• Journal of the Korean Society for Precision Engineering
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• v.19 no.4
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• pp.168-178
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• 2002

The CNC machine toots has two kinds of fault. One is the fault due to degraded parts and the other is the fault due to operation disability. The phenomena of degradation is predictable but the operational fault is unpredictable because it occurred without any warning. The major faults of CNC machine tool are operational faults which are charged over 70%. This paper describes the model of remote service and the intelligent fault diagnosis system to diagnosis operational faults of CNC machine tools. To generalize fault diagnosis, two diagnosis models such as SF(Switching Function) and SSF(Step Switching Function) are proposed. The SF is static model and SSF is dynamic model for expression of fault. The SF and SSF model can be generated using SFG(Switching Function Generator) which is developed in this research. The three major operational faults such as emergency stop error, cycle start disability and machine ready disability are applied to experiment of fault modeling. To remote service of faults fur CNC machine tool, the web server and client system based internet are proposed as the suitable environment. The developed two technologies are implemented with the internal function of open architecture controller. The implemental results for two technologies are presented to validate the proposed scheme.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.9
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• pp.114-120
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• 1995

It is important to test the various kinds of interconnect faults between chips on a card/module. When boundary scan design techniques are adopted, the chip to chip interconnection test generation and application of test patterns is greatly simplified. Various test generation algorithms have been developed for interconnect faults. A new interconnect test generation algorithm is introduced. It reduces the number of test patterns by half over present techniques. It also guarantees the complete diagnosis of mutiple interconnect faults.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.665-670
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• 1999

This paper introduces a fuzzy expert systems for fault diagnosis, where the causal relationships between faults and protective devices are defined as fuzzy relations. The uncertainties existing in the fault diagnosis are figured out using the possibility theory and the possibility measure is associated with the fuzzy relation to evaluate the possibilities of faults. Besides, the knowledge base in the expert system is described and explained. In this way, multiple-fault can be handled easily and simultaneously together with single faults.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.988-990
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• 1999

Using neural network approach, the diagnosis of faults in industrial process that requires observing multiple data simultaneously are studied in this paper. Two-stage diagnosis is proposed as the basic structure. The first stage detects the dynamic trend of each measurements and the second stage diagnosis the faults. This paper makes up for the disadvantage of neural about unknown faults. The potential of this approach is demonstrated in simulation using a model of tank reactor.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.10a
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• pp.36-39
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• 2000

This paper presents the technology of condition diagnosis & life estimation on insulation system of the traction motor. In the non-destructive methods for diagnosis of coil insulation state, residual dielectric strength is estimated by the D-map which consist of the partial discharge quantity Q and average degradation degree $\Delta$. In the operating history of machine, the N-Y life estimation method is based on the stop-starting numbers and operating times with considering each degradation factor by the thermal, electrical and heat-cycle stress. With the on-line conditioning monitoring on the currents of traction motors, detecting the abnormal operating state due to bearing faults, stator or armature faults, eccentricity related faults and broken rotor bars can be performed.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.37-44
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• 2014

This paper proposes a new diagnosis algorithm to detect broken rotor bars (BRBs) faults in induction motors. The proposed algorithm is composed of a frequency signal dimension order (FSDO) estimator and a fault decision module. The FSDO estimator finds a number of fault-related frequencies in the stator current signature. In the fault decision module, the fault diagnostic index from the FSDO estimator is used depending on the load conditions of the induction motors. Experimental results obtained in a 75 kW three-phase squirrel-cage induction motor show that the proposed diagnosis algorithm is capable of detecting BRB faults with an accuracy that is superior to a zoom multiple signal classification (ZMUSIC) and a zoom estimation of signal parameters via rotational invariance techniques (ZESPRIT).

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2175-2177
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• 2001

This paper presents a faults diagnosis technique of induction motors based on a neural network. Only stator current is measured, transformed by using FFT and normalized for the training. Healthy, bearing fault, stator fault and rotor end-ring fault motors are prepared to obtain the learning data and diagnose the several faults. For more effective diagnosis, the load rate is changed by 100%, 60%, 30% of full load and the obtained are applied to the learning process. The experimental results show the proposed method is very detectable and applicable to the real diagnosis system.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.4
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• pp.280-284
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• 2016

Stability and reliability of a power system in many respects depend on the condition of power transformers. Essential devices as power transformers are in a transmission and distribution system. Being one of the most expensive and important elements, a power transformer is a highly essential element, whose failures and damage may cause the outage of a power system. To detect the power transformer faults, dissolved gas analysis (DGA) is a widely-used method because of its high sensitivity to small amount of electrical faults. Among the various diagnosis methods, Rogers diagonsis method has been widely used in transformer in service. But this method cannot offer accurate diagnosis for all the faults. This paper proposes a fault diagnosis method of oil-filled power transformers using PNN(Probability Neural Network) based Rogers diagnosis method. The test result show better performance than conventional Rogers diagnosis method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2741-2749
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• 2009

There have been many researches to detect and recover from faults on smart home environment, because these faults should lower its reliability. while, most of these researches have addressed functional defects of devices or software malfunction, few attempts have been made to deal with faults which may occur due to the inter relationships among devices. In this paper, we define the relationships among devices as rules, and propose a method for generating fault diagnosis knowledge which defines the symptoms and causes of faults. We classify the contexts of devices into two sets, depending on whether it satisfies the rules or not. when this method is applied to smart home environment, it is feasible not only to detect faults that may occur due to the relationships among devices but to identify their causes at real time.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1614-1622
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• 2018

The research related to fault diagnosis in permanent magnet synchronous motors (PMSMs) has attracted considerable attention in recent years because various faults such as permanent magnet demagnetization and short-circuited turns can occur and result in unexpected failure of motor related system. Several conventional current and back electromotive force (BEMF) analysis techniques were proposed to detect certain faults in PMSMs; however, they generally deal with a single fault only. On the contrary, cases of multiple faults are common in PMSMs. We propose a fault diagnosis method for PMSMs with single and multiple combined faults. Our method uses three phase BEMF voltages based on the fast Fourier transform (FFT), support vector machine(SVM), and visualization tools for identifying fault types and severities in PMSMs. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) are used to visualize the high-dimensional data into two-dimensional space. Experimental results show good visualization performance and high classification accuracy to identify fault types and severities for single and multiple faults in PMSMs.