DS 1103.

  AtıfWoS8·OpenAlex11

  İncele
• MakaleÖn baskısı açık2014Scopus SJRQ3/r/munzur/4067
  Sensorless vector control of PMSM with non-sinusoidal flux using observer based on FEM

  Oksuztepe, Eyyup; Omac, Zeki; Kurum, Hasan

  Back electromotive force method has been widely used in the position and speed sensorless control of permanent magnet synchronous motors. In this method, it is supposed that the back electromotive forces induced on the stator windings by the rotor permanent magnets are sinusoidal. This assumption causes errors in the estimation of speed and position. In this paper, an observer using the stator winding linkage flux model obtained by using finite element method for position and speed sensorless field-oriented control of permanent magnet motor is proposed. The validity of the proposed method is demonstrated using DS 1104.

  AtıfWoS11·OpenAlex12

  İncele