• Economic and Environmental Geology
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• v.33 no.1
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• pp.61-75
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• 2000

Through modeling fault network using thin plate finite element technique in the San Andreas Fault system with slip rate over 1mm/year, as well as elevation, heat flow, earthquakes, geodetic data and crustal thickness, we compare the results with velocity boundary conditions of plate based on the NUVEL-1 plate model and the approximation of deformation in the Great Basin region. The frictional and dislocation creep constants of the crust are calculated to reproduce the observed variations in the maximum depth of seismicity which corresponds to the temperature ranging from $350^{\circ}C$ to $410^{\circ}C$. The rheologic constants are defined by the coefficient of friction on faults, and the apparent activation energy for creep in the lower crust. Two parameters above represent systematic variations in three experiments. The pattern of model indicates that the friction coefficient of major faults is 0.17~0.25. we test whether the weakness of faults is uniform or proportional to net slip. The geologic data show a good agreement when fault weakness is a trend of an additional 30% slip dependent weakening of the San Andreas. The results of study suggest that all weakening is slip dependent. The best models can be explained by the available data with RMS mismatch of as little as 3mm/year, so their predictions can be closely related with seismic hazard estimation, at least along faults where no data are available.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2326-2333
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• 2015

Stator turn faults in permanent magnet synchronous motors (PMSMs) are more dangerous than those in induction motors (IMs) because of the presence of spinning rotor magnets that can be turned off at will. Condition monitoring and fault detection and diagnosis of the PMSM have been receiving a growing amount of attention among scientists and engineers in the past few years. The aim of this study is to propose a new detection technique of stator winding faults in a three-phase PMSM. This technique is based on the image analysis and recognition of the stator current Concordia patterns, and will allow the identification of turn faults in the stator winding as well as its correspondent fault index severity. A test bench of a vector controlled PMSM motor behaviors under short circuited turn in two phases stator windings has been built. Some experimental results of the phase to phase short circuits have been performed for diagnosis purpose.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2741-2749
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• 2009

There have been many researches to detect and recover from faults on smart home environment, because these faults should lower its reliability. while, most of these researches have addressed functional defects of devices or software malfunction, few attempts have been made to deal with faults which may occur due to the inter relationships among devices. In this paper, we define the relationships among devices as rules, and propose a method for generating fault diagnosis knowledge which defines the symptoms and causes of faults. We classify the contexts of devices into two sets, depending on whether it satisfies the rules or not. when this method is applied to smart home environment, it is feasible not only to detect faults that may occur due to the relationships among devices but to identify their causes at real time.

• Journal of Mechanical Science and Technology
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• v.19 no.8
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• pp.1597-1610
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• 2005

The conventional computerized numerical controller (CNC) of machine tools has been increasingly replaced by a PC-based open architecture CNC (OAC) that is independent of a CNC vendor. The OAC and machine tools with an OAC have led to a convenient environment in which user-defined applications can be efficiently implemented within a CNC. This paper proposes a method of diagnosing the cause of operational faults. The method is based on the status of a programmable logic controller in machine tools with an OAC. An operational fault is defined as a disability that occurs during the normal operation of machine tools. Operational faults constitute more than 70 percent of all faults and are also unpredictable because most of them occur without any warning. To quickly and correctly diagnose the cause of an operational fault, two diagnostic models are proposed: the switching function and the step switching function. The cause of the fault is logically diagnosed through a fault diagnosis system using diagnostic models. A suitable interface environment between a CNC and developed application modules is constructed to implement the diagnostic functions in the CNC domain. The results of the diagnosis were displayed on a CNC monitor for machine operators and transmitted to a remote site through a Web browser. The proposed diagnostic method and its results were useful to unskilled machine operators and reduced the machine downtime.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.28-34
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• 2009

In a Gas Insulated Substation (GIS), Very Fast Transients (VFTs) are generated mainly due to switching operations. These transients may cause internal faults, i.e., layer-to-layer faults in a capacitively graded bushing as it is one of the most important terminal equipment for GIS. The healthiness of the bushing is generally verified by measuring its leakage current. However, the change in current magnitude/pattern is only marginal for different types of fault conditions. Leakage current monitoring (LCM) systems generate large amounts of data and computer aided interpretation of defects may be of great assistance when analyzing this data. In view of the above, ANN techniques have been used in this study for identification of these minor faults. A single layer perceptron network, a two layer feed-forward back propagation network and cascade correlation (CC) network models are used to identify interlayer faults in the bushing. The effectiveness of the CC network over perceptron and back propagation networks in identification of a fault has been analysed as part of the paper.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1614-1622
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• 2018

The research related to fault diagnosis in permanent magnet synchronous motors (PMSMs) has attracted considerable attention in recent years because various faults such as permanent magnet demagnetization and short-circuited turns can occur and result in unexpected failure of motor related system. Several conventional current and back electromotive force (BEMF) analysis techniques were proposed to detect certain faults in PMSMs; however, they generally deal with a single fault only. On the contrary, cases of multiple faults are common in PMSMs. We propose a fault diagnosis method for PMSMs with single and multiple combined faults. Our method uses three phase BEMF voltages based on the fast Fourier transform (FFT), support vector machine(SVM), and visualization tools for identifying fault types and severities in PMSMs. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) are used to visualize the high-dimensional data into two-dimensional space. Experimental results show good visualization performance and high classification accuracy to identify fault types and severities for single and multiple faults in PMSMs.

• Journal of Power Electronics
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• v.8 no.2
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• pp.181-191
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• 2008

The task performed by induction motors grows increasingly complex in modern industry and hence improvements are sought in the field of fault diagnosis. It is essential to diagnose faults at their very inception, as unscheduled machine down time can upset critical dead lines and cause heavy financial losses. Artificial intelligence (AI) techniques have proved their ability in detection of incipient faults in electrical machines. This paper presents an application of AI techniques for the detection of inter-turn insulation and bearing wear faults in single-phase induction motors. The single-phase induction motor is considered a proto type model to create inter-turn insulation and bearing wear faults. The experimental data for motor intake current, rotor speed, stator winding temperature, bearing temperature and noise of the motor under running condition was generated in the laboratory. The different types of fault detectors were developed based upon three different AI techniques. The input parameters for these detectors were varied from two to five sequentially. The comparisons were made and the best fault detector was determined.

• Journal of The Geomorphological Association of Korea
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• v.28 no.3
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• pp.63-83
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• 2021

In the southeastern part of the Korean Peninsula, the Yangsan Fault, an active fault zone, has developed. Many earthquakes occur around these faults, and the possibility of earthquakes occurring along the branch faults is being discussed. On the other hand, the Yeongdeok Fault is reported in Yeongdeok-gun, which is the northern part of the Yangsan fault. In this study, goemorphic characteristics of a set faults found on the outcrop of the gentle slope of the coast of Byeonggok-myeon were analyzed and granulometric and geochemical characteristics of sediments and other materials, including fault gouges were analyzed. The outcrop of Byeonggok-myeon is the part of the fault core and can be divided into two parts. Theses fault are formed on the upper part of the Mesozoic bedrock and the tertiary sedimentary layer of red sand-supported clasts are covered in several sedimentary units. The faults were normal fault sets, and a number of vertical cracks were developed, and glossy surfaces were observed in the fault area. It appears that these faults have occurred after alluvial deposition had been formed. In the case of samples from fault gouges, there were differences in particle size and geochemical characteristics from the surrounding area.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.341-344
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• 1997

This paper presents a dynamics failure detection algorithm developed for the two-motor-driven electric vehicle system. The algorithm is based on the application of the fault detection filter. The fault detection includes the identification of sudden pressure drops of the two rear tires in driving axis and dynamics faults of the two inverter-motor-paired actuators An E.V. dynamics simulator is developed, which includes the modeling of the E.V. dynamics as well as the driving dynamics. The simulator, which allows the generation of various fault situations, is utilized in the verification of the developed fault detection algorithm. The results of the simulations are also presented.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1272-1278
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• 1999

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to fault diagnosis. In this paper, we proposes an FDI system for nonlinear systems using neuro-fuzzy inference system. The proposed diagnostic system consists of two neuro-fuzzy inference systems which operate in two different modes (parallel and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis Function) network to identify the faults. The proposed FDI scheme has been tested by simulation on two-tank system.