• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.2
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• pp.29-37
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• 1998

This paper describes the fault diagnosis based on real-time data of the inverter system for brush less DC motor drive. After identifying all the fault types in the inverter system, a preliminary typical analysis of fault types has been classified into the key fault symptoms. The predicted fault performances are then substantiated by using ACSL(Advanced Continuous Simulation Language), and the simulated results are composed of knowledge-base. The real-time measured data from the inverter system are compared with the simulated knowledge-base through the inference engine of expert system, which have been used to diagnose the fault causes. If some faults may occur in the inverter system, this system will be stopped. And then the expertise of elimination and remedial strategies about the fault causes, will be supplied rapidly to operator who doesn't know well about the inverter drive system.system.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1623-1631
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• 2014

Eccentricity faults more or less exist in all rotating electrical machines. This paper establishes a more precise model of dynamic eccentricity (DE) in electrical machines named as curved dynamic eccentricity. It is a kind of axial unequal eccentricity which has not been investigated in detail so far but occurs in large electrical machines. The inductances of a large three-phase squirrel-cage induction machine (SCIM) under different levels of curved DE conditions are evaluated using winding function approach (WFA). These inductances include the stator self and mutual inductances, rotor self and mutual inductances, and mutual inductances between stator phases and rotor loops. A comparison is made between the calculation results under curved DE and the corresponding pure DE conditions. It indicates that the eccentricity condition will be more terrible than the monitored eccentricity based on the conventional pure DE model.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.6
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• pp.43-50
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• 2000

Design and experiments of a nondestructive testing system with a solenoid eddy current sensor to inspect deterioration of ASCR-OC (ACSR Outdoor Cross-linked Polyethylene Insulated Wires) usually used in HV overhead distribution lines in domestic areas in presented in this paper. Through corrosion mechanisms and deterioration results for ACSR-OC conductors are examined, it is shown that corrosion may lead to the reduction of the effective cross section area of conductors is proposed. The measurement system consisting of a constant current source with a RF frequency, a signal processing unit and a motor driver/ controller is designed and implemented. This instrument has such capabilities as detecting the sensor output and estimating diameter change of the testing conductors, continuously. As a result, it was verified that such corrosion detector system with an eddy current sensor can be shown good effectiveness for estimating the serious faults due to deterioration in overhead distribution lines and giving an early warming before severe aged conductor may lead to fail.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1392-1401
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• 2017

In this study, the design methodology of pattern classification is introduced for avoiding faults through partial discharge occurring in the power facilities and local sites. In order to classify some partial discharge types according to the characteristics of each feature, the model is constructed by using the Radial Basis Function Neural Networks(RBFNNs) and Particle Swarm Optimization(PSO). In the input layer of the RBFNNs, the feature vector is searched and the dimension is reduced through Principal Component Analysis(PCA) and PSO. In the hidden layer, the fuzzy coefficients of the fuzzy clustering method(FCM) are tuned using PSO. Raw datasets for partial discharge are obtained through the Motor Insulation Monitoring System(MIMS) instrument using an Epoxy Mica Coupling(EMC) sensor. The preprocessed datasets for partial discharge are acquired through the Phase Resolved Partial Discharge Analysis(PRPDA) preprocessing algorithm to obtain partial discharge types such as void, corona, surface, and slot discharges. Also, when the amplitude size is considered as two types of both the maximum value and the average value in the process for extracting the preprocessed datasets, two different kinds of feature datasets are produced. In this study, the classification ratio between the proposed RBFNNs model and other classifiers is shown by using the two different kinds of feature datasets, and also we demonstrate the proposed model shows superiority from the viewpoint of classification performance.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.8
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• pp.36-41
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• 2006

Computer Loads can be found in all of modern society. The switching mode power supplies used in personal computers are major sources of harmonic currents. Harmonic currents can cause lots of harmonic problems such as disruption in computer performance. A series resonant filter is very effective in harmonic reduction for personal computer loads. Voltage sags are short duration reductions in rms voltage. The main causes of voltage sags at faults, motor starting, and transformer energizing. Personal computers are another example of devices sensitive to voltage sags. A serious voltage sag at the terminals way lead mis-operation of the equipment. This paper presents an in depth analysis to evaluate the effect of harmonics reduction based on the IEC 61000-3-2 and the effect of voltage sag using ITI curve by applying a series resonant filter for personal computer loads.

• Journal of the Korean Society for Railway
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• v.16 no.1
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• pp.26-31
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• 2013

The reduction unit is one of the most important components for railway vehicles because the torque of the motor must be transmitted to the wheels of the vehicle by the reduction unit. The faults in the reduction units of high-speed trains are caused by damage such as gear, fatigue. These have serious impacts on safety of the train during operation. To address this development of a system for monitoring, fault diagnosis of the reduction unit is needed to keep the vehicle running safely. Before that can be accomplished, it is most important to understand the vibration characteristics of the reduction unit in a normal state. Vibration diagnosis technology using characteristic-analysis of vibration waveform and frequency is known to be the most effective method for fault diagnosis. In this paper, we analyzed the vibration characteristics of the reduction units two Korean high-speed trains (KTX and KTX II), under normal conditions, by two test methods (driving gear test, full-vehicle test).

• Journal of IKEEE
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• v.27 no.4
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• pp.518-523
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• 2023

The diagnosis of Inter Turn Short Circuits (ITSC) in induction motors is critical due to the escalating severity of faults resulting from even minor disruptions in the stator windings. However, diagnosing ITSC presents significant challenges due to similarities in noise and losses shared with 3-phase induction motors. Although artificial intelligence techniques have been explored for efficient diagnosis, practical applications heavily rely on model-based methods, necessitating further research to enhance diagnostic performance. This study proposed a diagnostic method applied the Clarke Transformation approach, focusing solely on current components while disregarding changes in rotating flux. Experimental results conducted over a 30-minute period, encompassing both normal and ITSC conditions, demonstrate the effectiveness of the proposed approach, with FAR(False Accept Rates) of 0.2% for normal-to-ITSC FRR(False Rejection Rates) and 0.26% for ITSC-to-normal FRR. These findings underscore the efficacy of the proposed approach.

• Journal of Digital Convergence
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• v.13 no.5
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• pp.213-218
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• 2015

In parallel with evolving information communication technology, M2M(Machine-to-Machine) industry has implemented multi-functional and high-performance systems, and made great strides with IoT(Internet of Things) and IoE(Internet of Everything). Authentication, confidentiality, anonymity, non-repudiation, data reliability, connectionless and traceability are prerequisites for communication security. Yet, the wireless transmission section in M2M communication is exposed to intruders' attacks. Any security issues attributable to M2M wireless communication protocols may lead to serious concerns including system faults, information leakage and privacy challenges. Therefore, mutual authentication and security are key components of protocol design. Recently, secure communication protocols have been regarded as highly important and explored as such. The present paper draws on hash function, random numbers, secret keys and session keys to design a secure communication protocol. Also, this paper tests the proposed protocol with a formal verification tool, Casper/FDR, to demonstrate its security against a range of intruders' attacks. In brief, the proposed protocol meets the security requirements, addressing the challenges without any problems.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.445-454
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• 2023

Recently, various fault diagnosis studies are being conducted utilizing data from collaborative robots. Existing studies performing fault diagnosis on collaborative robots use static data collected based on the assumed operation of predefined devices. Therefore, the fault diagnosis model has a limitation of increasing dependency on the learned data patterns. Additionally, there is a limitation in that a diagnosis reflecting the characteristics of collaborative robots operating with multiple joints could not be conducted due to experiments using a single motor. This paper proposes an LSTM diagnostic model that can overcome these two limitations. The proposed method selects representative normal patterns using the correlation analysis of vibration and current data in single-axis and multi-axis work environments, and generates residual patterns through differences from the normal representative patterns. An LSTM model that can perform gear wear diagnosis for each axis is created using the generated residual patterns as inputs. This fault diagnosis model can not only reduce the dependence on the model's learning data patterns through representative patterns for each operation, but also diagnose faults occurring during multi-axis operation. Finally, reflecting both internal and external data characteristics, the fault diagnosis performance was improved, showing a high diagnostic performance of 98.57%.