• Nuclear Engineering and Technology
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• v.47 no.5
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• pp.624-632
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• 2015

A self-diagnostic monitoring system is a system that has the ability to measure various physical quantities such as temperature, pressure, or acceleration from sensors scattered over a mechanical system such as a power plant, in order to monitor its various states, and to make a decision about its health status. We have developed a self-diagnostic monitoring system for an air-operated valve system to be used in a nuclear power plant. In this study, we have tried to improve the self-diagnostic monitoring system to increase its reliability. We have implemented three different machine learning algorithms, i.e., logistic regression, an artificial neural network, and a support vector machine. After each algorithm performs the decision process independently, the decision-making module collects these individual decisions and makes a final decision using a majority vote scheme. With this, we performed some simulations and presented some of its results. The contribution of this study is that, by employing more robust and stable algorithms, each of the algorithms performs the recognition task more accurately. Moreover, by integrating these results and employing the majority vote scheme, we can make a definite decision, which makes the self-diagnostic monitoring system more reliable.

• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.214-216
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• 2001

In this paper, we developed the on-line Diagnostic & Monitoring system of distribution transformers using wireless communication. The Diagnostic system consists of the top oil temperature sensing, ambient temperature sensing. load current sensing, acoustic emission peak-value sensing, acoustic emission pulse counter and wireless communication part. The Monitoring system consists of the communication setting, online monitoring and database construction part. The Diagnostic system send the date acquired from the some sensors of the distribution transformers to the host PC. The Monitoring program of the host PC store the data to database. And the Monitoring system estimates the loss of life from the DB. Thus it could be managed the career and the functional lifetime of the transformer more efficiently than existing methodologies.

• Smart Structures and Systems
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• v.5 no.4
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• pp.397-413
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• 2009

Composite bridges offer many advantages compared to current steel and aluminum bridges. This paper presents the results of a comprehensive on-going research program to develop innovative Diagnostic Prognostic System (DPS) and a structural evaluation of Composite Army Bridge (CAB) system. The DPS is founded on three technologies: optical fiber sensing, remote data transmission, and virtual testing. In developing this system, both laboratory and virtual test were used in different damage scenarios. Health monitoring with DPS entailed comparing live strain data to archived strained data in various bridge locations. For field repairs, a family of composite chords was subjected to simple ramp loads in search of ultimate strength. As such, composite bridge specimens showcased their strengths, heralded the viability of virtual testing, highlighted the efficacy of field repair, and confirmed the merits of health monitoring.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.112-122
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• 1996

In order to monitior machine tool condition and diagnose alarm states due to electrical and mechanical faults, and expert system using diagnostic parameters of NC machine tools was developed. A model-based knowledge base was constructed via searching and comparing procedures of diagnostic parameters and state parameters of the machine tool. Diagnostic monitoring results generate through a successive type inference engine were graphically displayed on the screen of the console. The validity and reliability of the expert system was rcrified on a vertical machining center equipped with FANUC OMC through a series of experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.709-711
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• 2016

An automatic control system to control a machinery system is managed by the PLC(Program Logic Control) and measured a machinery status from the sensor information to improve productivity in the industry field. This paper to propose a diagnostic monitoring system consists of a relay module, a one-chip processor module and a computer monitoring system. To improve productive capacity, the system is to check an auxiliary relay's trouble by a real-time monitoring.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.132-137
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• 2004

On-Board Diagnostic(OBD) systems are in most cars and light trucks on the load today. During the 1970's and early 1980's manufacturers started using electronic means to control engine functions and diagnose engine problems. The CARB's diagnostic requirements to meet EPA emission standards have been designated as OBD with a goal of monitoring all of the emissions-related components, as well as the chassis, body, accessory devices and the diagnostic control network of the vehicle for proper operation. In this paper, we present a remote measurement system for the wireless monitoring of diagnosis signal and sensors output signals of ECU adopted KWP2000, united the OBD communication protocol, on OBD-compliant vehicle using the wirless communication technique of Bluetooth. In order to measure the ECU signals, the interface circuit is designed to communicate ECU and designed terminal wirelessly according to the ISO, SAE regulation of communication protocol standard. A microprocessor S3C3410X is used for communicating ECU signals. The embedded system's software is programmed to measure the ECU signals using the ARM compiler and ANCI C based on MicroC/OS kernel to communicate between bluetooth modules using bluetooth stack. The diagnostic system is developed using Visual C++ MFC and protocol stack of bluetooth for Windows environment. The self-diagnosis and sensor output signals of ECU is able to monitor using PC with bluetooth board connected in serial port of PC. The algorithms for measuring the ECU sensor output and self-diagnostic signals are verified to monitor ECU state. At the same time, the information to fix the vehicle's problem can be shown on the developed monitoring software. The possibility for remote measurement of self-diagnosis and sensor signals of ECU adopted KWP2000 in embedded system verified through the developed systems and algorithms.

• Nuclear Engineering and Technology
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• v.55 no.2
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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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.3
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• pp.131-139
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• 2001

As increasing the accident of Distribution Transformer (DT), we need to manage them efficiently. In this paper, we discusses with the possibility of the diagnostic technique for distribution transformer using relative ageing rate calculation in this paper. The relative ageing rate of the DT could be calculated from the measured top oil temperature, ambient temperature and load current. In order on apply the proposed diagnostic technique we developed an on line Monitoring and Diagnostic System (MDS) which has hardware and software part. Diagnostic device is developed to measure the state information of DT and to send them with R/F(radio frequency) communication. Host computer monitors and saves the receive data. The database is constructed from the data of distributed DT and it is used for estimating loss-of-life in the MDS.

• Proceedings of the KIEE Conference
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• 2004.05b
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• pp.91-93
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• 2004

This study presents recent trends on the on-line monitoring and diagnostic systems for oil-immersed transformers. Specially, our system on Yeo Su thermal power plant is introduced for high sensitivity and accuracy of ours. It is combined with communication networks to provide an on-line remote monitoring system. Several alarm criteria are formulated to enable a superimposed monitoring system to perform decisive action. The reasons for monitoring the condition and maintaining the health of electrical apparatus were discussed. The experience at the fields and the criteria for the judgment are also discussed in detail.

• Transactions on Electrical and Electronic Materials
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• v.7 no.2
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• pp.90-95
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• 2006

An on-line diagnostic test has been studied and performed for the pumped storage generator/motor. This study aims at reducing the diagnostic cost, minimizing the technical dependency on third party manufactures. Further design of on-line diagnostic system such as shorted-turn of rotor winding, partial discharge (PD) of stator winding and air-gap between stator and rotor for pumped storage generator/motor has been verified. In addition, it needs to be validated on site performance of the developed continuous on-line monitoring system for corresponding tasks for the improvement of the availability & reliability during operation.