• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.8
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• pp.1355-1361
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• 2016

The inter-turn fault (ITF) causes the negative sequence components in the d -q voltage equation due to an increase in the unbalance of three-phase input currents. For this reason, d -q voltage equation become complicate as the voltage equation is classified into positive and negative components. In this study, we propose a simplified d -q equivalent circuit of an interior permanent magnet synchronous motor under ITF state. First, we proposed modeling method for d -q current based on the finite element method simulation results. Then, we developed the simplified d -q equivalent circuit by applying the proposed d -q current modeling.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.416-424
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• 2016

In this study, automatic detection of stator winding inter-turn short circuit fault (SWISCFs) in surface-mounted permanent magnet synchronous motors (SPMSMs) and automatic classification of fault severity via a pattern recognition system (PRS) are presented. In the case of a stator short circuit fault, performance losses become an important issue for SPMSMs. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, an artificial neural network (ANN)-based PRS was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To validate the proposed method, both simulation results and experimental results are presented.

• Journal of Power Electronics
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• v.8 no.2
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• pp.181-191
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• 2008

The task performed by induction motors grows increasingly complex in modern industry and hence improvements are sought in the field of fault diagnosis. It is essential to diagnose faults at their very inception, as unscheduled machine down time can upset critical dead lines and cause heavy financial losses. Artificial intelligence (AI) techniques have proved their ability in detection of incipient faults in electrical machines. This paper presents an application of AI techniques for the detection of inter-turn insulation and bearing wear faults in single-phase induction motors. The single-phase induction motor is considered a proto type model to create inter-turn insulation and bearing wear faults. The experimental data for motor intake current, rotor speed, stator winding temperature, bearing temperature and noise of the motor under running condition was generated in the laboratory. The different types of fault detectors were developed based upon three different AI techniques. The input parameters for these detectors were varied from two to five sequentially. The comparisons were made and the best fault detector was determined.

• The Transactions of the Korean Institute of Power Electronics
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• v.15 no.3
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• pp.167-178
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• 2010

To diagnose a stator winding fault caused by a short-circuited turn in a permanent magnet synchronous motor (PMSM), an on-line based fault detecting scheme during motor operation is presented. The proposed scheme is based on monitoring the second-order harmonic components in q-axis current obtained through the harmonic analysis and a winding fault is detected by comparing these components with those in normal conditions. The linear interpolation method is employed to determine harmonic data in arbitrary normal operating conditions. To verify the effectiveness of the proposed fault detecting scheme, a test motor to allow inter-turn short in the stator winding has been built. The entire control system including harmonic analysis algorithm and fault detecting algorithm is implemented using DSP TMS320F28335. The proposed scheme does not require any additional hardware and can effectively detect a fault during motor operation so long as the steady-state condition is satisfied.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1291-1296
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• 2003

This paper deals with efficient diagnostic for stator winding fault of 3-phase induction motor using a current Park's vector approach. This method firstly transforms 3-phase stator current to vertical axis current and horizontal axis current of Park's Vector, and then obtains the each Park's Vector Pattern and detects stator winding fault by comparing to Park's Vector Pattern of healthy and fault. Experimental results, obtained by using induction motor having inter-turn fault of 2, 10, 20 turn, demonstrate the effectiveness of the proposed technique, for detecting the presence of stator winding fault under 25%, 50%, and 100% of full load condition.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.5
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• pp.94-105
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• 2010

To detect faults in an inverter-fed permanent magnet synchronous motor (PMSM) drive under the circumstance having faults in a stator winding and inverter switch, an on-line basis fault detecting scheme during operation is presented. The proposed scheme is achieved by monitoring the second-order harmonic component in q-axis current and the fault is detected by comparing these components with those in normal conditions. The linear interpolation method is employed to determine the harmonic data in normal operating conditions. As soon as the fault is detected, the operating mode is changed to identify a fault type using the phase current waveform. To verify the effectiveness of the proposed fault detecting scheme, a test motor to allow inter-turn short in the stator winding has been built. The entire control algorithm is implemented using DSP TMS320F28335. Without requiring an additional hardware, the fault can be effectively detected by the proposed scheme during operation so long as the steady-state condition is satisfied.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.5
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• pp.18-28
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• 2009

To analyze influences under faults caused by a stator winding short and to evaluate an effectiveness of a diagnostic algorithm a faulty model for an inverter-driven permanent magnet synchronous motor is presented. Even though the conventional dq motor model obtained through the transformation of phase voltage model is widely used to analyze and control the motor, it can not be used in the analysis of a faulty motor since the 3-phase balanced condition is no longer hold under the fault caused by a stator winding short, and thus, it is very difficult to obtain motor input voltages from the pole voltage of an inverter. To overcome this problem, a faulty model for an inverter-driven permanent magnet synchronous motor is proposed by considering the line voltage of 3-phase variables. The effectiveness of the proposed faulty model is verified through comparative simulations and experiments using DSP TMS320F28335 and motor built to allow a partial short of inter-turn.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1079-1080
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• 2011

This paper deals with the operating characteristics of induction motors with broken rotor bar, stator winding inter-turn short and their complex fault conditions. The considered operating characteristics are phase current, torque and speed. Since the operating characteristics of induction motors are directly related to their slip conditions, this paper built the experimental set to adjust the speed of induction motor with a permanent magnet synchronous generator connected to a load bank. From the various experimental results, it is shown that the faults do not highly affect on the operating characteristics of induction motors in low slip conditions, but the fault characteristics can be easily found in larger slip conditions.

• Journal of IKEEE
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• v.28 no.2
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• pp.180-186
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• 2024

This paper proposes an efficient fault diagnosis method for ITSC(Inter-Turn Short Circuit) in three-phase induction motors using CNN. By utilizing only the D-axis component of the D-Q synchronous coordinate system, it compares SWM(Slide Window Method) and GAF(Gramian Angular Field) methods for image encoding. Results show GAF achieving ~74% accuracy, while SWM achieves ~65%, indicating GAF's superiority by 9%. Learning time (~14.74s) remains consistent, particularly with epochs ≤ 100, showcasing faster learning.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.539-542
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• 2003

A motor is the workhorse of our industry. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. Different internal motor faults (e.g., inter-turn short circuits, broken bearings, broken rotor bars) along with external motor faults (e.g., phase failure, mechanical overload, blocked rotor) are expected to happen sooner or later. This paper introduces the fault detection technique of induction motors based upon the stator current. The fault motors have rotor bar broken or rotor unbalance defect, respectively. The stator currents are measured by the current meters and stored by the time domain. The time domain is not suitable to represent the current signals, so the frequency domain is applied to display the signals. The Fourier Transformer is used for the conversion of the signal. After the conversion of the signals, the features of the signals have to be extracted by the signal processing methods like a wavelet analysis, a spectrum analysis, etc. The discovered features are entered to the pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, etc. This paper describes the fault detection results that use wavelet decomposition. The wavelet analysis is very useful method for the time and frequency domain each. Also it is powerful method to detect the features in the signals.