• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.12
• /
• pp.2333-2338
• /
• 2011

In this paper, we develope a wavelet transform based time-frequency domain reflectometry (WTFDR) for the fault localization of underground power cable. The conventional TFDR (CTFDR) is more accurate than other reflectometries to localize the cable fault. However, the CTFDR has some weak points such as long computation time and hard implementation because of the nonlinearity of the Wigner-Ville distribution used in the CTFDR. To solve the problem, we use the complex wavelet transform (CWT) because the CWT has the linearity and the reference signal in the TFDR has a complex form. To confirm the effectiveness and accuracy of the proposed method, the actual experiments are carried out for various fault types of the underground power cable.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.154-157
• /
• 2003

In order for testing faults of combinatorial logic circuit, the authors have developed a new diagnosis method: "Neural Network (NN) fault diagnosis", based on fm error back propagation functions. This method has proved the capability to test gate faults of wider range including so called SSA (single stuck-at) faults, without assuming neither any set of test data nor diagnosis dictionaries. In this paper, it is further shown that what kind of fault models can be detected in the NN fault diagnosis, and the simply modified one can extend to test delay faults, e.g. logic hazard as long as the delays are confined to those due to gates, not to signal lines.

• Journal of Electrical Engineering and Technology
• /
• v.6 no.6
• /
• pp.742-749
• /
• 2011

Power transmission lines are one of the most important components of electric power system. Failures in the operation of power transmission lines can result in serious power system problems. Hence, fault diagnosis (transient or permanent) in power transmission lines is very important to ensure the reliable operation of the power system. A hidden Markov model (HMM), a powerful pattern recognizer, classifies events in a probabilistic manner based on fault signal waveform and characteristics. This paper presents application of HMM to classify faults in overhead power transmission lines. The algorithm uses voltage samples of one-fourth cycle from the inception of the fault. The simulation performed in EMTPWorks and MATLAB environments validates the fast response of the classifier, which provides fast and accurate protection scheme for power transmission lines.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.135_136
• /
• 2009

This paper proposes the new reclosing scheme considering the recovery time of SFCL in distribution systems which is based on variable dead time control. The main idea is that it uses the carrier signal to distinguish whether the fault is instantaneous or permanent following initial recloser opening. The system and SFCL is modeled by using Electromagnetic Transient Program(EMTP) and the validity of the reclosing scheme represented in this paper is analyzed according to the value of the fault resistance and the time of the fault removal.

• Proceedings of the KIEE Conference
• /
• 2008.04b
• /
• pp.171-173
• /
• 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.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.5
• /
• pp.881-888
• /
• 2008

We present a novel fault diagnosis methodology using acoustic sensor systems and neural independent component analysis for large-scaled power machines. Acoustic sensors are carried out to measure sounds generated from power machines whose signal is used to determine whether fault is occurred or not. Acoustic measurements are independently mixed and deteriorated from original source signals. We propose a demixing algorithm against such mixed signals by means of independent component analysis which is achieved based on information theory and higher-order statistics to derive learning mechanism.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.1503-1504
• /
• 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 Mechanical Science and Technology
• /
• v.14 no.7
• /
• pp.773-781
• /
• 2000

The focus of this study is the development of a credible fault detection system of the cylindrical plunge grinding process. The acoustic emission (AE) signals generated during machining were analyzed to determine the relationship between grinding-related faults and characteristics of changes in signals. Furthermore, a neural network, which has excellent ability in pattern classification, was applied to the diagnosis system. The neural network was optimized with a momentum coefficient, a learning rate, and a structure of the hidden layer in the iterative learning process. The success rates of fault detection were verified.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.7 no.5
• /
• pp.268-277
• /
• 1999

Impulsive acoustic and vibration signals within gear are often induced by impacting of fault tooths in gear. Thus the detection of these impulses can be useful for fault diagnosis. Recently there is an increasing trend towards the use of higher order statistics for fault detection within mechanical systems based on the observation that impulsive signals then to increase the kurtosis values. We show that the fourth order Wigner Moment Spectrum, called the Wigner Trispectrum, has found superior detection performance to second order Wigner distribution for typical impulsive signals in a condition monitoring application. These methods are also applied to data sets measured within an industrial gear box.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.53 no.4
• /
• pp.214-220
• /
• 2004

This paper describes a busbar current differential relay in conjunction with a current transformer(CT) compensating algorithm irrespective of the level of the remanent flux. The compensating algorithm detects the start of first saturation if the third-difference function of the current exceeds the threshold; it estimates the core flux at the first saturation start by inserting the negative value of the third-difference function of the current into the magnetization curve; thereafter, it calculates the core flux during the fault and compensates the distorted current using the magnetization curve. The algorithm estimates the correct secondary current irrespective of the level of the remanent flux and needs no saturation point of the magnetization curve. The proposed relay can improve not only security of the relay on an external fault with CT saturation but sensitivity of the relay on an internal fault; the relay can improve the operating speed on n internal fault with CT saturation. This paper concludes by implementing the relay into a digital signal processor based prototype relay.