• Smart Structures and Systems
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• v.11 no.5
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• pp.477-496
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• 2013

Due to their cost-effectiveness and ease of installation, wireless smart sensors (WSS) have received considerable recent attention for structural health monitoring of civil infrastructure. Though various wireless smart sensor networks (WSSN) have been successfully implemented for full-scale structural health monitoring (SHM) applications, monitoring of low-level ambient strain still remains a challenging problem for WSS due to A/D converter (ADC) resolution, inherent circuit noise, and the need for automatic operation. In this paper, the design and validation of high-precision strain sensor board for the Imote2 WSS platform and its application to SHM of a cable-stayed bridge are presented. By accurate and automated balancing of the Wheatstone bridge, signal amplification of up to 2507-times can be obtained, while keeping signal mean close to the center of the ADC span, which allows utilization of the full span of the ADC. For better applicability to SHM for real-world structures, temperature compensation and shunt calibration are also implemented. Moreover, the sensor board has been designed to accommodate a friction-type magnet strain sensor, in addition to traditional foil-type strain gages, facilitating fast and easy deployment. The wireless strain sensor board performance is verified through both laboratory-scale tests and deployment on a full-scale cable-stayed bridge.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1031-1053
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• 2016

The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.58-65
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• 2006

Accurate and efficient monitoring of dynamically changing environments is one of the most important requirements for ubiquitous network environments. Ubiquitous computing provides intelligent environments which are aware of spatial conditions and can provide timely and useful information to users or devices. Also, the growth of embedded systems and wireless communication technology has made it possible for sensor network environments to develop on a large scale and at low-cost. In this paper, we present the design and implementation of a monitoring system that collects, analyzes, and controls the status information of each sensor, following sensor data extracted from each sensor node. The monitoring system adopts Web technology for the implementation of a simple but efficient user interface that allows an operator to visualize any of the processes, elements, or related information in a convenient graphic form.

• Journal of Sensor Science and Technology
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• v.25 no.5
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• pp.333-336
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• 2016

In this study, a beta monitoring sensor was developed as a part of basic research for quantitative beta monitoring underwater, and its performance was evaluated using a calibration source. A beta detection sensor was manufactured by using SiPM(silicon photomultiplier) and $CaF_2$:Eu, YAG:Ce, YAP:Ce scintillator. A large-area light guide was introduced to improve beta-ray detection efficiency. As calibration sources, the Beta source $^{90}Sr$, which is the main fission product of a nuclear accident, and the gamma source $^{137}Cs$ are used. In the performance evaluation, it is confirmed that scintillator $CaF_2:Eu$ gives the highest beta-ray detection response. Compared to gamma ray, beta-ray detection responsivity and detection efficiency are verified. Therefore, this study is expected to contribute to basic research in the development of an underwater beta-ray monitoring system.

• Journal of Sensor Science and Technology
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• v.24 no.5
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• pp.299-305
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• 2015

Since the natural disasters such as floods, droughts, heat wave and cold wave are increasing, the need for risk management is necessary to minimize the damage with utilizing IT technology. Also, the monitoring services of disaster response type have been developed and applied. Recently, the open source hardware based on the signal of the sensor, or the monitoring studies have been carried. In this paper, by analyzing a low-cost open source hardware platform such as Beagle board, we examine the utilization of the hardware-based module for sensor monitoring.

• Nuclear Engineering and Technology
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• v.39 no.2
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• pp.133-142
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• 2007

The Electric Power Research Institute (EPRI) demonstrated a method for monitoring the performance of instrument channels in Topical Report (TR) 104965, 'On-Line Monitoring of Instrument Channel Performance.' This paper presents the results of several models originally developed by EPRI to monitor three nuclear plant sensor sets: Pressurizer Level, Reactor Protection System (RPS) Loop A, and Reactor Coolant System (RCS) Loop A Steam Generator (SG) Level. The sensor sets investigated include one redundant sensor model and two non-redundant sensor models. Each model employs an Auto-Associative Kernel Regression (AAKR) model architecture to predict correct sensor behavior. Performance of each of the developed models is evaluated using four metrics: accuracy, auto-sensitivity, cross-sensitivity, and newly developed Error Uncertainty Limit Monitoring (EULM) detectability. The uncertainty estimate for each model is also calculated through two methods: analytic formulas and Monte Carlo estimation. The uncertainty estimates are verified by calculating confidence interval coverages to assure that 95% of the measured data fall within the confidence intervals. The model performance evaluation identified the Pressurizer Level model as acceptable for on-line monitoring (OLM) implementation. The other two models, RPS Loop A and RCS Loop A SG Level, highlight two common problems that occur in model development and evaluation, namely faulty data and poor signal selection

• Transactions of Materials Processing
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• v.18 no.2
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• pp.144-149
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• 2009

Recently, the monitoring technology of machining process is very important to improve productivity and quality in manufacturing filed. Such monitoring technology has been performed to measurement using vibration signal, acoustic emission signal and tool dynamometer. However, micro machining is limited small-scale parts machining because micro tool is very small and weakness to generate signal in micro machining process. Therefore, this study has efficient sensing technology for real monitoring system in micro machine that is proposed to supplement a disadvantage of single-sensor by multi sensor. From experimental result, it was evaluated tool wear and cutting situation according to repetitive slot cutting condition and changing cutting condition, and it was performed monitoring spindle rpm and condition according to compare acceleration signal with current signal.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.167-173
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• 1999

This paper presents results of monitoring grinding processes with on-the-machine measurement of surface roughness by using nux ratios of scattered lights. A compact, on-the-machine surface roughness sensor. which consists of a diodelaser and several optic units. is developed. The control unit is also developed. The developed sensor together with the controller is applied for monitoring grinding processes of two different grinding machines. Experimental results show that the nux ratios and their standard deviations measured by the developed sensor over the entire ground surface are useful for monitoring grinding conditions. In particular, the sensor and the control unit are proved to be useful for monitoring grinding processes in order to detect abnormal grinding conditions and dressing time.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.315-318
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• 1997

In terms of productivity, the speed of machining process has been increasing in most of engineering part. But the tapping process does not reach at enough level compared with other machining processes because of its complicate cutting mechanism. In the high speed tapping process, the one of important elements is tool monitoring system to prevent tool breakage. This paper describes tool monitoring system by acoustic emission(AE) in the tapping process. We used 2 types of AE sensors in this test. The one is commercial sensor which is used in other machining monitoring system like polishing and the other is a self-fabricated sensor for this test. In this test we purpose to find out the frequency of AE signal in tapping process and verify the possibility of applying AE sensor in in-process tapping monitoring system. Also grasp of characteristic of tapping process by AE signal is handled.

• Journal of information and communication convergence engineering
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• v.7 no.3
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• pp.258-262
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• 2009

A Practical application of environmental monitoring system based on wireless sensor node network with the core of embedded system STR711FR2 microprocessor is presented in the paper. The adaptable and classifiable wireless sensor node network is used to achieve the data acquisition and multi-hop wireless communication of parameters of the monitoring base station environment including repeaters. The structure of the system is proposed and the hardware architecture of the system is designed, and the system operating procedures is proposed. As a result of field test, designed hardware platform operated with 50kbps bit rate and 5MHz channel spacing at 2040Hz. The wireless monitoring system can be managed and swiftly retreated without support of base station environmental monitoring.