• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.3
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• pp.238-247
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• 2007

Genetic Algorithms(GA) which searches optimum solution using natural selection and the law of heredity has been applied to learning algorithms in order to estimate performance deterioration of the aircraft gas turbine engine. The compressor, gas generator turbine and power turbine are considered for engine performance deterioration and estimation for performance deterioration of a single component at design point was conducted. As a result of that, defect diagnostics has been conducted. The input criteria for the genetic algorithm to guarantee the high stability and reliability was discussed as increasing learning data sets. As a result, the accuracy of defect estimation and diagnostics were verified with its RMS error within 3%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.640-646
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• 2017

A GEN TEST of the auxiliary power unit of a T-50 series aircraft is performed as part of the operational test of its emergency power system on the ground before flight. At this time, the auxiliary power unit should be automatically turned off via the response signal of the ECU when power is not normally supplied to the emergency power system. If the correct operation of the emergency power system cannot be confirmed on the ground, it is not possible to proceed with the flight. This kind of defect is a major factor causing the operation rate of the aircraft to be decreased. The defect code identified by the ECU was confirmed as a defect in the inverter. However, the same defect was found after replacing the inverter. This report presents an improved method of identifying the cause of the defect by analyzing the response characteristics of the ECU and emergency power system and allows the ECU to be recognized as the cause of the defect if the inverter does not generate a voltage within a certain time. Also, the application of the improved method confirmed that it can satisfy the output request time of the emergency power system and effectively prevent the auto-shutdown of the auxiliary power unit.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.558-567
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• 2020

This study examined the defect characteristics of fuel flow proportioner-mounted structures to analyze the causes of structural defects during aircraft operation. System vibrations and single component vibrations that occur during aircraft operations are usually the cause of structural defects. The fuel flow proportioner causes a defect in the support structure due to the vibration caused by the pressure change caused by the sudden increase in the flow rate. Defects in the support structure of the fuel flow proportioner are not correlated directly with the cracking of the maneuver, and flight time according to aircraft operation analysis is related to the use of A/B. The structural reinforcement configuration was confirmed through static and life analysis of the cracks of the bracket mounted under the fuel flow proportioner for improvement of the defect. An analysis of the reinforcement revealed a minimum structural strength of +0.15. Structural life analysis confirmed that the stress acted on the site under 15Ksi. The fatigue life was confirmed to be more than 7,700 Cycles.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.209-212
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• 2006

In this study, Support Vector Machine(SVM) and Artificial Neural Network(ANN) are used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine. Effect of altitude variation on the Defect Diagnostics algorithm has been included and evaluated. Separate learning Algorithm(SLA) suggested with ANN to loam the performance data selectively after classifying the position of defects by SVM improves the classification speed and accuracy.

• Composites Research
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• v.28 no.6
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• pp.348-355
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• 2015

NDT (Non Destructive Test) of the adhesive joints is very important because their strengths have greatly affected by the worker's skill and environmental condition. Recently, the electric impedance method in which 1-2 wt% CNT was dispersed in the adhesive and the electric resistance of the adhesive joint was measured was suggested for the defect detection of the adhesive joint. The uniform dispersion of CNT in the electric impedance method is very important to make a constant electric resistance of the adhesive joint and the accuracy of defect detection depends on the uniform dispersion. In this paper, the adhesive joints in which CNT was dispersed in the adhesive by the four dispersion methods were made and their electric resistance were measured. The pre-process and evaporation process of CNT using the ultrasonic method and agitation method was used and the effective dispersion method was suggested. Also, the criteria to evaluate the dispersivity was proposed.

• Journal of the Korean Society of Propulsion Engineers
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• v.11 no.1
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• pp.43-50
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• 2007

In this study, Hybrid Separate Learning Algorithm(SLA) consisting of Support Vector Machine(SVM) and Artificial Neural Network(ANN) has been used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine in the off-design range considering altitude variation. Although the number of teaming data and test data highly increases more than 6 times compared with those required for the design condition, the proposed defect diagnostics of gas turbine engine using SLA was verified to give the high defect classification accuracy in the off-design range considering altitude variation.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.11a
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• pp.23-27
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• 2008

In this study, Real Coded Genetic Algorithm(RCGA) and Artificial Neural Network(ANN) are used for developing the defect diagnostics of the aircraft turbo-shaft engine. ANN accompanied with large amount data has a most serious problem to fall in the local minima. Because of this weak point, it becomes very difficult to obtain good convergence ratio and high accuracy. To solve this problem, GA based ANN has been suggested. GA is able to search the global minima better than ANN. GA based ANN has shown the RMS defect error of 5% less in single and dual defect cases.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.285-288
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• 2006

Genetic Algorithms(GA) which searches optimum solution using natural selection and the law of heredity has been applied to teaming algorithms in order to estimate performance deterioration of the aircraft gas turbine engine. The compressor, gas generation turbine and power turbine are considered for estimation for performance deterioration of a complex component at design point was conducted. As a result of that, complex defect diagnostics has been conducted. As a result, the accuracy of diagnostics were verified with its relative error with in 10% at each component.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.627-632
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• 2016

In this paper, we propose a new vision-based defect inspection system for the surface of hot slabs. To minimize the influence of self-emission from slab surfaces with high temperature, an optic method based on blue LED light and a blue pass filter is proposed. Because the slab surface is partially covered with scales, which are unavoidable oxidized substances caused during manufacturing, it is difficult to distinguish between vertical cracks and scale. In order to resolve this problem and to improve the detection performance, the use of a Gabor filter and dynamic programming are proposed. Finally, the effectiveness of the proposed method is shown by means of experiments conducted on images of hot slabs that were obtained from an actual slab production line.

• Convergence Security Journal
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• v.20 no.2
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• pp.145-153
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• 2020

The aviation safety policy master plan is promoting the development of aviation safety management technology applying the 4th industrial revolution technology with the goal of establishing a flawless aviation safety management system and establishing a future aviation safety infrastructure. The master plan includes the establishment of various aviation safety management systems such as aircraft fault management using AI & Big data and flight training system using VR/AR. Currently, the Air Force is promoting a flight safety management system using new technology under the goal of building smart air force. Therefore, this study intends to apply the 4th Industrial Revolution technology to the aircraft condition check system that finally checks the safety of the aircraft before flight. The Air Force conducts airframe flaw checks and pre-flight aircraft check. In this study, we architect the airframe flaw check system using AI and drones, and the pre-flight aircraft condition check system using the IoT and big data for more precise and detailed check of aircraft condition and flawlessness check.