• 소성∙가공
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• 제15권1호
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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.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2005년도 금형가공,미세가공,플라스틱가공 공동 심포지엄
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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.

• 한국멀티미디어학회논문지
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• 제25권2호
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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.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2001년도 제34회 추계학술대회 개최
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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.

• 산업공학
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• 제19권1호
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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.

• 한국산학기술학회논문지
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• 제10권11호
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• pp.3115-3120
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• 2009

전기선로전환기는 열차가 진행하고자 하는 진로로 운행할 수 있도록 분기기를 전환시키는 설비로서 도시철도 및 간선철도를 비롯한 대부분의 철도에서 많이 설치되어 사용되고 있다. 이러한 전기선로전환기의 유지보수 작업대부분은 유지보수자들이 포터블 계측기를 이용하여 수작업으로 현장에서 모터 전원, 제어 전원, 표시 전원에 대한 측정을 하는 것이다. 특히 NS형 또는 NS-AM형 전기선로전환기의 경우에는 유지보수 주기가 너무 잦기 때문에 이러한 측정에 많은 인력이 요구되고 있을 뿐만 아니라 작업 도중 부주의에 의한 사상사고도 발생하고 있다. 본 논문에서는 이러한 유지보수 작업의 향상을 위해 현장에 설치된 전기선로전환기의 상태를 모니터링하는 시스템을 제안하고 있으며, 이러한 모니터링 시스템은 전기선로전환기에 대한 각종 측정값을 실시간으로 확인하여 유지보수 업무의 향상을 이룰 수 있을 것으로 기대된다.

• Smart Structures and Systems
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• 제29권1호
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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.

• 정보처리학회논문지C
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• 제18C권3호
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• pp.157-166
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• 2011

가상화 계층은 커널 보다 높은 권한 계층에서 수행되어 운영체제가 사용하고 있는 자원 정보를 모니터링 하는데 적합하다. 하지만 기존 가상화 기반 모니터링 시스템은 CPU나 메모리 사용률과 같은 기초적인 정보만을 제공하고 있다. 본 논문에서 메모리, 레지스터 GDT, IDT 그리고 시스템 콜과 같은 동적인 시스템 커널 객체를 모니터링하기 위하여 전가상화 방식의 모니터링 시스템을 제안한다. 모니터링 시스템을 검증하기 위해 커널의 수정 없이 바로 리눅스 커널에 적용된 전가상화 방식의 KVM을 기반으로 시스템을 구현하였다. 구현된 시스템은 KVM 내부 객체에 접근하기 위한 KvmAccess 모듈, 그리고 가상머신 모니터링 결과를 외부 모듈에서도 사용할 수 있도록 API를 제공하였다. 구현된 모니터링 시스템의 성능을 측정한 결과 1초 주기로 시스템을 모니터링을 하더라도 0.37% 정도의 CPU 점유율을 차지하여 그 성능 부하가 아주 작았다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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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.

• 한국기계가공학회지
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• 제2권3호
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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.