• Proceedings of the KIEE Conference
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• summer
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• pp.412-414
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• 2004

In this paper, a novel algorithm of underground cable fault location based on the analysis of distributed parameter circuit is proposed. The proposed method makes voltage and current equations about core and sheath, and then establishes a function of the fault distance according to the analysis of fault conditions. Finally gets the solution of this function through Newton-Raphson iteration method. The effectiveness of proposed algorithm has been verified by Matlab program, and the cable parameters such as impedance and admittance are from EMTP simulation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.802-808
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• 2000

This paper characterizes the electromechanical parameters due to the fault of rotor bars in a squirrel cage induction motor. Simulation is performed to investigate how broken rotor bars have effect on them by solving the time-stepping finite element equation coupled with magnetic field equation, circuit equation and mechanical equation of motion. It shows that the asymmetry of magnetic flux due to the broken rotor bar introduces the beating phenomenon in time domain and the sideband frequencies in frequency spectra, respectively, to the stator current, torque, speed, magnetic force and vibration of a rotor. However, vibration of a rotor would be the most effective monitoring parameters to detect the faults of rotor bars.

• The Journal of Information Technology
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• v.7 no.4
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• pp.61-69
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• 2004

On-line monitoring of fault discharge is an important approach for indicating the condition of electrical insulation of stator winding in high voltage motor. In this paper, several key aspects of on-line monitoring system are discussed, involving the characteristics of fault discharge of stator winding in high voltage motor, spectrum analysis of four simulation fault signals, feature extraction of internal fault discharge from apply voltage to breakdown. The study of the partial discharge activities allows to highlight the ageing stage in the winding fault under test. During the life of the winding insulation fault, the shape of PD signal change relating to the ageing stage. The ageing of stator winding insulation fault of high voltage motor is investigated based on the characteristics of partial discharge pulse distribution and statistical parameters, such as maximum, skewness and kurtosis using discrete wavelet trnasform coefficients.

• Transactions on Electrical and Electronic Materials
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• v.6 no.6
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• pp.262-271
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• 2005

In order to achieve timely and accurate fault detection of plasma etching process, neural network based time series modeling has been applied to reactive ion etching (RIE) using two different in-situ plasma-monitoring sensors called optical emission spectroscopy (OES) and residual gas analyzer (RGA). Four different subsystems of RIE (such as RF power, chamber pressure, and two gas flows) were considered as potential sources of fault, and multiple degrees of faults were tested. OES and RGA data were simultaneously collected while the etching of benzocyclobutene (BCB) in a $SF_6/O_2$ plasma was taking place. To simulate established TSNNs as incipient fault detectors, each TSNN was trained to learn the parameters at t, t+T, ... , and t+4T. This prediction scheme could effectively compensate run-time-delay (RTD) caused by data preprocessing and computation. Satisfying results are presented in this paper, and it turned out that OES is more sensitive to RF power and RGA is to chamber pressure and gas flows. Therefore, the combination of these two sensors is recommended for better fault detection, and they show a potential to the applications of not only incipient fault detection but also incipient real-time diagnosis.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.23 no.11
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• pp.705-710
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• 2011

Fault detection has to be proceeded by steady state filtering to get rid of transient effect associated with thermal capacity. Coefficient of variance (COV), ratio of standard deviation devided by moving average, was employed as steady-state filter. Engine speed and refrigerant pressures were selected as parameters representing system dynamics. The filtered values were registered as members of steady-state DB. They were found to show good functional relationship with ambient temperature. The relationship was fitted with a second order polynomial and the distribution bounds of the data around the fitted curve were expressed by visual inspection because of varying average and random data interval. Fault data were compared with the steady-state data obtained during normal operation. The fault data were easily isolated from the fault-free one. To make such isolation reliable, tests to construct good DB should be designed in a systematic way.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Journal of the Earthquake Engineering Society of Korea
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• v.19 no.6
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• pp.265-271
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• 2015

The tsunami hazard analysis is performed for testing the application of probabilistic tsunami hazard analysis to nuclear power plant sites in the Korean Peninsula. Tsunami hazard analysis is based on the seismic hazard analysis. Probabilistic method is adopted for considering the uncertainties caused by insufficient information of tsunamigenic fault sources. Logic tree approach is used. Uljin nuclear power plant (NPP) site is selected for this study. The tsunamigenic fault sources in the western part of Japan (East Sea) are used for this study because those are well known fault sources in the East Sea and had several records of tsunami hazards. We have performed numerical simulations of tsunami propagation for those fault sources in the previous study. Therefore we use the wave parameters obtained from the previous study. We follow the method of probabilistic tsunami hazard analysis (PTHA) suggested by the atomic energy society of Japan (AESJ). Annual exceedance probabilities for wave height level are calculated for the site by using the information about the recurrence interval, the magnitude range, the wave parameters, the truncation of lognormal distribution of wave height, and the deviation based on the difference between simulation and record. Effects of each parameters on tsunami hazard are tested by the sensitivity analysis, which shows that the recurrence interval and the deviation dominantly affects the annual exceedance probability and the wave heigh level, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.673-681
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• 2005

As it is the most important to make sure security and reliability for nuclear Power Plant, it's considered the most crucial issues to develop a fault detective and diagnostic system in spite of multiple hardware redundancy in itself. To develop an algorithm for a fault diagnosis in the nuclear PWR steam generator, this paper proposes a method based on ART2(adaptive resonance theory 2) neural network that senses and classifies troubles occurred in the system. The fault diagnosis system consists of fault detective part to sense occurred troubles, parameter estimation part to identify changed system parameters and fault classification part to understand types of troubles occurred. The fault classification part Is composed of a fault classifier that uses ART2 neural network. The Performance of the proposed fault diagnosis a18orithm was corroborated by applying in the steam generator.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.4
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• pp.535-550
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• 2016

Securing the safety and the reliability of liquid-propellant rocket engines (LREs) for space vehicles is indispensable as engines consist of many complex components and operate under extremely high energy-dense conditions. Thus, health monitoring has become a mandatory requirement, especially for the reusable LREs that are currently being developed. In this context, a dynamic simulation program based on MATLAB/Simulink was developed in the current research on the Space Shuttle Main Engine (SSME), a partly reusable engine. Then, a series of fault simulations using this program was conducted: at a steady state operating condition (104% Rated Propulsion Level), various simulated fault conditions were artificially injected into the simulation models for the five major valves, the pumps, and the turbines of the SSME. The consequent effects due to each fault were analyzed based on the time responses of the major parameters of the engine. It is believed that this research topic is an essential pre-step for the development of fault detection and diagnosis algorithms for reusable engines in the future.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.3
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• pp.376-382
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• 2014

E-Actuator is an essential part of an eVGT, it receives the command from the main ECU and controls the vane. An e-Actuator failure can cause an abrupt change in engine output and it may induce an accident. Therefore, it is required to detect anomalies in the e-Actuator in real time to prevent accidents. In this paper, an e-Actuator fault detection method using on-line parameter identification is proposed. To implement on-line fault detection algorithm, many constraints are considered. The test input and sampling rate are selected considering the constraints. And new recursive system identification algorithm is proposed which reduces the memory and MCU power dramatically. The relationship between the identified parameters and real elements such as gears, spring and motor are derived. The fault detection method using the relationship is proposed. The experiments with the real broken gears show the effectiveness of the proposed algorithm. It is expected that the real time fault detection is possible and it can improve the safety of eVGT system.