• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3184-3186
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• 2000

This paper describes the remote monitoring & diagnosis system of tower parking facilities. This system consists of central station, monitoring equipments and parking system control panel. The central station is developed under client/server architecture, and the monitoring systems are connected to central station by LAN using RAS constructed PSTN. This system offers real-time fault detection and data acquisition of tower parking system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.878-882
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• 2004

In this paper, we propose a hierarchical hybrid neural network for detecting faults of induction motor. Implementing the classifier based on the input and output data, we apply appropriate transform and classification method at each step. In the proposed method, after obtaining the current of state of motor for each period, we transform it by Principle Component Analysis(PCA) to reduce its dimension. Before the training process, we use the conditional Fuzzy C-means(FCM) for obtaining the initial parameters of neural network for more effective learning procedure. From the various simulations, we find that the proposed method shows better performance to detect and diagnosis of induction motor and compare than other methods.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.3
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• pp.185-192
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• 2015

Since real-time monitoring systems, such as early fault detection, have been very important, an infrared thermography technique was proposed as a new diagnosis method. This study focused on damage detection and temperature characteristic analysis of ball bearings using the non-destructive, infrared thermography method. In this paper, for the reliability assessment, infrared experimental data were compared with finite element analysis (FEA) results from ANSYS. In this investigation, the temperature characteristics of ball bearing were analyzed under various loading conditions. Finally, it was confirmed that the infrared thermography technique was useful for the real-time detection of damage to bearings.

• Progress in Superconductivity
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• v.10 no.1
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• pp.60-67
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• 2008

We present domestic efforts for superconducting fault current limiter (SFCL) application in the Korea Electric Power Corporation (KEPCO) grid and pending points at issue. KEPCO's decision to upgrade the 154 kV/22.9 kV main transformer from 60 MVA to 100 MVA cast a problem of high fault current in the 22.9 kV distribution lines. The grid planners supported adopting an SFCL to control the fault current. This environment friendly to SFCL application must be highly dependent upon the successful development of SFCL having specifications that domestic utility required. The required conditions are (1) small size of not greater than twice of 22.9 kV gas insulated switch-gear (GIS), (2) sustainability of current limitation without the line breaking by circuit breakers (CB) for maximum 1.5 seconds. Also, optionally, recommended is (3) the reclosing capability. Conventional resistive SFCLs do not meet (1) $\sim$ (3) all together. A hybrid SFCL is an excellent solution to meet the conditions. The hybrid SFCL consists of HTS SFCL components for fault detection and line commutation, a fast switch (FS) to break the primary path, and a limiter. This characteristic structure not only enables excellent current limiting performances and the reclosing capability, but also allows drastic reduction of HTS volume and small size of the cryostat, resulting in economic feasibility and compactness of the equipment. External current limiter also enables long term limitation since it is far less sensitive to heat generation than HTS. Semi-active operation is another advantage of the hybrid structure. We will discuss more pending points at issues such as maintenance-free long term operation, small size to accommodate the in-house substation, passive and active control, back-up plans, diagnosis, and so on.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.7
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• pp.555-561
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• 2014

Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet for the rotating machinery diagnosis. Therefore, in this paper two methods which are processed by Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94 % classification of averaged accuracy with the parameter of the RBF 0.08, 12 feature selection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.515-522
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• 2021

In this paper, we present and analyze a method of applying a machine learning to ultrasonic test signals for constant monitoring of the welding faults in a tubular steel tower. For the machine learning, feature selection based on genetic algorithm and fault signal classification using a support vector machine have been used. In the feature selection, the peak value, histogram lower bound, and normal negative log-likelihood from 30 features are selected. Those features clearly indicate the difference of signals according to the depth of faults. In addition, as a result of applying the selected features to the support vector machine, it has been possible to perfectly distinguish between the regions with and without faults. Hence, it is expected that the results of this study will be useful in the development of an early detection system for fault growth based on ultrasonic signals and in the energy transmission related industries in the future.

• Journal of Power Electronics
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• v.16 no.1
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• pp.48-56
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• 2016

This paper proposes a method for detecting irreversible demagnetization using the harmonic analysis of back electromotive force (BEMF) in interior permanent magnet-type brushless DC motors. First, demagnetization patterns, such as equality, inequality, and weighted demagnetizations, are defined and classified by considering the possibility of demagnetization resulting from motor operating characteristics. Second, an available diagnostic model for the harmonic analysis of BEMFs is defined according to pole-slot coefficients because the characteristics of BEMFs under demagnetization conditions are affected by the combination of poles and slots. Third, BEMFs and their harmonic components under normal and demagnetization conditions are analyzed through simulation and experiment to verify the proposed demagnetization detection technique.

• Transactions on Electrical and Electronic Materials
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• v.14 no.2
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• pp.71-77
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• 2013

Advanced semiconductor manufacturing technology requires methods to maximize tool efficiency and improve product quality by reducing process variability. Real-time plasma process monitoring and diagnosis have become crucial for fault detection and classification (FDC) and advanced process control (APC). Additional sensors may increase the accuracy of detection of process anomalies, and optical monitoring methods are non-invasive. In this paper, we propose the use of a chromatic data acquisition system for real-time in-situ plasma process monitoring called the Plasma Eyes Chromatic System (PECS). The proposed system was initially tested in a six-inch research tool, and it was then further evaluated for its potential to detect process anomalies in an eight-inch production tool for etching blanket oxide films. Chromatic representation of the PECS output shows a clear correlation with small changes in process parameters, such as RF power, pressure, and gas flow. We also present how the PECS may be adapted as an in-situ plasma arc detector. The proposed system can provide useful indications of a faulty process in a timely and non-invasive manner for successful run-to-run (R2R) control and FDC.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.9
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• pp.449-456
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• 2005

The preventive diagnostic technique prevents transformers from power failure through giving alarm and observing transformers in service. And it helps to establish the plan for optimum maintenance of the transformer as well as to find location or cause of fault using accumulated data. Data detection and experience of the preventive diagnostic system need to establish the preventive diagnostic algorithm regarding interrelationship between detected data and deterioration of equipment. Therefore in-depth analysis about the preventive diagnosis system is required. KEPCO has adopted the preventive diagnostic system at nine 345kV substations since 1997. Techniques for component sensors of the preventive diagnosis system were settled but diagnosis algorithm, diagnostic criteria and practical use of accumulated data are not yet established. This paper, to build up the base of preventive diagnostic algorithm for the Power transformer. investigated the preventive diagnostic criteria for the power transformer.