• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.1503-1504
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• 2008

This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

• Journal of Electrical Engineering and Technology
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• 제9권1호
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• pp.170-177
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• 2014

This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.

• 대한전자공학회논문지
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• 제25권12호
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• pp.1522-1531
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• 1988

The purpose of this paper is to explain eigenstructure based spatial spectrum estimation algorithms computing better estimates than the other approaches. Also, as an approach to overcome performance degradations in a multipath environments, the notion of forward and backwark spatial smoothing is discussed. Intensive simulation results,which include the comparisons of the eigenbased spatial spectral estimation algorithms in the situations of faulty estimation of the number of signals, are presented. The simulation results have shown that overestimation of the number of signals is more desirable than underestimation in using EV (Eigen Vector) and MUSIC (Multiple Signal Classification) algorithms and that underestimation of the number of signals is better strategy than overestimation in using eigenstructure based LP(Linear Prediction) algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권5호
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• pp.2375-2393
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• 2016

Key agreement is paramount in secure wireless communications. A promising approach to address key agreement schemes is to extract secure keys from channel characteristics. However, because channels lack randomness, it is difficult for wireless networks with stationary communicating terminals to generate robust keys. In this paper, we propose a Robust Secure Key Agreement (RSKA) scheme from Received Signal Strength (RSS) in stationary wireless networks. In order to mitigate the asymmetry in RSS measurements for communicating parties, the sender and receiver normalize RSS measurements and quantize them into q-bit sequences. They then reshape bit sequences into new l-bit sequences. These bit sequences work as key sources. Rather than extracting the key from the key sources directly, the sender randomly generates a bit sequence as a key and hides it in a promise. This is created from a polynomial constructed on the sender's key source and key. The receiver recovers the key by reconstructing a polynomial from its key source and the promise. Our analysis shows that the shared key generated by our proposed RSKA scheme has features of high randomness and a high bit rate compared to traditional RSS-based key agreement schemes.

• Nuclear Engineering and Technology
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• 제39권2호
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• pp.133-142
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• 2007

The Electric Power Research Institute (EPRI) demonstrated a method for monitoring the performance of instrument channels in Topical Report (TR) 104965, 'On-Line Monitoring of Instrument Channel Performance.' This paper presents the results of several models originally developed by EPRI to monitor three nuclear plant sensor sets: Pressurizer Level, Reactor Protection System (RPS) Loop A, and Reactor Coolant System (RCS) Loop A Steam Generator (SG) Level. The sensor sets investigated include one redundant sensor model and two non-redundant sensor models. Each model employs an Auto-Associative Kernel Regression (AAKR) model architecture to predict correct sensor behavior. Performance of each of the developed models is evaluated using four metrics: accuracy, auto-sensitivity, cross-sensitivity, and newly developed Error Uncertainty Limit Monitoring (EULM) detectability. The uncertainty estimate for each model is also calculated through two methods: analytic formulas and Monte Carlo estimation. The uncertainty estimates are verified by calculating confidence interval coverages to assure that 95% of the measured data fall within the confidence intervals. The model performance evaluation identified the Pressurizer Level model as acceptable for on-line monitoring (OLM) implementation. The other two models, RPS Loop A and RCS Loop A SG Level, highlight two common problems that occur in model development and evaluation, namely faulty data and poor signal selection

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.533-540
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• 2017

Distance relay identifies the type and location of fault by measuring the transmission line impedance. However any other factors that cause miss calculating the measured impedance, makes the relay detect the fault in incorrect location or do not detect the fault at all. One of the important factors which directly changes the measured impedance by the relay is series capacitive compensation (SCC). Another factor that changes the calculated impedance by distance relay is fault resistance. This paper provides a method based on the combination of distance and differential protection. At first, faulty transmission line is detected according to the current data of buses. After that the fault location is calculated using the proposed algorithm on the transmission line. This algorithm is based on active power calculation of the buses. Fault resistance is calculated from the active powers and its effect will be deducted from calculated impedance by the algorithm. This method measures the voltage across SCC by phasor measurement units (PMUs) and transmits them to the relay location via communication channels. The transmitted signals are utilized to modify the voltage signal which is measured by the relay. Different operating modes of SCC and as well as different faults such as phase-to-phase and phase-to-ground faults are examined by simulations.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.1401-1409
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• 2007

This paper presents a detecting technique for the improvement in quality by appling the various vibrational characteristics theory. The object of this study is to objectively point out faulty gear by developing the program which can be used to analyze and predict the vibrational characteristics caused by gear wear, deformation and nick of auto-transmission. The fault detection methods by vibrational signal analysis of gear have been progressed in the various fields of industry. These methods have the advantage of being easy to attach the accelerometer without discontinuance of the structure. But not all the methods are efficient for finding early faults. So in the thesis, we completed development of the inspection system of vibration by appling the most efficient detecting methods and verified the system's reliability through experiments.

• 조명전기설비학회논문지
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• 제23권4호
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• pp.62-71
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• 2009

본 논문에서는 주성분 분석 기법에 의한 BLDC 전동기의 고장진단 알고리즘과 임베디드 타입의 실시간 고장진단 시스템을 구현하였다. 우선 오프라인 상태에서 제안된 고장진단 알고리즘을 검증하기 위해 BLDC 고장진단 실험장치를 구현한 후 LabVIEW 프로그램에 의해 다양한 고장 데이터를 취득하였다. 취득된 데이터는 신호특성에 맞는 전 처리과정을 수행한 후 주성분분석 기법에 의해 고장특성을 나타내는 특징을 추출하고 최종적으로 BLDC 전동기의 진단은 유클리디안 거리 유사도 방법에 의해 수행된다. 이러한 결과를 바탕으로 임베디드 타입의 실시간 BLDC 고장진단 시스템을 구현하였다. 제안된 방법은 다양한 실험을 통하여 성능을 평가하였다.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2485-2492
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• 2018

The rapid development of large-scale integrated circuits has brought great challenges to the circuit testing and diagnosis, and due to the lack of exact fault models, inaccurate analog components tolerance, and some nonlinear factors, the analog circuit fault diagnosis is still regarded as an extremely difficult problem. To cope with the problem that it's difficult to extract fault features effectively from masses of original data of the nonlinear continuous analog circuit output signal, a novel approach of feature extraction and dimension reduction for analog circuit fault diagnosis based on wavelet packet decomposition, local linear embedding algorithm, and clone selection algorithm (WPD-LLE-CSA) is proposed. The proposed method can identify faulty components in complicated analog circuits with a high accuracy above 99%. Compared with the existing feature extraction methods, the proposed method can significantly reduce the quantity of features with less time spent under the premise of maintaining a high level of diagnosing rate, and also the ratio of dimensionality reduction was discussed. Several groups of experiments are conducted to demonstrate the efficiency of the proposed method.

• 한국항행학회논문지
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• 제7권2호
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• pp.171-179
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• 2003

본 논문에서는 항공기 센서와 구동장치 고장시 신경회로망을 이용하여 이를 대처하는 고장보완시스템에 대해서 고려한다. 센서 고장의 경우에는 비행동역학적 관계식을 적절히 이용하여 신경회로망으로 센서고장을 진단/규명하고 고장난 센서를 대체할 수 있는 시스템을 설계하고 또한 구동장치의 고장이나 조종면의 일부 파손시 이를 진단/규명하고 보완하여 새로운 트림상태로 안정화시키는 제어시스템을 제안한다. 설계된 두 보완시스템을 하나의 가격함수로 통합하여 운용하는 알고리즘을 제안하며 이의 검증을 위해 센서와 구동장치의 고장을 가상적으로 설정하여 시뮬레이션함으로써 보완시스템의 성능을 확인한다.