• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.3
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• pp.254-262
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• 2008

The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.1
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• pp.22-30
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• 2002

ln this paper, it was reported the developed results of the monitoring unit for 72.5kY GIS which it is one part of the monitoring system for the substation that Korea Railway Company is promoting. In order to monitor the operational status of GIS, four parameters were chosen: the number of times of the circuit breaker switching, tightness of the gas-sealed units (circuit breaker / disconnecting switch / earthing switch), the number of times of the lightning arrestor operating and the leakage current of the lighting arrester. We constructed the monitoring system that can be judged the operating condition of the GIS from the signal of the suitable sensor for the purpose. Therefore, it is possible to on line monitoring for the condition of the GlS without efforts of the Periodic inspection.

• Journal of Ocean Engineering and Technology
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• v.24 no.1
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• pp.90-98
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• 2010

In this paper, vibration-based structural health monitoring methods that are suitable for caisson-type structures are examined by an experimental evaluation. To achieve the objective, four approaches are implemented. First, vibration-based structural health monitoring methods are selected to monitor the structural condition of caisson-type breakwaters. Second, a lab-scaled caisson structure is constructed to verify the selected monitoring methods. Third, the vibration characteristics are numerically analyzed using an FE model due to the change in the rubble mound condition. Finally, experimental vibration tests of the lab-scaled caisson structure are performed to monitor the vibration responses due to changes in rubble mound conditions and the performances of the selected methods are examined from the monitoring results.

• Journal of Power System Engineering
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• v.9 no.4
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• pp.181-186
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• 2005

Because of advanced control technology, Shop floor control system of various kinds of equipment and machinery need a web based remote monitoring to control process efficiently. This paper presents the development of Operation Control System. Operation Control System(OCS) is based on intelligent web monitoring, so that OCS is improved the working condition for the line of heat treatment process and the product's quality. The developed OCS is consisted of Atmega128(MCU) based on embedded system, running the data logging of the line of heat treatment process. Web monitoring system is based on CS8900 ethernet controller and TCP/IP for remote monitoring responsibility between a server and clients and controlling the progress of entire system. The developed OCS is implemented on the line of heat treatment process and shows the improvement of environment condition, product's quality and efficiency of process line.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.838-845
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• 2006

Characterization of sediment in the riverbed is of importance for effective water quality management, yet have not been monitored sufficiently. This paper reports monitoring results of nutrient concentrations of sediments. Surface waters and sediments were sampled four times during rainy season at five monitoring points. Organics of overlying water were increased after high flow condition followed by decreasing tendencies. Soluble phosphorus fraction among total phosphorus was increased after high flow condition while total phosphorus was in decreasing tendencies. Monitoring result suggested that more extended monitoring scheme for flow rate, scouring velocity, and suspended material is required for analyzing relationship between water quality and sediment.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.617-620
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• 2017

The periodic monitoring of crop conditions and timely estimation of crop yield are of great importance for supporting agricultural decision-makings, as well as for effectively coping with food security issues. Remote sensing has been regarded as one of effective tools for crop condition monitoring and crop type classification. Since 2010, RDA (Rural Development Administration) has been developing technology for monitoring on crop condition using remote sensing and model. These special papers address recent state-of-the-art of remote sensing and geospatial technologies for providing operational agricultural information, such as, crop yield estimation methods using remote sensing data and process-oriented model, crop classification algorithm, monitoring and prediction of weather and climate based on remote sensing data,system design and architecture of crop monitoring system, history on rice yield forecasting method.

• Structural Monitoring and Maintenance
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• v.1 no.3
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• pp.249-271
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• 2014

This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.7
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• pp.611-617
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• 2015

In the development of a vibration-based condition monitoring system in gearbox, one of the most important research topics is a quantitative analysis and test of the effect of gear damage on vibration of gearbox. This paper presents the evaluation result of vibration condition indicator according to the gear tooth damage through the vibration test of gearbox. The dynamic load test was performed with high speed railway (KTX)'s gearbox. The vibration of gearbox was measured according to a rotational speed change with the common gear fault modes, such as pitting and tooth breakage. The characteristics and the possibility of applying of vibration condition indicator on condition monitoring system were analyzed. As a result, the value of most condition indicator is gradually increased with the severity of gear faults. The NA6 indicator shows a low variation with the rotational speed change and high sensitivity in accordance with the gear fault.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.6
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• pp.558-563
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• 2010

In this paper, the novel approach for the fault diagnosis of the bearing equipped with rotational mechanical facilities was studied. As research works, by applying the ball bearing used extensively in many industrial fields, experiments were conducted in order to propose the new prognostic method about the condition monitoring for the rotational bodies based on the condition analysis of infrared thermography. Also, by using the vibration spectrum analysis, the real time monitoring was performed. As results, it was confirmed that infrared thermography method could be adapted into monitor and diagnose the fault for bearing by evaluating quantitatively and qualitatively the temperature characteristics according to the condition of the ball bearing.

• 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.