• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.11a
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• pp.499-505
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• 2010

The UAV(Unmanned Aerial Vehicle) which is remotely operating with long endurance in high altitude must have a very reliable propulsion system. The precise fault diagnostic system of the turboprop engine as a propulsion system of this type UAV can promote reliability and availability. This work proposes a diagnostic method which can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. It is found by evaluation examples that the proposed diagnostic method can detect well not only single type faults but also multiple type faults.

• Geomechanics and Engineering
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• v.37 no.2
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• pp.189-195
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• 2024

Buried pipelines can be classified as continuous and segmented pipelines. These infrastructures can be damaged either by ground movement or by seismic wave propagation during an earthquake. Permanent ground deformations (PGD) include surface faulting, liquefaction-induced lateral spreading and landslide. Liquefaction is a major problem for both superstructures and infrastructures. Buyukcekmece lake zone, which is the studied region in this paper, is a liquefaction prone area located near the North Anatolian Fault Line. It is an active fault line in Turkey and a major earthquake with a magnitude of around 7.5 is expected in this investigated region in Istanbul. It is planned to be constructed a new 12" steel natural gas pipeline from one side of the lake to the other side. In this study, this case has been examined in terms of two different support conditions. Firstly, it has been defined as a beam in liquefied soil and has built-in supports at both ends. In the other approach, this case has been modeled as a beam in liquefied soil and has vertical elastic pinned supports at both ends. These models have been examined and some solution proposals have been produced according to the obtained results. In this study, based on this sample, it is aimed to determine the behaviors of buried continuous pipelines subject to liquefaction effects in terms of buoyancy.

• 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.

• Progress in Superconductivity
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• v.9 no.2
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• pp.188-192
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• 2008

We investigated the size effect on quench development in $Au/YBa_2Cu_3O_7$ (YBCO) thin film meander lines on sapphire substrates. The meander lines were fabricated by patterning YBCO films coated with gold layers. The lines were subjected to simulated AC fault current, and immersed in liquid nitrogen during the experiment. After the initial rapid rise, the resistance increased moderately and then slowly. In 4 inch-diameter meander lines, the period during which the resistance increased moderately was considerably longer than in 2 inch-diameter line. Moderate increase of resistance was originated from quench propagation. The film temperature was about 180 K at the point when the propagation was completed. The rate of resistance increase after the quench completion was not affected by the film size.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.10
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• pp.965-975
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• 2005

In the case of the fault in stator windings of a high voltage motor. it facilitates certain destructive characteristics in insulations. This will result in a decreased reliability in power supplies and will prevent the generation of electricity, which will result in huge economic losses. This study simulates motor windings using normal windings and four faulty windings for an actual fault in stator winding of a high voltage motor. The partial discharge signals produced in each faulty winding were measured using an 80 PF epoxy/mica coupler sensor. In order to quantified signal waves its a way of feature extraction for each faulty signal, the signal wave of winding was quantified to measure the degree of skewness shape and kurtosis, which are both types of statistical parameters, using a discrete wavelet transformation method for each faulty type. Wave types present different types lot each faulty type, and the skewness and kurtosis also present different quantified values. The result of feature extraction was used as a preprocessing stage to identify a certain fault in stater windings. It is evident that the type of faulty signals can be classified from the test results using faulty signals that were randomly selected from the signal, which was not applied in the training after the training and learning period, by applying it to a back-propagation algorithm due to the supervising and learning method in a neural network in order to classify the faulty type. This becomes an important basis for studying diagnosis methods using the classification of faulty signals with a feature extraction algorithm, which can diagnose the fault of stator windings in the future.

• The Journal of Korean Association of Computer Education
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• v.8 no.5
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• pp.129-137
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• 2005

Criticality prediction models that identify fault-prone spots using system design specifications play an important role in reducing development costs of large systems such as telecommunication systems. Many criticality prediction models using complexity metrics have been suggested. But most of them need training data set for model training. And they are classification models that can only classify design entities into fault-prone group and non fault-prone group. To solve this problem, this paper builds a new prediction model, HMM, using two styled hybrid metrics. HMM has strong point that it does not need training data and it enables comparison between design entities by criticality. HMM is implemented and compared with a well-known prediction model, BackPropagation neural network Model(BPM), considering internal characteristics and accuracy of prediction.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.836-842
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• 2016

When an accident occurs in the nuclear power plant, the faulted information might mislead to the high possibility of aggravating the accident. At the Fukushima accident, the operators misunderstood that there was no core exposure despite in the processing of core damage, because the instrument information of the reactor water level was provided to the operators optimistically other than the actual situation. Thus, this misunderstanding actually caused to much confusions on the rapid countermeasure on the accident, and then resulted in multiplying the accident propagation. It is necessary to be equipped with the function that informs operators the status of instrument integrity in real time. If plant operators verify that the instruments are working properly during accident conditions, they are able to make a decision more safely. In this study, we have performed various tests for the fault detection sensitivity of an data-driven empirical model to review the usability of the model in the accident conditions. The test was performed by using simulation data from the compact nuclear simulator that is numerically simulated to PWR type nuclear power plant. As a result of the test, the proposed model has shown good performance for detecting the specified instrument faults during normal plant conditions. Although the instrument fault detection sensitivity during plant accident conditions is lower than that during normal condition, the data-drive empirical model can be detected an instrument fault during early stage of plant accidents.

• Nuclear Engineering and Technology
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• v.43 no.3
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• pp.271-278
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• 2011

When performing fire PSA in a nuclear power plant, an event mapping method, using an internal event PSA model, is widely used to reduce the resources used by fire PSA model development. Feasible initiating events and component failure events due to fire are identified to transform the fault tree (FT) for an internal event PSA into one for a fire PSA using the event mapping method. A surrogate event or damage term method is used to condition the FT of the internal PSA. The surrogate event or the damage term plays the role of flagging whether the system/component in a fire compartment is damaged or not, depending on the fire being initiated from a specified compartment. These methods usually require explicit states of all compartments to be modeled in a fire area. Fire event scenarios, when using explicit identification, such as surrogate or damage terms, have two problems: (1) there is no consideration of multiple fire propagation beyond a single propagation to an adjacent compartment, and (2) there is no consideration of simultaneous fire propagations in which an initiating fire event is propagated to multiple paths simultaneously. The present paper suggests a fire propagation equation to identify all possible fire event scenarios for an explicitly treated fire event scenario in the fire PSA. Also, a method for separating fire events was developed to make all fire events a set of mutually exclusive events, which can facilitate arithmetic summation in fire risk quantification. A simple example is given to confirm the applicability of the present method for a $2{\times}3$ rectangular fire area. Also, a feasible asymptotic approach is discussed to reduce the computational burden for fire risk quantification.

• Tunnel and Underground Space
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• v.29 no.3
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• pp.197-213
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• 2019

We simulated the fault reactivation experiment conducted at 'Main Fault' intersecting the low permeability clay formations of Mont Terri Underground Research Laboratory in Switzerland using TOUGH-FLAC simulator. The fluid flow along a fault was modelled with solid elements and governed by Darcy's law with the cubic law in TOUGH2, whereas the mechanical behavior of a single fault was represented by creating interface elements between two separating rock blocks in FLAC3D. We formulate the hydro-mechanical coupling relation of hydraulic aperture to consider the elastic fracture opening and failure-induced dilation for reproducing the abrupt changes in injection flow rate and monitoring pressure at fracture opening pressure. A parametric study was conducted to examine the effects of in-situ stress condition and fault deformation and strength parameters and to find the optimal parameter set to reproduce the field observations. In the best matching simulation, the fracture opening pressure and variations of injection flow rate and monitoring pressure showed good agreement with field experiment results, which suggests the capability of the numerical model to reasonably capture the fracture opening and propagation process. The model overestimated the fault displacement in shear direction and the range of reactivated zone, which was attributed to the progressive shear failures along the fault at high injection pressure. In the field experiment results, however, fracture tensile opening seems the dominant mechanism affecting the hydraulic aperture increase.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.446-460
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• 2011

The use of induction motors has been recently increasing with automation in aeronautical and automotive industries, and it playes a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of an induction motor in order to minimize economical damage caused by its fault. With this reason, this paper proposed feature vector extraction methods based on STE (short-time energy)+SVD (singular value decomposition) and DCT (discrete cosine transform)+SVD techniques to early detect and diagnose faults of induction motors, and classified faults of an induction motor into different types of them by using extracted features as inputs of BPNN (back propagation neural network) and multi-layer SVM (support vector machine). When BPNN and multi-lay SVM are used as classifiers for fault classification, there are many settings that affect classification performance: the number of input layers, the number of hidden layers and learning algorithms for BPNN, and standard deviation values of Gaussian radial basis function for multi-layer SVM. Therefore, this paper quantitatively simulated to find appropriate settings for those classifiers yielding higher classification performance than others.