• 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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.500-501
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• 2007

Automation systems for higher performance and efficiency in railway electrification systems are driving ever more demanding needs for new condition monitering systems which can consist of sensors connected to the substation and catenary systems. This paper reviews the recently developed condition monitering system, based on a IP network-based multi-agent system, wireless communications and sensor networks for railway electrification system. A new concept for information management, condition monitoring and control of power transmission are considered as railway automation in electrification system.

• 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.

• Smart Structures and Systems
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• v.20 no.2
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• pp.139-150
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• 2017

The structural strain plays a significant role in structural condition assessment of in-service bridges in terms of structural bearing capacity, structural reliability level and entire safety redundancy. Therefore, it has been one of the most important parameters concerned by researchers and engineers engaged in structural health monitoring (SHM) practices. In this paper, an SHM system instrumented on the Jiubao Bridge located in Hangzhou, China is firstly introduced. This system involves nine subsystems and has been continuously operated for five years since 2012. As part of the SHM system, a total of 166 fiber Bragg grating (FBG) strain sensors are installed on the bridge to measure the dynamic strain responses of key structural components. Based on the strain monitoring data acquired in recent two years, the strain-based structural condition assessment of the Jiubao Bridge is carried out. The wavelet multi-resolution algorithm is applied to separate the temperature effect from the raw strain data. The obtained strain data under the normal traffic and wind condition and under the typhoon condition are examined for structural safety evaluation. The structural condition rating of the bridge in accordance with the AASHTO specification for condition evaluation and load and resistance factor rating of highway bridges is performed by use of the processed strain data in combination with finite element analysis. The analysis framework presented in this study can be used as a reference for facilitating the assessment, inspection and maintenance activities of in-service bridges instrumented with long-term SHM system.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.54-60
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• 2000

Research about tool condition monitoring has been done until now for product automation and unmaned system. But it is hard to apply it to the industrial field due to its cost and reliability. This paper presents the new method of tool wear measurement using Marpos gauge. This is a kind of touch sensor, so its cost is lower than vision system. And it is not affected by dust and illumination, which are important in vision system. This proposed method use tool clearance angle to measure flank wear. Experimental results compared with vision system shows that this method is available for tool condition monitoring system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.11
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• pp.1732-1736
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• 2012

To increase speed up of train, in the field of catenary system, it is necessary to develop of new monitoring methods for dynamic interaction between pantograph and contact wire. Also, there is a need to develop technologies that constantly measure are from various railway structure such as uplift of contact wire, vibration of catenary, dynamic strain of contact line in tunnel. In this paper condition monitoring systems for dynamic performance of catenary systems in tunnel were proposed. An advanced method and results of field tests using high speed camera for monitoring of vertical upward movement of the grooved contact wire due to the force produced from the pantograph were presented. The proposed uplift measurement system of contact wire is expected to enhance precision of current collection quality performance assessment methods at high-speed lines.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.6_spc
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• pp.651-659
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• 2016

Condition monitoring (CM) encounters a large data problem due to sensors that measure vibration data with a continuous, and sometimes, high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate the efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer samples compared to traditional sampling methods. For the experiments a built-in rotating system was used and all data were compressively sampled to obtain compressed data. Optimal signal features were then selected without the reconstruction process and were used to detect and classify damage. The experimental results show that the proposed method could improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.148-152
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• 2000

Detection of tool wear is very important in automated manufacturing. This paper presents tool condition monitoring system based on the wavelet analysis of the AC servo motro current in drilling and milling process. The current measurement system is relatively simple and its mounting will not affect machining operations. The discrete wavelet transform was used to decompose the current signal of a spindle AC servo motor in time - frequency domain. The feature vectors were extracted from the decomposed signals and compared for normal and wear condition. The results show the possibility for the effective application of wavelet analysis to tool condition monitoring.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.3
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• pp.136-142
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• 2008

Mobile robots can offer services like intelligent monitoring in an indoor environment using network connection with remote users. In this paper, we designed and developed a networked security robot system with various sensors, such as flame detector, gas detector, sound monitoring module, and temperature sensor, etc. The robot can be accessed through a web service and the user can check the status of the environment. Using ADAMS software, we defined the motor specification for a worst-case condition of climbing over a obstacle. We applied the robot system in monitoring office condition.