• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.95-113
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• 1997

This paper outlines a framework for a diagnosis of a complex system with uncertain information. Sensor validation ploys a vital role in the ability of the overall system to correctly determine the state of a system monitored by imperfect sensors. Here, emphases are put on the heuristic technology and post-processor for reasoning. Heuristic Sensor Validation (HSV) exploits deeper knowledge about parameter interaction within the plant to cull sensor faults from the data stream. Finally the modified probability distributions and validated data are used as input to the reasoning scheme which is the runtime version of the influence diagram. The output of the influence diagram is a diagnostic mapping from the symptoms or sensor readings to a determination of likely failure modes. Once likely failure modes are identified, a detailed diagnostic knowledge base suggests corrective actions to improve performance. This framework for a diagnostic expert system with sensor validation and reasoning under uncertainty applies in $HEATXPRT^{TM}$ a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants [1].

• Journal of the Korean Geotechnical Society
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• v.19 no.6
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• pp.397-406
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• 2003

Several sensor systems are used to estimate the reinforcing effect of stabilizing pile in slopes, and to find a failure surface in slopes effectively. FBG(Fiber Brags Crating) sensor, V/W(Vibrating Wire) sensor and inclinometer have shown a great potentiality to serve real time health monitoring of the reinforcing structures. Field tests and test results have shown great solutions for sensor systems of Smart Structures. The purpose of this research is to seek for the relationships among the slope movement and the reinforcing effect of stabilizing pile, and the strain distribution of stabilizing pile in a active zone by analyzing the data from the in-situ measurement so that the possible failure surface should be well defined based on the relationships. The field test results have shown that the data by FBG sensor are well coincided with those of V/W sensor and inclinometer, and the reinforcing effect of the stabilizing pile is good enough.

• Design & Manufacturing
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• v.16 no.4
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• pp.52-59
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• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.87-91
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• 2019

The diagnosis of the failure for the existing electrical facilities was based on regular preventive maintenance, but this preventive maintenance was limited in preventing a lot of cost loss and sudden system failure. To overcome these shortcomings, fault prediction and diagnostic techniques are critical to increasing system reliability by monitoring electrical installations in real time and detecting abnormal conditions in the facility early. As the performance and quality deterioration problem occurs frequently due to the increase in the number of users of the motor pump, the purpose is to build an intelligent control system that can control the motor pump to maximize the performance and to improve the quality and reliability. To this end, a vibration sensor, temperature sensor, pressure sensor, and low water level sensor are used to detect vibrations, temperatures, pressures, and low water levels that can occur in the motor pump, and to build a system that can identify and diagnose information to users in real time.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.4
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• pp.71-85
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• 2012

This paper aims to develop an event-driven failure analysis and prognosis system that is able to monitor ship status in real time, and efficiently react unforeseen system failures. In general, huge amount of recorded sensor data must be effectively interpreted for failure analysis, but unfortunately noise and redundant information in the gathered sensor data are obstacles to a successful analysis. This paper therefore applies 'Equal-frequency binning' and 'Entropy' techniques to extract only important information from the raw sensor data while minimizing information loss. The efficiency of the developed failure analysis system is demonstrated with the collected sensor data from a marine diesel engine.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.53-62
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• 2023

Due to the recent increase in new and renewable energy, gas turbine generators start and stop every day to supply high-quality power, and accordingly, the life span of high-temperature parts is shortened and the failure of combustion chamber temperature sensors increases. Therefore, in this study, we proposed a fuzzy logic-based failure diagnosis algorithm that can accurately diagnose and systematically detect the failure of the sensor when the dual temperature sensor used for gas turbine control fails, and to confirm the usefulness of the proposed algorithm We tried to confirm the usefulness of the proposed algorithm by performing various simulations under the matlab/simulink environment.

• Journal of the Korean Geophysical Society
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• v.8 no.3
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• pp.123-130
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• 2005

It is necessary, in the light of the importance of long-term slope stability problem, to develop a simple method or tool which can figure out the possible failure zone resulted from weathering effect and other factors. The FBG sensor system is used to estimate the correlations between the temperature and the slope in Kimhae, and to find a failure zone in slopes effectively. This research is to seek for the correlation between the soil temperature distribution and the strain distribution in a active zone by analyzing the data from the in-situ measurement so that the possible failure zone should be well defined based on the correlation. For instance, the zone of high temperature fluctuation can be regarded as one of the possible sliding zone due to the weathering effect while the constant temperature depth of the ground, if exists would not be relatively affected by the weathering process.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.3
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• pp.216-224
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• 2004

In this paper we address reliable output feedback control problem for a class of linear systems with actuator/sensor failures. An output feedback control method is proposed which stabilizes the plant and guarantees $H_\inftyt$-norm constraint against all admissible actuator/sensor failures. The controller can be obtainer by solving some LMls that cover all failure cases. Effectiveness of this controller is validated via a numerical example. This paper addresses reliable output feedback control problem for a class of linear systems with actuator/sensor failures. An output feedback control method is proposed which stabilizes the plant and guarantees $H_\inftyt$-norm constraint against all admissible actuator/sensor failures. The controller can be obtained by solving some LMls that cover all failure cases. Effectiveness of this controller is validated via numerical example.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2003.04a
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• pp.42-45
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• 2003

Optical fiber vibrations sensors (OFVSs) and extrinsic Fabry-Perot interferometer (EFPI) were used in damage monitoring of fiber-metal laminates(FML). The optical fiber vibration sensor and EFPI were applied in order to detect and evaluate the strain, damage and failure of FML. Damages in composites, such as matrix cracks, delamination and fiber breakage may occur as a result of excessive load, fatigue and low-velocity impacts. Indentation test was performed with the measurement of optical signal and acoustic emission (AE). The signals of the optical fiber vibration sensor due to damages were quantitatively evaluated by wavelet transform. It was found that damage information of comparable in quality to acoustic emission data could be obtained from the optical fiber vibration sensor signals.

• Journal of the Korea Society for Simulation
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• v.25 no.2
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• pp.31-40
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• 2016

Wireless sensor networks have been used in many applications such as marine environment, army installation, etc. The sensor data is very important, because all these applications depend on sensor data. The possibility of communication failures becomes high since the surrounding environment of a wireless sense network has an sensitive effect on its communications. In particular, communication failures in underwater communications occur more frequently because of a narrow bandwidth, slow transmission speed, noises from the surrounding environments and so on. In cases of communication failures, the sensor data can be lost in the sensor data delivery process and these kinds of sensor data losses can make critical huge physical damages on human or environments in applications such as fire surveillance systems. For this reason, although a few of studies for storing and compressing sensor data have been proposed, there are lots of difficulties in actual realization of the studies due to none-existence of the framework using network communications. In this paper, we propose a framework for reducing loss of the sensor data and analyze its performance. The our analyzed results in non-framework application show a decreasing data recovery rate, T/t, as t time passes after a network failure, where T is a time period to fill the storage with sensor data after the network failure. Moreover, all the sensor data generated after a network failure are the errors impossible to recover. But, on the other hand, the analyzed results in framework application show 100% data recovery rate with 2~6% data error rate after data recovery.