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Öğe A Correlation Between Eccentricity Fault and AC Winding Losses of Permanent Magnet Synchronous Motors(Institute of Electrical and Electronics Engineers Inc., 2024) Gunay, Hicret Yetis; Goktas, Taner; Mese, Erkan; Arkan, Muslum; Ozkan, Emine BostanciIn this study, the variation of AC winding losses in a permanent magnet synchronous motor (PMSM) under eccentricity fault conditions is investigated. To analyze how AC winding losses change during eccentricity faults, the motor is simulated using ANSYS@Motor-CAD software under static, dynamic, and mixed (static + dynamic) eccentricity faults. Analyses are conducted with three different stator conductor types and at various operating frequencies. The obtained results are extensively analyzed and compared for both healthy and eccentric motors. The analysis results show that the AC winding losses in the faulty condition increase compared to the healthy condition. © 2024 IEEE.Öğe Analysis of Stator Inter-turn Short-circuit Fault Signatures for Inverter-fed Permanent Magnet Synchronous Motors(Ieee, 2016) Cira, Ferhat; Arkan, Muslum; Gumus, Bilal; Goktas, TanerIt is quite important to detect stator short-circuit fault, which is the most common fault type, at incipient stage. It is possible to carry out fault detection using Motor Current Signature Analysis (MCSA) method. In this study, stator current and voltage space vectors of PMSMs were analyzed with MCSA under various load torque, speed and fault percentages conditions. The Negative & positive harmonics were obtained by applying Fast Fourier Transform (FFT) to space vectors of stator current and voltage. It is suggested that by using the obtained fault signatures, stator inter-turn fault estimation can be achieved accurately. The results of comprehensive analysis carried out under various load torque and speed conditions show that characteristics fault signatures are both present in the current and the voltage space vectors spectra.Öğe Broken Rotor Bar Fault Monitoring based on Fluxgate Sensor Measurement of Leakage Flux(Ieee, 2017) Goktas, Taner; Arkan, Muslum; Mamis, M. Salih; Akin, BilalBroken rotor bar fault in induction motors significantly affects the motor dynamic performance and increases the mechanical oscillations in torque and speed. Motor current signature analysis has widely been used to detect such fault, yet it has some shortcomings due to motor topology, stator winding and load type dependencies. In this paper, radial leakage flux which contains most critical fault related information is analyzed using a fluxgate sensor to detect broken bar fault in induction motors (IMs). The 2D-Time Stepping Finite Element Method (2D-TSFEM) is used to analyze fault patterns in leakage flux. A 2-Dimensional (2D) finite element analysis and experimental results show that using leakage flux can provide superior and more reliable results than classical motor stator current analysis in IMs.Öğe A Comparative Study of Current, Vibration and Stray Magnetic Flux Based Detection for Parallel Misalignment Fault in Induction Motors(Ieee, 2021) Goktas, Taner; Arkan, Muslum; Gurusamy, V.The misalignment fault is commonly caused by incorrect shaft positions between motor and load in electrical machines. It affects the mechanical symmetry of machine and thus causes mechanical oscillation on the shaft. In this paper, the parallel misalignment fault is analyzed based on stator current, vibration and stray flux in induction motors (IMs). The three-axis vibration sensor and an integrated flux sensor are used in order to stream vibration and stray flux for diagnostics process, respectively. The comparative results between stator current, vibration and stray flux are presented. Experimental results show that stator current and vibration-based analyses provide highly reliable results than stray flux for parallel misalignment fault. It is also shown that the proposed signatures in current and vibration vary very little with respect to load and motor drive type. Moreover, Multilayer Perceptron (MLP) based machine learning algorithm using vibration and stator current is carried out and it has excellent performance in the automatic detection of parallel misalignment fault.Öğe Comprehensive Analysis of Magnet Defect Fault Monitoring Through Leakage Flux(Ieee-Inst Electrical Electronics Engineers Inc, 2017) Goktas, Taner; Zafarani, Mohsen; Lee, Kun Wang; Akin, Bilal; Sculley, TerryThis paper presents a detailed magnet defect fault detection analysis through fluxgate sensors by monitoring the leakage flux around permanent magnet synchronous motors. The flux spectra of electric machines contain direct and most critical information to monitor and characterize magnet defect faults and their progressions. In the mainstream diagnosis techniques based on phase current and back-EMF analysis, the fault corresponding signature characteristics may vary and cause misleading results depending on motor topology, winding configuration, number and location of defective magnets, and controller parameters. In this paper, it is shown that leakage flux analysis provides some superior results for magnet defect diagnosis. For this purpose, the fault patterns in the leakage flux spectrum are exhaustively analyzed at different torque/speed profiles. Simulation and experimental results show that the deployment of a direction sensitive fluxgate sensor in magnet defect fault detection yields very promising results both in time and frequency domain analyses.Öğe Design, Simulation and Application of Buck Converter with Digital PI Controller(2021) Sucu, Hasan; Goktas, Taner; Arkan, MuslumIn this paper, a dc-dc buck converter with digital PIcontrolled is analyzed and designed considering all design parameters such as inductance current variation, output voltage ripple etc. The designed dc-dc buck converter provides stable output voltage against to load changes and output voltage variations. Buck converter control method relies on voltage mode controlled PWM (Pulse width Modulation) with digital PI (Proportional Integral) controller. The design criteria, operating mode selection, suitable material selection, etc. of low cost and high-performance buck converter are explained in detail. Finally, the designed converter is carried out experimentally and the experimental results shows the effectiveness of designed converter under different load profiles.Öğe Detection of Rotor Bar Fault through Stray Flux Based Analytical Signal Angular Fluctuation Method(Ieee, 2023) Goktas, Taner; Arkan, MuslumStray flux analysis has increasing trend to monitor the machine condition including fault diagnosis. In this paper, the rotor bar fault is detected through the Stray Flux based Analytical Signal Angular Fluctuation (SF-ASAF) method in induction motors (IMs). For this purpose, the stray flux is collected from the vicinity of motor frame and analytical signal of stray flux is calculated using Hilbert Transform (HT). Then, angular fluctuation of obtained signal is utilized to see the harmonic content of stray flux-analytical signal. It is shown that there are some effective signals such as fs-3sfs, 2sfs to detect rotor bar fault in stray flux based analytical signal angular fluctuation spectrum. The presented 2D- FEM based simulation and experimental results prove that the Stray Flux based Analytical Signal Angular Fluctuation (SF-ASAF) method can provide superior and reliable results rather than classical stray flux analysis in induction motors.Öğe Detection of rotor fault in three-phase induction motor in case of low-frequency load oscillation(Springer, 2015) Goktas, Taner; Arkan, Muslum; Ozguven, Omer FarukThis 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.Öğe Diagnosis of Broken Rotor Fault in Inverter-Fed IM by Using Analytical Signal Angular Fluctuation(Ieee, 2014) Goktas, Taner; Arkan, MuslumThe aim of this paper is to detect broken rotor bar fault at the presence of low frequency load torque oscillation in inverter-fed induction motors. The low frequency load torque oscillation in induction motor may sometimes have the same effect as broken rotor bar fault on the stator current. Especially, when load torque oscillation frequency is close to twice the slip frequency, additional processing need to be done to separate these two effects from each other. To discern these two effects, Analytical Signal Angular Fluctuation (ASAF) spectrum is used. Experimental results are presented for separating broken rotor bar fault from low frequency load torque oscillation.Öğe Discerning broken rotor bar failure from low-frequency load torque oscillation in DTC induction motor drives(Sage Publications Ltd, 2018) Goktas, Taner; Arkan, MuslumThis paper proposes a method for separation of broken rotor bar failures from low-frequency load torque oscillation in direct torque control (DTC) induction motor drives by using v(q) voltage and i(q) current components' spectra. The effect of load torque oscillation should be considered in induction motor drives for reliable broken bar fault detection. Induction machine drivers are run in DTC mode to control its torque and speed. In practice, the presence of load torque fluctuation may sometimes cause false positive alarms on stator current spectrum. However, discerning of broken rotor bar failure from low-frequency load variation for DTC drives remains unexplored. Experimental results show that by using the proposed method broken rotor bar failure can be reliably detected in the presence of low-frequency load torque oscillation in DTC induction motor drives.Öğe Discriminating of Rotor Fault and Low Frequency Load Torque Oscillation Using Motor Square Current Signature Analysis(Ieee, 2018) Goktas, Taner; Arkan, MuslumThis paper proposes a method to discern broken bar rotor fault from low frequency load torque oscillation in induction motors. If a motor is subjected to load fluctuation, the sideband components show up in phase current and they can exhibit similar behavior that of broken bar which leads misleading diagnostics. Thus, broken rotor bar and load oscillation related harmonics may sometimes overlap in the same spot. In this study, Motor Square Current Signature Analysis (MSCSA) method is used to detect broken rotor bar fault when load torque oscillation frequency overlaps with that of broken bar fault. This method is quite simple, but effective for false positive indication. The 2D-FEM simulations and experiments are carried out to prove the efficacy of proposed method. Based on the presented results, it is shown that broken bar related signatures such as 4f(s)-2ksf(s) do exist, whereas there is no load oscillation related signatures at the sidebands of 4f(s) in the square of phase current spectrum.Öğe Identification of Static Eccentricity and Load Current Unbalance via Space Vector Stray Flux in Permanent Magnet Synchronous Generators(Mdpi, 2025) Aladag, Ilyas; Goktas, Taner; Arkan, Muslum; Yaniktepe, BulentPermanent Magnet Synchronous Generators (PMSGs) have become increasingly important in industrial applications such as wind turbine systems due to their high efficiency and power density. However, their operational reliability can be affected by asymmetries such as static eccentricity (SE) and load current unbalance (UnB), which exhibit similar spectral features and are therefore difficult to differentiate using conventional techniques such as Motor Current Signature Analysis (MCSA). Stray flux analysis provides an alternative diagnostic approach, yet single-point measurements often lack the sensitivity required for accurate fault discrimination. This study introduces a diagnostic methodology based on the Space Vector Stray Flux (SVSF) for identifying static eccentricity (SE) and load current unbalance (UnB) faults in PMSG-based systems. The SVSF is derived from three external stray flux sensors placed 120 degrees electrical degrees apart and analyzed through symmetrical component decomposition, focusing on the +5fs positive-sequence harmonic. Two-dimensional Finite Element Analysis (FEA) conducted on a 36-slot/12-pole PMSG model shows that the amplitude of the +5fs harmonic increases markedly under static eccentricity, while it remains nearly unchanged under load current unbalance. To validate the simulation findings, comprehensive experiments have been conducted on a dedicated test rig equipped with high-sensitivity fluxgate sensors. The experimental results confirm the robustness of the proposed SVSF method against practical constraints such as sensor placement asymmetry, 3D axial flux effects, and electromagnetic interference (EMI). The identified harmonic thus serves as a distinct and reliable indicator for differentiating static eccentricity from load current unbalance faults. The proposed SVSF-based approach significantly enhances the accuracy and robustness of fault detection and provides a practical tool for condition monitoring in PMSG.Öğe Interturn Short-Circuit Faults in Permanent Magnet Synchronous Machines: An Extended Review and Comprehensive Analysis(Ieee-Inst Electrical Electronics Engineers Inc, 2018) Zafarani, Mohsen; Bostanci, Emine; Qi, Yuan; Goktas, Taner; Akin, BilalThis paper presents an extended review and recent advances in modeling and diagnosis of interturn short circuit (ITSC) faults in permanent magnet synchronous machines, supported with in-depth fault analysis. In the analysis part, the influence of the fault intensity on the machine's parameters and performance is analyzed at various operating conditions through finite-element analysis, drive system models, and test bench measurements. The presented findings shed light on the fault signatures and factors that are affecting signature dynamics such as the fault intensity, operating conditions, resistance of the short-circuiting path, and controller action. Following the characterization of the ITSC fault, a detailed literature review on fault signature types and state-of-the art diagnosis methods are presented. The corresponding trends, shortcomings of current solutions, and potential research topics are discussed exhaustively.Öğe An Investigation of Motor Topology Impacts on Magnet Defect Fault Signatures(Ieee-Inst Electrical Electronics Engineers Inc, 2017) Zafarani, Mohsen; Goktas, Taner; Akin, Bilal; Fedigan, Stephen E.This paper presents a study on the topology-dependent magnet defect fault signatures in permanent-magnet motors. A new analytical approach is introduced to characterize the fault signatures in stator back electromotive force (EMF) and current waveforms using magnetic equivalent circuit. Stator winding configuration, winding connection type and location of damaged rotor magnets are some of the physical properties affecting the fault signature characteristics. Several cases with different number of pole and slot are investigated through the proposed method. In addition, different winding connections (including star and delta connection), different winding configurations (including single and double layer, fractional and full coil pitch), and different magnet defect number and location are scrutinized. It is shown that there are some cases exhibiting different fault patterns than the ones obtained through well-known fault models defined in the literature. It is essential to take these discrepancies into account in order to avoid false alarms. In addition, it is observed that some of the fault signatures show up in the stator back EMF spectrum but not in the current spectrum due to location and severity of magnet defect, and design specs. Comparative 2-D finite-element simulations and experimental results justify the theoretical magnet defect fault analysis and show the efficacy of the proposed approach.Öğe Monitoring of Leakage Flux for Rotor Fault Detection under non-adjacent broken rotor bars in Induction Motors(Ieee, 2019) Goktas, Taner; Ekinci, Imran; Yuklu, Nihat; Arkan, Muslum; Mamis, M. SalihIn this paper, leakage flux is monitored when induction motors expose to non-adjacent broken rotor bars fault. The detection of rotor faults based on stator current analysis may sometimes become challenging if the corresponding broken bar positions are in half and full pole-pitch. In such cases, electromagnetic distribution can be keep symmetrical due to induced magnetic field by corresponding broken bar positions. This leads to reduce diagnostics ability for rotor bar fault. In order to minimize the such problems, leakage flux is examined using finite element analysis (FEA) in induction motors. Comparative results for stator current and leakage flux are presented to show the diagnostics ability and advantages of leakage flux based analysis. The 2D-FEA results show that some characteristics signatures such as 3sf(s) and f(r)+sf(s) in leakage flux are more reliable signatures to detect broken rotor bar fault even the positions of broken bars are in half-and full pole pitch distance.Öğe The Performance Evaluation of Machine Learning based Techniques via Stator Current and Stray Flux for Broken Bar Fault in Induction Motors(Ieee, 2021) Younas, M. B.; Ullah, N.; Goktas, Taner; Arkan, Muslum; Gurusamy, V.In this paper, the machine learning based techniques are evaluated using stator current and stray flux for broken bar fault in induction motors (IMs). The feature extraction is achieved from Discrete Wavelet Transform (DWT) for both healthy and faulty operations. In order to analyze the performance of different classifier, six fundamental classifications with 23 sub-classifiers are used via a toolbox. It has been observed that 18 out of 23 classifiers have shown great performance (100% accuracy) and two more classifier results at accuracy of greater than 90% for stray flux. Both simulation and experimental results show that stray flux provides better diagnostics results than stator current using different machine learning based classification algorithms in IMs.Öğe Separation Harmonics for Detecting Broken Bar Fault in case of Load Torque Oscillation(Ieee, 2015) Goktas, Taner; Arkan, Muslum; Zafarani, Mohsen; Akin, BilalThis paper presents separation harmonics to discriminate rotor failure from low frequency load torque oscillations in three phase induction motors. The most common method for detecting broken rotor bar faults is to analyze the corresponding sidebands through motor current signature analysis (MCSA). If a motor is subjected to load fluctuation, then the oscillation related sidebands exhibit similar behaviors as well. Particularly, when the load fluctuation frequency is close or equal to that of broken bars, the stator current spectrum analysis can be misleading. In this study, torque and motor phase voltage waveforms are exhaustively analyzed to discriminate broken rotor bar fault from low frequency load torque oscillation in three phase induction motors. In order to extract and justify the separation patterns, 2-D Time Stepping Finite Element Method (TSFEM) is used. The simulation and experimental results show that the proposed approach can successfully be applied to fault separation process in star connected motors.Öğe Separation of Induction Motor Rotor Faults and Low Frequency Load Oscillations Through the Radial Leakage Flux(Ieee, 2017) Goktas, Taner; Arkan, Muslum; Mamis, M. Salih; Akin, BilalThis paper presents a separation method to discern broken rotor bar fault from low frequency load torque oscillation thorough radial leakage flux spectrums in induction motors (IMs). Broken rotor bar fault can usually be detected using classical motor current based analysis (MCSA), but it may not provide reliable results since its performance depend on motor topology, stator winding and load type. Particularly, if a motor is subjected to load fluctuation, then oscillation related signatures exhibit similar behavior that of broken bar which leads misleading signatures. In this paper, radial leakage flux spectrum is exhaustively analyzed thorough a fluxgate sensor to discern these two effects in IMs. It is shown that there are some additional characteristics broken bar signatures such as 3sf(s) and (f(s)-f(r))+/- 2sf(s) in radial leakage flux which do not appear in low frequency load torque oscillation case. A 2-Dimensional (2D) finite element analysis and experiments are carried to show that using leakage flux can provide a separation method and more reliable results than classical MCSA in IMs.Öğe The Role of Speed-Related Frequencies in Vibration-Based Mechanical Faults Detection in Induction Motor(Institute of Electrical and Electronics Engineers Inc., 2024) Ayas, G. Nur; Goktas, Taner; Arkan, MuslumIn induction motors, possible faults can affect the dynamic performance of the motor and reduce the overall efficiency of the motor-driven systems. Therefore, it is crucial to detect potential faults early and accurately. This study investigates the role of speed-related frequencies in vibration-based detection of mechanical faults in induction motors. For this purpose, a test setup is built, and different mechanical faults are created to analyze the frequency spectra of 3-axis vibration data under different torque profiles. According to the experimental results, effective and reliable fault detection can be provided through frequency-related signals (fr (1X) and 2fr (2X)) and their sidebands (2fr+2sfs). © 2024 IEEE.Öğe Vibration-Based Detection and Classification of Mechanical Defects in Induction Motor-Driven Systems During the Starting Transient(Ieee-Inst Electrical Electronics Engineers Inc, 2025) Battulga, Byambasuren; Shaikh, Muhamad Faizan; Goktas, Taner; Arkan, Muslum; Lee, Sang BinVibration analysis is considered the most common and effective means of detecting mechanical faults such as imbalance, misalignment, and looseness in induction motor driven systems. Most mechanical problems result in an increase in vibration at multiples of the rotor speed frequency (1x) making it difficult to discern the source of vibration. In case of a fault alarm, the maintenance engineer usually performs a walk-around test to identify the source of vibration for planning maintenance, and therefore, is exposed to safety risks. In this paper, a new remote and automated test method for identifying the source of mechanical vibration during the starting transient of induction motors is proposed. The level and speed-dependency of vibration during rotor acceleration are used for identifying imbalance from other mechanical defects that produce 1x vibration. Test results on a 380 V, 5.5 kW induction motor under mechanical defects are given for verification. It is shown that the proposed method can provide automated identification of the source of vibration enabling maintenance to be performed in a safe, low cost, and efficient manner. The data acquired and analyzed for the testing are described and shared through this paper.
