• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2612-2616
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• 2003

Method for performance monitoring and diagnosis of a MIMO control system has been studied aiming at application to model predictive control (MPC) for industrial processes. The performance monitoring part is designed on the basis of the traditional SPC/SQC method. To meet the underlying premise of Schwart chart observation that the observed variable should be univariate and independent, the process variables are decorrelated temporally as well as spatially before monitoring. The diagnosis part was designed to identify the root of performance degradation among the controller, process, and disturbance. For this, a method to estimate the model-error and disturbance signal has been devised. The proposed methods were evaluated through numerical examples.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2198-2202
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• 2005

In this paper we have presented a condition monitoring method of check valve using neural network. The acoustic emission sensor was used to acquire the condition signals of check valve in direct vessel injection (DVI) test loop. The acquired sensor signal pass through a signal conditioning which are consisted of steps; rejection of background noise, amplification, analogue to digital conversion, extract of feature points. The extracted feature points which represent the condition of check valve was utilized input values of fault diagnosis algorithms using pre-learned neural network. The fault diagnosis algorithm proceeds fault detection, fault isolation and fault identification within limited ranges. The developed algorithm enables timely diagnosis of failure of check valve’s degradation and service aging so that maintenance and replacement could be preformed prior to loss of the safety function. The overall process has been experimented and the results are given to show its effectiveness.

• Proceedings of the KIEE Conference
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• 1996.07c
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• pp.1831-1834
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• 1996

Diagnosis of cable insulation degradation has important meaning from a viewpoint of reliability enhancement of CV cable systems. So, we diagnosed 3.3kV & 6.6kV CV distribution cable on live-line which is judged to a most poor installation condition because they have been used in water condition after installation at the Yeochon chemical complex. The paper describes the analysis of the measured data, the problems at diagnosis, and the items which are considered at the development of diagnosis technology and equipment hearafter.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.914-919
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• 2000

In most case high temperature components in fossil power plant are damaged by fatigue, creep and degradation. Design of power plant components is based on ideal loading such as temperature, pressure and so on. But in many cases unexpected loadings are applied at components. A key ingredient in plant life extension is the preventive diagnosis technology and remaining-life-assessment technology. This paper describes diagnosis technology and life-assessment technology for power plant boiler. It helps in setting up proper inspection schedules, maintenance procedures, and operating procedure.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05c
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• pp.148-151
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• 2002

In order to prevent the failures of underground distribution power cables we need to measure insulation condition in the field. Until now we used DC high voltage as a power source for the cable diagnosis but it was not proper method to the XLPE insulation cables because DC high voltage can affect sound insulation and can't diagnose exactly insulation degradation. For these reasons we imported isothermal relaxation current measurement system called by KDA-1 from germany but it's reliability did not proved in our URD cables. DC voltage decay measurement system was developed by domestic company but they don't have field experience. In this paper we tried to prove reliability of these two systems in the field. Through the field diagnosis and Ac breakdown test the two systems showed similar results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.355-357
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• 2017

With the development of automation technology and the increase of large-scale automation projects, sensors used for state monitor and parameter measurement have become more and more important. Once the sensor faults occur, which will lead to the degradation of automation system's performance, and even disastrous consequences. In this paper, sensor output value modeling is performed using Kung's Algorithm for direct fault diagnosis of sensor, and fault diagnosis method based on decision theory is presented.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.11a
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• pp.191-192
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• 2018

This study presents the standard methods of diagnosis and repair to prevent and maintain the durability degradation caused by leakage cracks in underground structures in Korea. The third part after the announcement of the existing maintenance plan, The environmental characteristics and leakage condition of underground structures, the causes of deterioration of underground concrete structures, and countermeasures and leakage equipment were described.

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1998.06a
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• pp.297-300
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• 1998

In this paper, we obtained the data, which is required in training the neural network and diagnosing the degradation degree, by introducing the AE detection that is effective method in ordinary degradation diagnosis on activation. Automatic detection system to detect acoustic. As the results of generalization tests by appling neural network to the unknown AE patterns obtained from specimens, firstly as to evaluate an objective performance of neural network, the recognition ratio for no-void specimen is appeared. Also, in the evaluation for the adaptability of neural network with a untrained type of no-void specimen, it is confirmed that the result appears.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.1
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• pp.57-62
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• 2017

The purpose of this study is to develop a method for diagnosing the degree of gradual degradation of a reciprocating pump caused by continuous use as a water supply pump in a nuclear power plant. Normally, the progress of such degradation is too slow to be noticed. Hence, it is difficult to determine the degree of degradation using the existing diagnostic methods. In this paper, we propose a new method by which the normal state and the degraded state of the pump can be differentiated, so that the degree of degradation can be identified. First, an emulator was developed using FPGA by providing the parameters of the pump under normal state, so that the emulator generates the information of the pump in the healthy state. Then, by comparing this information with the parameters received from various output sensors of the emulator during the current state, it is possible to identify and measure the degree of gradual degradation. This paper presents some of the results obtained during the development process, and results that show how the emulator operates, by comparing the data collected from an actual pump.