• Smart Structures and Systems
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• v.32 no.4
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• pp.253-266
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• 2023

The time-reversal method is employed to improve the ability of pipeline defect detection, and a new approach of identifying the pipeline defect depth is proposed in this research. When the L(0,2) mode ultrasonic guided wave excited through a lead zirconate titinate (PZT) transduce array propagates along the pipeline with a defect, it will interact with the defect and be partially converted to flexural F(n, m) modes and longitudinal L(0,1) mode. Using a receiving PZT array attached axisymmetrically around the pipeline, the L(0,2) reflection signal as well as the mode conversion signals at the defect are obtained. An appropriate rectangle window is used to intercept the L(0,2) reflection signal and the mode conversion signals from the obtained direct detection signals. The intercepted signals are time reversed and re-excited in the pipeline again, result in the guided wave energy focusing on the pipeline defect, the L(0,2) reflection and the L(0,1) mode conversion signals being enhanced to a higher level, especially for the small defect in the early crack stage. Besides the L(0,2) reflection signal, the L(0,1) mode conversion signal also contains useful pipeline defect information. It is possible to identify the pipeline defect depth by monitoring the variation trend of L(0,2) and L(0,1) reflection coefficients. The finite element method (FEM) simulation and experiment results are given in the paper, the enhancement of pipeline defect reflection signals by time-reversal method is obvious, and the way to identify pipeline defect depth is demonstrated to be effective.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.4
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• pp.311-316
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• 2010

Defects positioning of underground gas pipelines using MFL(magnetic flux leakage) inspection which is one of non-destructive evaluation techniques is proposed in this paper. MFL signals acquired from MFL PIG(pipeline inspection gauge) have nonlinearity and distortion caused by various external disturbances. SQI(self quotient image), a compensation technique for nonlinearity and distortion of MFL signal, is used to correct positioning of pipeline defects. Through the experiments using artificial defects carved in the KOGAS pipeline simulation facility, it is found that the performance of proposed defect detection is greatly improved compared to that of the conventional DCT(discrete cosine transform) coefficients based detection.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1177-1185
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• 2022

The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.

• Journal of the Korean Society for Nondestructive Testing
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• v.26 no.2
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• pp.122-129
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• 2006

Shearography is one of optical methods that has been applied to nondestructive testing (NDT) and strain/stress analysis. The technique has the merit of the directly measuring relative displacement, which is insensitive to environmental vibration disturbance. Previous studies about the method have emphasized on extending its application to new fields and lack insufficient research on effective parameters for qualitative and quantitative evaluation of defects. In this paper, the influence of shearing amount on the detection of an internal defect is investigated. In experiment, slender defects along longitudinal direction of pipeline are artificially designed and detection results according to the change of shearing amount are analyzed. Based on the investigation, we propose the technique for the determination of defect size and accurate source location.

• Journal of Magnetics
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• v.18 no.3
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• pp.357-360
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• 2013

A modified SQI method using magnetic leakage flux (MFL) signal for underground gas pipelines' defect detection and characterization is presented in this paper. Raw signals gathered using MFL signals include many unexpected noises and high frequency signals, uneven background signals, signals caused by real defects, etc. The MFL signals of defect free pipelines primarily consist of two kinds of signals, uneven low frequency signals and uncertain high frequency noises. Leakage flux signals caused by defects are added to the case of pipelines having defects. Even though the SQI (Self Quotient Image) is a useful tool to gradually remove the varying backgrounds as well as to characterize the defects, it uses the division and floating point operations. A modified SQI having low computational complexity without time-consuming division operations is presented in this paper. By using defects carved in real pipelines in the pipeline simulation facility (PSF) and real MFL data, the performance of the proposed method is compared with that of the original SQI.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.211-220
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• 2022

Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.

• Smart Structures and Systems
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• v.9 no.5
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• pp.461-472
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• 2012

This study proposes an optics-based active sensing system for continuous monitoring of underground pipelines in nuclear power plants (NPPs). The proposed system generates and measures guided waves using a single laser source and optical cables. First, a tunable laser is used as a common power source for guided wave generation and sensing. This source laser beam is transmitted through an optical fiber, and the fiber is split into two. One of them is used to actuate macro fiber composite (MFC) transducers for guided wave generation, and the other optical fiber is used with fiber Bragg grating (FBG) sensors to measure guided wave responses. The MFC transducers placed along a circumferential direction of a pipe at one end generate longitudinal and flexural modes, and the corresponding responses are measured using FBG sensors instrumented in the same configuration at the other end. The generated guided waves interact with a defect, and this interaction causes changes in response signals. Then, a damage-sensitive feature is extracted from the response signals using the axi-symmetry nature of the measured pitch-catch signals. The feasibility of the proposed system has been examined through a laboratory experiment.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.298-303
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• 2003

In recent years, shearogrpahy has significantly improved capabilities in the areas of unbond and separation detection in tires. Although shearography has many advantages for qualitative evaluation, the technique remains the problem of quantitative analysis of inside defects, because shearography needs several effective factors including the amount of shearing, shearing direction and induced load, which exist as barrier for the quantitative analysis of inside defects. Since the factors are highly dependent on inspectors skill and also affect the in-situ workability. The factors were optimized and the size of cracks inside of pipeline and tire has been quantitatively determined.

• Smart Media Journal
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• v.12 no.9
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• pp.45-59
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• 2023

Weakly supervised object localization (WSOL) is a task of localizing an object in an image using only image-level labels. Previous studies have followed the conventional class activation mapping (CAM) pipeline. However, we reveal the current CAM approach suffers from problems which cause original CAM could not capture the complete defects features. This work utilizes a convolutional neural network (CNN) pretrained on image-level labels to generate class activation maps in a multi-scale manner to highlight discriminative regions. Additionally, a vision transformer (ViT) pretrained was treated to produce multi-head attention maps as an auxiliary detector. By integrating the CNN-based CAMs and attention maps, our approach localizes defective regions without requiring bounding box or pixel-level supervision during training. We evaluate our approach on a dataset of apple images with only image-level labels of defect categories. Experiments demonstrate our proposed method aligns with several Object Detection models performance, hold a promise for improving localization.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.2
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• pp.177-184
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• 2012

An optical defect detection method using ESPI(electronic speckle pattern interferometry) is proposed. ESPI is widely used as a non-contact measurement system which show deformation and phase map in real time. ESPI can be divided as the in-plane, out-of-plane and shearography by operation principle and target object and also divided with bulk type and optic fiber type by the optic configurations. This paper is focused on optic fiber type out-of-plane ESPI, which has the following advantages: (1) low cost; (2) reduction of the unreliable factors generated by separated optic components; (3) simplification of the optic configuration; (4) great reduction of volume; (5) flexibility, to be easily designed into different structures to adapt to inaccessible environments such as pipeline cavity and so on.