• Smart Structures and Systems
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• v.20 no.1
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• pp.43-52
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• 2017

Structural health monitoring has drawn great attention in the field of civil engineering in past two decades. These structural health monitoring methods evaluate structural integrity through high-quality sensor measurements of structures. Due to electronic deterioration or aging problems, sensors may yield biased signals. Therefore, the objective of this study is to develop a fault detection method that identifies malfunctioning sensors in a sensor network. This method exploits the autoregressive modeling technique to generate a bank of Kalman estimators, and the faulty sensors are then recognized by comparing the measurements with these estimated signals. Three types of faults are considered in this study including the additive, multiplicative, and slowly drifting faults. To assess the effectiveness of detecting faulty sensors, a numerical example is provided, while an experimental investigation with faults added artificially is studied. As a result, the proposed method is capable of determining the faulty occurrences and types.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.12
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• pp.643-650
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• 2000

Using neural network approach, the diagnosis of faults in industrial process that requires observing multiple data simultaneously are studied. Two-stage diagnosis is proposed to analyze system faults. By using neural network, the first stage detects the dynamic trend of each normalized date patterns by comparing a proposed pattern. Instead of using neural network, the difference between stored fault pattern and real time data is used for fault diagnosis in the second stage. This method reduces the amount of calculation and saves storing space. Also, we dealt with unknown faults by normalizing the data and calculating the difference between the value of steady state and the data in case of fault. A model of tank reactor is given to verify that the proposed method is useful and effective to noise.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.9
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• pp.973-983
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• 1992

A very fast algorithm, using fast Haar transformation with half a cycle dc-offset free data, to extract the power frequency components and to detect faults in power systems is proposed. For the speed-up, two important techniques are used. First, according to the symmetric characteristics of sine and cosine functions, fundamental frequency components are calculated with only half a cycle sample data. For using these characteristics, post-fault de-offset components must be removed beforehand. Therefore, secondly, a newly designed digital filter is used to remove exponentially decaying dc-offset from the post-fault signal. In accordance with series simulations, transmission line faults can be detected in around half a cycle after faults.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.895-902
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• 2007

Rotating machinery has two typical faults with clearance, one is partial rub and the other is looseness. Due to these faults, non-linear and non-stationary signals are occurred. Therefore, time-frequency analysis is necessary for exact fault diagnosis of rotating machinery. In this paper newly developed time-frequency analysis method, HHT(Hilbert-Huang Transform) is applied to fault diagnosis and compared with other method of FFT, SFFT and CWT. The results show that HHT can represent better resolution than any other method. Consequently, the faults of rotating machinery are diagnosed efficiently by using HHT.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1558-1565
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• 2015

In this paper, we propose a new method for detecting bearing faults using vibration signals. The proposed method is based on support vector machines (SVMs), which treat the harmonics of fault-related frequencies from vibration signals as fault indices. Using SVMs, the cross-validations are used for a training process, and a two-stage classification process is used for detecting bearing faults and their status. The proposed approach is applied to outer-race bearing fault detection in three-phase squirrel-cage induction motors. The experimental results show that the proposed method can effectively identify the bearing faults and their status, hence improving the accuracy of fault diagnosis.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.9
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• pp.678-684
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• 1987

The identification problem of instrument faults in PWR pressurizer is considered. The instrument failure detection technique in this paper consists of two filters, a normal-mode Kalman filter which estimates plant states in normal operation and a bias estimator which estimates the magnitudes and directions of bias faults. The concept of threshold based on the residual of a Kalman filter in normal operation is introduced. The bias estimator is driven when the absolute value of residual exceeds the threshold. The suggested failure detection algorithm is applied to a PWR pressurizer. Computer simulations show that the prompt detection of bias fault can be performed very successfully when there exist instrument faults.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1771-1772
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• 2006

In this paper, we considered multiple faults detection on a coaxial cable through Time-Frequency Domain Reflectometry (TFDR). It is well known that TFDR has high resolution accuracy for detecting and estimating the fault detection on a coaxial cable. This approach was based on time-frequency signal analysis and utilized a chirp signal multiplied by a Gaussian time envelope. The Gaussian envelope provided time localization, while the chirp allowed one to excite the system interest. We carried out experiments with 10C-FBT coaxial cable having either one or two faults. The result shows TFDR can be extended to detect multiple faults with high accuracy on a coaxial cable.

• Proceedings of the KIEE Conference
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• summer
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• pp.619-621
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• 2004

This paper presents the rapid and accurate algorithm for fault discrimination in transmission lines. When faults occur in transmission lines, fault discrimination is very important. If high impedance faults occur in transmission lines, it cannot be detected by overcurrent relays. The method using current and voltage cannot discriminate high impedance fault. Because of this reason this paper uses voltage and zero sequence current, and the proposed algorithm uses fuzzy logic method. This algorithm uses voltage and zero sequence current per period in case of faults. Single line ground fault and three-phase fault can be detective using voltage. Two-line ground fault and line to line fault and high impedance can be detected using zero sequence current. To prove the performance of the algorithm, it test algorithm with signal obtained from ATPDraw simulation.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.4
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• pp.399-405
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• 2008

Recently, robot technology is actively going on progress to the field of various services such as home care, medical care, entertainment, and etc. Because these service robots are in use nearby person, they need to be operated safely even though hardware and software faults occur. This paper proposes a Fault-Tolerant middleware for a robot system, which has following two characteristics: supporting of heterogeneous network interface and processing of software components and network faults. The Fault-Tolerant middleware consists of a Service Layer(SL), a Network Adaptation Layer(NAL), a Network Interface Layer(NIL), a Operating System ion Layer(OSAL), and a Fault-Tolerant Manager(FTM). Especially, the Fault-Tolerant Manager consists of 4 components: Monitor, Fault Detector, Fault Notifier, and Fault Recover to detect and recover the faults effectively. This paper implements and tests the proposed middleware. Some experiment results show that the proposed Fault-Tolerant middleware is working well.