• Journal of KIISE:Computer Systems and Theory
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• v.33 no.1_2
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• pp.105-109
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• 2006

Hypercube has a regular and hierarchical structure, therefore it can be applied to the development of efficient diagnosis algorithm. Kranakis and Pelc [7] have proposed HYP-DIAG algorithm to implement different method of HADA/IHADA and adaptive cube partition method after embedding the small size of ring that includes all the faulty nodes. In this paper, we propose new method to reduce testing rounds by analyzing the syndrome of RGC-rings gained in the first step of HYP-DIAG and analyze the proposed algorithm.

• Computers and Concrete
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• v.2 no.6
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• pp.439-454
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• 2005

When deterioration of concrete is observed in a structure, it is highly desirable to determine the cause of such deterioration. Only by understanding the cause can an appropriate repair strategy be implemented to address both the cause and the symptom. In colder climates, bridge deck deterioration is often caused by chlorides from de-icing salts, which penetrate the concrete and depassivate the embedded reinforcement, causing corrosion. Bridge decks can also suffer from other deterioration mechanisms, such as alkali-silica reaction, freeze-thaw, and shrinkage. There is a need for a comprehensive and integrative system to help with the inspection and evaluation of concrete bridge deck deterioration before decisions are made on the best way to repair it. The purpose of this research was to develop a model to help with the diagnosis of concrete bridge deck deterioration that integrates the symptoms observed during an inspection, various deterioration mechanisms, and the probability of their occurrence given the available data. The model displays the diagnosis result as the probability that one of four deterioration mechanisms, namely shrinkage, corrosion of reinforcement, freeze-thaw and alkali-silica reaction, is at fault. Sensitivity analysis was performed to determine which probabilities in the model require refinement. Two case studies are included in this investigation.

• Proceedings of the KIEE Conference
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• 2006.07e
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• pp.19-22
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• 2006

The CMD(Condition Monitoring & Diagnosis) System is used to monitor and analyze the PD(Partial Discharge), Arc, Temperature, Pressure, Gas in Oil and so on for reliability and availability of the substation. Although this system comes into the spotlight as the forecast and management of the failure and fault, there are some problems. For example, the unified standard was not defined, and the effective management of the layed communication network does not performed, limitation of physical space, etc. To resolve above problems, this paper suggests the IEC61850 compatible eCMD system architecture for monitoring and analyzing the CMD factor in substations. The suggested eCMD system consists of CMD-LU(Condition Monitoring & Diagnosis-Local Unit), IED(Intelligent Electronic Device), and engineering centers. The IEC61850 is the international standard that defines communication networks and system in substation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.338-345
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• 2013

Induction motors play an increasing importance in industrial manufacturing. Therefore, the state monitoring systems also have been considering as the key in dealing with their negative effect by absorbing faulty symptoms in motors. There are numerous proposed systems in literature, in which, several kinds of signals are utilized as the input. To solve the multiple faults problem of induction motors, like the proposed system, the vibration signals is good candidate. In this study, a new signal processing scheme was utilized, which transforms the time domain vibration signal into the spatial domain as an image. Then the spatial features of converted image then have been extracted by applying the dominant neighbourhood structure (DNS) algorithm. In addition, these feature vectors were evaluated to obtain the fruitful dimensions, which support to discriminate between states of motors. Because of reliability, the conventional one-against-all (OAA) multi-class support vector machines (MCSVM) have been utilized in the proposed system as classifier module. Even though examined in severity levels of signal-to-noise ratio (SNR), up to 15dB, the proposed system still reliable in term of two criteria: true positive (TF) and false positive (FP). Furthermore, it also offers better performance than five state-of-the-art systems.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.31-37
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• 2021

As the size of buildings increases due to urbanization due to the development of industry, the need to purify the air and maintain a comfortable indoor environment is also increasing. With the development of monitoring technology for refrigeration systems, it has become possible to manage the amount of electricity consumed in buildings. In particular, refrigeration systems account for about 40% of power consumption in commercial buildings. Therefore, in order to develop the refrigeration system failure diagnosis algorithm in this study, the purpose of this study was to understand the structure of the refrigeration system, collect and analyze data generated during the operation of the refrigeration system, and quickly detect and classify failure situations with various types and severity . In particular, in order to improve the classification accuracy of failure types that are difficult to classify, a three-step diagnosis and classification algorithm was developed and proposed. A model based on SVM and LGBM was presented as a classification model suitable for each stage after a number of experiments and hyper-parameter optimization process. In this study, the characteristics affecting failure were preserved as much as possible, and all failure types, including refrigerant-related failures, which had been difficult in previous studies, were derived with excellent results.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.625-630
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• 2014

Asynchronous digital circuits working in military and space environments are often subject to the adverse effects of radiation faults. In this paper, we propose a new hardening technique against radiation faults. The considered digital system has the structure of DMR (Double Modular Redundancy), in which two sub-systems conduct the same work simultaneously. Based on the output feedback, the proposed scheme diagnoses occurrences of radiation faults and realizes immediate recovery to the normal behavior by overriding parts of memory bits of the faulty sub-system. As a case study, the proposed control scheme is applied to an asynchronous dual ring counter implemented in VHDL code.

• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.136-137
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• 2019

본 논문에서는 ADALINE (Adaptive-Linear-Neuron) 구조의 ANN(Artificial Neural Network)을 이용한 3상 PWM 컨버터의 개방고장 진단 방법에 대해 제안한다. 3상 PMW 컨버터에서 스위치의 개방고장이 발생한 경우 보호회로에 의해 시스템이 중단되지 않으며, 개방고장으로 인한 상전류의 고조파와 직류 성분에 의해 주변 기기에 고장에 의한 파급효과가 나타날 수 있다. 이에 본 논문에서는 ADALINE을 이용하여 각 상의 THD(Total Harmonics Distortion)와 직류 성분 얻고 대소비교를 통해 개방고장이 발생한 스위치를 진단하는 방법에 대해 제안한다.

• Electrical & Electronic Materials
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• v.11 no.11
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• pp.1-8
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• 1998

This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.282-283
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• 2020

3상 컨버터에서 스위치의 개방고장이 발생한 경우 고장 전류에 직류 및 고조파 성분이 발생할 수 있으며, 보호회로에 의한 고장 감지가 어려우므로 주변 기기에 2차 고장이 발생할 수 있다. 단일 및 이중 스위치 개방고장의 경우 21가지 고장 모드가 존재한다. 본 논문에서는 이러한 고장 모드를 진단하기 위해 정지 좌표계 d-q축 전류의 직류 및 고조파 성분을 활용하는 two-step 구조의 ANN(Artificial Neural Network)을 제안한다. 고장 시에 발생된 직류 및 고조파 성분 전류는 ADALINE(Adaptive-Linear Neuron)을 통해 얻는다. 고장 진단의 첫 번째 단계에서는 직류 성분을 기반으로 ANN을 이용하여 고장모드를 6개 영역으로 분류한다. 두 번째 단계에서는 6개의 각 영역에서 직류 성분과 전류의 THD(Total Harmonics Distortion)를 기반으로 ANN을 이용하여 개방고장이 발생한 스위치를 진단한다. 제안된 Two-step 방법으로 고장을 진단하므로써 간단한 구조로 ANN의 설계가 가능하다. 3.7kW급 3상 PWM 컨버터로 실험을 통해 제안된 방법의 효용성을 검증하였다.

• Korean Chemical Engineering Research
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• v.50 no.6
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• pp.980-987
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• 2012

Nonpoint source pollution causes leaks and overtopping, depending on the state of the sewer network as well as aggravates the pollution load of the aqueous water system as it is introduced into the sewer by wash-off. According, the need for efficient sewer monitoring system which can manage the sewage flowrate, water quality, inflow/infiltration and overflow has increased for sewer maintenance and the prevention of environmental pollution. However, the sewer monitoring is not easy since the sewer network is built in underground with the complex nature of its structure and connections. Sewer decontamination mechanism as well as pipe network monitoring and fault diagnosis of water network system on system analysis proposed in this study. First, the pollution removal pattern and behavior of contaminants in the sewer pipe network is analyzed by using sewer process simulation program, stormwater & wastewater management model for expert (XP-SWMM). Second, the sewer network fault diagnosis was performed using the multivariate statistical monitoring to monitor water quality in the sewer and detect the sewer leakage and burst. Sewer decontamination mechanism analysis with static and dynamic state system results showed that loads of total nitrogen (TN) and total phosphorous (TP) during rainfall are greatly increased than non-rainfall, which will aggravate the pollution load of the water system. Accordingly, the sewer outflow in pipe network is analyzed due to the increased flow and inflow of pollutant concentration caused by rainfall. The proposed sewer network monitoring and fault diagnosis technique can be used effectively for the nonpoint source pollution management of the urban watershed as well as continuous monitoring system.