• 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.

• Smart Structures and Systems
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• v.24 no.6
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• pp.693-707
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• 2019

This paper proposes an integrated safety assessment method that can take multiple sources data into consideration based on a data fusion approach. Data cleaning using the Kalman filter method (KF) was conducted first for monitoring data from each sensor. The inclination data from the four tilt sensors of the same monitoring section have been associated to synchronize in time. Secondly, the finite element method (FEM) model was established to physically correlate the external forces with various structural responses of the shield tunnel, including the measured inclination. Response surface method (RSM) was adopted to express the relationship between external forces and the structural responses. Then, the external forces were updated based on the in situ monitoring data from tilt sensors using the extended Kalman filter method (EKF). Finally, mechanics parameters of the tunnel lining were estimated based on the updated data to make an integrated safety assessment. An application example of the proposed method was presented for an urban tunnel during a nearby deep excavation with multiple source monitoring plans. The change of tunnel convergence, bolt stress and segment internal forces can also be calculated based on the real time deformation monitoring of the shield tunnel. The proposed method was verified by predicting the data using the other three sensors in the same section. The correlation among different monitoring data has been discussed before the conclusion was drawn.

• Journal of Korean Society for Atmospheric Environment
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• v.3 no.1
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• pp.47-54
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• 1987

A new method for designing air pollution monitoring newtork is presented in this study. In this method, the magnitudes and the correlation coefficients of predicted concentrations in each grid points are examined and the monitoring stations are assigned to those stations which cover the damage cost the most. This method was applied to the Ulsan-Onsan Industrial Complex. This method turned out to be much more efficient than the method of TM coordinates and the method of concentric circles prescribed in the Standard Methods for Pollution Measurement as well as the existing monitoring system established in the area. The 21 stations selected by the method of TM coordinates could cover only 64.4% of the damage cost in the area, the 16 stations by the method of concentric circles 72.1%, and the existing 21 stations 67.8%, while 11 stations were enough to cover 90% of the damage cost in the area with this method. It also was found that this method required only 24 stations to cover the entire area.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.6
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• pp.689-698
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• 2007

Particular attention is paid to the validity of PM-10 measurement using beta attenuation method. In this study, the inter-comparison studies were undertaken for PM-10 sampler with different measuring methods. The three beta attenuation monitoring instruments (BAM1020, FH C-14, and DIGS-150) and filter based gravitational method using High Volume Air Sampler were collocated and mass concentrations were compared from August to November, 2006. The results of the investigation show that beta attenuation monitoring instruments are comparable with gravitational method within 10% of measurement error. For specific conditions under high humidity and high concentrations, the inter-comparison results also showed a relatively good agreement and this can be explained as the effect of inlet heating of beta attenuation instrument. Subsequently, this study shows that beta attenuation monitoring instruments can be equivalently used with reasonable validity when compared gravimetric method.

• Tribology and Lubricants
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• v.36 no.6
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• pp.307-314
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• 2020

In this paper, studies on application cases of machine condition monitoring using oil sensors are reviewed. Owing to rapid industrial advancements, maintenance strategies play a crucial role in reducing the cost of downtime and improving system reliability. Consequently, machine condition monitoring plays an important role in maintaining operation stability and extending the period of usage for various machines. Machine condition monitoring through oil analysis is an effective method for assessing a machine's condition and providing early warnings regarding a machine's breakdown or failure. Among the three prevalent methods, the online analysis method is predominantly employed because this method incorporates oil sensors in real-time and has several advantages (such as prevention of human errors). Wear debris sensors are widely employed for implementing machine condition monitoring through oil sensors. Furthermore, various types of oil sensors are used in different machines and systems. Integrated oil sensors that can measure various oil attributes by incorporating a single sensor are becoming popular. By monitoring wear debris, machine condition monitoring using oil sensors is implemented for engines, automotive transmission, tanks, armored vehicles, and construction equipment. Additionally, such monitoring systems are incorporated in aircrafts such as passenger airplanes, fighter airplanes, and helicopters. Such monitoring systems are also employed in chemical plants and power plants for managing overall safety. Furthermore, widespread application of oil condition diagnosis requires the development of diagnostic programs.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.2
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• pp.40-47
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• 2003

This paper presents a new evaporative system monitoring method using a virtual HC sensor for an automotive on-board diagnosis. A development was made at providing mathematical expressions from the lambda control information to estimate the HC mass flow purged into the intake manifold from the canister for implementing a virtual HC sensor. The change of the lambda averagevalue reflected the influence of the additional fuel from purging results the sensor estimation of the purged HC amount. Based on this virtual HC sensor, a new evaporative system monitoring method was proposed comparing the amount of purged HC amount with the amount of the HC gas evaporated from the fuel tank and absorbed into the canister. Finally, the method was validated with a simulation using the data logged from the retail car.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.17 no.1
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• pp.13-22
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• 2014

Esophageal pH monitoring is considered the gold standard for the diagnosis of gastroesophageal reflux disease because of the normal ranges across the pediatric age range. However, this method can only detect acid reflux. Multichannel intraluminal impedance-pH (MII-pH) monitoring has recently been used for the detection of bolus reflux in infants and children. This method allows for the detection of liquid, gas or mixed reflux in addition to acid, weakly acidic or weakly alkaline reflux. MII-pH monitoring can record the direction of flow and the height of reflux, which are useful parameters to identify an association between symptoms and reflux. However, the technique is limited by its high cost and the lack of normative data of MII-pH in the pediatric population. Despite certain limitations, MII-pH monitoring will become more common and gradually replace pH monitoring in the future, because pH monitoring is part of MII-pH.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.5
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• pp.225-234
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• 2018

Control flow errors are caused by the vulnerability of memory and result in system failure. Signature-based control flow monitoring is a representative method for alleviating the problem. The method commonly consists of two routines; one routine is signature update and the other is signature verification. However, in the existing signature-based control flow monitoring, monitoring target application is tightly combined with the monitoring code, and the operation of monitoring in a single thread is the basic model. This makes the signature-based monitoring method difficult to expect performance improvement that can be taken in multi-thread and multi-core environments. In this paper, we propose a new signature-based control flow monitoring model that separates signature update and signature verification in thread level. The signature update is combined with application thread and signature verification runs on a separate monitor thread. In the proposed model, the application thread and the monitor thread are separated from each other, so that we can expect a performance improvement that can be taken in a multi-core and multi-thread environment.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.11
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• pp.918-923
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• 2008

Since crack detection for laminated composites in-service is effective to improve the structural reliability of laminated composites, it have been tried to detect cracks of laminated composites by various nondestructive methods. An electric potential method is one of the widely used approaches for detection of cracks for carbon fiber composites, since the electric potential method adopts the electric conductive carbon fibers as reinforcements and sensors and the adoption of carbon fibers as sensors does not bring strength reduction induced by embedding sensors into the structures such as optical fibers. However, the application of the electric method is limited only to electrically conductive composite materials. Recently, a piezoelectric method using piezoelectric characteristics of epoxy adhesives has been successfully developed for the adhesive joints because it can monitor continuously the damage of adhesively bonded structures without producing any defects. Polymeric materials for the matrix of composite materials have piezoelectric characteristics similarly to adhesive materials, and the fracture of composite materials should lead to the fracture of polymeric matrix. Therefore, it seems to be valid that the piezoelectric method can be applied to monitoring the damage of composite materials. In this research, therefore, the feasibility study of the damage monitoring for composite materials by piezoelectric method was conducted. Using carbon fiber epoxy composite and glass fiber composite, charge output signals were measured and analyzed during the static and fatigue tests, and the effect of fiber materials on the damage monitoring of composite materials by the piezoelectric method was investigated.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1074-1081
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• 2011

As robotics systems are becoming more complex there is the need to promote component based robot development, where systems can be constructed as the composition and integration of reusable building block. One of the most important challenges facing component based robot development is safeguarding against software component failures and malfunctions. The health monitoring of the robot software is most fundamental factors not only to manage system at runtime but also to analysis information of software component in design phase of the robot application. And also as a lot of monitoring events are occurred during the execution of the robot software components, a simple data treatment and efficient memory management method is required. In this paper, we propose an efficient events monitoring and data management method by modeling robot software component and monitoring factors based on robot software framework. The monitoring factors, such as component execution runtime exception, Input/Output data, execution time, checkpoint-rollback are deduced and the detail monitoring events are defined. Furthermore, we define event record and monitor record pool suitable for robot software components and propose a efficient data management method. To verify the effectiveness and usefulness of the proposed approach, a monitoring module and user interface has been implemented using OPRoS robot software framework. The proposed monitoring module can be used as monitoring tool to analysis the software components in robot design phase and plugged into self-healing system to monitor the system health status at runtime in robot systems.