• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.3
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• pp.240-246
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• 1990

The security control can be defined as all control actions and counter-measures to return the operating state of the system to a normal state. In an emergency state, fault clearing and/or overload suppression is enabled as a security control in order to prevent the extension of the fault. In the alert state, counter-measures should be set up in advance for the dangerous points of the system operation in drder to protect the system from expected accidents. In the normal state, the routine scenario is conducted to analyze system state. In the decision-making of the classification of system states, the heuristic and experienced knowledge can be well applied and thus application of expert system to this area attains considerable achievements. In this study, it is attempted to extract empirical rules through heuristic analysis and establish the knowledge base. Finally, the incorporation method with the conventional program packages in proposed. The expert system is designed to select an appropriate method and to perform the corresponding package. The input data can be automatically set up by using the data base. The computation results can be automatically added to the data base.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.23-26
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• 2005

This paper describes the algorithm for deciding the status of the operating machines in the power plants. It is very important to decide whether the status of the operating machines is good or not in the industry to protect the accidents of machines and improve the operation efficiency of the plants. There are two steps to analyze the status of the running machines. First, we extract the features from the input original data. Second, we classify those features into normal/abnormal condition of the machines using the wavelet transform and the input RMS vector through the K-means algorithm. In this paper we developed the algorithm to detect the fault operation using the K-means method from the sound of the operating machines.

• Economic and Environmental Geology
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• v.30 no.5
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• pp.459-467
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• 1997

In order to examine the primary trends and characteristics of geological lineaments along the southwestern boundary of Okcheon zone, we carried out the analysis of geological lineament trends over six selected sub-areas using Landsat-5 TM images and digital elevation model. The trends of lineaments is determined by a minimum variance method, and the resulting geological lineament map can be obtained through generalized Hough transform. We have corrected look direction biases reduces the interpretability of remotely sensed image. An approach of histogram modification is also adopted to extract drainage pattern specifically in alluvial plains. The lineament extracting method adopted in this study is very effective to analyze geological lineaments, and that helps estimate geological trends associated various with the tectonic events. In six sub-areas, the general trends of lineaments are characterized NW, NNW, NS-NNE, and NE directions. NW trends in Cretaceous volcanic rocks and Jurassic granite areas may represent tension joints that developed by rejuvenated end of the Early Cretaceous left-lateral strike-slip motion along the Honam Shear Zone, while NE and NS-NNE trends correspond to fault directions which are parallel to the above Shear Zone. NE and NW trends in Granitic Gneiss are parallel to the direction of schitosity, and NS-NNE and NE trends are interpreted the lineation by compressive force which acted by right-lateral strike-slip fault from late Triassic to Jurassic. And in foliated Granite, NE and NNE trends are coincided with directions of ductile foliation and Honam Shear Zone, and NW-NNW trends may be interpreted direction of another compressional foliation (Triassic to Early Jurassic) or end of the Early Cretaceous tensional joints. We interpreted NS-NNE direction lineation is related with the rejuvenated Chugaryung Fault System.

• The Journal of the Acoustical Society of Korea
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• v.20 no.5
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• pp.21-27
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• 2001

Impulsive sound and vibration signals in gear system are often associated with their faults. Thus these impulsive sound and vibration signals can be used as indicators in condition monitoring of gear system. The traditional continuous wavelet transform has been used for detection of impulsive signals. However, it is often difficult for the continuous wavelet transform to identify spikes at high frequency and meshing frequencies at low frequency simultaneously since the continuous wavelet transform is to apply the linear scaling (a-dilation) to the mother wavelet. In this paper, the spike wavelet transform is developed to extract these impulsive sound and vibration signals. Since the spike wavelet transform is to apply the non-linear scaling, it has better time resolution at high frequency and frequency resolution at low frequency than that of the continuous wavelet transform respectively. The spike wavelet transform can be, therefore, used to detect fault position clearly without the loss of information for the damage of a gear system. The spike wavelet transform is successfully is applied to detection of the gear fault with tip breakage.

• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.23-35
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• 2013

This paper proposes an effective technique that is used to automatically extract feature vectors from vibration signals for fault classification systems. Conventional mel-frequency cepstral coefficients (MFCCs) are sensitive to noise of vibration signals, degrading classification accuracy. To solve this problem, this paper proposes spectral envelope cepstral coefficients (SECC) analysis, where a 4-step filter bank based on spectral envelopes of vibration signals is used: (1) a linear predictive coding (LPC) algorithm is used to specify spectral envelopes of all faulty vibration signals, (2) all envelopes are averaged to get general spectral shape, (3) a gradient descent method is used to find extremes of the average envelope and its frequencies, (4) a non-overlapped filter is used to have centers calculated from distances between valley frequencies of the envelope. This 4-step filter bank is then used in cepstral coefficients computation to extract feature vectors. Finally, a multi-layer support vector machine (MLSVM) with various sigma values uses these special parameters to identify faulty types of induction motors. Experimental results indicate that the proposed extraction method outperforms other feature extraction algorithms, yielding more than about 99.65% of classification accuracy.

• Environmental Engineering Research
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• v.11 no.2
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• pp.63-76
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• 2006

Multivariate analysis and batch monitoring on a pilot-scale sequencing batch reactor (SBR) are described for integrated wastewater treatment management system, where a batchwise multiway independent component analysis method (MICA) are used to extract meaningful hidden information from non-Gaussian wastewater treatment data. Three-way batch data of SBR are unfolded batch-wisely, and then a non-Gaussian multivariate monitoring method is used to capture the non-Gaussian characteristics of normal batches in biological wastewater treatment plant. It is successfully applied to an 80L SBR for biological wastewater treatment, which is characterized by a variety of error sources with non-Gaussian characteristics. The batchwise multivariate monitoring results of a pilot-scale SBR for integrated wastewater treatment management system showed more powerful monitoring performance on a WWTP application than the conventional method since it can extract non-Gaussian source signals which are independent and cross-correlation of variables.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2486-2491
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• 2013

There are invisible defects by naked eyes in ceramic material images such as internal stomata, cracks and foreign substances. In this paper we propose a method to detect and extract such defects from ceramic pipe weld zone by applying ART2 learning. In pre-processing, we apply Ends-in Search Stretching to enhance the intensity and then perform fuzzy binarization with triangle type membership function followed by enhanced ART2 that interacts with random input patterns to extract such invisible defects. The experiment verifies that this proposed method is sufficiently effective.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.1
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• pp.84-92
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• 2001

In this papers, we proposed the new method in order to diagnosis aging state of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion skewness, kurtosis) about each acoustic emission signal. Also, these coefficients are used to identify normal and fault signal of internal partial discharge in transformer. As improved method for classification use neural network. Extracted statistical parameters are input into an back-propagation neural network. The number of neurons of hidden layer are obtained through Result of Cross-Validation. The network, after training, can decide whether the test signal is early aging state, alst aging state or normal state. In quantity analysis, capability of proposed method is superior to compared that of classical method.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.87-88
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• 2006

Generator has responded quickly with variation of power system voltage and is controlled at real time. As a reactive power source, generator is revalued better than power condenser and as a reactive power sink, is revalued. But Domestic generators scarcely have been tested and operated to reactive power capability. In case of power system fault, operators wouldn't quickly take a follow-up measures about reactive power disturbance. Therefore generator reactive power capability verification strategy must be developed, several generators is tested as a exhibition since 2004. This paper is extract from the test contents.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.99-101
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• 1995

This paper presents a method for the extraction of the fundamental frequency from transient signals using Prony's analysis. There exists transient voltages and current after a fault including an exponetially decaying do component and harmonics as well as the fundamental frequency. As Prony's analysis uses exponetially decaying functions as basis functions it can extract the fundamental frequency precisely from transient signals. The results of comparison with DFT are also shown.