• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.4
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• pp.154-159
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• 2012

Condition monitoring(CM) is a method based on Non-destructive test(NDT). Therefore, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days also. Because its sensitivity is higher than normal accelerometers and it can detect low energy vibration signals. A machine tool consist of many parts such as the bearings, gears, process tools, shaft, hydro-system, and so on. Condition of Every part is connected with product quality finally. To increase the quality of products, condition monitoring of the components of machine tool is done completely. Therefore, in this paper, acoustic emission method is used to detect a machine fault seeded in a gearbox. The AE signals is saved, and power spectrums and feature values, peak value, mean value, RMS, skewness, kurtosis and shape factor, were determined through Matlab.

• Tribology and Lubricants
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• v.26 no.1
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• pp.73-77
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• 2010

One of the important trends for condition monitoring in the 21st century is the development of smart sensors that will permit the cost-effective continuous monitoring of key machine equipments. In this study, an integrated in-line oil monitoring sensor assigned for continuous in situ monitoring multiple parameters of oil performance is presented. The sensor estimates oil deterioration based on the information about chemical degradation, total contamination, water content of oil and oil temperature. The oil oxidation is estimated by "chromatic ratio", total contamination is measured by the changes in optical density of oil in three optical wave-bands ('Red', 'Green' and 'Blue') and water content is evaluated as relative saturation of oil by water. In order to evaluate the sensor's effectiveness, the sensor was applied to several used oil samples in steel making industry and the results were compared with those measured by standard test methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.1005-1010
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• 2000

Internet service has widely used in office automation such as, manufacturing management purchase, and material resource. Nowadays, factory automation and shop floor control system including CAD/CAM department need a web based monitoring and diagnosis to achieve global collaboration and tole-service. This raper deal with design of web based remote monitoring and diagnosis system which concerned with open architecture controller for machine tool.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.27-30
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• 2019

Factory monitoring systems are gaining importance in wide areas of industry. Especially, there have been many efforts in implementation of vibration measurement and analysis for monitoring the status of rotating machines. In this paper, a digital signal processor (DSP) based monitoring system dedicated to the vibration monitoring and analysis on rotating machines is discussed. Vibration signals are acquired and processed for the continuous monitoring of the machine status. Time domain signals and fast Fourier transform (FFT) are used for vibration analysis. All of the signal processing procedures are done in the DSP to reduce the production and maintenance cost. The developed system could also provide remote and mobile monitoring capabilities to operator via internet connection. This paper describes the overview of the functional blocks of the implemented system. Test results based on signals from small-size single phase motors are discussed for monitoring and defect diagnosis of the machine status.

• Journal of KSNVE
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• v.11 no.1
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• pp.125-131
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• 2001

There is a clearance between the parts of a machine due to design tolerance, manufacturing error, wear, looseness, or misalignment. If the clearance is large, the vibration and noise of the machine is generally large. Therefore, the analysis on the nitration and noise of a machine can tell the clearance of the machine, which reveals the condition of the machine, i.e., the existence of faults and the safety of the machine. The investigation of this kind of research should be on the basis of experimental results. A link mechanism with a clearance at a joint between the coupler and locker is made for the investigation of the condition monitoring of a machine due to clearance. The vibration and sound are measured from the link driving system during the operation. The signals are clarified using line enhancement technique. The noise removed signals are used to develop the dynamic model of the system for a model based fault diagnosis. Also this study showed that the clarified signals can be used for the calculation of the joint forces between the coupler and rocker and for the correlation between the vibration and sound levels and the clearance sizes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1516-1539
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• 2022

This article shows a set of physical information fusion IoT systems that we designed for smart buildings. Its essence is a computer system that combines physical quantities in buildings with quantitative analysis and control. In the part of the Internet of Things, its mechanism is controlled by a monitoring system based on sensor networks and computer-based algorithms. Based on the design idea of the agent, we have realized human-machine interaction (HMI) and machine-machine interaction (MMI). Among them, HMI is realized through human-machine interaction, while MMI is realized through embedded computing, sensors, controllers, and execution. Device and wireless communication network. This article mainly focuses on the function of wireless sensor networks and MMI in environmental monitoring. This function plays a fundamental role in building security, environmental control, HVAC, and other smart building control systems. The article not only discusses various network applications and their implementation based on agent design but also demonstrates our collaborative information fusion strategy. This strategy can provide a stable incentive method for the system through collaborative information fusion when the sensor system is unstable in the physical measurements, thereby preventing system jitter and unstable response caused by uncertain disturbances and environmental factors. This article also gives the results of the system test. The results show that through the CPS interaction of HMI and MMI, the intelligent building IoT system can achieve comprehensive monitoring, thereby providing support and expansion for advanced automation management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2801-2815
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• 2022

With the advent and realization of Software Defined Network (SDN) architecture, many organizations are now shifting towards this paradigm. SDN brings more control, higher scalability, and serene elasticity. The SDN spontaneously changes the network configuration according to the dynamic network requirements inside the constrained environments. Therefore, a monitoring system that can monitor the physical and virtual entities is needed to operate this type of network technology with high efficiency and proficiency. In this manuscript, we propose a real-time monitoring system for data collection and visualization that includes the Prometheus, node exporter, and Grafana. A node exporter is configured on the physical devices to collect the physical and virtual entities resources utilization logs. A real-time Prometheus database is configured to collect and store the data from all the exporters. Furthermore, the Grafana is affixed with Prometheus to visualize the current network status and device provisioning. A monitoring system is deployed on the physical infrastructure of the KOREN topology. Data collected by the monitoring system is further pre-processed and restructured into a dataset. A monitoring system is further enhanced by including machine learning techniques applied on the formatted datasets to identify the elephant flows. Additionally, a Random Forest is trained on our generated labeled datasets, and the classification models' performance are verified using accuracy metrics.

• Tribology and Lubricants
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• v.14 no.3
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• pp.74-80
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• 1998

The morphologies of the wear particles are directly indicative of wear process occuring in the machine. The analysis of wear particle morphology can therefore provide very early detection of a fault and can also ofen facilitate a dignosis. For this work, the neural network was applied to identify friction coefficient through four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris generated from the machine. The averages of these parameters were used as inputs to the network. It is shown that collect identification of friction coefficient depends on the ranges of these shape parameters learned. The various kinds of the wear debris had a different pattern characteristics and recognized relation between the friction condition and materials very well by neural network. We discuss how the network determines difference in wear debris feature, and this approach can be applied for machine condition monitoring and fault diagnosis.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2010.03b
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• pp.46-46
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• 2010

In order to detect exact corona discharge point at stator winding of a rotating machine, an inductively coupled magnetic probe has been developed, which consists of U-shaped and truncated manganese ferrite inductor as a helix. The measured current intensity is somewhat higher than commercially developed probe. It has been shown that the measured intensity of proposed probe is suitable for manual localization as to off-line stator winding monitoring of rotating machine.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.233-238
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• 2000

CNC machine tool is assembled by central processor, PLC(Programmable Logic Controller), and actuator. The sequential control of machine generally controlled by a PLC. The main fault occured at PLC in 3 control parts. In LC faults, operational fault is charged over 70%. This paper describes diagnosis model and data processing for remote monitoring and diagnosis system in machine tools with open architecture controller. Two diagnostic models based on the ladder diagram. Logical Diagnosis Model(LDM), Sequential Diagnosis Model(SDM), are proposed. Data processing structure is proposed ST(Structured Text) based on IEC1131-3. The faults from CNC are received message form open architecture controller and faults from PLC are gathered by sequential data.. To do this, CNC and PLC's logical and sequential data is constructed database.