• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.2
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• pp.73-78
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• 2005

The management of technological systems will become increasingly complex. Safe and reliable software operation is a significant requirement for many types of system. So, with software fault tolerance, we want to prevent failures by tolerating faults whose occurrences are known when errors are detected. This paper presents a fault location algorithm for single-phase-to-ground faults on the teed circuit of a parallel transmission line using software fault tolerance technique. To find the fault location of transmission line, we have to solve the 3rd order transmission line equation. A significant improvement in the identification of the fault location was accomplished using the N-Version Programming (NVP) design paradigm. The delivered new algorithm has been tested with the simulation data obtained from the versatile EMTP simulator.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.1
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• pp.27-32
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• 2017

This paper presents a Daubechies wavelet-based fault detection method for fault identification in transmission lines. After the Daubechies wavelet coefficients are calculated, the proposed algorithm has been implemented difference equation using C language. We have modeled a 154kV transmission line using the ATPDraw software and have acquired test data. In order to evaluate effects of DC offset, simulations carried out while varying an inception angle of the voltage $0^{\circ}$, $45^{\circ}$, $90^{\circ}$. For performance evaluation, fault distance was varied. As we can see from the off-line simulation, the proposed algorithm shows rapid and accurate fault detection. Also we can see the proposed algorithm is not affected by the fault inception angle change.

• Aerospace Engineering and Technology
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• v.6 no.2
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• pp.21-28
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• 2007

The goal of this paper is to represent a technique of fault detection for the control surface as a base research of the fault tolerant control system for safety improvement of UAV. The real-time system identification based on the recursive Fourier Transform was implemented for the fault detection of the control surface and verified through the HILS and flight test. The failures of the control surface are detected by comparing the control derivatives in fault condition with the normal condition. As a result from the flight test, we have confirmed that the control derivatives of fault condition less than normal condition.

• Nuclear Engineering and Technology
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• v.37 no.3
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• pp.259-264
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• 2005

This paper presents a vital area identification (VAI) method based on the current fault tree analysis (FTA) and probabilistic safety assessment (PSA) techniques for the physical protection of nuclear power plants. A structured framework of a top event prevention set analysis (TEPA) application to the VAI of nuclear power plants is also delineated. One of the important processes for physical protection in a nuclear power plant is VAI that is a process for identifying areas containing nuclear materials, structures, systems or components (SSCs) to be protected from sabotage, which could directly or indirectly lead to core damage and unacceptable radiological consequences. A software VIP (Vital area Identification Package based on the PSA method) is being developed by KAERI for the VAI of nuclear power plants. Furthermore, the KAERI fault tree solver FTREX (Fault Tree Reliability Evaluation eXpert) is specialized for the VIP to generate the candidates of the vital areas. FTREX can generate numerous MCSs for a huge fault tree with the lowest truncation limit and all possible prevention sets.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.852-856
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• 2004

An induction motor is one of the most popular electrical apparatuses owing to its simple structure and robust construction. Parameter identification of the induction motor has long been researched either for a vector control technique or fault detection. Since vector control is a well-established technique for induction motor control, this paper concentrates on successive identification of physical parameters with on-load data for the purpose of condition monitoring and/or fault detection. For extracting six physical parameters from the on-load data in the framework of the induction motor state equation, unmeasured initial state values and profiles of load torque have to be estimated as well. However, the analytic optimization methods in general fail to estimate these auxiliary but significant parameters owing to the difficulty of obtaining their gradient information. In this paper, the univariate dynamic encoding algorithm for searches (uDEAS) newly developed is applied to the identification of whole unknown parameters in the mathematical equations of an induction motor with normal operating data. Profiles of identified parameters appear to be reasonable and therefore the proposed approach is available for fault diagnosis of induction motors by monitoring physical parameters.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.549-551
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• 1999

Recently, Several type of motors are used more widely in Fan system because of their low cost and high reliability. Therefore, the importance of fault detection and isolation of fan system significantly increases. The motor is a important factor bring out the fan system fault. So the problem of a fault detection for motor based on a parameter identification will be considered in this paper. After an introduction into fault detection with parameter estimation, a mathematical model for motor with special emphasis on motor itself. In the fault detection system, current and motor speed are used as parameter. Finally, simulation results are used to demonstrate the efficiency of the fault detection system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.11
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• pp.1279-1289
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• 1992

Fault-tolerant system in improved the reliability and safety by using hardware and software redundancy. Fault mask and detection, identification techniques are conditionally used with system's application areas. Here DMR system is operated with standby and fail-safe module method that has minimal hardware and software redundancy, then its reliablity and safety comparison is presented respectively. Also this paper proposed an effective methods of dealing with transient faults as compared system's MTTFs to transient faults tolerance capabilities of self-diagnosis program.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1406-1416
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• 2017

In complex and large-scale industries, properly designed fault detection and identification (FDI) systems considerably improve safety, reliability and availability of target processes. In thermal power plants (TPPs), generating units operate under very dangerous conditions; system failures can cause severe loss of life and property. In this paper, we propose a bagged auto-associative kernel regression (AAKR)-based FDI approach for steam boilers in TPPs. AAKR estimates new query vectors by online local modeling, and is suitable for TPPs operating under various load levels. By combining the bagging method, more stable and reliable estimations can be achieved, since the effects of random fluctuations decrease because of ensemble averaging. To validate performance, the proposed method and comparison methods (i.e., a clustering-based method and principal component analysis) are applied to failure data due to water wall tube leakage gathered from a 250 MW coal-fired TPP. Experimental results show that the proposed method fulfills reasonable false alarm rates and, at the same time, achieves better fault detection performance than the comparison methods. After performing fault detection, contribution analysis is carried out to identify fault variables; this helps operators to confirm the types of faults and efficiently take preventive actions.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1798-1804
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• 2017

This paper presents an approach of the Gustafson-Kessel (GK) clustering algorithm's performance in fault identification on power transmission lines. The clustering algorithm is incorporated in a scheme that uses hybrid intelligent technique to combine artificial neural network and a fuzzy inference system, known as adaptive neuro-fuzzy inference system (ANFIS). The scheme is used to identify the type of fault that occurs on a power transmission line, either single line to ground, double line, double line to ground or three phase. The scheme is also capable an analyzing the fault location without information on line parameters. The range of error estimation is within 0.10 to 0.85 relative to five values of fault resistances. This paper also presents the performance of the GK clustering algorithm compared to fuzzy clustering means (FCM), which is particularly implemented in structuring a data. Results show that the GK algorithm may be implemented in fault identification on power system transmission and performs better than FCM.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.359-362
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• 2000

In this paper, we propose a test pattern generation algorithm on the basis of the identification of undetectable faults for Speed-Independent(SI) asynchronous control circuits. The proposed methodology generates tests from the specification of a target circuit, which describes the behavior of the circuit in the form of Signal Transition Graph (STG). The proposed identification method uses only topological information of a target circuit and reachability information of a fault-free circuit, which is generated in the form of Binary Decision Diagram(BDD) during pre-processing. Experimental results show that high fault coverage over single input stuck-at fault model is obtained for several synthesized SI circuits and the use of the identification process as a preprocessing decreases execution time of the proposed test generation with negligible costs.