• Steel and Composite Structures
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• v.49 no.6
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• pp.701-711
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• 2023

In order to fully reflect variation characteristics of composite concrete dam health state, the monitoring data is applied to diagnose composite concrete dam health state. Composite concrete dam lesion development to wreckage is a precursor, and its health status can be judged. The monitoring data are generally non-linear and unsteady time series, which contain chaotic information that cannot be characterized. Thus, it could generate huge influence for the construction of monitoring models and the formulation of corresponding health diagnostic indicators. This multi-scale diagnosis process is from point to whole. Chaotic characteristics are often contained in the monitoring data. If chaotic characteristics could be extracted for reflecting concrete dam health state and the corresponding diagnostic indicators will be formulated, the theory and method of diagnosing concrete dam health state can be huge improved. Therefore, the chaotic characteristics of monitoring data are considered. And, the extracting method of the chaotic components is studied from monitoring data based on fuzzy dynamic cross-correlation factor method. Finally, a method is proposed for formulating composite concrete dam health state indicators. This method can effectively distinguish chaotic systems from deterministic systems and reflect the health state of concrete dam in service.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.472-477
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• 2003

Spindle state monitoring is getting more and more important according to the technology trend of spindle that is accurate and automated. Spindle state monitoring is to measure the state of rotation vibrations. The spindle rotation error motion detected by sensing device includes rotation object's unbalance, external forced vibrations, shape error of spindle, as well as measuring error of monitoring device. In this paper, we have inspected the runout characteristics. Also, we introduce the way to exclude the runout element that appear while you monitor a spindle state.

• Earthquakes and Structures
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• v.14 no.2
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• pp.143-153
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• 2018

Correlation among different factors must be considered for selection of influencing factors in safety monitoring of high dam including positive correlation of variables. Therefore, a new factor selection method was constructed based on Copula entropy and mutual information theory, which was deduced and optimized. Considering the small sample size in high dam monitoring and distribution of daily monitoring samples, a computing method that avoids causality of structure as much as possible is needed. The two-dimensional normal information diffusion and fuzzy reasoning of pattern recognition field are based on the weight theory, which avoids complicated causes of the studying structure. Hence, it is used to dam safety monitoring field and simplified, which increases sample information appropriately. Next, a complete system integrating high dam monitoring and uncertainty prediction method was established by combining Copula entropy theory and information diffusion theory. Finally, the proposed method was applied in seepage monitoring of Nuozhadu clay core-wall rockfill dam. Its selection of influencing factors and processing of sample data were compared with different models. Results demonstrated that the proposed method increases the prediction accuracy to some extent.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.2
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• pp.84-91
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• 2014

This paper introduces the application of supervisory monitoring system for substation automation based on IEC 61850. The objective of proposed system is detection of such a malfunction or degradation of devices. The supervisory monitoring procedure consists of a two step - topology processor and state estimation. The topology processor using artificial intelligence is a preprocessing step of state estimation. Topology processor identifies the topology structure of switches in substation and detects an error of ON/OFF state data. The state estimation is an algorithm that minimizes an error between optimal estimation values and real values. The proposed system is applied to standard digital substation based on IEC 61850 for performance verification.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.1
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• pp.38-43
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• 2012

For safe and stable operations of power plants, it is essential to monitor closely crucial measurement values related to power plant trips. In this paper, an intelligent power plant operating state monitoring technique enabling the operating crew member to monitor conveniently the status of the important measurement values and to perceive almost instantly the significance of the implications of those measurement values is developed. The proposed technique is called a "POST(Plant Operating State Tracking) Chart" technique and provides the foundations in developing an intelligent and integrated power plant operating state monitoring support system called the "P-OASIS"(Plant Operation Assessment and Support Intelligent System). The P-OASIS is applied to a thermal power plant of 500[MW] capacity and exhibited impressive performances.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.6
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• pp.133-140
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• 1999

In this study, a monitoring method to detect chip disposal state in drilling system based on neural network was proposed and its performance was evaluated. If chip flow is bad during drilling, not only the static component but also the fluctuation of dynamic component of drilling. Drilling torque is indirectly measured by sensing spindle motor power through a AC spindle motor drive system. Spindle motor power being measured drilling, four quantities such as variance/mean, mean absolute deviation, gradient, event count were calculated as feature vectors and then presented to the neural network to make a decision on chip disposal state. The selected features are sensitive to the change of chip disposal state but comparatively insensitive to the change of drilling condition. The 3 layerd neural network with error back propagation algorithm has been used. Experimental results show that the proposed monitoring system can successfully recognize the chip disposal state over a wide range of drilling condition even though it is trained under a certain drilling condition.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.30
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• pp.107-116
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• 1994

This paper formulates a dynamic monitoring and control model with a machine state by quality variations in a single lot production system. A monitoring model is based on estimate of machine state obtained using control theory. The model studied in this paper has a great advance from a point of view the combination between quality control (Sampling, Control Chart) and automatic control theory, and can be extended in a several ways.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.6 s.171
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• pp.109-117
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• 2005

In case of fine machining processes, the cutting state monitoring by a skilled operator is impossible because the physical changes generated during fine machining are very weak. To realize the high efficient and precise fine machining, it is necessary to develop the sensor based monitoring system which is able to detect the fine changes of cutting state. In this paper, the fine acoustic emission monitoring system is developed to monitor the state of the fine machining process. The developed system consists of the AE sensor and the AE signal processing unit. And this has the high-sensitivity and bandwidth which can detect fine AE signal generated during fine machining process. In order to investigate the feasibility of the developed system, evaluation experiments were performed in the fine fixed-abrasive machining processes such as polishing and glass ferrule slicing. Experimental results show that the developed monitoring system possesses an excellent real-time monitoring capability at fine machining processes.

• Structural Monitoring and Maintenance
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• v.3 no.1
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• pp.1-32
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• 2016

This paper describes the backgrounds, motivations and recent history of structural health monitoring (SHM) developments to various types of engineering structures. Extensive applications of SHM technologies in bridges, high-rise buildings, sport avenues, offshore platforms, underground structures, dams, etc. in mainland China are summarily categorized and listed in tables. Sensors used in implementations, their deployment, damage identification strategies if applicable, preliminary monitoring achievements and experience are presented in the lists. Finally, existing problems and promising research efforts in civil SHM are discussed, highlighting challenges and future trends.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2D
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• pp.163-169
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• 2010

Construction monitoring and monitoring industry have been rapidly developed with advance of IT industry and telecommunication. However institutional, legal supports that need to settle the construction monitoring are practically inadequate. So development plans that are reflected enquete results about the present state of construction monitoring are considered. A way that consider establishment Korea construction monitoring association and society, deal with relevant law appointing monitoring industry, consider enactment and revision of construction monitoring design standard and specifications, appoint practical construction monitoring educational institution, appoint an efficiency test specialty institution of monitoring equipment, consider the term of guarantee of monitoring equipment consider enactment and revision of estimating cost of construction measurement, consider ratio of monitoring equipment loss for developing construction monitoring and monitoring industry are proposed.