밸브의 내부 누설 현상은 밸브의 내부 부품의 손상에 의해 발생하며 배관 시스템의 사고와 운전정지를 일으키는 주요 요인이 다. 본 연구는 버터플라이형 밸브의 내부 누설에 따라 배관계에서 발생하는 음향방출 신호를 이용하여 배관 가동 중 실시간 누설 진단의 가능성을 검토하였다. 이를 위해 밸브의 작동 모드별로 측정한 시간영역의 AE 원시신호를 취득하였으며 이로부터 구축한 데이터셋은 데 이터 기반의 인공지능 알고리즘에 적용하여 밸브의 내부 누설 유무를 진단하는 모델을 생성하였다. 누설 유무진단을 분류의 문제로 정의 하여 SVM 기반의 머신러닝과 CNN 기반의 딥러닝 분류 알고리즘을 적용하였다. 데이터의 특징 추출에 기반한 SVM 분류 모델의 경우, 이 진분류 모델에서 구축된 모델에 따라 83~90%의 정확도를 나타냈으며, 다중 클래스인 경우 분류 정확도가 66%로 감소하였다. 반면, CNN 기반의 다중 클래스 분류 모델의 경우 99.85%의 분류 정확도를 얻을 수 있었다. 결론적으로 밸브 내부 누설 진단을 위한 SVM 분류모델은 다중 클래스의 정확도 향상을 위해 적절한 특징 추출이 필요하며, CNN 기반의 분류모델은 프로세서의 성능 저하만 없다면 누설진단과 밸브 개도 분류에 효율적인 접근방법임을 확인하였다.

The security accidents occurring in ships and at seas and ports became very serious, and in particular, the maritime terrorism and abduction by pirates have emerged at the international level as a problem. The international maritime organization (IMO), accordingly, entered into such forces as the SOLAS chapter and measures in order to reinforce the maritime security and the security for ships and port facilities in 2004. In this study, the JDS-S4 improved as an oriented speaker to reinforce the ship security by enabling the clear communication even at long distance was tested by using the conducted emission test(CE101) and a standard test of the US military standard (MIL-STD-461F). Also, the result of this study was shown to satisfy the standard.

To estimate weld quality of the resistance spot-welding, the acoustic emission features are investigated from the total acoustic emission signal at the single-spot weld. Typically, the resistance spot welding process consists of several stages: set-down of the electrodes, squeeze, current flow, forging, hold time, and lift-off. Various types of acoustic emission response corresponding to each stage can be separately analyzed by using back-propagation neural network classifier and wavelet transform technique. The presented machine learning results provide a validation for using back-propagation neural network and wavelet transform technique as a valuable insights into the resistance spot-welding process. Especially, a wavelet transform technique is demonstrated and the plots are very powerful in the recognition of the acoustic emission features

In this study, carbon/epoxy composite DCB(double cantilever beam) specimens based on K-means clustering and wavelet transform analyses are presented. For the fracture Mode I, the fiber orientation θ = [0 ]24 and θ = [±45]12 both shown up stable crack growth in DCB testing. For the fiber orientation θ = [0 ]24 , the continuous type AE signal showed at central frequency 130～270kHz, which means that matrix micro cracking was occurred. The Burst type AE signal was occurred at central frequency 200～300kHz due to fiber bridging and fiber breaking. Other burst type AE signals were occurred at central frequency 130～180kHz with very high amplitude due to fiber bridging. For the fiber orientation θ = [±45]12 , the burst type signal showed at central frequency 220～300kHz, which means that fiber breaking was occurred. Mixed type of burst and continuous signals were captured at central frequency 250～480kHz due to fiber friction.

This is a study on the distribution of acoustic emission parameters during a burst test for a type-II CNG vehicle fuel tank. A resonant AE sensor with a central frequency of 150 kHz was attached to the composite materials in the center of the fuel tank. The pressure was increased from 30 to 100% of the expected burst pressure and was maintained for 10 minutes at each level. Damage at 70% of expected burst pressure occurred by various damage mechanisms including fiber breakage and delamination, while that of below 60% only occurred by matrix crack initiation and growth. The count, duration and rise time of the AE signal at 60% of the expected burst pressure are distributed below 500, 5000 μs and 300 μs, respectively. Then, at above 70% they increased with pressure by superimposing of individual AE signal generated at a nearby place. These results confirmed that the analysis of the distribution of AE parameters is an effective tool for estimating damage of a CNG fuel tank.

This study deals with the high frequency induction hardening (HF at 850℃, 120kHz & 50kW condition) SM45C steel. (1) The HF specimen, which was tempered at 150℃, did not show any tempering effect. A brittle fracture occurred at rounded area of the tensile specimen. AE (acoustic emission) amplitude distribution showed between 45dB and 60dB. (2) A slip and fracture occurred at the hole area of the HF specimen which was tempered at 300℃. As they pass the yield point, the AE energy is increased intermittently and AE amplitude distribution exists between 70dB and 85dB. In addition, after imposing the maximum tensile load, AE signals showed high amplitude and energy distribution. The AE amplitude showed between 45dB and 70dB. (3) A brittle fracture occurred at HF specimen which was tempered at 450℃ as if it is torn in the direction of 45° on parallel area over the both sides of the tensile specimen, which lead to several peak appeared in AE energy. It was found that the AE amplitude was relatively low and the AE energy was high.

This study investigated tensile deformation of the stress aging heat-treated SM45C steel which are aging temperature at 250℃ and 300℃; aging time at 1 hour and, 3 hours; applied load at 300N and 400N by using an acoustic emission techniques (AEs). A signal processing technology is applied to evaluate an AE source characterization of different AE measurement systems DiSP & PCI-2. In this study, most suitable aging condition appeared at 250℃, 3 hours and 300N. But in cases of 250℃, 3 hours, 400N and 300℃, 3 hours, 400N conditions, yield load decreased compare to other conditions according to the over-aging phenomena. On the other hand, when arranged via AE amplitude results by K-means clustering pattern recognition of AE raw signals, tendency of signal strength appeared non-heat treatment condition, 'Class 1 < Class 2 < Class 3'; optimal condition, 'Class 3 < Class 2'; over-aging condition, 'Class 3 < Class 2 < Class 1'. This is judged by emitting a lot of AE energy when material causes plastic deformation because ductility increases on factor by over-aging phenomenon.

This study is deal with the high frequency induction hardening (HF at 850℃, 120kHz & 50kW condition) SM45C steel. (1) The HF specimen which was tempered at 150℃, did not appear any tempering effect. A brittle fracture occurred at rounded area of the tensile specimen. AE amplitude distribution showed between 45~60dB. (2) The HF specimen which was tempered at 300℃, slip and fracture occurred at the hole area of the tensile specimen. As it passes the yield point, the AE energy increased intermittently and AE amplitude distribution showed between 70~85dB. In addition, after the maximum tensile load, it showed high amplitude and energy distribution. The AE amplitude showed between 45~70dB. (3) The HF specimen which was tempered at 450℃, a brittle fracture occurred as if it is torn in the direction of 45℃ on parallel area over the both sides of the tensile specimen, which led to several peak to be appeared in AE energy. It was found that the AE amplitude was relatively low and the AE energy was high.

This paper investigates tensile characteristics of the stress aging heat-treated SM45C steel which are aging temperature at 250℃, 300℃, aging time at 1, 3 hours, and applied load at 300, 400N conditions by using acoustic emission. Most suitable aging condition was aging temperature 300℃, aging time 1 hour, and aging applied load 300N. And increased yield load 28.3% than non-treatment specimen in this condition. AE energy in elastic limit increased about 16.7 times than non-treatment specimen. When aging time is 3 hours, yield load decreased than other conditions that possibility is high to have itself defect on inside the specimen or coarse grain size precipitation is different in happened over-aging phenomenon. Especially, in case of 300℃, 3 hours and 400N condition appeared AE energy in elastic limit fairly high about 30 times than non-treatment specimen. This is considered by emit a lot of energies when material causes plastic deformation because the ductility increases on specimen by over-aging phenomenon.

A study of fracture to material is getting interest in nuclear and aerospace industry as a viewpoint of safety. Acoustic emission (AE) is a non-destructive testing and new technology to evaluate safety on structures. In previous research continuously, all tensile tests on the pre-defected coupons were performed using the universal testing machine, which machine crosshead was move at a constant speed of 5mm/min. This study is to evaluate an AE source characterization of SM45C steel by using k-nearest neighbor classifier, k-NNC. For this, we used K-means clustering as an unsupervised learning method for obtained multi -variate AE main data sets, and we applied k-NNC as a supervised learning pattern recognition algorithm for obtained multi-variate AE working data sets. As a result, the criteria of Wilk's λ, D&B(Rij) & Tou are discussed.

The objective of this study is to evaluate the mechanical behaviors and structural integrity of the weldment of high strength steel by using an acoustic emission (AE) techniques. Monotonic simple tension and AE tests were conducted against the 3 kinds of welded specimen. In order to analysis the effectiveness of weldability, joinability and structural integrity, we used K-means clustering method as a unsupervised learning pattern recognition algorithm for obtained multi-variate AE main data sets, such as AE counts, energy, amplitude, hits, risetime, duration, counts to peak and rms signals. Through the experimental results, the effectiveness of the proposed method is discussed.

The objective of this study is to evaluate the mechanical behaviors and structural integrity of the weldment of high strength steel by using an acoustic emission (AE) techniques. Simple tension and AE tests were conducted against the 3 kind of welding test specimens. In order to analysis the effectiveness of weldability, joinability and structural integrity, we used K-means clustering method as a unsupervised learning pattern recognition algorithm for obtained multivariate AE main data sets, such as AE counts, energy, amplitude, hits, risetime, duration, counts to peak and rms signals. Through the experimental results, the effectiveness of the proposed method is discussed.

bismaleimide취약성을 개선 하기 위하여 toughening agent인 TM120을 첨가하여 carbon/(± 45˚)2s를 제조하고 이들의 파손과 기계적 특성을 인당실험과 음향방출을 통해 자세히 논하였다. 첨가하는 TM120의 비율은 0, 5, 10, 15, 20, 25phr이었고, 1, 4-diazobicyclo-(2, 2, 2)-octane(DABCO) 0.2phr를 경화 촉진제로 사용하였다. 또한, 탄소 섬유는 Toray사의 T300를 사용하였고, 음향방출과 인장실험 결과로 TM120이 적당한 첨가량은 20phr이었으며, TM120은 cabon/(± 45˚)2s의 파손특성과 기계적물성에 많은 영향을 미쳤다.

응력부식균열(SCC) 감수성평가를 위한 여러 시험방법들중 저변형율시험방법은 비교적 ?은 시간내에 금속재료의 SCC감수성을 평가하기 위한 효과적인 시험방법이다. 그러나 저변형율 시험방법만으로 SCC과정의 미시적 파괴거동ㅇ르 분석하는 것은 매우 어렵다. 종래, 음향방출(AE)시험은 재료의 파괴과정시 미시균열의 개시 및 전파거동을 감시하는데 유효한 기법으로 잘 알려져 있다. 그러므로 본 논문에서는 저변형율시험과 음향방출시험을 이용하여 SCC의 전파과정과 AE신호 특성사이의 상호관계를 분석하였다. 실험결과, 재료의 미시파괴 과정에서 발생하는 AE신호들은 뚜렷히 시험환경에 의존하였으며, 인공해수중에서 SCC과정시 발생된 AE신호 특성은 Air상태 보다 상당히 크게 나타났다. 그리고 SCC거동은 AE신호의 진폭인자로서 명확하게 평가할 수 있다.

유리 원형 평판에서 힘의 세기가 1 dyne이고 면에 수직하게 작용하는 Heaviside계단 함수의 시간 의존성을 갖는 점 하중에 의한 진앙점에서 수직 변위를 이론적으로 계산하였다. 연필심 파괴시 방출되는 음향방출신호를 안정화회로가 부착된 Michelson 간섭계로 측정하여, 음향방출 발생원함수를 deconvolution방법을 이용하여 해석하였다. 연필심 파괴시 방출되는 음향방출 발생원을 파 전면에 약 0.7μsec의 지속시간이 갖는 dip부분과 약 0.5μsec인 계단 상승시간과 약 4.5N의 힘의 크기를 갖는 계단함수의 형태였다.

본 연구에서는 음향 방출 기법을 사용하여 강연선(7-wire strand)의 손상을 감지하기 위한 기초 실험을 수행하였다. 강연선은 주로 교량에 추가적인 인장력을 제공하기 위해 널리 사용되는 건설 자재이다. 프리스트레스 교량 또는 사장교가 대표적인 경우이다. 그러나 교량 노화가 급격히 진행되면서 강연선 부식 문제가 대두되고 있다. 이러한 이유로 케이블 점검을 위한 다양한 비파괴 방법이 연구되고 있고 현장 적용 이 시도되고 있다. 비파괴 방법 ??중 하나인 음향 방출 기법은 케이블 손상 및 파단을 감지하는 효과적인 기술로 알려져 있다. 본 연구에서는 음향 방출 기법의 교량에 대한 적용 가능성을 평가하기 위해 강연선의 손상에 따른 음향 방출 신호 특성을 인장 실험을 분석 하고, 현장 적용을 위한 최적 센서 주파수 타입을 선정하였다. 결과적으로, 음향 방출 기법을 활용하여 향후 교량 케이블의 부식 파단 및 파단 징후를 감지 할 수 있다 고 여겨진다.

This paper presents the assessment of 7-wire strand monitoring using acoustic emission technique for bridges. 7-wire strand is widely used construction materials to provide additional tensile force to bridges. PSC (PreStressed Concrete) bridge and cable-stayed bridge are representatives for such cases. However, as the bridge aging progresses recently, corrosion problems of strand are emerging. For this reason, various NDT (Non-Destructive Test) methods for cable inspection are being studied and applied to the field. One of the NDT methods, acoustic emission technique, is known as an effective technique to detect cable damage and breakage. In this study, to evaluate the applicability of acoustic emission technique to bridges, acoustic emission signals according to presence or absence of the strand were acquired and analyzed by tensile test. As a result, it is considered that the acoustic emission technique will be able to detect corrosion breakage and signs of rupture.