• The Journal of Engineering Geology
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• v.21 no.1
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• pp.87-95
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• 2011

To provide regulatory standards and technical guides that are likely to be required for the Fault Monitoring System (FMS), soon to be installed and operated at the Shin-Wolsong NPP #1, 2 site, technical trend analysis was performed for currently operating geodetic and/or earthquake monitoring systems and activities worldwide, resulting in the identification of several items of concern regarding the design, installation, and operation of FMS at Korean sites. The items are as follows: 1) characterization of site-specific properties (including local noise), 2) selection of measuring instruments/design of appropriate sensitivity for the tectonic behavior of the target, 3) data/electricity backup system, 4) cross-checking and/or cross-referencing system to enhance data quality and credibility, 5) transparent data operation and open-to-the-public policy, and 6) long-term operation in a stable environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.611-617
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• 2018

The application of health monitoring, including a fault detection technique, is needed to secure the structural safety of large structures. A 2-step crack identification method for detecting the crack location and size of the beam structure is presented. First, a crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape obtained from the distributed local strain data. The crack location and size were then identified based on the natural frequencies obtained from the acceleration data and the neural network technique for the pre-estimated crack occurrence region. The natural frequencies of a cracked beam were calculated based on an equivalent bending stiffness induced by the energy method, and used to generate the training patterns of the neural network. An experimental study was carried out on an aluminum cantilever beam to verify the present method for crack identification. Cracks were produced on the beam, and free vibration tests were performed. A crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape, and the crack location and size were assessed using the natural frequencies and neural network technique. The identified crack occurrence region agrees well with the exact one, and the accuracy of the estimation results for the crack location and size could be enhanced considerably for 3 damage cases. The presented method could be applied effectively to the structural health monitoring of large structures.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.2 s.95
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• pp.211-217
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• 1999

감지기의 고장 발견, 확인, 보완은 복잡한 항공 시스템의 중요한 문제로 부각되어 왔으며, 그동안 칼만 필터를 이용한 기존 추정기술 혹은 온라인 학습 인공지능 알고리듬 등이 이 같은 문제를 해결하기 위해 제시되어 왔다. 본 연구에서는 여분의 감지기가 없는 항공제어계에 대해 온라인 학습 신경망을 이용한 감지기의 고장 발견, 확인, 그리고 보완에 관해 초점을 둔다. 이 내고장성 항공제어계는 주 신경조직망과 n개의 국소 신경조직망으로 이루어지는데, 포괄적인 감지기의 고장을 발견하는 능력을 가진다. 어떤 경우에서는 기존의 감지기 고장 발견 방법의 성능을 향상시키기 위해 수정된 감지방법이 소개되고 그 보완된 감지방법을 이용하여 기존의 방법과 성능비교가 이루어졌다.

• Journal of the korean Society of Automotive Engineers
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• v.14 no.5
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• pp.83-94
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• 1992

There has been a suggestion of many techniques as the methods of diagnosis for rotational machinery. In this study, HFRT was used as the analysis method for ball bearing of automobile and was compared with the conventional ANC technique. And this paper presented the computer simulation process about fault types and noise for the validity of the algorithm and identification of the physical meanings of HFRT. Also, experiment was performed using ball bearing and the results showed that HFRT was much more effective than the conventional methods in diagnostic process.

• International Journal of Aeronautical and Space Sciences
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• v.4 no.2
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• pp.61-68
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• 2003

The space systems are being operated in a uncertain space environment and are desired to have autonomous capability for long periods of time without frequent telecommunications with the ground station. At the same time, requirements for new set of satellite system set of projects/systems calling for "autonomous" operations for long unattended periods of time are emerging. Since, by the nature of space systems, it is desired to perform its mission flawlessly and also it is of extreme importance to have fault-tolerant sensors and actuators for the purpose of validating science measurement data for the mission success. This studies focused on the identification/demonstration of critical technology innovations that will be applied to the Validation Control System.

• Journal of Computing Science and Engineering
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• v.7 no.4
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• pp.272-284
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• 2013

In this paper, we propose a new framework for anomaly detection in medical wireless sensor networks, which are used for remote monitoring of patient vital signs. The proposed framework performs sequential data analysis on a mini gateway used as a base station to detect abnormal changes and to cope with unreliable measurements in collected data without prior knowledge of anomalous events or normal data patterns. The proposed approach is based on the Mahalanobis distance for spatial analysis, and a kernel density estimator for the identification of abnormal temporal patterns. Our main objective is to distinguish between faulty measurements and clinical emergencies in order to reduce false alarms triggered by faulty measurements or ill-behaved sensors. Our experimental results on both real and synthetic medical datasets show that the proposed approach can achieve good detection accuracy with a low false alarm rate (less than 5.5%).

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.9
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• pp.678-684
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• 1987

The identification problem of instrument faults in PWR pressurizer is considered. The instrument failure detection technique in this paper consists of two filters, a normal-mode Kalman filter which estimates plant states in normal operation and a bias estimator which estimates the magnitudes and directions of bias faults. The concept of threshold based on the residual of a Kalman filter in normal operation is introduced. The bias estimator is driven when the absolute value of residual exceeds the threshold. The suggested failure detection algorithm is applied to a PWR pressurizer. Computer simulations show that the prompt detection of bias fault can be performed very successfully when there exist instrument faults.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.3063-3065
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• 2005

In the raper, the noise involved on the active circuit is identified using correlation function. In order to identify the unknown noise source location, signals from each points on the system are detected and the location is identified by a concept calico Noise Source Surface. The fault diagnosis method is suggested for each element by identifying the noise source in active circuit using SVM. Experiment is conducted to confirm the validity of the proposed method. Also a method to reduce and control the noise in the system signal by using Matrix Pencil Method is introduced.

• Journal of the Korea Institute of Military Science and Technology
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• v.1 no.1
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• pp.82-91
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• 1998

This paper presents a neural networks based approach for the problem of sensor failure detection and accommodation for ship without physical redundancy in the sensors. The designed model consists of two neural networks. The first neural network is responsible for the failure detection and the second neural network is responsible for the failure identification and accommodation. On the yaw rate sensor of ship, simulation results indicates that the proposed method can be useful as failure detector and sensor estimator.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1465-1467
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• 1999

An Algorithm is devised to discriminate power distribution faults from other activites utilizing the arc activity and changes in the loading. This scheme intends to determine the cause of the transients and identify them. the discrimination algorithm was tested using recorded event data.