• Journal of Institute of Control, Robotics and Systems
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• v.4 no.1
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• pp.123-133
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• 1998

In this paper, an algorithm of fault detection and diagnosis during operation of induction motors under the condition of various loads and rates is investigated. For this purpose, the spectrum pattern of input currents is used in monitoring the state of induction motors, and by clustering the spectrum pattern of input currents, the newly occurrence of spectrum patterns caused by faults are detected. For the diagnosis of the fault detected, a fuzzy fault tree is designed, and the fuzzy relation equation representing the relation between an induction motor fault and each fault type, is solved. The solution of the fuzzy relation equation shows the possibility of occurence of each fault. The results obtained are summarized as follows : (1) Using clustering algorithm by unsupervised learning, an on-line fault detection method unaffected by the characteristics of loads and rates is implemented, and the degree of dependency for experts during fault detection is reduced. (2) With the fuzzy fault tree, the fault diagnosis process become systematic and expandable to the whole system, and the diagnosis for sub-systems can be made as an object-oriented module.

• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.276-286
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• 2008

In this paper, we introduce an adaptive e-Learning system for engineering mathematics which is based on computer algebra system (Mathematica) and on-line authoring environment. The system provides an assessment tool for individual diagnosis using Bayesian inference network. Using this system, an instructor can easily develop mathematical web contents via web interface. Examples of such content development are illustrated in the area of linear algebra, differential equation and discrete mathematics. The diagnostic module traces a student's knowledge level based on statistical inference using the conditional probability and Bayesian updating algorithm via Netica. As part of formative evaluation, we brought this system into real university settings and analyzed students' feedback using survey.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.264-266
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• 2005

Recently, on-line diagnosis methods through wired and wireless networks are widely adopted in the diagnosis of industrial Electric Facilities, such as generators, transformers and motors. Also smart sensors which includes sensors, signal conditioning circuits and micro-controller in one board are widely studied in the field of condition monitoring. This paper suggests an self-powered system suitable for condition-monitoring smart sensors, which uses parasitic vibrations of the facilities as energy source. First, vibration-driven noise patterns of the electric facilities are presented. And then, an electromagnetic generator which uses mechanical mass-spring vibration resonance are suggested and designed. Finally energy consumption of the presented smart sensor, which consists of MEMS vibration sensors, signal conditioning circuits, a low-power consumption micro-controller, and a ZIGBEE wireless tranceiver, are presented. The usefulness and limits of the presented electromagnetic generators in the field of electric facility monitoring are also suggested.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1082-1084
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• 1998

In this paper, we developed a diagnosis device for ZnO arrester to detect on-line leakage current and acquire data from the power distribution system. The arrester is important power equipment used in power transmission and distribution systems to protect the generator and the main transformer from surge and overvoltage. First of all we developed a diagnosis device for ZnO arrester leakage current. And then we detect the total leakage current by the developed device without disconnecting the arrester ground wire and analysis the 3rd order harmonic by Fast Fourier Transform(FFT) to diagnose the ZnO arrester deterioration. With measuring the total current and the resistive current of power distribution system in operation, we analysis the trend of resistive current component in the total leakage current. We expect the result will be promote the method to protect electrical utility and customer from accident.

• Journal of the Korean Society for Railway
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• v.20 no.2
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• pp.223-233
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• 2017

The railway turnout system is one of the most important systems that set train routes. Turnout system integrity should be guaranteed for robust train operation. To diagnose the turnout system status, LVDT and accelerometers are installed on a turnout system in a high speed line. The LVDT and accelerometers produce signals containing physical meaning of the turnout systems. The LVDT produces the displacement of the rail gauge and vibration when point moving or a train passes on turnout systems and the accelerometer produces impact forces induced by wheel sets. We performed data extraction from the measured signals and parameterized the extracted signals into meaningful quantities. The parameters are used for classifying whether the turnout status is normal. We proposed two methods for the classification, one uses probabilistic distribution and the other artificial neuron networks. The probabilistic distribution is used for the parameter being classified by the quantities and the artificial neuron networks for the form classification. Finally, we show how to learn the normal status of a turnout system.

• Journal of the Korean Society for Railway
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• v.20 no.2
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• pp.210-222
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• 2017

Railway turnout systems play a key role in railway systems that change train directions. The turnout systems are one of the weakest systems in railway systems, and consecutive maintenance is required. A turnout diagnostic system can automatically measure the turnout status and its deterioration. To diagnose the turnout systems, we follow conventional maintenance procedures and need to identify their physical characteristics to coincide the procedures and the characteristics. According to the physical characteristics, we should choose and install adequate sensors on the turnout systems to measure their physical characteristics. We studied the phenomenon of the turnout system responses for point moving and train running on the turnout systems. We installed sensors on the turnout system in a revenue line to measure the identified physical quantities and to reveal the robustness of the sensors under the turnout system environment.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.1
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• pp.27-32
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• 2018

In this paper, we developed an algorithm to detect arc of PV power plant through frequency analysis. For arc detection based on frequency analysis, the filter should be designed to emphasize the difference between the arc state and the normal state. Therefore, in this paper, we analyzed the arc detection performance according to various filter structures. The arc detection algorithm developed in this paper extracts the filtering signal on current by using various filters and then calculates the frequency components and total energy using the FFT. In the final step, the arc is detected using the calculated energy magnitude. In order to verify the performance of the proposed arc detection algorithm, experiments were conducted on 51 kW solar inverters connected to power line. Through various experiments, it was confirmed that the proposed method effectively detects the arc.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.5
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• pp.80-86
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• 2005

Induction motors are a critical component of many industrial processes and are frequently integrated in commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motor applications. Fault tolerant control (FTC) strives to make the system stable and retain acceptable performance under the system faults. All present FTC method can be classified into two groups. The first group is based on fault detection and diagnostics (FDD). The second group is includes of FDD and includes methods such as integrity control, reliable stabilization and simultaneous stabilization. This paper presents the fundamental FDD-based FTC methods, which are capable of on-line detection and diagnose of the induction motors. Therefore, our group has developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. This paper presents its architecture. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module processes the stator current, voltage, temperatures, vibration signal of the motor.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.48-53
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• 2008

The performances of the building service equipment relay on the individual superintendent's share for the assessment of performance, fault detection, deterioration diagnosis of the building service equipment. A major use of building energy management system(BEMS) is for monitoring plant and building's energy consumption. National building management system (N-BMS) presented in this study links buildings into a network group in order to monitor and control the buildings. How to construct the N-BMS was considered to save energy resource and to conserve performance of building service equipment. The FEMIS, facility, energy/environmental management & information system, for building service offer management process integrated with BAS, FMS and EMS and so on.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.2
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• pp.168-174
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• 2008

Diagnosis on setteability based on human operator's experimental knowledge, which could be established by long term operation, is a limit factor to construction of automation control system in wastewater treatment plant. On-line SVI(Sludge Volume Index) analyzer was developed which can measure SV30 automatically by image capture and image analysis method. In this paper, information got by settling process was studied using On-line SVI analyzer for better operation & management of WWTPs. First, SV30 detection algorithm was developed using image capture and image analysis for settling test and it showed that automatic detection is feasible even if deflocculation and bulking was occurred. Second, turbidity assessment model was developed using image analysis.