고속철도 차륜의 건전성 평가를 위한 스캔형 자기카메라 개발 및 지능형 결함평가

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As the global climate changes, green growth has emerged as a new value so that railroad is considered to be the technology that leads the green growth in the transportation sector. Railroad is an optimal and environmentally friendly means that consumes less energy and emits less carbon. However, railroad train that weighs in thousands tons has enormous kinetic energy when moving at the speed of hundreds kilometers per hour. In addition, some damages may have been caused to train axle, train wheel and rail due to many operating hours, quick braking and other problems. Unfortunately, railroad train can stay only for less than two hours in a maintenance depot for maintenance and management. Consequently, it is really necessary to adopt a technology for measuring damages in a high speed, a high precision and a real time. In the meantime, the existing nondestructive testing technology indicates that there can be some changes in capability of detecting defects due to internal factors such as operation mistake, involuntary action, misunderstanding, nervousness, and loss of motivation and to external factors such as work intensity, unsafe environment, natural disaster, defects in machinery equipment, and insufficient supervision. There is an error or risk when a worker determines whether or not damage has been caused due to internal and external causes.
The testing method by using electromagnetic phenomenon is useful to detect defects on the surface of or in the vicinity of the surface of ferromagnetic structure such as train wheel. For this study, we developed the scan type magnetic camera to resolve issues on shortcomings and restrictions of the magnetic particle testing (MT), the magnetic flux leakage testing (MFLT) and the eddy current testing (ECT) among nondestructive examination methods for train wheel that utilize electromagnetic phenomenon. In addition, we suggested the algorithm to detect defects automatically, locate them and make an estimate to quantify their shape, size and volume with a view to reducing man-made error and risk.

First of all, we conducted the researches as below to examine express train wheel that rotated at the speed of approximately 30km/h, which was equivalent to the speed of railroad train entering into a maintenance depot.
(1) We explained magnetic source useful for ferromagnetic materials such as train wheel and verified the usefulness of magnetic source type and magnetizing method by making numerical analysis.
(2) We compared the existing magnetic sensor arrangement method and the improved arrangement method that is, sensors are arranged according to wheel profile and flange shape.
(3) We developed the amplification circuit with the high signal-to-noise ratio and the filter circuit for removing noise. And we also developed the high-speed signal process circuit that included parallel comparison type A/D converter.
(4) We analyzed magnetic images from the scan type magnetic camera by using the dipole model. And we compared the experiment results with the numerical analysis results to compare and analyze the effects of length, width and height of defects on magnetic field distribution.
In addition, we developed the intelligent damage evaluation algorithm as below to minimize man-made errors such as worker's misunderstanding and operation mistake due to inexperience.
(5) We analyzed the relation between signal and noise by making frequency spectrum analysis. And we performed the histogram analysis to examine the relation between existence or non-existence of defects and size distribution of the defects. Based on such analysis of the relations, we suggested the algorithm that could readily and quickly acquire the coordinate, length, shape, direction, and volume of defects. In addition, we proposed the method to express location of defects in measured object in a three-dimensional way. The method can improve worker’s understanding of damage analysis.
If the scan type magnetic camera technology developed in this study is used, it is possible to make an estimate to quantify in a high speed and a higher precision defects that existed on the surface or in the vicinity of the surface of ferromagnetic materials used for large equipment and various structures in petrochemical plant, iron and steel manufacturing plant, heavy industry, and vessel as well as nuclear and thermal power plant. In addition, the suggested algorithm and the three-dimension tangible software can help a worker make a more precise judgment.
Alternative Title
Development of a Scan Type Magnetic Camera and Intellectual Nondestructive Evaluation for Health Monitoring of Express Train Wheels
Alternative Author(s)
Hwang, Ji Seong
일반대학원 정보통신공학과
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Table Of Contents
제 1장 서 론 1
제 2장 구성 요소 및 원리 13
제 1절 자 원 4
제 2절 자기센서 배열 및 신호 처리 회로 23
1. 종래의 자기센서 배열 방법 25
2. 본 연구의 제안 방법 29
3. 저잡음 고속 차동 증폭 회로 32
4. 필 터 34
가. 아날로그 필터 34
나. 디지털 필터 35
제 3절 A/D 변환기 38
제 4절 인터페이스 40
제 5절 결함 정보 평가 48
1. 다이폴 모델 48
2. 개선된 다이폴 모델 53
3. 결함 평가 59
제 3장 지능형 결함평가 알고리즘 및 3차원 표현 64
제 1절 1차 자기영상 취득 및 스펙트럼 분석 66
제 2절 2차 자기영상 취득 및 히스토그램 분석 68
제 3절 3차 자기영상 취득 및 평가 71
1. 결함의 좌표 추정 71
2. 결함의 길이, 형상, 방향 추정 72
3. 결함의 체적 추정 74
제 4절 3차원 실감형 소프트웨어 75
제 4장 실험 및 고찰 78
제 1절 시험편 79
제 2절 곡면형 LIHaS 85
제 3절 실시간 신호 처리 및 실험 장치 89
제 4절 실험 결과 및 지능형 손상 평가 알고리즘 적용 92
1. 1차 자기영상 취득 및 스펙트럼 분석 92
2. 2차 자기영상 취득 및 히스토그램 분석 95
3. 3차 자기영상 취득 및 평가 97
가. 결함의 좌표, 길이, 형상, 방향 추정 102
나. 결함의 체적 추정 114
4. 3차원 실감형 소프트웨어 116
제 5장 결 론 120
Acknowledgments 129
조선대학교 일반대학원
황지성. (2009). 고속철도 차륜의 건전성 평가를 위한 스캔형 자기카메라 개발 및 지능형 결함평가.
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General Graduate School > 4. Theses(Ph.D)
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