• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2012년도 춘계학술대회 논문집
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• pp.607-608
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• 2012

• Korean Journal of Acupuncture
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• 제28권1호
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• pp.23-37
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• 2011

Objectives : The pulse diagnosis is an important method in Oriental Medicine. The aim of this study is to measure the similarity of the diagnosis by a traditional method using doctor's hand for feeling of pulse and by pulse diagnosis apparatus using Hwang-Je (HJ) pulse analyser, Hui-Su (HS) pulse analyser on Chon, Kwan and Chuk. Methods : Four korean medical doctors and HJ pulse analyser, HS pulse analyser have measured the speed (遲數), the size (微細弱緩大), and the depth (浮沈) of pulse waves of 23 volunteers. First, four korean medical doctors measured pulse waves of volunteers. And then, the pulse waves of volunteers were measured by HJ pulse analyser, HS pulse analyser. This was performed on the right Chon, Kwan and Chuk. Results : The traditional method and the HJ pulse analyser method had the 60.9% matches on the values of the pulse speed condition, the HS pulse analyser method had the 78.3% matches on the values of the pulse speed condition. The traditional method and the HJ pulse analyser method had the 56.5% (Chon), 65.2% (Kwan), 78.3% (Chuk) matches on the values of the pulse size condition, the HS pulse analyser method had the 65.2% (Chon), 13.0% (Kwan), 39.1% (Chuk) matches on the values of the pulse size condition. The traditional method and the HJ pulse analyser method had the 43.5% (Chon), 26.1% (Kwan), 47.8% (Chuk) matches on the values of the pulse depth condition, the HS pulse analyser method had the 45.5% (Chon), 30.4% (Kwan), 36.8% (Chuk) matches on the values of the pulse depth condition. Conclusions : According to these results, we suggest that the pulse analyser is necessary to develope for its high similarities with the traditional pulse diagnosis.

• 한국항해항만학회지
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• 제46권6호
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• pp.562-569
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• 2022

기존 운항선박에 적용되어 있는 알람 모니터링 기술은 온도, 압력 등의 데이터 항목을 AMS(Alarm Monitoring System)으로 관리하고 해당 센싱 데이터가 정상 수준 범위를 초과할 경우만 선원에게 알람을 제공한다. 또한 기존 선박의 정비는 PMS(Planned Maintenance System)를 따른다. 이는 장비로부터 측정된 센싱 데이터가 설정범위 이상으로 측정되어 이에 따른 알람을 통해 정비하거나, 대상 기기의 고장 유무에 관계없이 일정 시간 사용 후 해당 부품을 사전에 교체하는 방식으로 운영되고 있다. 하지만 선박 기관운영의 신뢰성과 운항 안전성을 확보하기 위해서는 실시간 상태 모니터링 데이터 기반의 사전적 진단 및 예측이 가능해야 한다. 그러기 위해서 실선 데이터를 종합적으로측정하여 데이터베이스화 하고 이를 선박의 보조기기와 배관의 상태기반 예지보전을 위한 상태 진단 모니터링 시스템을 구현하고자 한다. 특히 반응형 웹 기반으로 선박의 보조기기와 배관 상태 정보를 관리할 수 있도록 하였으며, 선내 개인용 컴퓨터(Personal Computer, PC)에서 보는 용도뿐만 아니라 스마트폰 등 다양한 모바일 기기의 접근 및 활용이 가능하도록 화면과 해상도에 맞춰 최적화된 상태 관리가 가능하도록 하여 업데이트 비용이 적게 들며, 관리 방법도 쉽다. 본 논문에서는 자율운항선박 핵심 기술인 상태기반정비(Condition Based Management, CBM) 기술력을 확보하기 위해 선박의 보조기기 중 펌프와 청정기, 그리고 배관 중 해수 및 스팀 배관의 상태 진단 모니터링을 통해 이상 현상을 파악하고, 이를 통해 융합 분석할 수 있도록 선박 보조기기 및 배관의 성능 진단 및 고장 예측에 활용하여 예방정비 의사결정을 지원하고자 한다.

• Nuclear Engineering and Technology
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• 제46권6호
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• pp.737-752
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• 2014

As condition-based maintenance (CBM) has risen as a new trend, there has been an active movement to apply information technology for effective implementation of CBM in power plants. This motivation is widespread in operations and maintenance, including monitoring, diagnosis, prognosis, and decision-making on asset management. Thermal efficiency analysis in nuclear power plants (NPPs) is a longstanding concern being updated with new methodologies in an advanced IT environment. It is also a prominent way to differentiate competitiveness in terms of operations and maintenance costs. Although thermal performance tests implemented using industrial codes and standards can provide officially trustworthy results, they are essentially resource-consuming and maybe even a hind-sighted technique rather than a foresighted one, considering their periodicity. Therefore, if more accurate performance monitoring can be achieved using advanced data analysis techniques, we can expect more optimized operations and maintenance. This paper proposes a framework and describes associated methodologies for in-situ thermal performance analysis, which differs from conventional performance monitoring. The methodologies are effective for monitoring, diagnosis, and prognosis in pursuit of CBM. Our enabling techniques cover the intelligent removal of random and systematic errors, deviation detection between a best condition and a currently measured condition, degradation diagnosis using a structured knowledge base, and prognosis for decision-making about maintenance tasks. We also discuss how our new methods can be incorporated with existing performance tests. We provide guidance and directions for developers and end-users interested in in-situ thermal performance management, particularly in NPPs with large steam turbines.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Nuclear Engineering and Technology
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• 제50권4호
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• pp.599-605
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• 2018

Fault diagnosis depends critically on the selection of sensors monitoring crucial process variables. Boolean network (BN) is composed of nodes and directed edges, where the node state is quantized to the Boolean values of True or False and is determined by the logical functions of the network parameters and the states of other nodes with edges directed to this node. Since BN can describe the fault propagation in a sensor network, it can be applied to propose sensor selection strategy for fault diagnosis. In this article, a sufficient condition for parameter identifiability of BN is first proposed, based on which the sufficient condition for fault identifiability of a sensor network is given. Then, the fault identifiability condition induces a sensor selection strategy for sensor selection. Finally, the theoretical result is applied to the fault diagnosis-oriented sensor selection for a nuclear heating reactor plant, and both the numerical computation and simulation results verify the feasibility of the newly built BN-based sensor selection strategy.

• 대한안전경영과학회지
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• 제6권3호
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• pp.237-247
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• 2004

The condition of the manufacturing process in a factory should be diagnosed and maintained efficiently because any unexpected disorder in the process will be reason to decrease the efficiency of the overall system. However, if an expert experienced in this system leaves, there will be a problem for the efficient process diagnosis and maintenance, because disorder diagnosis within the process is normally dependent on the expert's experience. This paper suggests a process diagnosis using data mining based on the collected data from the coil-spring manufacturing process. The rules are generated for the relations between the attributes of the process and the output class of the product using a decision tree after selecting the effective attributes. Using the generated rules from decision tree, the condition of the current process is diagnosed and the possible maintenance actions are identified to correct any abnormal condition. Then, the appropriate maintenance action is recommended using the decision network.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 추계학술대회 논문집 전기물성,응용부문
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• pp.224-226
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• 2001

In this paper, a novel high-voltage motor monitoring system (HVMMS) is proposed. This system monitors the insulation condition of the stator winding by on-line measurements of partial discharge (PD). Sensor, EMC (Epoxy-Mica Coupler) is used for PD measurement PD signals are continuously measured and digitized with a peak-hold A/D converter to build the database of the high-voltage motor's insulation condition. Also, this system can communicate with the central monitoring system via RS-485. This helps more efficient operation and maintenance of the generator.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 추계학술대회논문집 II
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• pp.633-639
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• 2001

This paper proposes a knowledge-based expert system. which is assembled into hardware organized with sensor module. AID converter, USB. data acquisition PC and software composed of monitoring and diagnosis module combined with a frame-based method using Sohre's chart and a rule-based method. Vibration signals using various sensors are acquired by AID converter. transferred into PC and processed to obtain a continuous monitoring of the machine status displayed into several plots. Through combining frame-base which covers wide vibration causes with rule-base which gives relatively specified diagnosis results, high accuracy of fault diagnosis can be guaranteed and knowledge base can be easily extended by adding new causes or symptoms. Some examples using experimental data show the good feasibility of the proposed algorithm for condition monitoring and diagnosis of industrial rotating machinery.

• Journal of Yeungnam Medical Science
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• 제36권2호
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• pp.85-91
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• 2019

The incidence of gastric mucosa-associated lymphoid tissue (MALT) lymphoma is increasing worldwide, but the diagnosis is difficult. Most patients are asymptomatic or complain of nonspecific gastrointestinal symptoms. As the endoscopic features of gastric MALT lymphoma are variable and nonspecific, the possibility of this condition may be overlooked during esophagogastroduodenoscopy, and it remain undiagnosed. Therefore, this condition needs to be considered when an abnormal mucosa is observed during this procedure. Biopsy performed during endoscopy is the primary diagnostic test, but false negative results are possible; large numbers of samples should be collected from both normal and abnormal mucosae. Endoscopic ultrasonography is useful to assess the depth of invasion and to predict the treatment response. After treatment, follow-up tests are required every 3 months until complete remission is achieved, and annually thereafter. Early diagnosis of gastric MALT lymphoma is difficult, and its diagnosis and follow-up require wide experience and competent endoscopic technique.