• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.6
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• pp.241-251
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• 2002

The effect of sensor faults in direct torque control(DTC) based induction motor drives is analyzed and a new Instrument fault detection isolation scheme(IFDIS) is proposed. The proposed IFDIS, which operated in real-time, detects and isolates the incipient fault(s) of speed sensor and current sensors that provide the feedback information. The scheme consists of an adaptive gain scheduling observer as a residual generator and a special sequential test logic unit. The observer provides not only the estimate of stator flux, a key variable in DTC system, but also the estimates of stator current and rotor speed that are useful for fault detection. With the test logic, the IFDIS has the functionality of fault isolation that only multiple estimator based IFDIS schemes can have. Simulation results for various type of sensor faults show the detection and isolation performance of the IFDIS and the applicability of this scheme to fault tolerant control system design.

• Proceedings of the KIEE Conference
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• 2006.07e
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• pp.59-60
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• 2006

A shorted-turn test was performed at the Pyungtaek combined cycle power plant on gas turbine generators. The test was conducted using a permanent flux probe and on-line shorted-turn diagnosis system. The permanent flux probe installed in the generator air gap senses the field winding slot leakage flux and produces a voltage proportional to the rate of change signature unique to each field winding. We have also applied a voltage waveform analysis technique that can identify the pole location, slot number and number of shorted-turn with each slot.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.111-116
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• 2022

The causes of motor burnout include overload, phase loss, restraint, interlayer short circuit, winding ground fault, instantaneous overvoltage, and the rotor contacting the stator, leading to insulation breakdown, leading to breakdown or electrical accidents. Therefore, equipment failure causes not only loss due to cost required for equipment maintenance/repair, but also huge economic loss due to productivity decrease due to process stop because the process itself including the motor is stopped. The current level of technology for diagnosing motor failures uses vibration, heat, and power analysis methods, but there is a limit to analyzing the problems only after a considerable amount of time has passed according to the failure. Therefore, in this paper, a device and algorithm for measuring insulation resistance using DC AMP signal was applied to an industrial motor to solve this problem. And by following the insulation resistance state value, we propose a diagnosis of deterioration and failure of the motor that cannot be solved by the existing method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.106-109
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• 2005

산업화와 더불어 자동화의 요구에 따라 여러 분야에서 유도전동기의 응용 사례가 늘어나고 있다. 본 연구는 인버터단의 전류센서에서 실시간 얻어진 전류신호로 부터 대표적인 전기, 기계적인 4가지 고장(Bearing Fault, Broken Rotor bar, Misalignment, Unbalance)을 검출하여 예기치 못한 고장에 대비할 수 있는 실시간 진단 알고리듬을 제시한다. 실시간 진단의 핵심요소인 동기화 방법으로서 Hilbert Transform을 응용하였다. 총 40세트의 정상 모터 데이터를 임의로 선택하여 두 데이터의 차를 이용하여 0에 근사한 정상 모터 뎀플릿 값을 설정하였다. 이를 이용하여 진단 대상 전동기의 고장 유무를 미리 판단하게 된다. 만약 기준치 이상의 오차가 나타나게 되면, 이와 비교하기 위한 미리 수집된 각4가지 고장 전동기의 미소신호 템플릿 데이터와의 유사성을 비교하여 고장의 종류를 표시하며, 고장의 종류가 진단되지 않은 대상은 고장 유무만 표시된다.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.1
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• pp.100-110
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• 2008

It is not easy to monitor and identify all engine faults and conditions using conventional fault detection approaches like the GPA (Gas Path Analysis) method due to the nature and complexity of the faults. This study therefore focuses on a model based diagnostic method using Neural Network algorithms proposed for fault detection on a turbo shaft engine (PW 206C) selected as the power plant for a tilt rotor type unmanned aerial vehicle (Smart UAV). The model based diagnosis should be performed by a precise performance model. However component maps for the performance model were not provided by the engine manufacturer. Therefore they were generated by a new component map generation method, namely hybrid method using system identification and genetic algorithms that identifies inversely component characteristics from limited performance deck data provided by the engine manufacturer. Performance simulations at different operating conditions were performed on the PW206C turbo shaft engine using SIMULINK. In order to train the proposed BPNN (Back Propagation Neural Network), performance data sets obtained from performance analysis results using various implanted component degradations were used. The trained NN system could reasonably detect the faulted components including the fault pattern and quantity of the study engine at various operating conditions.

• Journal of Power Electronics
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• v.8 no.2
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• pp.181-191
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• 2008

The task performed by induction motors grows increasingly complex in modern industry and hence improvements are sought in the field of fault diagnosis. It is essential to diagnose faults at their very inception, as unscheduled machine down time can upset critical dead lines and cause heavy financial losses. Artificial intelligence (AI) techniques have proved their ability in detection of incipient faults in electrical machines. This paper presents an application of AI techniques for the detection of inter-turn insulation and bearing wear faults in single-phase induction motors. The single-phase induction motor is considered a proto type model to create inter-turn insulation and bearing wear faults. The experimental data for motor intake current, rotor speed, stator winding temperature, bearing temperature and noise of the motor under running condition was generated in the laboratory. The different types of fault detectors were developed based upon three different AI techniques. The input parameters for these detectors were varied from two to five sequentially. The comparisons were made and the best fault detector was determined.

• Proceedings of the KIEE Conference
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• 2006.07e
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• pp.47-48
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• 2006

본 논문에서는 LabVIEW를 이용한 휴대용 3상 유도전동기 회전자 바 고장 진단 시스템을 개발하였다. LabVIEW는 NI사에서 개발한 그래픽컬 프로그래밍 언어로서 VI(Virtual Instrument)기술을 이용하여 계측, 분석, 표현 프로그램에 최적화된 언어이다. 유도전동기의 입력단에 Current Probe를 연결하여 전류를 측정하였고, NI사의 USB-DAQ보드를 이용하여 데이터를 수집한 후 노트북 컴퓨터에서 실행되는 LabVIEW 프로그램에 의해 고장을 진단할 수 있는 실시간 휴대용 진단 시스템을 개발하였다. 회전자 바에 고장이 일어나면 선 전류의 불균형, 손실 증가, 토크맥동과 평균 토크의 감소를 가져올 수 있다. 고장 진단 방법으로는 회전자 바 고장의 특징 주파수를 찾아 진단하는 방법이 있다. 회전자 바 고장에 대하여 1개와 2개 바를 절단한 회전자를 사용하여 각각 실험하였으며, FFT 분석에 의하여 실시간으로 유도전동기 회전자 바 고장을 진단 할 수 있음을 보였다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.119-122
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• 2005

A study on motor current signature analysis has been executed for monitoring the fault of journal bearing due to wear. The air gap eccentricity of motor produces specific frequencies in motor current, the supplied current frequency plus and minus rotational rotor frequency. The air gap eccentricity is simulated by the clearance of Journal bearing. The amplitudes of the specific frequencies increase with the increasing clearances. The amplitudes of the specific frequencies continue to increase over the wear limit that is used in the manufacturer of the test motor. Though clear relations between the amplitudes of the specific frequencies and the clearances are not obtained in this paper, the specific frequencies can be used as an indicator of a journal bearing fault. Further study is necessary to make out the quantitative relations between the specific frequencies and the clearances.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.11
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• pp.874-882
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• 2003

The condition assessment of engineering systems has increased in importance because the manpower needed to operate and supervise various plants has been reduced. Especially, induction motors are at the core of most engineering processes, and there is an indispensable need to monitor their health and performance. So detection and diagnosis of motor faults is a base to improve efficiency of the industrial plant. In this paper, a model-based fault detection system is developed for induction motors, using steady state vibration signals. Early various fault detection systems using vibration signals are a trivial method and those methods are prone to have missed fault or false alarms. The suggested motor fault detection system was developed using a model-based reference value. The stationary signal had been extracted from the non-stationary signal using a data segmentation method. The signal processing method applied in this research is FFT. A reference model with spectra signal is developed and then the residuals of the vibration signal are generated. The ratio of RMS values of vibration residuals is proposed as a fault indicator for detecting faults. The developed fault detection system is tested on 800 hp motor and it is shown to be effective for detecting faults in the air-gap eccentricities and broken rotor bars. The suggested system is shown to be effective for reducing missed faults and false alarms. Moreover, the suggested system has advantages in the automation of fault detection algorithms in a random signal system, and the reference model is not complicated.

• Journal of the Korean Vacuum Society
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• v.6 no.4
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• pp.308-313
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• 1997

TDS (Thermal Lkso~ption Spectroscopy)system, for diagnosis of CRT manufacturing process, was designed and constructed. Outgassings and themla1 desorptions from the part or materials of CRT can be measured and analysed with this system at various temperatures. The system is consisted of 3 pirrts. vacuum chamher and pumping system with variable conductance, sample heating stages & their controller, and outgassing measurement devices, like as ion gauge or quadrupole mass spectrometer. The ultimate pressure of the system was under $1\times10^{-7}$ Pa. With the variable conductance system, the effective pumping speed of the chamber could he controlled from sub 11s to 100 11s. The effective pumping speed values were determined by dynamic flow measurement principle. The temperatures and ramp rate of sample were controlled by tungsten heater and PID controller up to $600^{\circ}C$ within t $\pm 1^{\circ}C$$difference to setting value. Ion gauge & QMS were calibrated for quantitative measurements. Some examples of TDS measurement data ;ind application on the CRT process analysis were shown.