• Tribology and Lubricants
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• v.14 no.3
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• pp.74-80
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• 1998

The morphologies of the wear particles are directly indicative of wear process occuring in the machine. The analysis of wear particle morphology can therefore provide very early detection of a fault and can also ofen facilitate a dignosis. For this work, the neural network was applied to identify friction coefficient through four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris generated from the machine. The averages of these parameters were used as inputs to the network. It is shown that collect identification of friction coefficient depends on the ranges of these shape parameters learned. The various kinds of the wear debris had a different pattern characteristics and recognized relation between the friction condition and materials very well by neural network. We discuss how the network determines difference in wear debris feature, and this approach can be applied for machine condition monitoring and fault diagnosis.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.928-934
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• 2002

A natural way to cope with fault tolerant control (FTC) problems is to modify the control parameters according to an online identification of the system parameters when a fault occurs. However. due to not only difficulties Inherent to the online multivariable identification in closed-loop systems, such as modeling errors, noise or the lack of excitation signals, but also long time requirement to identify the post-fault system and implemeutation of control problems during the identification process, we propose an alternative approach based on the observer-based fault detection and isolation (FDI) and model reference adaptive control (MRAC). The proposed robust fault diagnosis method is based on a bank of observers. We also propose a model reference adaptive control with changeable reference models according to the occurred faults. Simulation results of a flight control example show the validity and applicability of the proposed algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.244-251
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• 1997

In this paper, we propose a fault diagnosis algorithm to detect and isolate multiple faults in a system. The proposed fault diagnosis algorithm is based on a multiple fault classifier which consists of two ART2 NN(adaptive resonance theory2 neural network) modules and the algorithm is composed of three main parts - parameter estimation, fault detection and isolation. When a change in the system occurs, estimated parameters go through a transition zone in which residuals between the system output and the estimated output cross the threshold, and in this zone, estimated parameters are transferred to the multiple faults classifier for fault isolation. From the computer simulation results, it is verified that when the proposed diagnosis algorithm is performed successfully, it detects and isolates faults in the position control system of a DC motor.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.852-856
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• 2004

An induction motor is one of the most popular electrical apparatuses owing to its simple structure and robust construction. Parameter identification of the induction motor has long been researched either for a vector control technique or fault detection. Since vector control is a well-established technique for induction motor control, this paper concentrates on successive identification of physical parameters with on-load data for the purpose of condition monitoring and/or fault detection. For extracting six physical parameters from the on-load data in the framework of the induction motor state equation, unmeasured initial state values and profiles of load torque have to be estimated as well. However, the analytic optimization methods in general fail to estimate these auxiliary but significant parameters owing to the difficulty of obtaining their gradient information. In this paper, the univariate dynamic encoding algorithm for searches (uDEAS) newly developed is applied to the identification of whole unknown parameters in the mathematical equations of an induction motor with normal operating data. Profiles of identified parameters appear to be reasonable and therefore the proposed approach is available for fault diagnosis of induction motors by monitoring physical parameters.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.320-326
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• 2008

This paper studies the problem of fault estimation using adaptive fault diagnosis observer. A fast adaptive fault estimation (FAFE) approximator is proposed to improve the rapidity of fault estimation. Then based on linear matrix inequality (LMI) technique, a feasible algorithm is explored to solve the designed parameters. Furthermore, an extension to sensor fault case is investigated. Finally, simulation results are presented to illustrate the efficiency of the proposed FAFE methodology.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1344-1352
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• 2018

In this paper, we propose a two-port S-parameter data to diagnose the fault conditions of a single-phase transformer. Using the S-parameters we can measure the reflection and transmission characteristics of signal power at the port of a transformer, which can also be converted into ABCD parameters and Z parameters through a well-known conversion formulas. Transformer fault diagnoses can be performed based on the intuitive and qualitative/quantitative characteristics of the these parameters. In addition, we can obtain wide frequency characteristics at the primary and secondary sides of the transformer, which can be used to get time domain responses using the inverse Fourier transformation with some specific input waveform. In order to verify the effectiveness of the proposed method, the fault conditions were analyzed in simulation and experiment for 3 kVA single phase transformer with 15: 5 turns ratio, and the validity of the proposed method was verified.

• Structural Engineering and Mechanics
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• v.23 no.6
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• pp.615-634
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• 2006

Seismic response of the liquid storage tanks isolated by the elastomeric bearings and sliding systems is investigated under near-fault earthquake motions. The fault normal and parallel components of near-fault motion are applied in two horizontal directions of the tank. The continuous liquid mass of the tank is modeled as lumped masses known as sloshing mass, impulsive mass and rigid mass. The corresponding stiffness associated with these lumped masses has been worked out depending upon the properties of the tank wall and liquid mass. It is observed that the resultant response of the isolated tank is mainly governed by fault normal component with minor contribution from the fault parallel component. Further, a parametric study is also carried out to study the effects of important system parameters on the effectiveness of seismic isolation for liquid storage tanks. The various important parameters considered are: aspect ratio of tank, the period of isolation and the damping of isolation bearings. There exists an optimum value of isolation damping for which the base shear in the tank attains the minimum value under near-fault motion. The increase of damping beyond the optimum value will reduce the bearing and sloshing displacements but increases the base shear. A comparative performance of five isolation systems for liquid storage tanks is also studied under normal component of near-fault motion and found that the EDF type isolation system may be a better choice for design of isolated tank in near-fault locations. Finally, it is also observed that the satisfactory response can be obtained by analysing the base-isolated tanks under simple cycloidal pulse instead of complete acceleration history.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.136-144
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• 1999

This paper deals with the fault detection problem in uncertain linear multivariable systems and its application. A robust fault detection method presented by Kim et a. (1998) for MIMO (Multi Input/Multi Output) systems has been adopted and applied to the twin rotor MIMO experimental setup using industrial DSP. The system identification problem is formulated for the twin rotor MIMO system and its parameters are estimated using experimental data. Based on the estimated parameters, some fault detection simulations are performed using the robust fault detection method, which shows that the preformance is satisfied.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.883-885
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• 1997

High impedance fault(HIF) is random in its behavior even in a similar environment. The detection of Ire HIF has focused on the development of algorithms based on harmonic, parameters of the arc currents. However, a fact that proper selection of the harmonic parameters, rather than algorithm selection, is more important is shown in this paper by applying three different performance evaluation methods on two HIF detection algorithms using eight harmonic parameters.

• Progress in Superconductivity and Cryogenics
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• v.5 no.3
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• pp.26-29
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• 2003

Nowadays, one of the serious problems in KEPCO system is much higher fault current than the SCC(Short Circuit Capacity) of circuit breaker. As the superconductivity technology has developed, the HTS-FCL(High Temperature Superconductor-Fault Current Limiter) can be one of the attractive alternatives to solve the fault current problem. But the parameters of HTS-FCL should be designed optimally to have the best performance. Under this background, this paper presents the optimal design method of parameters for resistive type HTS-FCL using Monte Carlo technique.