• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.410-418
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• 1999

This paper describes the use of residual and parameter identification methods for fault detection in an air handling unit. Faults can be detected by comparing expected condition with the measured faulty data using residuals. Faults can also be detected by examining unmeasurable parameter changes in a model of a controlled system using a system identification technique. In this study, AutoRegressive Moving Average with seXtrnal input(ARMAX) and AutoRegressive with eXternal input(ARX) models with both single-input/single-input and multi-input/single-input structures are examined. Model parameters are determined using the Kalman filter recursive identification method. Regression equations are calculated from normal experimental data and are used to compute expected operating variables. These approaches are tested using experimental data from a laboratory's variable-air-volume air-handling-unit.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.124-126
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• 2001

A reliable fault type identification (phase selection) plays a very important role in transmission line protection, particularly in Extra High Voltage(EHV) networks. The conventional fault type identification algorithm used the phase difference between positive and negative sequence current excluding load current. But, it is difficult to pick out only fault current since we can not know when a fault occurs and identify the fault type in weak-infeed conditions that dominate zero-sequence current in phase current. The proposed algorithm can identify the accurately fault type using the sum of unit vectors which are calculated by positive-sequence votage and negative-sequence voltage.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.7-12
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• 1994

This paper describes an outline of pseudorandom M-sequence and its applications to measurement and control engineering. At first, generation and properties of M-sequence is briefly described and then its applications to delay time measurement, information transmission by use of M-array, two dimensional positioning, fault detection of logical circuit, fault detection of RAM, linear and nonlinear system identification.

• The Journal of the Korea institute of electronic communication sciences
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• v.3 no.4
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• pp.276-281
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• 2008

Because the distribution systems experience frequently the fault by several causes, the identification task of fault location plays very important role in the view point of power supply reliability. The distribution systems are designed as radial structure with three-phase and single-phase branch line to supply the electric power to the widely dispersed loads, and it have a several load taps within each line segment. it makes the accurate fault distance determination difficult. Accordingly in this papers, the existing fault point determination methods are surveyed and analyzed, and then a fault distance determination method for distribution feeder is adopted which can be executed effectively in DAS center. Finally, the adopted method is verified using EMTP simulation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.2
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• pp.62-69
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• 2009

For the fault diagnosis of power transformer, we develop a diagnosis algorithm based on support vector machine. The proposed fault diagnosis system consists of data acquisition, fault/normal diagnosis, and identification of fault. In data acquisition part, concentrated gases are extracted from transformer for data gas analysis. In fault/normal diagnosis part, KEPCO based decision rule is performed to separate normal state from fault types. The determination of fault type is executed by multi-class SVM in identification part. As the simulation results to verify the effectiveness, the proposed method showed more improved classification results than conventional methods.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1272-1278
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• 1999

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to fault diagnosis. In this paper, we proposes an FDI system for nonlinear systems using neuro-fuzzy inference system. The proposed diagnostic system consists of two neuro-fuzzy inference systems which operate in two different modes (parallel and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis Function) network to identify the faults. The proposed FDI scheme has been tested by simulation on two-tank system.

• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.325-327
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• 2007

This paper proposes a novel sensorless fault detection algorithm for a brushless DC(BLDC) motor drive system. This proposed method is configured without the additional sensor for fault detection and identification. The fault detection and identification are achieved by a simple algorithm using the operating characteristic of the BLDC motor. This proposed method can also be embedded into existing BLDC motor drive systems as a subroutine without excessive computational effort. The feasibility of a novel sensorless fault detection algorithm is validated in simulation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.39-52
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• 2002

Research Direction The significant research direction in mechanical fault diagnosis area: Theorles and approaches for fault feature extracting and fault classification. Identification Complicated fault generating mechanism and its model Intelligent fault diagnosis system (including the expert system and network based remote diagnosis system) One of the Key Points： Fault feature extracting techniques based on (modern) signal processing(omitted)

• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.2
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• pp.88-95
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• 2008

This paper proposes a fast fault detection algorithm under open-circuit fault of a switch for a brushless DC(BLDC) motor drive system. This proposed method is configured without the additional devices for fault detection and identification. The fault detection and identification are achieved by a simple algorithm using the operating characteristic of the BLDC motor. After the fault identification, the drive system is reconfigured for continuous operation. This system is reconfigured by four-switch topology connecting a faulty leg to the middle point of DC-link bidirectional switches. This proposed method can also be embedded into existing BLDC motor drive systems as a subroutine without excessive computational effort. The feasibility of a the proposed fault detection algorithm is validated in simulation and experiment.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.1-13
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• 2020

In this paper, the author proposed a nuclear power plant (NPP) instrumentation signal faults identification algorithm. A variational autoencoder (VAE)-based model is trained by using only normal dataset as same as existing anomaly detection method, and trained model predicts which signal within the entire signal set is anomalous. Classification of anomalous signals is performed based on the reconstruction error for each kind of signal and partial derivatives of reconstruction error with respect to the specific part of an input. Simulation was conducted to acquire the data for the experiments. Through the experiments, it was identified that the proposed signal fault identification method can specify the anomalous signals within acceptable range of error.