• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.8B
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• pp.514-524
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• 2005

In optical burst switching(OBS) networks, the ingress edge router assembles packets in the same class queue into the appropriate size of burst. A burst control packet(BCP) is generated for channel reservation of corresponding data burst and sent earlier than the corresponding data burst with an offset time. Offset time is determined considering the number of hops from source to destination and the required quality of service(QoS). After offset time, the burst data is passed through tile pre-configured optical switches without any O/E/O conversion. But a failure in OBS networks may lead to the loss of bursts until the ingress nodes receive the failure indication signal. This results in a significant degradation in QoS. Therefore, in this paper, we propose a fault-tolerant node architecture based on sub-carrier multiplexing to reduce the effects of failure in OBS networks. The Performance of the proposed fault-tolerant node architecture exhibits considerable improvement as compared with the previous ones.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.6
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• pp.55-62
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• 2006

For the fault diagnosis of three-phase induction motors, we construct a experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the data, three-phase current is transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel principal component analysis(KPCA) and linear discriminant analysis(LDA). Finally, we used the classifier based on radial basis function(RBF) network. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.11-19
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• 2013

This paper proposes an algorithm using vibration signals and texture analysis for mechanical fault diagnosis of an induction motor. We analyze characteristics of contrast and pattern of an image converted from vibration signal and extract three texture features using gray-level co-occurrence model(GLCM). Then, the extracted features are used as inputs of a multi-level support vector machine(MLSVM) which utilizes the radial basis function(RBF) kernel function to classify each fault type. In addition, we evaluate the classification performance with varying the parameter from 0.3 to 1.0 for the RBF kernel function of MLSVM, and the proposed algorithm achieved 100% classification accuracy with the parameter of the RBF from 0.3 to 1.0. Moreover, the proposed algorithm achieved about 98% classification accuracy with 15dB and 20dB noise inserted vibration signals.

• Journal of IKEEE
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• v.26 no.3
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• pp.488-493
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• 2022

In manufacturing sites, bearing fault in eletrically driven motors cause the entire system to shut down. Stopping the operation of this environment causes huge losses in time and money. The reason of this bearing defects can be various factors such as wear due to continuous contact of rotating elements, excessive load addition, and operating environment. In this paper, a motor driving environment is created which is similar to the domestic manufacturing sites. In addition, based on the established environment, we propose a dataset for bearing fault detection by collecting changes in vibration characteristics that vary depending on normal and defective conditions. The sensor used to collect the vibration characteristics is Microphone G.R.A.S. 40PH-10. We used various machine learning models to build a prototype bearing fault detection system trained on the proposed dataset. As the result, based on the deep neural network model, it shows high accuracy performance of 92.3% in the time domain and 98.3% in the frequency domain.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.157.1-157
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• 2001

A new methodology is presented to diagnose faults in equipment plasma. This is accomplished by using neural networks as a pattern recognizer of radio frequency(rf) impedance match data. Using a realtime match monitor system, the match data were collected. The monitor system consisted mainly of a multifunction board and a signal flow diagram coded by Visual Designer. Plasma anomaly was effectively represented by electrical match positions. Twenty sets of fault-symptom patterns were experimentally simulated with experimental variations in process factors, which include rf source power, pressure, Ar and O$_2$ flow rates. As the inputs to neural networks, two means and standard deviations of positions were used ...

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.11a
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• pp.56-57
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• 2017

직렬 아크의 발생은 큰 화재를 일으킬 수 있다. 직렬 아크는 일반 차단기의 검출 범위보다 낮은 전류에서 일어날 수 있어 직렬 아크를 차단하는데 큰 어려움을 가진다. 직렬 아크를 판단하기 위해 웨이블릿 엔트로피를 사용하여 feature를 추출한 다음 신경망을 적용하여 직렬 아크를 검출한다.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.288-290
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• 1999

In view of the importance of DFT(Discrete Fourier Transform) in spectrum analysis, its computation efficiency is a topic. This paper presents calculation time to extract the power frequency at a fault signal using DFT. Furthermore, it is tested a relaying algorithm based on modified DFT and its application to Micro-controller.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.318-319
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• 2013

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.10
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• pp.976-983
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• 2006

Multi-resolution Signal Decomposition (MSD) Technique of Wavelet Transform has interesting properties of capturing the embedded horizontal, vertical and diagonal variations within an image in a separable form. This feature was exploited to identify individual partial discharge sources present in multi-source PD pattern, usually encountered during practical PD measurement. Employing the Daubechies wavelet, feature were extracted from the third level decomposed and reconstructed horizontal and vertical component images. These features were found to contain the necessary discriminating information corresponding to the individual PD sources and multi-PD soruces.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.566-570
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• 2001

Generally, grounding systems are responsible for the safe operation of a power system. Their performance guarantees equipment protection and personnel safety under condition of the limited ground potential rise and touch voltages as well as step voltages under ground fault conditions. Therefore, it is necessary to measure the ground resistance frequently for checking the performance of grounding system, In this paper the ground resistance measuring system using digital signal processor and high-performance L-C resonant band pass filter is presented. The signal current magnitude for measuring ground resistance in this system is $10^{-1}[A]\;to\;5\times10^{-2}[A]$ and the current frequency is 30[Hz].