• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.1439-1444
• /
• 2005

Induction motors are the most commonly used electrical drives because they are rugged, mechanically simple, adaptable to widely different operating conditions, and simple to control. The most common faults in squirrel-cage induction motors are bearing, stator and rotor faults. Surveys conducted by the IEEE and EPRI show that the most common fault in induction motor is bearing failure (${\sim}$40% of failure). Thence, this paper addresses experimental results for diagnosing faults with different rolling element bearing damage via motor current spectral analysis. Rolling element bearings generally consist of two rings, an inner and outer, between which a set of balls or rollers rotate in raceways. We set the experimental test bed to detect the rolling-element bearing misalignment of 3 type induction motors with normal condition bearing system, shaft deflection system by external force and a hole drilled through the outer race of the shaft end bearing of the four pole test motor. This paper takes the initial step of investigating the efficacy of current monitoring for bearing fault detection by incipient bearing failure. The failure modes are reviewed and the characteristics of bearing frequency associated with the physical construction of the bearings are defined. The effects on the stator current spectrum are described and related frequencies are also determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. We utilized the FFT, Wavelet analysis and averaging signal pattern by inner product tool to analyze stator current components. The test results clearly illustrate that the stator signature can be used to identify the presence of a bearing fault.

• 조명전기설비학회논문지
• /
• 제24권5호
• /
• pp.94-105
• /
• 2010

인버터 구동 영구자석 동기전동기에서 고정자 권선과 인버터 스위치에 고장이 존재할 때 동작 중 실시간으로 고장을 검출하고 고장의 유형을 판별할 수 있는 진단 기법이 제시된다. 제안된 기법은 q축 전류의 2차 고조파를 실시간으로 관찰함으로써 이루어지며 고장이 없는 정상 조건에서의 고조파 데이터와 비교를 통해서 고장을 판별한다. 임의의 정상 동작 조건에서의 고조파 데이터는 선형 보간법을 이용해 구해진다. 고장 검출 시 진단 알고리즘은 고장 유형 판별모드로 전환되어 상전류의 파형 분석을 통해 고장의 유형을 판별한다. 제안된 고장 검출 기법의 타당성을 입증하기 위해 내부 권선 단락이 가능한 전동기가 제작되었으며 전체 시스템과 고조파 분석 및 고장 검출 알고리즘이 DSP TMS320F28335에 의해 구현된다. 제안된 방법은 부가적인 진단 장비를 요구하지 않으며 정상상태 조건이 만족된다면 동작 중 실시간으로 고장을 검출할 수 있다.

• Journal of Advanced Marine Engineering and Technology
• /
• 제31권1호
• /
• pp.95-102
• /
• 2007

The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis and prognosis are of increasing importance. This study introduces a technique to detect and identify faults in induction motors. Stator currents were measured and stored by time domain. The time domain is not suitable for representing current signals, so wavelet transform is used to convert the signals onto frequency domain. The raw signals can not show the significant feature, therefore difference values between the signal of the health conditions and that of the fault conditions are applied. The difference values were transformed by wavelet transform and the features are extracted from the transformed signals. The dynamic time warping method was used to identify the fault type. This study describes the results of detecting fault using wavelet analysis.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2002년도 하계학술대회 논문집 C
• /
• pp.1721-1723
• /
• 2002

This paper deals with the analysis of field condition for earth leakage current alarming system in the stage lighting, stage sound stage machinery installation. These analyses of field condition were carried out in accordance with investigating an installation of earth leakage current alarming system with respect to a main line of power source, dimmer, sound equipment, machinery mobile unit equipment and so on. As a result of analyses. The earth leakage current alarming system has been installed only a part of the main line of power source and the probability of places which were installed was less than 50(%). Therefore, it is desirable that the earth leakage current alarming system is installed at places which are suitable, for example, dimmer, each kind machinery etc. in order to prevent electrical hazard.

• 전기학회논문지
• /
• 제57권3호
• /
• pp.398-402
• /
• 2008

Recently, the development of superconducting fault current limiters (SFCLs) has been required as power demands increase in the power system. A distribution-level prototype resistive SFCL using coated conductor (CC) has been developed by Hyundai Heavy Industries Co., Ltd. and Yonsei University for the first time in the world. The ratings of the SFCL are 13.2kV/630A at normal operating condition. A novel non-inductive winding method is used in fabricating coils so there is almost zero impedance during normal operation. The distribution SFCL is cooled by sub-cooled liquid nitrogen $(LN_2)$ of 65K and 3 bar to enhance cryo-dielectric performance, critical current density, and thermal conductivity. In order to make reliable operation of an SFCL in real power systems, we monitored and controled its operation conditions by using supervisory control and data acquisition (SCADA) method. Thus, a monitoring system for the SFCL employing information technology (IT) is proposed and developed to be on the lookout for the operation conditions such as inside temperature, inside pressure, $LN_2$ level, voltage and current. Since operation temperature should be kept constant, bang-bang control for temperature feedback with a heater attached to the cold head of cryo-cooler is applied to the system. Short-circuit tests with prospective fault current of 10kA and AC dielectric withstand voltage tests up to 143kV for 1 minute were successfully performed at Korea Electrotechnology Research Institute. This paper deals with the development of a distribution level SFCL and its monitoring system for reliable operation.

• 조명전기설비학회논문지
• /
• 제19권6호
• /
• pp.36-45
• /
• 2005

이 논문은 다른 종류의 유도전동기 구름베어링 손상을 유도전동기 고정자 전류신호해석을 통하여 검출하고 실시간으로 손상을 진단하는 알고리즘을 개발하였다. 유도전동기 구름베어링의 손상을 검출하기 위하여 정상적인 베어링을 갖는 유도전동기, 측정열에 불량을 가지고 있는 전동기와 베어링 외륜에 구멍을 가지고 있는 2가지 종류의 비정상 베어링을 갖는 유도전동기 3set를 실험시스템을 구축하였다. 또한 유도전동기의 구름베어링시스템의 비정상적인 상태에서 고정자전류을 검출하기 위하여 TMS320F2407 DSP 칩을 이용하여 데이터 획득보드를 개발하였다. 이 고정자전류신호를 해석을 통하여 베어링 손상을 검출하기 위한 방법으로 FFT, 웨이브렛 분석 및 내적에 의한 평균 신호패던에 의한 분석결과를 제시하였다. 특히 내적에 의한 신호분석 온 통하여 베어링 손상 여부를 실시간으로 진단할 수 있는 새로운 알고리즘과 분석방법을 제시하였다.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
• /
• pp.539-542
• /
• 2003

A motor is the workhorse of our industry. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. Different internal motor faults (e.g., inter-turn short circuits, broken bearings, broken rotor bars) along with external motor faults (e.g., phase failure, mechanical overload, blocked rotor) are expected to happen sooner or later. This paper introduces the fault detection technique of induction motors based upon the stator current. The fault motors have rotor bar broken or rotor unbalance defect, respectively. The stator currents are measured by the current meters and stored by the time domain. The time domain is not suitable to represent the current signals, so the frequency domain is applied to display the signals. The Fourier Transformer is used for the conversion of the signal. After the conversion of the signals, the features of the signals have to be extracted by the signal processing methods like a wavelet analysis, a spectrum analysis, etc. The discovered features are entered to the pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, etc. This paper describes the fault detection results that use wavelet decomposition. The wavelet analysis is very useful method for the time and frequency domain each. Also it is powerful method to detect the features in the signals.

• 한국철도학회논문집
• /
• 제20권5호
• /
• pp.595-601
• /
• 2017

전철화 확대의 영향으로 전차선로 변의 고장발생 빈도는 지속적으로 증가하여, 고장위치를 표정하는 시스템의 중요도는 향상되고 있다. 그럼에도 전차선로의 정확한 고장위치 표정은 매우 어려운 현실이다. 본 논문에서는 기존 회로방정식으로 제안된 전차선로 임피던스 계산이론에 대해 현장실측 데이터를 활용하여 보편화 정리하고 변전소 및 운행차량의 전압, 전류 데이터를 동기화 실측하여 전차선로의 임피던스를 예측 하였다. 또한 예측된 전차선로 임피던스값의 유효성 확인을 위해 동일지점에서 열차전류에 따른 임피던스 변화 정도를 시뮬레이션으로 확인하였다. 끝으로 측정 데이터와 지락시험을 통한 데이터를 비교 분석하였다. 본 기법은 강제지락시험, 인공지락시험을 통해 임피던스를 예측하는 현행 방식 대비 열차운행 중단 없이 다수의 데이터를 확보할 수 있고 시간경과에 따른 임피던스 변화에 대해서도 신속하고 편리하게 대응할 수 있는 장점이 있는 것으로 판단된다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2007년도 추계학술대회 논문집 전력기술부문
• /
• pp.37-39
• /
• 2007

In the faults of power line, single line ground and line-to-line fault make power system to unbalanced. These fault currents make unbalanced power system. This paper suggests the simulation results of dynamic characteristic of HTS cable system under unbalanced faults condition using EMTDC, Quench phenomenon and current limiting effects are observed. However, quench on the HTS is destroy cable system, coordination with SFCL has to be considered.

• Nuclear Engineering and Technology
• /
• 제52권9호
• /
• pp.1998-2008
• /
• 2020

Solenoid operated valves (SOV) play important roles in industrial process to control the flow of fluids. Solenoid valves can be found in so many industries as well as the nuclear plant. The ability to be able to detect the presence of faults and predicting the remaining useful life (RUL) of the SOV is important in maintenance planning and also prevent unexpected interruptions in the flow of process fluids. This paper proposes a fault diagnosis method for the alternating current (AC) powered SOV. Previous research work have been focused on direct current (DC) powered SOV where the current waveform or vibrations are monitored. There are many features hidden in the AC waveform that require further signal analysis. The analysis of the AC powered SOV waveform was done in the time and frequency domain. A total of sixteen features were obtained and these were used to classify the different operating modes of the SOV by applying a machine learning technique for classification. Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. The results of this paper can be used to improve on the condition based monitoring of the SOV.