• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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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

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.737-747
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• 2011

5678SMRT has installed various sensor for operating conditions(field of electric, facilities, signal, communication equipment and track) and environment of Every Function Room for remotely detecting and monitoring. Installed sound sensor for analyzed after remotely heard the noise of every equipment at Every Function Room and temperature sensor for check the temperature condition of Every Function Room. Additional installed voltage sensor in signal equipment room for monitoring RF track-circuit's voltage condition. Installed displacement sensor at The Chungdam bridge's railway for measuring and monitoring track displacement caused by temperature change and Pan/Tilt camera at sub-station and drainage for remotely field monitoring. Installed sensor for each equipment's operating condition and failure at Every Function Room then periodic check of workforce turned to around-the-clock surveillance by sensor therefore improvement of operating equipment. SMRT is lots of prevent a failure by Immediately detect of precondition of equipment failure by analyzed the sensor data. If the occurrence of an failure, become detected Immediately so possibility correct diagnosis and order by remotely field check by installed camera and sound sensor at field.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2000.11a
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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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• v.69 no.4
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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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• v.33 no.5
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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.

• Journal of Hydrospace Technology
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• v.1 no.1
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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.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2001.05a
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• pp.254-257
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• 2001

An intensity-based optical fiber vibration sensor is applied to detect and evaluate damages and fiber failure of composites. The optical fiber vibration sensor is constructed by placing two cleaved fiber end, one of which is cantilevered in a hollow glass tube. The movement of the cantilevered section lags behind the rest of the sensor in response to an applied vibration and the amount of light coupled between the two fibers is thereby modulated. Vibration characteristics of the optical fiber vibration sensor are investigated. Surface mounted optical fiber vibration sensor is used in tensile and indentation test. Experimental results show that the optical fiber sensor can detect damages and fiber failure of composites correctly.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.547-552
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• 2001

An intensity-based optical fiber vibration sensor is applied to detect and evaluate damages and fiber failure of composites. The optical fiber vibration sensor is constructed by placing two cleaved fiber end, one of which is cantilevered in a hollow glass tube. The movement of the cantilevered section lags behind the rest of the sensor in response to an applied vibration and the amount of light coupled between the two fibers is thereby modulated. Vibration characteristics of the optical fiber vibration sensor are investigated. Surface mounted optical fiber vibration sensor is used in tensile and indentation test. Experimental results show that the optical fiber sensor can detect damages and fiber failure of composites correctly.

• 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.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.5B no.1
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• pp.78-83
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• 2005

Currently in Japan, the use of the small wind turbine is an upward trend. There are already many well established small wind turbine generators in use and their various failures have been reported. The most commonly sighted failure is blade damage. Thus the research purpose was set to develop a simple failure diagnostic system, where an Acoustic Emission (AE) signal was produced from the failure part of a blade which was measured by AE sensor. The failure diagnostic technique was thoroughly examined. Concurrently, the damage part of the blade was imitated, the AE signal was measured, and a FFT(Fast Fourier Transform) analysis was carried out, and was compared with the output characteristic. When one sheet of a blade was damaged 40mm or more, the level was computed at which failure could be diagnosed.