• Smart Structures and Systems
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• v.10 no.3
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• pp.271-298
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• 2012

Researchers have made significant progress in recent years towards realizing effective structural health monitoring (SHM) utilizing wireless smart sensor networks (WSSNs). These efforts have focused on improving the performance and robustness of such networks to achieve high quality data acquisition and distributed, in-network processing. One of the primary challenges still facing the use of smart sensors for long-term monitoring deployments is their limited power resources. Periodically accessing the sensor nodes to change batteries is not feasible or economical in many deployment cases. While energy harvesting techniques show promise for prolonging unattended network life, low power design and operation are still critically important. This research presents the WiSeMote: a new, fully integrated ultra-low power wireless smart sensor node and a flexible base station, both designed for long-term SHM deployments. The power consumption of the sensor nodes and base station has been minimized through careful hardware selection and the implementation of power-aware network software, without sacrificing flexibility and functionality.

• IEMEK Journal of Embedded Systems and Applications
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• v.2 no.2
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• pp.101-106
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• 2007

Recently, according as USN application service models have been integrated each other and have become various, the need on the development of monitoring programs corresponding each service model has been risen steadily. However, if we stick to the traditional method developing independently each service model, we might pay unnecessary expenses. In this paper, we propose the monitoring program authoring tool for USN. Furthermore, we introduce an implementing example of the forest fire prevention monitoring system for verification of the proposed tool. The tool adopted the framework based programming and the S/W reusability techniques for minimizing waste of redeveloping cost.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.30-37
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• 1995

The mechanical seals, which are installed in rotating machines like pump and compressor, are gengrally used as sealing devices in the many fields of industries. The failure of mechanical seals such as leakage,fast and severe wear, excessive torque, and squeaking results in big problems. To monitor the failure of mechanical seals and to propose the proper monitoring techniques with artificial neural network, sliding wear experiments were conducted. Torque and temperature of the mechanical seals were measured during experiments. Optical microstructure was observed for the wear processing after every 10 minute sliding at rotation speed of 1750 rpm and scanning electron microscopy was also observed. During the experiment, the variation of torque and temperature that meant an abnormal phenomenon, was observed. That experimental data recorded were applied to the developed monitoring system with artificial neural network. This study concludes that torque and temperature of mechanical seals wil be used to identify and to monitor the condition of sliding motion of mechanical seals. An availability to monitor the mechanical seal failure with artificial neural network was confirmed.

• Structural Monitoring and Maintenance
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• v.1 no.1
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• pp.89-110
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• 2014

Structural Health Monitoring (SHM) is an effective alternative to conventional inspections which are time-consuming and subjective. SHM can detect damage early and reduce maintenance cost and thereby help reduce the likelihood of catastrophic structural events to infrastructure such as bridges. After reviewing the Damage Index Method (DIM), an Iterative Damage Index Method (IDIM) is proposed to improve the accuracy of damage detection. These two damage detection techniques are compared based on damage on two structures, a simply supported beam and a pedestrian bridge. Compared to the traditional damage detection algorithm, the proposed IDIM is shown to be less arbitrary and more accurate.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1069-1074
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• 2008

Tool condition monitoring (TCM) is crucial for improvement of productivity in manufacturing process. However, TCM techniques have not been applied to monitor tool failure in an industrial gear shaving application. Therefore, this work studied a statistical TCM method for monitoring gear shaving tool condition. The method modeled the shaving process using beta probability distribution in order to extract the effective features. Modeling includes rectifying for converting a bi-modal distribution into a unimodal distribution, estimating parameters of beta probability distribution based on method of moments. The usefulness of features obtained from the proposed method was evaluated and discussed.

• Korean Journal of Hydrosciences
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• v.10
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• pp.47-58
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• 1999

Protection of groundwater resources from contamination has been of increasing concern throughout the past decades. In practice, however, groundwater monitoring is performed based on the experience and intuition of experts or on the convenience. In dealing with groundwater contamination, we need to know what contaminants have the potential to threat the water quality and the distribution and concentration of the plumes. Monitoring of the subsurface environment through remote geophysical techniques or direct sampling from wells can provide such information. Once known, the plume can be properly menaged. Evaluation of existing methodologies for groundwater monitoring network design revealed that one should select an appropriate design method based on the purpose of the network and the avaliability of field information. Integer programming approach, one of the general purpose network design tools, and a cost-to-go function evaluation approach for special purpose network design were tested for field applicability. For the same contaminated aquifer, two approaches resulted in different well locations. The amount of information, however, was about the same.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.950-955
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• 2003

This paper introduces an enhanced condition monitoring and diagnosis system recently developed for rotating machinery. In the system, the data aquisition/monitoring signal processing, machine condition classifier, case-based reasoning and demonstration modules are effectively integrated with user-friendliness so that machine operators can easily monitor and diagnose the status of rotating machinery in operation. Some of the new features include the directional spectrum, case-based reasoning and neural network techniques. And the demonstrator modules for fault diagnosis of a Bear driving system and for basic understanding of the rotor dynamics are provided to help the potential users better understand the system.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1191-1200
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• 2008

Recently classroom monitoring is becoming important since the lecture is being held in the classroom and academic institutions are interested in the quality assurance. Some institutions have adopted ISO 9000 systems and constructed monitoring system through measurement, analysis and improvement. In this study quality assurance problems in academic institutions and the requirements of ISO 9001:2000 will be briefly discussed. Next we will investigate how to monitor the lecture in the classroom(in-class) using statistical process control techniques such as control charts. Then case study will be given to illustrate the technique to use appropriate statistics. Finally how to monitor the learning process during in-class and after-class will be proposed.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.1
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• pp.124-130
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• 2014

In highly reliable and durable systems, failures due to aging might result in catastrophes. Aging monitoring techniques to prevent catastrophes by predicting such a failure are required. This paper presents a scan-based on-line aging monitoring scheme which monitors aging during normal operation and gives an alarm if aging is detected so that the system users take action before a failure occurs. We illustrate our modified scan chain architecture and aging monitoring control method. Experimental results show our simulation results to verify the functions of the proposed scheme.

• Smart Structures and Systems
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• v.14 no.2
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• pp.129-144
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• 2014

In this paper, a fuzzy inference system and an analytical hierarchy process-based online evaluation technique is developed to monitor the condition of the 32-km Donghai Bridge in Shanghai. The system has 478 sensors distributed along eight segments selected from the whole bridge. An online evaluation subsystem is realized, which uses raw data and extracted features or indices to give a set of hierarchically organized condition evaluations. The thresholds of each index were set to an initial value obtained from a structure damage and performance evolution analysis of the bridge. After one year of baseline monitoring, the initial threshold system was updated from the collected data. The results show that the techniques described are valid and reliable. The online method fulfills long-term infrastructure health monitoring requirements for the Donghai Bridge.