• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.264-266
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• 2005

Recently, on-line diagnosis methods through wired and wireless networks are widely adopted in the diagnosis of industrial Electric Facilities, such as generators, transformers and motors. Also smart sensors which includes sensors, signal conditioning circuits and micro-controller in one board are widely studied in the field of condition monitoring. This paper suggests an self-powered system suitable for condition-monitoring smart sensors, which uses parasitic vibrations of the facilities as energy source. First, vibration-driven noise patterns of the electric facilities are presented. And then, an electromagnetic generator which uses mechanical mass-spring vibration resonance are suggested and designed. Finally energy consumption of the presented smart sensor, which consists of MEMS vibration sensors, signal conditioning circuits, a low-power consumption micro-controller, and a ZIGBEE wireless tranceiver, are presented. The usefulness and limits of the presented electromagnetic generators in the field of electric facility monitoring are also suggested.

• Smart Structures and Systems
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• v.7 no.2
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• pp.83-102
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• 2011

As a testbed for various structural health monitoring (SHM) technologies, a super-tall structure - the 610 m-tall Guangzhou Television and Sightseeing Tower (GTST) in southern China - is currently under construction. This study aims to explore state-of-the-art wireless sensing technologies for monitoring the ambient vibration of such a super-tall structure during construction. The very nature of wireless sensing frees the system from the need for extensive cabling and renders the system suitable for use on construction sites where conditions continuously change. On the other hand, unique technical hurdles exist when deploying wireless sensors in real-life structural monitoring applications. For example, the low-frequency and low-amplitude ambient vibration of the GTST poses significant challenges to sensor signal conditioning and digitization. Reliable wireless transmission over long distances is another technical challenge when utilized in such a super-tall structure. In this study, wireless sensing measurements are conducted at multiple heights of the GTST tower. Data transmission between a wireless sensing device installed at the upper levels of the tower and a base station located at the ground level (a distance that exceeds 443 m) is implemented. To verify the quality of the wireless measurements, the wireless data is compared with data collected by a conventional cable-based monitoring system. This preliminary study demonstrates that wireless sensing technologies have the capability of monitoring the low-amplitude and low-frequency ambient vibration of a super-tall and slender structure like the GTST.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.10
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• pp.1011-1016
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• 2015

Gas piping systems in power plants and factories are always influenced by the mechanical vibrations of rotational machines such as pumps, blowers, and compressors. Unusual vibrations in a gas piping system influence possible leakages of liquids or gases, which can lead to large explosive accidents. Real-time measurements of unusual vibrations in piping systems in situ prohibit them from being possible leakages owing to the repeated fatigue of vibrations. In this paper, a non-contact 3-dimensional measurement system that can detect the vibrations of a solid body and monitor its vibrational modes is introduced. To detect the displacements of a body, a stereoscopic camera system is used, through which the major vibration types of solid bodies (such as X-axis-major, Y-axis-major, and Z-axis-major vibrations) can be monitored. In order to judge the vibration types, an artificial neural network is used. The measurement system consists of a host computer, stereoscopic camera system (two-camera system, high-speed high-resolution camera), and a measurement target. Through practical application on a flat plate, the measured data from the non-contact measurement system showed good agreement with those from the original vibration mode produced by an accelerator.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.578-579
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• 2010

최근 내연 기관에서 엔진상태 및 잠재적인 결함의 발견을 위해 연료분사, 실린더 내 연소과정, 실린더 내부와 피스톤 마모상태, 흡 배기 밸브 및 과급기 (turbocharger) 등의 상태 모니터링을 통해서 결함에 대한 진단 및 경향관리에 대한 연구들이 진행되고 있다. 본 연구에서는 기 개발된 선박용 대형디젤엔진에 대한 상시 모니터링 시스템을 이용하여 엔진상태 및 잠재적인 결함을 진단하고자 하였으며, 일차적으로 가속도 신호를 이용하여 실린더 내 연소과정에서 정상적인 상태와 착화실패에 따른 진동 양상을 비교 분석하였다. 신호 분석 기법으로 시간-주파수 분산 기법들은 충격파 신호인 엔진 폭발신호를 분석하는데 적합하였으며, 그 기법들 중 Short Time Fourier Transform 기법과 Wavelet Transform 기법을 이용하였다.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.2
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• pp.64-70
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results. it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.84-88
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results, it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• Smart Structures and Systems
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• v.9 no.3
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• pp.253-271
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• 2012

Finite element (FE) model updating is a useful tool for global damage detection technique, which identifies the damage of the structure using measured vibration data. This paper presents the application of a finite element model updating method to detect the damage of a small-scale reinforced concrete building structure using measured acceleration data from shaking table tests. An iterative FE model updating strategy using the least-squares solution based on sensitivity of frequency response functions and natural frequencies was provided. In addition, a side constraint to mitigate numerical difficulties associated with ill-conditioning was described. The test structure was subjected to six El Centro 1942 ground motion histories with different Peak Ground Accelerations (PGA) ranging from 0.06 g to 0.5 g, and analytical models corresponding to each stage of the shaking were obtained using the model updating method. Flexural stiffness values of the structural members were chosen as the updating parameters. In model updating at each stage of shaking, the initial values of the parameter were set to those obtained from the previous stage. Severity of damage at each stage of shaking was determined from the change of the updated stiffness values. Results indicated that larger reductions in stiffness values occurred at the slab members than at the wall members, and this was consistent with the observed damage pattern of the test structure.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.345-349
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• 2007

In this study a fuzzy trend monitoring method for detecting the engine mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration. etc. Using engine condition data set as a input which generated by linear regression analysis of real engine instrument data, an application of fuzzy logic in diagnostics estimate a cause of fault in each components.

• Journal of the Korea Safety Management & Science
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• v.23 no.4
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• pp.67-72
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• 2021

To ensure the safety and functionality of a railroad bridge, maintaining the integrity of the bridge via continuous structural health monitoring is important. However, most structural integrity monitoring methods proposed to date are based on modal responses which require the extracting process and have limited availability. In this paper, the applicability of the existing damage identification method based on free-vibration reponses to time-domain deflection shapes due to moving train load is investigated. Since the proposed method directly utilizes the time-domain responses of the structure due to the moving vehicles, the extracting process for modal responses can be avoided, and the applicability of structural health evaluation can be enhanced. The feasibility of the presented method is verified via a numerical example of a simple plate girder bridge.

• Smart Structures and Systems
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• v.22 no.1
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• pp.13-25
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• 2018

Pedro $G{\acute{o}}mez$ Bosque footbridge is a slender and lightweight structure that creates a pedestrian link over the Pisuerga River, Valladolid, Spain. This footbridge is a singular stress ribbon structure with one span of 85 m consisting on a steel plate and precast concrete slabs laying on it. Rubber pavement and a railing made of stainless steel and glass complete the footbridge. Because of its lively dynamics, prone to oscillate, a simple and affordable structural health monitoring system was installed in order to continuously evaluate its structural serviceability and to estimate its modal parameters. Once certain problems (conditioning and 3D orientation of the triaxial accelerometers) are overcome, the monitoring system is validated by comparison with a general purpose laboratory portable analyzer. Representative data is presented, including acceleration magnitudes and modal estimates. The evolution of these parameters has been analysed over one-year time.