• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.51-55
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• 1997

This study is devote to developing navigation filter for detecting sensor failure in multi-sensor navigation system. In multi-sensor navigation system, Kalman filter is generally used to fuse data of each sensors. Sensor failure is fatal in case that the sensor is used as external measurement of Kalman filter therefore detection and recovery of sensor failure is one the important feature of navigation filter. Generally each sensors have its specific feature in measuring navigational information. Fuzzy theory is proposed to detect external sensor failure and provide valid external measurement to Kalman filter avoiding filter divergence and instability. This idea is applied to Autonomous Underwater Vehicle(AUV) which has two navigation sensor i. e self contained inertial sensor and acoustic external sensor. 2 dimensional simulation result shows acceptable failure detection and recovery

• 한국복합재료학회:학술대회논문집
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• 한국복합재료학회 2000년도 추계학술발표대회 논문집
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• pp.128-133
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• 2000

The objective of this research is to develop real-time failure detection techniques for damage assessment of composite materials using optical fiber sensors. Signals from matrix cracking or fiber fracture in composite laminates are treated by signal processing unit in real-time. This paper describes the implementation of time-frequency analysis such as the Short Time Fourier Transform(STFT) to determine the time of occurrence of failure. In order to verify the performance of the optical fiber sensor for stress wave detection, we performed pencil break test with EFPI sensor and compared it with that of PZT. The EFPI sensor was embedded in composite beam to sense the failure signals and a tensile test was performed. The signals of the fiber optic sensor when damage occurred were characterized using STFT and wavelet transform. Failure detection system detected the moment of failure accurately and showed good sensitivity with the infinitesimal failure signal.

• Structural Engineering and Mechanics
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• 제69권4호
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• pp.407-416
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• 2019

This paper develops a wireless sensor for online fatigue crack detection and failure warning based on crack-induced nonlinear ultrasonic modulation. The wireless sensor consists of packaged piezoelectric (PZT) module, an excitation/sensing module, a data acquisition/processing module, a wireless communication module, and a power supply module. The packaged PZT and the excitation/sensing module generate ultrasonic waves on a structure and capture the response. Based on nonlinear ultrasonic modulation created by a crack, the data acquisition/processing module periodically performs fatigue crack diagnosis and provides failure warning if a component failure is imminent. The outcomes are transmitted to a base through the wireless communication module where two-levels duty cycling media access control (MAC) is implemented. The uniqueness of the paper lies in that 1) the proposed wireless sensor is developed specifically for online fatigue crack detection and failure warning, 2) failure warning as well as crack diagnosis are provided based on crack-induced nonlinear ultrasonic modulation, 3) event-driven operation of the sensor, considering rare extreme events such as earthquakes, is made possible with a power minimization strategy, and 4) the applicability of the wireless sensor to steel welded members is examined through field and laboratory tests. A fatigue crack on a steel welded specimen was successfully detected when the overall width of the crack was around $30{\mu}m$, and a failure warnings were provided when about 97.6% of the remaining useful fatigue lives were reached. Four wireless sensors were deployed on Yeongjong Grand Bridge in Souht Korea. The wireless sensor consumed 282.95 J for 3 weeks, and the processed results on the sensor were transmitted up to 20 m with over 90% success rate.

• Nuclear Engineering and Technology
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• 제33권5호
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• pp.483-497
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• 2001

In this work, a neuro-fuzzy inference system combined with the wavelet denoising, PCA (principal component analysis) and SPRT (sequential probability ratio test) methods is developed to detect the relevant sensor failure using other sensor signals. The wavelet denoising technique is applied to remove noise components in input signals into the neuro-fuzzy system The PCA is used to reduce the dimension of an input space without losing a significant amount of information. The PCA makes easy the selection of the input signals into the neuro-fuzzy system. Also, a lower dimensional input space usually reduces the time necessary to train a neuro-fuzzy system. The parameters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The residuals between the estimated signals and the measured signals are used to detect whether the sensors are failed or not. The SPRT is used in this failure detection algorithm. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level and the hot-leg flowrate sensors in pressurized water reactors.

• 한국군사과학기술학회지
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• 제1권1호
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• pp.82-91
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• 1998

본 논문에서는 실제 물리적인 여유 센서를 가지지 않는 수중운동체의 센서 고장진단 및 극복에 관한 문제를 신경회로망을 사용하여 접근하였다. 이를 위하여 설계된 신경회로망은 센서 고장 진단을 위한 신경회로망과 고장 확인 및 대체정보 생성을 위한 신경회로망으로 구성하였으며, 온라인(on-line) 학습을 위하여 확장 역전(Extended Back-Propagation) 학습법을 사용하였다. 시뮬레이션은 수중운동체의 방위변화율 센서에 대하여 수행하였으며, 제안된 기법이 센서에 대한 고장진단기와 센서 추정기로 사용할 수 있음을 확인하였다.

• Structural Monitoring and Maintenance
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• 제1권2호
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• pp.213-230
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• 2014

A novel real-time actuator failure detection algorithm is developed in this paper. Actuator fails when the input to the structure is different from the commanded one. Previous research has shown that one error function can be formulated for each actuator through interaction matrix method. For output without noise, non-zero values in the actuator functions indicate the instant failure of the actuator regardless the working status of other actuators. In this paper, it is further demonstrated that the actuator's error function coefficients will be directly calculated from the healthy input of the examined actuator and all outputs. Hence, the need for structural information is no longer needed. This approach is termed as direct method. Experimental results from a NASA eight bay truss show the successful application of the direct method for isolating and identifying the real-time actuator failure. Further, it is shown that the developed method can be used for real-time sensor failure detection.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.175-177
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• 1995

The sensor signal validation and failure detection system must be able to detect, isolate, and identify sensor degradation as well as provide a reconstruction of the measurements. In this study, this is accomplished by combining the neural network, the Generalized Consistency Checking(GCC), and the Sequential Probability Ratio Test(SPRT) method in a decision estimator module. The GCC method is a computationally efficient system for redundant sensors, while the SPRT provides the ability to make decisions based on the degradation history of a sensor. The methodology is also extended to the detection of noise degradation. The acceptability of the proposed method is demonstration by using the simulation data in safety injection accident of nuclear power plants. The results show that the signal validation and sensor failure detection system is able to detect and isolate a bias failure and noise type failures under transient conditions. And also, the system is able to provide the validated signal by reconstructing the measurement signals in the failure conditions considered.

• Journal of Hydrospace Technology
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• 제1권1호
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• pp.75-88
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• 1995

Auto-Pilot System uses heading angle information via the position sensor and the rudder device to control the ship's direction. Most of the control logics are composed of the state estimation and control algorithms assuming that the measurement device and the actuator have no fault except the measurement noise. But such asumptions could bring the danger in real situation. For example, if the heading angle measuring device is out of order the control action based on those false position information could bring serious safety problem. In this study, the control system including improved method for processing the position information is applied to the Auto-Pilot System. To show the difference between general state estimator and F.D.F., BJDFs for the sensor and the actuator failure detection are designed and the performance are tested. And it is shown that bias error in sensor could be detected by state-augmented estimator. So the residual confined in the 2-dimension in the presence of the sensor failure could be unidirectional in output space and bias sensor error is much easier to be detected.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.437-440
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• 1989

In the paper a failure detection and diagnosis method of a stochastic system is proposed. It is based on the comparison of the moving averages generated from outputs of the real plant and a modeled normal plant. The proposed method allows us to locate the failed sensor and can be efficiently used for the failure detection and diagnosis of a plant with many sensors.

• 대한조선학회논문집
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• 제30권4호
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• pp.8-16
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• 1993

자동항법장치(Auto-Pilot System)에 의한 방향제어는 방위계측센서에 의해 계측된 위치 정보와 선미조타장치를 바탕으로 이루어진다. 대부분의 제어시스템들은 센서 잡음을 제외하고는 고장이 없는 계측장비와 고장없는 actuator를 가정하여 상태추정 빛 제어알고리듬을 구현하고 있다. 그러나 실제 상황에서는 이러한 가정이 위험한 경우가 많다. 즉, 방위 계측장비가 고장인 난 경우, 이 잘못된 위치 정보에 기초한 제어기능은 심각한 안전상의 문제까지도 야기시킬 수 있는 것이다. 본 연구에서는 개선된 위치정보처리 방법을 포함시킨 제어시스템을 Auto-Pilot 시스템에 적용하여 보았다. 그 방법으로 센서 고장 진단 및 actuator 고장 진단용 BJDF(Beard-Jones Detection Filter)를 설계하여 그 기능을 파악하였고 일반적인 상태변수추정기와의 차이점을 보였다. 특히 센서의 Bias Error의 경우 상태변수 확장기법을 이용하여 actuator 고장진단의 모형으로 모형화 할 수 있음을 보였다. 이로 인하여 센서 고장의 경우 2차원 평면에 국한된 residual이 일정 방향의 residual로 되므로 고장진단이 용이함을 알 수 있었다.