• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.2
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• pp.83-92
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• 2005

An electromagnetic acoustic transducer (EMAT) is a unique probe that does not require a couplant or gel and also can usually generate or detect an ultrasonic wave into specimens across a small gap. It, therefore can be applied in a noncontact mode with a high degree of reproducibility. Especially stiffness of composites depends on layup sequence of CFRP(carbon fiber reinforced plastics) laminates. It is very important to evaluate the layup errors in prepreg laminates. A nondestructive technique can therefore serve as a useful measurement for detecting layup errors. This shear wave for detecting the presence of the errors is very sensitive. A decomposition model has been used in the interpretation and prediction of test results. Test results have been com pared with model data. It is found that the high probability shows between tests and the model utilized in characterizing cured layups of the laminates. Also a C-scan method was used for detecting layup of the laminates because of extracting fiber orientation information from the ultrasonic reflection caused by structural imperfections in the laminates. Therefore, it was found that interface C-scan images show the fiber orientation information by using two-dimensional fast Fourier transform (2-D FFT).

• Journal of the Korean Society of Safety
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• v.12 no.3
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• pp.166-172
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• 1997

The BEF on the radial distribution line refers to a class of ground faults in which the load-side power line only is grounded, with the distribution line broken into two parts, the source-side and the load-side. Because its mechanism is remarkably different from that of other earth faults, the fault current is very low, and then difficult to detect the BEF. Thus, it is necessary to analyze its properties and to find an appropriate method that can economically protect the BEF of nonautomation area in the substation. As a result of analyzing the BEF data obtained by the RTDS, EMTP simulation, and the field test data of ETSA, we believe that it is the dominant factor in distinguishing the BEF from normal conditions by a criterion value that is appropriately handled from the zero-sequence current. Thus, with this criterion value, a BEF detecting algorithm is constructed which measures the variations of the zero-sequence current and processes then properly so as to make the fault decision. To prove the accuracy of this algorithm, it is compared with the field test data of ETSA under various conditions. The results show that the proposed algorithm is accurate.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.450-463
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• 2018

Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sounds being inevitably corrupted by random disturbance, the most important part of the diagnosis consists of discovering the hidden elements inside the data that can reveal the faulty patterns. This paper presents a novel feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by the drills. Using the Fourier analysis, the magnitude spectrum of the sounds are extracted, converted into two-dimensional vectors and uniformly normalized in such a way that they can be represented as 8-bit grayscale images. Histogram equalization is then performed over the obtained images in order to adjust their very poor contrast. The obtained contrast enhanced images will be used as the features of our diagnosis system. Finally, principal component analysis is performed over the image features for reducing their dimensions and a nonlinear classifier is adopted to produce the final response. Unlike the conventional features, the results demonstrate that the proposed feature extraction method manages to capture the hidden health patterns of the sound.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1541-1548
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• 2012

This paper proposes a new multipath-based routing protocol on MANETs with Fast-Recovery of failures. The proposed protocol establishes the primary and secondary paths between a source and a destination considering the end-to-end packet reception reliability of routes. The primary path is used to transmit messages, and the secondary path is used to recover the path when detecting failures on the primary path. If a node detects a link failure during message transmission, it can recover the path locally by switching from the primary to the secondary path. By allowing the intermediate nodes to recover locally the route failure, the proposed protocol can reduce the number of packet loss and the amount of control packets for setting up new paths. The simulation result using QualNet simulator shows that the proposed protocol was about 10-20% higher than other protocols in terms of end-to-end message delivery ratio and the fault recovery time in case of link fault was about 3 times faster than the other protocols.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.224-229
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• 2020

We propose a machine learning-based method for proactively detecting faults in LDPE processes and predicting equipment lifespan. It is important to detect and prevent unexpected faults in chemical processes in order to maximize safety and productivity. Since LDPE process is a high-pressure process up to 3,000 kg/㎠g or more, once ESD occurs, it can result in productivity loss due to increased maintenance periods. By collecting key variables operation data of the process and using unsupervised machine leaning methods, we developed a fault detection model which detected 4 ESDs 2.4 days prior to the occurrence. In addition, it was confirmed that the life expectancy of a hyper compressor can be predicted by using the physically significant key variables.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.19-27
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• 2020

In this paper, we propose a machine learning method for diagnosing the failure of a gas pressure regulator. Originally, when implementing a machine learning model for detecting abnormal operation of a facility, it is common to install sensors to collect data. However, failure of a gas pressure regulator can lead to fatal safety problems, so that installing an additional sensor on a gas pressure regulator is not simple. In this paper, we propose various machine learning approach for diagnosing the abnormal operation of a gas pressure regulator with only the flow rate and gas pressure data collected from a gas pressure regulator itself. Since the fault data of a gas pressure regulator is not enough, the model is trained in all classes by applying the over-sampling method. The classification model was implemented using Gradient boosting, 1D Convolutional Neural Networks, and LSTM algorithm, and gradient boosting model showed the best performance among classification models with 99.975% accuracy.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2655-2657
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• 2005

When a fault relating to the urgent alarm occurs, we must prevent control rods from dropping and make one of two grippers in Control Rod Drive Mechanism (CRDM) grip the drive rod taking a control rod assembly. To enhance the reliability of holding control rods, we order two grippers to hold the drive rod. This action is called the double holding. In the middle of the movement of the drive rod, the latching of the drive rod can cause friction between a gripper and the drive rod. This state may give damage to both the gripper and the drive rod. In this paper, we have devised the method which can have two grippers hold the drive rod more stably, without damaging the equipment.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.5
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• pp.699-708
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• 1990

This paper proposes two test procedures for detecting functional faults in semiconductor random access memories (RAM's) and a new testimg scheme to execute the proposed test procedures. The first test procedure detects stuck-at faults, coupling faults and decoder faults, and requires 19N operations, which is an improvement over conventional procedures. The second detects restricted patternsensitive faults and requires 69N operations. The proposed scheme uses Built-In Self Testing (BIST) techniques. The scheme can write into more memory cells than I/O pins can in a write cycle in test mode. By using the scheme, the number of write operations is reduced and then much testing time is saved.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.8
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• pp.390-398
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• 2000

Leakage impulse current of the contaminated insulators by using experiment data were studied. The impulse current in phase-time relationship was analyzed on line post insulators. Also, frequency components and crest factor of the leakage current were investigated to provide a scheme for an early detection of insulator incipient failure. The study shows that the phase-time characteristic is non-stationary and random and, non-harmonic component and crest factor can be promising parameters for detecting insulator leakage currents.

• Proceedings of the KIEE Conference
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• 2001.07c
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• pp.1667-1669
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• 2001

The clack of Insulator, which results from internal or external surges such as electrical, mechanical, and thermal stress, might cause ground fault and a power failure without proper repairs. In order to minimize losses caused by a short-term or long-term power failure, it is necessary to detect the deteriorated insulator as early as possible. To do so, we will develop a new device, which can detect and trace an deteriorated insulator by monitoring its physical change the variation of frequency spectrum. This device will do so by finding a Periodic wave sharp (120 Hz), a peculiar wave generated by defused electricity.