• Journal of the Korean Society of Propulsion Engineers
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• v.21 no.3
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• pp.91-100
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• 2017

The solution was developed for the maintenance decision support of combined cycle power plant gas turbine. The developed solution was applied to MHI501G gas turbine and is, in present, on the process of field test at GUNSAN combined cycle power plant, South Korea. The developed solution provides the calculated result of optimal overhaul maintenance period through following modules: Real Time Performance Monitoring, Model-Based Diagnostics, Performance Trend Analysis, Optimal Overhaul Maintenance Interval, Compressor Washing Period Management, and Blade Path Temperature Analysis. Model-Based Diagnostics module analyzed the differences between the data of gas turbine performance model and the online measurement. Compressor washing management module suggests the optimal point of balancing between the compressor performance and the maintenance cost.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.10
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• pp.101-108
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• 2001

In this study, the static and dynamic characteristics of endmilling process were modelled and the analytic realization of chatter mechanism was discussed. In this reward, We have discussed on the comparative assessment of recursive time series modeling algorithms that cal represent time machining process and detect the abnormal machining behaviors in precision endmilling operation. In this study, simulation and experimental works were performed to show the malfunctional behaviors. For this purpose, new recursive algorithm(RLSM) was adopted for the oil-line system identification and monitoring of a machining process, we can apply these new algorithms in real process for detection of abnormal chatter. Also, The stability lobe of chatter was analysed by varying parameter of cutting dynamics in regenerative chatter mechanics.

• Clinical and Experimental Pediatrics
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• v.58 no.8
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• pp.301-308
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• 2015

Purpose: We evaluated three blood glucose self-monitoring for measuring whole blood glucose levels in preterm and low-birth-weight infants. Methods: Between December 1, 2012 and March 31, 2013, 230 blood samples were collected from 50 newborns, who weighed, ${\leq}2,300g$ or were ${\leq}36$ weeks old, in the the neonatal intensive care unit of Eulji University Hospital. Three blood glucose self-monitoring (A: Precision Pcx, Abbott; B: One-Touch Verio, Johnson & Johnson; C: LifeScan SureStep Flexx, Johnson & Johnson) were used for the blood glucose measurements. The results were compared to those obtained using laboratory equipment (D: Advia chemical analyzer, Siemens Healthcare Diagnostics Inc.). Results: The correlation coefficients between laboratory equipment and the three blood glucose self-monitoring (A, B, and C) were found to be 0.888, 0.884, and 0.900, respectively. For glucose levels ${\leq}60mg/dL$, the correlation coefficients were 0.674, 0.687, and 0.679, respectively. For glucose levels>60 mg/dL, the correlation coefficients were 0.822, 0.819, and 0.839, respectively. All correlation coefficients were statistically significant. And the values from the blood glucose self-monitoring were not significantly different from the value of the laboratory equipment, after correcting for each device's average value (P>0.05). When using laboratory equipment (blood glucose ${\leq}60mg/dL$), each device had a sensitivity of 0.458, 0.604, and 0.688 and a specificity of 0.995, 0.989, and 0.989, respectively. Conclusion: Significant difference is not found between three blood glucose self-monitoring and laboratory equipment. But correlation between the measured values from blood glucose self-monitoring and laboratory equipment is lower in preterm or low-birth-weight infants than adults.

• Journal of Aerospace System Engineering
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• v.7 no.1
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• pp.8-18
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• 2013

Recently, the advanced condition monitoring methods such as the model-based method and the artificial intelligent method have been applied to maximize the availability as well as to minimize the maintenance cost of the aircraft gas turbines. Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the linear GPA, fuzzy logic and neural networks. Therefore this work applies both the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and in case of having sensor noise and bias it is confirmed that the GA is better than the GPA through the comparison of two methods.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.2
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• pp.34-42
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• 2009

Gas turbine performance diagnostics is a method for detecting, isolating and quantifying faults in gas turbine gas path components. On-line precise fault diagnosis can promote greatly reliability and availability of gas turbine in real time operation. This work proposes a GUI-type on-line diagnostic program using SIMULINK and Fuzzy-Neuro algorithms for a helicopter turboshaft engine. During development of the diagnostic program, a look-up table type base performance module are used for reducing computer calculating time and a signal generation module for simulating real time performance data. This program is composed of the on-line condition monitoring program to monitor on-line measuring performance condition, the fuzzy inference system to isolate the faults from measuring data and the neural network to quantify the isolated faults. Evaluation of the proposed on-line diagnostic program is performed through application to the helicopter engine health monitoring.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.5
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• pp.87-95
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• 2010

Lots of bridge structure generated the change of outward formation according to durability capability and decrepitude by long use. Especially, in case of the typhoon, snowing and earthquake is going to make rapidly more worse formation about the old structural facilitys. Also, outward formation by irregular and micro-distortion in bridge structure could not easily checked by normal diagnostics method. As a result, performance-capability of structure facility is getting to make a decline in standard of structure performance. Recently, real-time monitoring technology by wireless environment go into the study of irregular movement for structure facility. This paper presents the development of sensor to displacement checking about bridge structure. Sensing method of developed sensor put bring into the gyroscope technology using the acceleration speed and angular acceleration speed. This paper also will simulated to verified the monitoring capability of developed sensor against random vibration, frequency and distortion in simulated equipment.

• Journal of the Korea Convergence Society
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• v.2 no.4
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• pp.15-19
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• 2011

The transportation and logistics industries, the constructions of a green transport methods for logistics systems are presented. They aims the greenhouse gas emissions 37% savings compared to BAU(Business As Usual) for a green growth of national strategy by 2020. An eco-driving training for truck drivers is a typical way. However there are few studies such as the integrated data collection automatically and the diagnostic Assessments and monitoring methods of logistics network status to show the effects of the eco-driving. To solve this problem, this paper provides previous research on logistics network diagnostics assessments and monitoring service and eco-driving. Then this paper shows developments of the logistics network diagnostic assessments and monitoring service business flow to promote eco-driving behaviors for truck drivers based on a smart phone application. Also, it shows an efficiency through an application results in field test.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.417-423
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• 2017

This paper intend to implement monitoring systems for individual customized diagnostics to maintain ongoing disease management to promote human health. Analyze the threshold of a measured biological signal using a number of measuring sensors. Performance assessment revealed that the SVM algorithm for bio-signal analysis showed an average error rate of 2 %. The accuracy of the classification is 97.2%, and reduced the maximum of 19.2% of the storage space when you split the window into 5,000 pieces. Out of the total 5,000 bio-signals, 84 results showed that results from the system were differently the results of the expert's diagnosis and showed about 98 % accuracy. However, the results of the monitoring system did not occur when the results of the monitoring system were lower than that of experts. And About 98% accuracy was shown.

• Nuclear Engineering and Technology
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• v.49 no.3
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• pp.484-494
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• 2017

In industrial plants such as nuclear power plants, system operations are performed by embedded controllers orchestrated by Supervisory Control and Data Acquisition (SCADA) software. A targeted attack (also termed a control aware attack) on the controller/SCADA software can lead a control system to operate in an unsafe mode or sometimes to complete shutdown of the plant. Such malware attacks can result in tremendous cost to the organization for recovery, cleanup, and maintenance activity. SCADA systems in operational mode generate huge log files. These files are useful in analysis of the plant behavior and diagnostics during an ongoing attack. However, they are bulky and difficult for manual inspection. Data mining techniques such as least squares approximation and computational methods can be used in the analysis of logs and to take proactive actions when required. This paper explores methodologies and algorithms so as to develop an effective monitoring scheme against control aware cyber attacks. It also explains soft computation techniques such as the computational geometric method and least squares approximation that can be effective in monitor design. This paper provides insights into diagnostic monitoring of its effectiveness by attack simulations on a four-tank model and using computation techniques to diagnose it. Cyber security of instrumentation and control systems used in nuclear power plants is of paramount importance and hence could be a possible target of such applications.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.263-269
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• 2012

Condition-based maintenance (CBM) has been used as a useful concept for optimizing maintenance plan and decreasing maintenance cost in several kinds of plant sites. This study introduced an example that developed an integrated management system for maintenance parameters and hydraulic turbine of hydro-power plant in order to improve its maintenance system as applying CBM techinique. The integrated management system consists of three parts. One is a hardware part including PDA inspection system and several kind of precision measuring instruments. Another is a vibration monitoring system on hydraulic turbine. The other is a software part that takes charge of making hierarchy tree of maintenance parameters and their inspection route, managing accumulated database, assessing health condition of components, and supporting interface with other enterprise management system. The system has been installed at Chuncheon Hydro-power plant for test and demonstration. It is expected that the system can contribute database construction for diagnostics and prognostics on facility health condition and systematic accumulation of know-how on operation and maintenance of plant.