• Transactions of Materials Processing
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• v.15 no.1 s.82
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• pp.82-88
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• 2006

In this paper, the web-based monitoring system is developed for the effective process monitoring of mold manufacturing using web. In developed system, the cutting force for monitoring the manufacturing condition is measured using hall-sensor that is low cost and useful to be installed in a machine tool indirectly. Specially, the current of main spindle in a machine tool is converted into cutting force by various experiments. For effective web-based monitoring, the program which runs in the local computer of client is made to exchange message between a server and a client by making of ActiveX control and the result of manufacturing is shown on web-browser by Ch language. The developed system in this study is the foundation of establishing E-manufacturing in mold factory.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.09a
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• pp.121-125
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• 2005

In this paper, the web-based monitoring system is developed for the process monitoring of mold manufacturing. The cutting force is measured by hall-sensors which is low cost and useful to be installed in machine tool indirectly. Specially, the current of main spindle in machine tool is converted into cutting force by various experiments. For effective emote monitoring, the interface that is able to offer the information of current process and cutting signal to client is establish.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.197-205
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• 2022

The Cyber Physical System(CPS) is an important concept in achieving SMSs(Smart Manufacturing Systems). Generally, CPS consists of physical and virtual elements. The former involves manufacturing devices in the field space, whereas the latter includes the technologies such as network, data collection and analysis, security, and monitoring and control technologies in the cyber space. Currently, all these elements are being integrated for achieving SMSs in which we can control and analyze various kinds of producing and diagnostic issues in the cyber space without the need for human intervention. In this study, we focus on implementing a production equipment monitoring system related to building a SMS. First, we describe the development of a fog-based gateway system that links physical manufacturing devices with virtual elements. This system also interacts with the cloud server in a multimedia network environment. Second, we explain the proposed network infrastructure to implement a monitoring system operating on a cloud server. Then, we discuss our monitoring applications, and explain the experience of how to apply the ML(Machine Learning) method for predictive diagnostics.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2001.11a
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• pp.43-52
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• 2001

Condition monitoring plays a vital role since it sustains the reliable operation of industrial plant and machinery in the pursuit of economic whole life operation. In order to achieve this goal, it is needed to monitor various parameters of mechanical system such as vibration, wear, temperature, and etc., and finally to diagnosis the root causes of any possible abnormal machine condition. In this work, we constructed a rotor system where various types of functional machine failures occurred frequently in industry were induced. Characteristics of the machine failure were monitored simultaneously by the on-line measurement of vibration, wear and temperature. Result showed that these parameters responded differently to the induced functional machine failure. The availability of each parameter on effective condition monitoring was discussed in this work.

• IE interfaces
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• v.19 no.1
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• pp.43-52
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• 2006

In order to make best use of NC machine tools with minimal labor costs, they need to be in operation 24 hours a day without being attended by human operators except for setup and tool changes. Thus, unattended machining is becoming a dream of every modern machine shop. However, without a proper mechanism for real-time monitoring of the machining processes, unattended machine could lead to a disaster. Investigated in this paper are ways to using PC camera as a real-time monitoring system for unattended NC milling operations. This study defined five machining states READY, NORMAL MACHINING, ABNORMAL MACHINING, COLLISION and END-OF-MACHINING and modeled them with DEVS (discrete event system) formalism. An image change detection algorithm has been developed to detect the table movements and a flame and smoke detection algorithm to detect unstable cutting process. Spindle on/off and cutting status could be successfully detected from the sound signals. Initial experimentation shows that the PC camera could be used as a reliable monitoring system for unattended NC machining.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3115-3120
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• 2009

An electric point-machine which changes over a turnout so that it enables a train to be operated in the routine that a train is to proceed has been installed and used in most of the railways such as not only urban railways but also main-line railways. Most of the maintenance works in this electric point-machine is that the maintenance workers manually measure motor power, control power and display power in the site with the use of portable counters. Especially in case of NS or NS-AM electric point machine, the maintenance cycle is so frequent that during the operation casualty accidents caused by carelessness occurs as well as more staff is required for this measurement. In this paper we propose a system for monitoring the state of electric point machine installed in the site to improve maintenance efficiency and it is expected that this monitoring system confirms any measured values on electric point machines in real-time and improves maintenance efficiency.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• The KIPS Transactions:PartC
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• v.18C no.3
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• pp.157-166
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• 2011

The virtualization layer is executed in higher authority layer than kernel layer and suitable for monitoring operating systems. However, existing virtualization monitoring systems provide simple information about the usage rate of CPU or memory. In this paper, the monitoring system using full virtualization technique is proposed, which can monitor virtual machine's dynamic kernel object as memory, register, GDT, IDT and system call table. To verify the monitoring system, the proposed system was implemented based on KVM(Kernel-based Virtual Machine) with full virtualization that is directly applied to linux kernel without any modification. The proposed system consists of KvmAccess module to access KVM's internal object and API to provide other external modules with monitoring result. In experiments, the CPU utilization for monitoring operations in the proposed monitering system is 0.35% when the system is monitored with 1-second period. The proposed monitoring system has a little performance degradation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.121-124
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• 2003

The conventional CNC(Computerized Numerical Controller) of machine tool, which is dependent to CNC maker, was a closed architecture type. Therefore, it is impossible to implement a special user-define function to CNC. But recently, CNC is changed to OAC(Open Architecture Controller) type increasingly and the general function of CNC can be upgrade efficiently. This paper describes web service application for remote monitoring regarding the faults or machine tool with open architecture CNC. The major faults of CNC machine tool can be defined to the operational faults to be charged over 70%. Those faults are unpredictable because of being occurred without any warning. To generalize the fault diagnosis efficiently. two diagnosis models such as SF(Switching Function) and SSF(Step Switching Function) are proposed and the function of fault diagnosis is implemented to internal function of OAC. Also, to service remotely the faults of CNC machine tool. the suitable web environment is proposed and practical function is programmed to evaluate its operation on web.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.3
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• pp.13-19
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• 2003

The high speed machining technology has been improved remarkably in die/mold industry with the growth of parts and materials industries. Though the spindle speed of machine tool increases, the condition monitoring techniques of the machine tool, tool and workpiece in high speed machining ate incomplete. In tins study, efficient sensing technology in high speed machining is suggested by observing the characteristics of cutting force, gap sensor and accelerometer signal also, machinability of high-speed machining is experimentally evaluated sensing technique to monitor the machine tool and machining conditions was performed.