• Smart Structures and Systems
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• 제29권3호
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• pp.421-431
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• 2022

Concrete slab cracks monitoring of modern high-speed railway is important for safety and reliability of train operation, to prevent catastrophic failure, and to reduce maintenance costs. This paper proposes a curvature filtering improved crack detection method in concrete slabs of high-speed railway via graph-based anomalies calculation. Firstly, large curvature information contained in the images is extracted for the crack identification based on an improved curvature filtering method. Secondly, a graph-based model is developed for the image sub-blocks anomalies calculation where the baseline of the sub-blocks is acquired by crack-free samples. Once the anomaly is large than the acquired baseline, the sub-block is considered as crack-contained block. The experimental results indicate that the proposed method performs better than convolutional neural network method even under different curvature structures and illumination conditions. This work therefore provides a useful tool for concrete slabs crack detection and is broadly applicable to variety of infrastructure systems.

• Smart Structures and Systems
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• 제10권6호
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• pp.547-555
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• 2012

Measuring crack length in concrete fracture test is not a trivial problem due to high speed crack propagation. In this study, mechanoluminascent (ML) material, which emits visible light under stress condition, was employed to visualize crack propagation during concrete fracture test. Three-point bending test was conducted with a notched concrete beam specimen. The cracking images due to ML phenomenon were recorded by using a high speed camera as a function of time and external loadings. The experimental results successfully demonstrated the capability of ML material as a promising visualization tool for concrete crack propagation. In addition, an interesting cracking behavior of concrete bending fracture was observed in which the crack propagated fast while the load decreased slowly at early fracture stage.

• 한국소음진동공학회논문집
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• 제20권2호
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• pp.185-190
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• 2010

Condition monitoring(CM) is a method based on non-destructive test(NDT). So, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days because of high sensitivity than common accelerometers and detectable low energy vibration signals. And crack is considered one of severe fault in the rotating machine. Therefore, in this paper, study on early detection using AE has been accomplished for the crack of the low-speed shaft. There is a seeded initial crack on the shaft then the AE signal had been measured with low-speed rotation as the applied load condition. The signal detected from crack in rotating machine was detected by the AE transducer then the trend of crack growth had found out by using some of feature values such as peak value, skewness, kurtosis, crest factor, frequency center value(FC), variance frequency value(VF) and so on.

• Computers and Concrete
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• 제23권6호
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• pp.445-457
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• 2019

Cracks are an important distress of concrete bridges, and may reduce the life and safety of bridges. However, the traditional manual crack detection means highly depend on the experience of inspectors. Furthermore, it is time-consuming, expensive, and often unsafe when inaccessible position of bridge is to be assessed, such as viaduct pier. To solve this question, the real-time automatic crack detecting system with unmanned aerial vehicle (UAV) become a choice. This paper designs a new automatic detection system based on real-time comprehensive image processing for bridge crack. It has small size, light weight, low power consumption and can be carried on a small UAV for real-time data acquisition and processing. The real-time comprehensive image processing algorithm used in this detection system combines the advantage of connected domain area, shape extremum, morphology and support vector data description (SVDD). The performance and validity of the proposed algorithm and system are verified. Compared with other detection method, the proposed system can effectively detect cracks with high detection accuracy and high speed. The designed system in this paper is suitable for practical engineering applications.

• Smart Structures and Systems
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• 제32권3호
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• pp.135-151
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• 2023

Using 3D asphalt pavement surface data, a deep and multiscale network named CrackNet-M is proposed in this paper for pixel-level crack detection for improvements in both accuracy and robustness. The CrackNet-M consists of four function-specific architectural modules: a central branch net (CBN), a crack map enhancement (CME) module, three pooling feature pyramids (PFP), and an output layer. The CBN maintains crack boundaries using no pooling reductions throughout all convolutional layers. The CME applies a pooling layer to enhance potential thin cracks for better continuity, consuming no data loss and attenuation when working jointly with CBN. The PFP modules implement direct down-sampling and pyramidal up-sampling with multiscale contexts specifically for the detection of thick cracks and exclusion of non-crack patterns. Finally, the output layer is optimized with a skip layer supervision technique proposed to further improve the network performance. Compared with traditional supervisions, the skip layer supervision brings about not only significant performance gains with respect to both accuracy and robustness but a faster convergence rate. CrackNet-M was trained on a total of 2,500 pixel-wise annotated 3D pavement images and finely scaled with another 200 images with full considerations on accuracy and efficiency. CrackNet-M can potentially achieve crack detection in real-time with a processing speed of 40 ms/image. The experimental results on 500 testing images demonstrate that CrackNet-M can effectively detect both thick and thin cracks from various pavement surfaces with a high level of Precision (94.28%), Recall (93.89%), and F-measure (94.04%). In addition, the proposed CrackNet-M compares favorably to other well-developed networks with respect to the detection of thin cracks as well as the removal of shoulder drop-offs.

• 비파괴검사학회지
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• 제36권6호
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• pp.451-459
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• 2016

프레스 공정은 제품의 대량 생산에 주로 사용되며, 제품의 생산관리를 위해 공정 중 발생하는 제품 결함 탐지는 중요하다. 결함 탐지를 위해 검출자의 육안을 통한 검출 방법이 주로 사용되고 있으나, 이 방법은 검출자에 따라 정확도 및 효율이 크게 좌우된다. 따라서 검출자의 영향을 받지 않고 일정 이상의 검출 능력을 갖춘 자동검출시스템이 필요하다. 본 연구에서는 프레스 라인의 영상촬영시스템을 활용하여 공정 중 패널이미지를 획득 및 패널 결함검출기법을 개발하였다. 결함이 없는 제품이미지를 기저이미지로 선정한 뒤, 이미지 내 외곽라인 요소들에 대한 히스토그램을 이용하여 결함이 존재하는 패널이미지와 비교를 통해 결함검출을 수행하였다. 또한 실험실 및 실제 프레스 라인 실험을 통하여 신뢰성을 확인하였다. 실험 결과 프레스 공정 중 생기는 패널의 크랙결함에 대한 탐지가 가능하였으며 추후 이미지 처리의 가속화 및 최적화 진행 시, 검출률 및 검출 속도 향상시 현장 적용 가능함을 확인하였다.

• 비파괴검사학회지
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• 제32권6호
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• pp.696-702
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• 2012

In this study, a noncontact laser ultrasonic system is proposed for rail defect detection. An Nd-Yag pulse laser is used for generation of ultrasonic waves, and the corresponding ultrasonic responses are measured by a laser Doppler vibrometer. For the detection of rail surface damages, the shape of the excitation laser beam is transformed into a line. On the other hand, a point source laser beam is used for the inspection of defects inside a rail head. Then, the interactions of propagating ultrasonic waves with defects are examined using actual rail specimens. Amplitude attenuation was mainly observed for a surface crack, and reflections were most noticeable from an internal damage. Finally, opportunities and challenges associated with real-time rail inspection from a high-speed train are discussed.

• 한국정밀공학회지
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• 제32권7호
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• pp.619-625
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• 2015

There is a large interest to find reliable and automatic methods for crack detection and quantification in the railway bogie frame. The non-destructive inspection of railway bogie frame has been performed by ultrasonic and magnetic particle testing in general inspection. The magnetic particle method has been utilized in the defect inspection of the bogie frame but the grinding process is required before inspection and the dust is developed resulting from the processing. The objective of this paper is to apply the inspection method of bogie frame using infra-red thermography. The infra-red thermography system using the excitation of eddy current was performed for the defect evaluation of weld specimen inserted artificial defects. The result shows that the infra-red thermography method can detect the surface and inner defects in weld specimen for bogie frame.

• 비파괴검사학회지
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• 제33권4호
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• pp.368-375
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• 2013

소구경 배관 용접부 손상은 원자력발전소에서 빈번하게 발생되고 있는 문제이며, 이러한 손상은 보수를 위한 불시정지를 야기하고 결과적으로 발전소에 경제적인 손실을 초래한다. 따라서 소켓용접부의 제작결함 검출과 초기 성장 결함의 검출은 매우 중요하다고 할 수 있다. 지금까지 해당부위에 대한 검사는 ASME Code Section XI 요건에 의하여 표면검사를 적용하여 왔으나 강화검사로 체적검사기법인 초음파검사를 병행하여 수행하고 있다. 그러나 가동중에 발생되는 피로균열을 검출하기 위하여 적용되고 있는 일반 수동 초음파검사는 소켓용접부의 접근성과 초음파 탐촉자의 접촉 문제 등으로 인하여 검사 결과의 신뢰성에 문제가 있어 왔다. 본 연구에서는 위상배열 초음파검사 기술을 적용하여 소구경 배관 소켓용접부의 검사 신뢰성과 속도를 향상하고자 하였다. 이를 위하여 소구경 배관 소켓용접부 검사를 위한 3.5 MHz 위상배열 초음파 탐촉자를 제작하고 탐촉자의 접촉 조건과 일정한 신호 품질을 유지하기 위하여 수동 엔코더 스캐너를 개발하였다. 또한 검사 시스템을 구성하고 현장 검사를 위한 절차서도 개발하였다.