• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2222-2224
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• 2002

Induction motors are critical components of many industrial machines and are frequently integrated in commercial equipment. The heavy economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motors supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the artificial neural network, and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.252-254
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• 2001

Accurate detection and classification of faults on transmission lines is vitally important. High impedance faults(HIF) in particular pose difficulties for the commonly employed conventional overcurrent and distance relays, and if not detected, can cause damage to expensive equipment, threaten life and cause fire hazards. Although HIFs are far less common than LIFs, it is imperative that any protection device should be able to satisfactorily deal with both HIFs and LIFs. This paper proposes an algorithm for fault detection and classification for both LIFs and HIFs using Adaptive Network-based Fuzzy Inference System(ANFIS). The performance of the proposed algorithm is tested on a typical 154[kV] Korean transmission line system under various fault conditions. Test results show that the ANFIS can detect and classify faults including (LIFs and HIFs) accurately within half a cycle.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.897-899
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• 1997

The analysis of distribution line faults is essential to the proper protections of the power system. A high impedance fault does not make enough current to cause conventional protective devices. It is well known that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional protection system. This paper describes an algorithm using back-propagation neural network for pattern recognition and detection of high impedance faults. Fractal dimensions are estimated for distinction between random noise and chaotic behavior in the power system. The fractal dimension of the line current is also used as a indication of the high impedance fault.

• Journal of the Korean Magnetics Society
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• v.6 no.5
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• pp.287-293
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• 1996

The squirrel cage rotors of induction motors may have several faults such as broken bars, bad spots in end ring and abnormal skew caused by improper processing. These faults may cause bad effects on the performance of the induction motor. This paper proposes the detecting technique of these faults by analyzing the induced current of the detecting electric magnet, using 2-D finite element method taking account of the rotor movement.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1972-1975
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• 2002

A new test approach based on a supply current test method is proposed for testing open faults in bipolar logic circuits. In the approach, only the open faults are detected by the supply current test method, which are difficult to be detected by functional test methods. The effectiveness of the approach is examined experimentally on open fault detection in TTL combinational circuits. The results shows that higher fault coverage can be established by applying a small number of test input vectors of the supply current test method after test vectors of functional test methods based on stuck-at models.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.6
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• pp.199-208
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• 1994

This paper proposes a delay fault test technique for ICs and PCBs with the boundary-scan architectures supporting ANSI/IEEE Std 1149.1-1990. The hybrid delay fault model, which comprises both of gate delay faults and path delay faults, is selected. We developed a procedure for testing delay faults in the circuits with typical boundary scan cells supporting the standard. Analyzing it,we concluded that it is impractical because the test clock must be 2.5 times faster than the system clock with the cell architect-ures following up the state transition of the TAP controller and test instruction set. We modified the boundary-scan cell and developed test instructions and the test procedure. The modified cell and the procedure need test clock two times slower than the system clock and support the ANSI/IEEE standard perfectly. A 4-bit ALU is selected for the circuits under test. and delay tests are simulated by the SILOS simulator. The simulation results ascertain the accurate operation and effectiveeness of the modified mechanism.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.4
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• pp.36-41
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• 2014

Most faults that occur on transmission lines are caused by extreme weather with lightning storms in the distance. These are not only prolongs the time of removing and recovering, but also increases economical damages. If faults can be precisely located, maintenance crews can reach them quickly, and remove the faults in time. So, the precise locating of the faulted point on a transmission line is very important to improve the system reliability, and decreases economic damages as an inherent consequence of long term outages. Also, fault location methods are becoming of much importance for utilities and research. In this paper, two single-terminal impedance-based fault location techniques will be investigated to show the reliability and evaluated the performance of reactance and Takagi method by using MATHCAD program simulations.

• ETRI Journal
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• v.30 no.4
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• pp.546-554
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• 2008

Phase-locked loops (PLLs) are among the most important mixed-signal building blocks of modern communication and control circuits, where they are used for frequency and phase synchronization, modulation, and demodulation as well as frequency synthesis. The growing popularity of PLLs has increased the need to test these devices during prototyping and production. The problem of distinguishing and classifying the responses of analog integrated circuits containing catastrophic faults has aroused recent interest. This is because most analog and mixed signal circuits are tested by their functionality, which is both time consuming and expensive. The problem is made more difficult when parametric variations are taken into account. Hence, statistical methods and techniques can be employed to automate fault classification. As a possible solution, we use the back propagation neural network (BPNN) to classify the faults in the designed charge-pump PLL. In order to classify the faults, the BPNN was trained with various training algorithms and their performance for the test structure was analyzed. The proposed method of fault classification gave fault coverage of 99.58%.

• Proceedings of the KSR Conference
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• 2000.05a
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• pp.330-335
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• 2000

Faults detection and containment requirements are typically allocated from a top-level specification as a percentage of total faults detection and containment, weighted by failure rate. This faults detection and containments are called as a fault coverage. The fault coverage requirements are typically allocated identically to all units in the system, without regard to complexity, cost of implementation or failure rate for each units. In this paper a simple methodology and mathematical model to support the allocation of system fault coverage rates to lower-level units by considering the inherent differences in reliability is presented. The models are formed as a form of constrained optimization. The objectives and constraints are modeled as a linear form and this problems are solved by linear programming. It is identified by simulation that the proposed solving methods for these problems are effective to such requirement allocating.

• Proceedings of the KSR Conference
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• 2003.10c
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• pp.529-534
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• 2003

Within industry induction motors have a broad application area to drive pumps, fans, elevators and electric trains. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.