• Journal of the Korea institute for structural maintenance and inspection
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• v.8 no.4
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• pp.123-133
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• 2004

The evaluation standard method of weld zone in rails is not exhibited in case of the domestic and the outside about ultrasonic inspection method. therefore, practical affairs a mans on the ground know very little about evaluation method of pass and failure. This paper discuss about ultrasonic exploration of weld zone in railway rails to know practical affairs a mans that the first, "problem and improvement direction of domestic track construction specifications applied according to a place ordering" and the second, "the method applied of ultrasonic exploration test of weld zone in railway rails".

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.1
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• pp.71-77
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• 2010

The concrete structure's durability and integrity is reduced owing to various deterioration phenomena. Therefore, it is important to prevent the deterioration phenomena. This study inquired into the various experimental results of specimens with different dilution concentration and impregnation time by the each solution to present the economic and efficient using method of the inorganic surface penetration agents. As a results, the reasonable dilution concentration and impregnation time of colloidal silica solutions are 15% and 5 minute and for the sodium alumina silicate solutions are 17% and 10 second.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.1
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• pp.165-174
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• 2010

In this paper, a two-phase structural health monitoring system using acceleration response signatures are presented to firstly alarm the change in structural condition and to secondly detect the changed location for full-scale concrete girder bridges. Firstly, Mihocheon Bridge which is a two-span continuous concrete girder bridge is selected as the target structure. The dynamic response features of Mihocheon Bridge are extracted by forced vibration test using bowling ball. Secondly, the damage alarming occurrence and the damage localization techniques are selected to design two-phase structural health monitoring system for Mihocheon Bridge. As the damage alarming techniques, auto-regressive model using time-domain signatures, correlation coefficient of frequency response function and frequency response ratio assurance criterion are selected. As the damage localization technique, modal strain energy-based damage index method is selected. Finally, the feasibility of two-phase structural health monitoring systems is evaluated from static loading tests using a dump truck.

• Journal of the Korean Society for Railway
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• v.20 no.2
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• pp.223-233
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• 2017

The railway turnout system is one of the most important systems that set train routes. Turnout system integrity should be guaranteed for robust train operation. To diagnose the turnout system status, LVDT and accelerometers are installed on a turnout system in a high speed line. The LVDT and accelerometers produce signals containing physical meaning of the turnout systems. The LVDT produces the displacement of the rail gauge and vibration when point moving or a train passes on turnout systems and the accelerometer produces impact forces induced by wheel sets. We performed data extraction from the measured signals and parameterized the extracted signals into meaningful quantities. The parameters are used for classifying whether the turnout status is normal. We proposed two methods for the classification, one uses probabilistic distribution and the other artificial neuron networks. The probabilistic distribution is used for the parameter being classified by the quantities and the artificial neuron networks for the form classification. Finally, we show how to learn the normal status of a turnout system.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.4
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• pp.396-409
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• 2004

Diagnosis f structural integrity is the basis for correct treatment of and countermeasures against progressive structural abnormalities. An exact diagnosis is at present the most reliable means for determining the soundness of structures during power plant operations. Acoustic emission(AE) technology has recently strengthened its application base, and practitioners' understanding of the technique's fundamentals. This paper presents the results of a survey and assessment on AE monitoring applications in nuclear, fossil and hydraulic power plant. The main objective of this paper was to obtain information on various applications of AE technology in electric power plant.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.3
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• pp.69-74
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• 2018

Fiber-Reinforced Cementitious Composites (FRCCs) have electrical conductivity by inserting reinforced conductive fibers into a cementitious matrix. Such characteristic allows us to utilize FRCCs for crack monitoring of a structure by measuring electrical responses without sensor installation. However, the electrical responses are often sensitively altered by temperature variation as well as crack initiation. The temperature variation may disturb crack detection on the measured electrical responses. Moreover, as sensing probes for measuring electrical reponses increase, undesired contact noises are often augmented. In this paper, a self-sensing impedance circuit is specially designed for reducing the number of sensing probes. The crack initiation and temperature variation effects on the self-sensing impedance responses of FRCCs are experimentally investigated using the self-sensing impedance circuit. The experiment results reveal that the electrical impedance response are more sensitively changed due to temperature variation than crack initiation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.2
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• pp.205-210
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• 2014

Recently, pattern recognition methods have been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov models (HMMs) and artificial neural networks (ANNs) have recently been used as pattern recognition methods in various fields. In this study, a HMM-ANN hybrid method for the fault diagnosis of a mechanical system is introduced, and a rotating wind turbine blade with a crack is selected for fault diagnosis. The existence, location, and depth of said crack are identified in this research. For improving the diagnostic accuracy of the method in spite of the presence of noise, a moment with a few specific frequencies is applied to the structure.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.995-996
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• 2013

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.293-294
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• 2014

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.445-454
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• 2023

Recently, various fault diagnosis studies are being conducted utilizing data from collaborative robots. Existing studies performing fault diagnosis on collaborative robots use static data collected based on the assumed operation of predefined devices. Therefore, the fault diagnosis model has a limitation of increasing dependency on the learned data patterns. Additionally, there is a limitation in that a diagnosis reflecting the characteristics of collaborative robots operating with multiple joints could not be conducted due to experiments using a single motor. This paper proposes an LSTM diagnostic model that can overcome these two limitations. The proposed method selects representative normal patterns using the correlation analysis of vibration and current data in single-axis and multi-axis work environments, and generates residual patterns through differences from the normal representative patterns. An LSTM model that can perform gear wear diagnosis for each axis is created using the generated residual patterns as inputs. This fault diagnosis model can not only reduce the dependence on the model's learning data patterns through representative patterns for each operation, but also diagnose faults occurring during multi-axis operation. Finally, reflecting both internal and external data characteristics, the fault diagnosis performance was improved, showing a high diagnostic performance of 98.57%.