• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.19-21
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• 2001

Induction motors are a critical component of many industrial machines and are frequently integrated in commercial equipment. The many economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system(motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses the motor's condition. The diagnostic system is constructed by using LabVIEW of National Instruments.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2222-2224
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• 2002

Induction motors are critical components of many industrial machines and are frequently integrated in commercial equipment. The heavy economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motors supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the artificial neural network, and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2172-2174
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• 2001

Induction motors are a critical component of many industrial machines and are frequently integrated in commercial equipment. The many economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses the motor's condition. The diagnostic system is constructed by using LabVIEW of National Instruments.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.65.1-65
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• 2001

Induction motors are a critical component of many industrial machines and are frequently integrated in commercial equipment. The many economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system motors, the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis MCSA method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyzes the motor´s supply current, since this diagnoses the motor´s condition. The diagnostic system is constructed by using LabVIEW of National Instruments.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.1
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• pp.37-44
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• 2016

Optical fiber temperature sensing systems have incomparable advantages over traditional electrical-cable-based monitoring systems. However, the fiber optic interrogators and sensors have often been rejected as a temperature monitoring technology in real-world industrial applications because of high cost and over-specification. This study proposes a multiplexed fiber optic temperature monitoring sensor system using an economical Optical Time-Domain Reflectometer (OTDR) and Hard-Polymer-Clad Fiber (HPCF). HPCF is a special optical fiber in which a hard polymer cladding made of fluoroacrylate acts as a protective coating for an inner silica core. An OTDR is an optical loss measurement system that provides optical loss and event distance measurement in real time. A temperature sensor array with the five sensor nodes at 10-m interval was economically and quickly made by locally stripping HPCF clad through photo-thermal and photo-chemical processes using a continuous/pulse hybrid-mode laser. The exposed cores created backscattering signals in the OTDR attenuation trace. It was demonstrated that the backscattering peaks were independently sensitive to temperature variation. Since the 1.5-mm-long exposed core showed a 5-m-wide backscattering peak, the OTDR with a spatial resolution of 40 mm allows for making a sensor node at every 5 m for independent multiplexing. The performance of the sensor node included an operating range of up to $120^{\circ}C$, a resolution of $0.59^{\circ}C$, and a temperature sensitivity of $-0.00967dB/^{\circ}C$. Temperature monitoring errors in the environment tests stood at $0.76^{\circ}C$ and $0.36^{\circ}C$ under the temperature variation of the unstrapped fiber region and the vibration of the sensor node. The small sensitivities to the environment and the economic feasibility of the highly multiplexed HPCF temperature monitoring sensor system will be important advantages for use as system-integrated temperature sensors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.11
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• pp.6399-6405
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• 2014

An external overload or impact is an important factor affecting the safety of large-scale structures. The proposal of this paper is the development of a system for detecting overload and impulse using a single PVDF film sensor. In large-scale structures, the load causes the structure to be deformed and the impulse generates vibration on the structure. Generally, low frequency deformation or bending of a structure is measured with a strain gauge and the high frequency vibration is detected by an accelerometer. On the other hand, a single sensor that can detect both deformation and vibration has not been developed. In this study, the development of a detection system integrated with a polyvinylidene fluoride (PVDF) film sensor, amplifier, and software was attempted to monitor deformation and impact through a single sensor. The system was verified by the possibility of detecting overload and impulse, and the two filtered signals of the PVDF were compared with a conventional strain gauge and an accelerometer.

• Journal of Korean Tunnelling and Underground Space Association
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• v.2 no.3
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• pp.59-69
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• 2000

Three-dimensional total station system which integrated the instrument with Target Pin and TEMS 3D software developed by SKEC R&D center was applied to a tunnel excavation for monitoring of convergence and crown settlement. The efficiency of the system was proved as the result in the aspects of exact monitoring and prediction of rock conditions ahead of the face. To monitor the behavior of tunnel lining at the maintenance stage, DOCS system was applied to the subway tunnel section. Such many effects as the vibration of sensors, verification of the system efficiency, the effect of test trains operation, the variation of temperature and the effect of high voltage was checked. Thus the management scheme for tunnel maintenance was laid out as a proposal.

• Smart Structures and Systems
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• v.25 no.1
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• pp.111-122
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• 2020

Researchers up to date have introduced several Structural Health Monitoring (SHM) techniques with varying advantages and drawbacks for each. Satellite positioning systems (GPS, GLONASS and GALILEO) based techniques proved to be promising, especially for high natural period structures. Particularly, the GPS has proved sufficient performance and reasonable accuracy in tracking real time dynamic displacements of flexible structures independent of atmospheric conditions, temperature variations and visibility of the monitored object. Tall structures are particularly sensitive to oscillations produced by different sources of dynamic actions; such as typhoons. Wind forces induce in the structure both longitudinal and perpendicular displacements with respect to the wind direction, resulting in torsional effects, which are usually more complex to be detected. To efficiently track the horizontal rotations of the in-plane sections of such flexible structures, two main issues have to be considered: a suitable sensor topology (i.e., location, installation, and combination of sensors), and the methodology used to process the data recorded by sensors. This paper reports the contributions of the measurements recorded from dual frequency GPS receivers and uni-axial accelerometers in a full-scale experimental campaign. The Canton tower in Guangzhou-China is the case study of this research, which is instrumented with a long-term structural health monitoring system deploying both accelerometers and GPS receivers. The elaboration of combining the obtained rather long records provided by these two types of sensors in detecting the torsional behavior of the tower under ambient vibration condition and during strong wind events is discussed in this paper. Results confirmed the reliability of GPS receivers in obtaining the dynamic characteristics of the system, and its ability to capture the torsional response of the tower when used alone or when they are combined with accelerometers integrated data.

• Earthquakes and Structures
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• v.22 no.2
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• pp.185-201
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• 2022

Structural Health Monitoring (SHM) is used to provide reliable information about the structure's integrity in near realtime following extreme incidents such as earthquakes, considering the inevitable aging and degradation that occurs in operating environments. This paper experimentally investigates an integrated wireless sensor network (Wi-SN) based monitoring technique for damage detection in concrete structures. An effective SHM technique can be used to detect potential structural damage based on post-earthquake data. Two novel methods are proposed for damage detection in reinforced concrete (RC) building structures including: (i) Jerk Energy Method (JEM), which is based on time-domain analysis, and (ii) Modal Contributing Parameter (MCP), which is based on frequency-domain analysis. Wireless accelerometer sensors are installed at each story level to monitor the dynamic responses from the building structure. Prior knowledge of the initial state (immediately after construction) of the structure is not required in these methods. Proposed methods only use responses recorded during ambient vibration state (i.e., operational state) to estimate the damage index. Herein, the experimental studies serve as an illustration of the procedures. In particular, (i) a 3-story shear-type steel frame model is analyzed for several damage scenarios and (ii) 2-story RC scaled down (at 1/6th) building models, simulated and verified under experimental tests on a shaking table. As a result, in addition to the usual benefits like system adaptability, and cost-effectiveness, the proposed sensing system does not require a cluster of sensors. The spatial information in the real-time recorded data is used in global damage identification stage of SHM. Whereas in next stage of SHM, the damage is detected at the story level. Experimental results also show the efficiency and superior performance of the proposed measuring techniques.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.