• Journal of the Korean housing association
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• v.26 no.2
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• pp.67-79
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• 2015

As the Korean construction market in the apartment housing has changed to a housing consumer focused market, interest and importance on efficient use and management on existing buildings has increased rather than demand for new buildings. Interest of housing consumers on apartment house quality has increased in this market paradigm, and this spontaneously is connected to quality flaw related defect disputes and lawsuits that the importance of defect diagnosis has continuously increased. This defect diagnosis is directly connected to maintenance charges in defect dispute and lawsuit processes that rather objective and highly credible progress of duty is required. However, most defect diagnosis firms today that progress defect diagnosis are using different diagnosis methods and depend on the experience of experienced professionals that there is no standardized defect diagnosis process. Therefore, the purpose of this study is to provide common defect diagnosis process model for defect diagnosis firms utilizing the BPMN (Business Process Modeling Notation) modeling method. It is expected that this will contribute to professional and reliable task performances of concerned defect diagnosis workers. Furthermore, it is expected that design lawsuit damage will be substantially reduced by standardizing defect diagnosis processes.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.107-114
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• 2000

Rollers and rolls in the continuous process systems are noes of key components that determine the quality of web products. The condition of rollers and rolls(ex. eccentricity wear) should be consistently monitored in order to maintain the process conditions (ex. tension, edge position) within a required specification. In this paper, a new diagnosis technique is suggested to detect the defect of rollers/rolls (ex. eccentricity, wear) based on frequency domain analysis of web tension signal. The kernel of this technique is to use the spectrum amplitude of tension signal which allows to identify the fault rollers/rolls and to also diagnose the degree of fault in corresponding rollers and rolls. The experimental results proved that the suggested diagnosis technique can be successfully used to identify the defect rollers and rolls as well as to diagnose the degree of the defect of those rollers. The suggested technique can be applied to monitor and diagnose the shape of rollers and rolls in various multi-span web transport systems.

• Smart Structures and Systems
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• v.31 no.1
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• pp.79-88
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• 2023

As the number of deteriorated buildings increases, the importance of safety diagnosis, maintenance, and the repair of buildings also increases. Traditionally, building condition assessments are performed by one person or one company and various inspections are needed. This entails a subjective judgment by the inspector, resulting in different assessment results, poor objectivity and a lack of reliability. Therefore, this study proposed a method to bring about accurate grading results of building conditions. The limitations of visual inspection and condition assessment processes previously conducted were identified by reviewing existing studies. Building defect data was collected using the reverse-engineered three-dimensional (3D) model. The accuracy of the results was verified by comparing them with the actual evaluation results. The results show a 50% time-saving to the same area with an accuracy of approximately 90%. Consequently, defect data with high objectivity and reliability were acquired by measuring the length, area, and width. In addition, the proposed method can improve the efficiency of the building diagnosis process.

• 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 of Manufacturing Process Engineers
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• v.18 no.5
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• pp.37-47
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• 2019

Recently, as demand for various paper containers has rapidly grown, it is inevitable that paper cup-forming machines have increased their manufacturing speed. However, the faster manufacturing speed naturally brings more frequent manufacturing failures, which decreases manufacturing efficiency. As such, it is necessary to develop a system that monitors the failures in real time and diagnoses the failure progress in advance. In this research, a paper cup-forming machine diagnosis system was developed. Three major failure targets, paper deviation, temperature failure, and abnormal vibration, which dominantly affect the manufacturing process when they occur, were monitored and diagnosed. To evaluate the developed diagnosis system, extensive experiments were performed with the actual data gathered from the paper cup-forming machine. Furthermore, the desired system validation was obtained. The proposed system is expected to anticipate and prevent serious promising failures in advance and lower the final defect rate considerably.

• Journal of the Korean institute of surface engineering
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• v.54 no.6
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• pp.348-356
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• 2021

In this study, optical diagnosis of plasma was performed for nitrogen doping in graphene using a horizontal inductively coupled plasma (ICP) system. Graphene was prepared by mechanical exfoliation and the ICP system using nitrogen gas was ignited for plasma-induced and defect-suppressed nitrogen doping. In order to derive the optimum condition for the doping, plasma power, working pressure, and treatment time were changed. Optical emission spectroscopy (OES) was used as plasma diagnosis method. The Boltzmann plot method was adopted to estimate the electron excitation temperature using obtained OES spectra. Ar ion peaks were interpreted as a reference peak. As a result, the change in the concentration of nitrogen active species and electron excitation temperature depending on process parameters were confirmed. Doping characteristics of graphene were quantitatively evaluated by comparison of intensity ratio of graphite (G)-band to 2-D band, peak position, and shape of G-band in Raman profiles. X-ray photoelectron spectroscopy also revealed the nitrogen doping in graphene.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.6
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• pp.594-602
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• 2022

Cast-resin transformers are widely installed in various electrical power systems because of their low operating cost and low influence on external environmental factors. However, when they have an internal defect during the manufacturing process or operation, a partial discharge (PD) occurs, and eventually destroys the insulation. In this paper, a Rogowski-type PD sensor was studied to replace commercial PD sensors used for the insulation diagnosis of power apparatus. The proposed PD sensor was manufactured with four different types of PCB-based winding structures, and it was analyzed in terms of the detection characteristics for standard calibration pulses and the changes of the output voltage according to the distance. The output increased linearly in accordance with the applied discharge amount. It was confirmed that the hexagon structure sensor had the highest sensitivity, because the winding cross-sectional area of the sensor was larger than others. In addition, as the distance from the defect increased, the output voltage of the sensors decreased by 7.32% on average. It was also confirmed that the attenuation rate according to the distance decreased as the input discharge amount increased. For the application of this new type sensor, PD electrode system was designed to simulate the void defect. Waveforms and PRPD patterns measured by the proposed PD sensors at DIV and 120% of DIV were the same as the results measured by MPD 600 based on IEC 60270. The proposed PD sensors can be installed on the inner wall of the transformer tank by coating its surfaces with a non-conductive material; therefore, it is possible to detect internal defects more effectively at a closer distance from the defect than the conventional sensors.

• Journal of Mechanical Science and Technology
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• v.16 no.12
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• pp.1604-1612
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• 2002

Rollers in the continuous process systems are ones of key components that determine the quality of web products. The condition of rollers (e.g. eccentricity, runout) should be consistently monitored in order to maintain the process conditions (e.g. tension, edge position) within a required specification. In this paper, a new diagnosis algorithm is suggested to detect the defective rollers based on the frequency analysis of web tension signals. The kernel of this technique is to use the characteristic features (RMS, Peak value, Power spectral density) of tension signals which allow the identification of the faulty rollers and the diagnosis of the degree of fault in the rollers. The characteristic features could be used to train an artificial neural network which could classify roller conditions into three groups (normal, warning, and faulty conditions) The simulation and experimental results showed that the suggested diagnosis algorithm can be successfully used to identify the defective rollers as well as to diagnose the degree of the defect of those rollers.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.5
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• pp.1347-1354
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• 1994

This paper deals with the experimental research, including measurement and analysis and field survey, on the causes of occurring noise defective gear boxes in hoist production plant in order to reduce the fraction of their occurrence. In this reserch following investigations are performed : measurement and gear-boxes, examination of each machining process of production, measurement and analysis of dimensional accuracy of each part, comparative vibration test with exchanging inaccurate parts. From these investigations, it is found that the machining accuracy of pinion gear tooth thickness is the most sensitive factor of noise problem. By maintaining the tooth thickness error within 0.05 mm tolerance in the gear cutting process, the fraction of noise defective gear-boxes are greatly reduced to less than 2%, where the usual rate of it has been 20-50%.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.9-17
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• 2023

In this paper, we propose a one-class vibration anomaly detection system for bearing defect diagnosis. In order to reduce the economic and time loss caused by bearing failure, an accurate defect diagnosis system is essential, and deep learning-based defect diagnosis systems are widely studied to solve the problem. However, it is difficult to obtain abnormal data in the actual data collection environment for deep learning learning, which causes data bias. Therefore, a one-class classification method using only normal data is used. As a general method, the characteristics of vibration data are extracted by learning the compression and restoration process through AutoEncoder. Anomaly detection is performed by learning a one-class classifier with the extracted features. However, this method cannot efficiently extract the characteristics of the vibration data because it does not consider the frequency characteristics of the vibration data. To solve this problem, we propose an AutoEncoder model that considers the frequency characteristics of vibration data. As for classification performance, accuracy 0.910, precision 1.0, recall 0.820, and f1-score 0.901 were obtained. The network design considering the vibration characteristics confirmed better performance than existing methods.