• Journal of the Korean Society for Precision Engineering
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• v.22 no.1
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• pp.53-60
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• 2005

The wire rope of container crane is a important component to container transfer system and is used in a myriad of various applications such as elevator, mine hoist, construction machinery, and so on. If it happen wire rope failures in operating, it may lead to the safety accident and economic loss, which is productivity decline, competitive decline of container terminal, etc. To solve this problem, we developed the active and portable wire rope fault detecting system. The developed system consists of three parts that are the fault detecting, signal processing, and remote monitoring part. All detected signal has external noise or disturbance according to circumstances. Therefore we applied to discrete wavelet transform to extract a signal from noisy data that was used filter. As experimental result, we can reduce the expense for container terminal because of extension of exchange period of wire rope for container crane and this system is possible to apply in several fields to use wire rope.

• Journal of Sensor Science and Technology
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• v.28 no.6
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• pp.371-376
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• 2019

We have developed an acoustic resonance inspection system to inspect the welding defects in the mechanical parts fabricated using friction stir welding method. The inspection system was consisted of a DAQ board, a microphone sensor, an impact hammer, and controlled by a PC software. The system was developed to collect and analyze the sound signal generated by hitting the sample with an impact hammer to determine whether it is defective. In this study, 100% welded good samples were compared with 95%, 90%, and 85% welded samples, respectively. The variation of the completeness in welding did not affect the visual appearance in the samples. As a result of analyzing the natural frequencies of the good samples, the five natural frequency peaks were identified. In the case of the defective samples, the frequency change was observed. The welding failure detection time was fast enough to be only 0.7 seconds. Employing our welding defect inspection system to the actual industrial field will maximize the efficiency of quality inspection and thus improve the productivity.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.5
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• pp.465-472
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• 2016

This study materialized an FPGA-based system to extract PCB patterns. The Printed Circuit Boards that are produced these days are becoming more detailed and complex. Therefore, the importance of a vision system to extract defects of detailed patterns is increasing. This study produced an FPGA-based system that has high speed handling for vision automation of the PCB production process. A vision library that is used to extract defect patterns was also materialized in IPs to optimize the system. The IPs materialized are Camera Link IP, pattern matching IP, VGA IP, edge extraction IP, and memory IP.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.12
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• pp.1061-1066
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• 2013

In the steel industry, the detection of tiny defects including its 3D characteristics on steel surfaces is very important from the point of view of quality control. A multi-spectral photometric stereo method is an attractive scheme because the shape of the defect can be obtained based on the images which are acquired at the same time by using a multi-channel camera. Moreover, the calculation time required for this scheme can be greatly reduced for real-time application with the aid of a GPU (Graphic Processing Unit). Although a more reliable shape reconstruction of defects can be possible when the numbers of available images are increased, it is not an easy task to construct a camera system which has more than 3 channels in the visible light range. In this paper, a new 6-channel camera system, which can distinguish the vertical/horizontal linearly polarized lights of RGB light sources, was developed by adopting two 3-CCD cameras and two polarized lenses based on the fact that the polarized light is preserved on the steel surface. The photometric stereo scheme with 6 images was accelerated by using a GPU, and the performance of the proposed system was validated through experiments.

• Journal of the Korea Safety Management & Science
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• v.23 no.3
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• pp.83-88
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• 2021

Recently, due to the aging of workers and the weakening of the labor base in the automobile industry, research on quality inspection methods through ICT(Information and Communication Technology) convergence is being actively conducted. A lot of research has already been done on the development of an automated system for quality inspection in the manufacturing process using image processing. However, there is a limit to detecting defects occurring in the automotive sunroof sealer application process, which is the subject of this study, only by image processing using a general camera. To solve this problem, this paper proposes a system construction method that collects image information using a infrared thermal imaging camera for the sunroof sealer application process and detects possible product defects based on the SVM(Support Vector Machine) algorithm. The proposed system construction method was actually tested and applied to auto parts makers equipped with the sunroof sealer application process, and as a result, the superiority, reliability, and field applicability of the proposed method were proven.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.11
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• pp.1279-1287
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• 2009

An increasingly competitive business environment has been concentrated on industries to reduce the operating costs. Industries such as gas, oil, petrochemical, chemical, and electric power have employed for the operation and used for large equipment or structures that require a high capital investment. In order to meet these requirements, the industries are increasingly moving toward saving the experimental verification and computer simulation. Therefore industries to reduce the maintenance costs without compromising the operational safety have been forced on finding for better and more efficient methods to inspect their equipment and structures. In this study, it focused on the development the real-time non-contract monitoring system as an efficient tool for the experimental study of weld defects based on the relationship between the measured voltage and input parameters.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.539-542
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• 2003

A motor is the workhorse of our industry. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. Different internal motor faults (e.g., inter-turn short circuits, broken bearings, broken rotor bars) along with external motor faults (e.g., phase failure, mechanical overload, blocked rotor) are expected to happen sooner or later. This paper introduces the fault detection technique of induction motors based upon the stator current. The fault motors have rotor bar broken or rotor unbalance defect, respectively. The stator currents are measured by the current meters and stored by the time domain. The time domain is not suitable to represent the current signals, so the frequency domain is applied to display the signals. The Fourier Transformer is used for the conversion of the signal. After the conversion of the signals, the features of the signals have to be extracted by the signal processing methods like a wavelet analysis, a spectrum analysis, etc. The discovered features are entered to the pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, etc. This paper describes the fault detection results that use wavelet decomposition. The wavelet analysis is very useful method for the time and frequency domain each. Also it is powerful method to detect the features in the signals.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.05a
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• pp.66-70
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• 2001

The partial discharge testing is widely used in diagnostic measuring technology because it gives low stress to power equipment which is undertaken tests. Therefore it is very useful method compare to previous destructive methods and effective diagnosis method in power system that requires on-line/on-site diagnosis. But partial discharges have very complex characteristics of discharge pattern, so it is required continuous research to development of precise analysis method. In recent, the study of partial discharge is carrying out discover of initial defect of power equipment through condition diagnosis and system development of degradation diagnosis using HFPD(High Frequency Partial Discharge) detection. In this study, simulated system is manufactured and HFPD occurred from those simulator is measured with broad-band antenna in real time, the degradation grade of system is analyzed through produced patterns in simulated target according to the AI/statistics processing.

• Korean Journal of Poultry Science
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• v.22 no.4
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• pp.233-238
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• 1995

A computer vision system was built to generate images of a single, stationary egg. This system includes a CGD camera, a frame grabber, and incandescent back lighting system. Image processing algorithms were developed to inspect egg shell and to sort eggs. Those values of both gray level and area of dark spots in the egg image were used as criteria to detect holes in egg and those values of both area and roundness of dark spots in the egg image were used to detect cracks in egg. For a sample of 300 eggs, this system was able to correctly analyze an egg for the presence of a defect 97.5% of the time. The weights of eggs were found to be linear to both the projected area and the perimeter of eggs viewed from above. Those two values were used as criteria to sort eggs. The coefficients of determination(r$^2$) for the regression equations between weights and those two values were 0.967 and 0.972 in the two sets of experiment. Accuracies in grading were found to be 95.6% and 96.7% as compared with results from sizing by electronic weight scale.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.834-841
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• 1996

Egg production in Korea is becoming automated with a large scale farm. Although many operations in egg production have been and cracks are regraded as a critical problem. A computer vision system was built to generate images of a single , stationary egg. This system includes a CCD camera, a frame grabber board, a personal computer (IBM PC AT 486) and an incandescent back lighting system. Image processing algorithms were developed to inspect egg shell and to sort eggs. Those values of both gray level and area of dark spots in the egg image were used as criteria to detect holes in egg and those values of both area and roundness of dark spots in the egg and those values of both area and roundness of dark spots in the egg image were used to detect cracks in egg. Fro a sample of 300 eggs. this system was able to correctly analyze an egg for the presence of a defect 97.5% of the time. The weights of eggs were found to be linear to both the projected area and the perimeter of eggs v ewed from above. Those two values were used as criteria to sort eggs. Accuracy in grading was found to be 96.7% as compared with results from weight by electronic scale.