• 제목/요약/키워드: Fault signal

검색결과 666건 처리시간 0.023초

진동 신호 분석을 이용한 전력용 변압기의 고장 판별 (Fault Discrimination of Power Transformers using Vibration Signal Analysis)

  • 윤용한;유치형;김재철;정찬수;이정진
    • 대한전기학회논문지:전력기술부문A
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    • 제48권1호
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    • pp.1-7
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    • 1999
  • In power transformers, vibration signals can occur at winding and core due to the change of current, voltage, and temperature and the deformation of winding and core. The deformation of winding and core occurs electromagnetic force induced by fault current in power systems. There firem the changes of vibration signals can be very different in normal or fault states of power transformers. We edtect and analyze the changes of vibration signals and use them as a tool for fault diagnosis of power transformers. This paper presents fault discriminating polliblility using the changes of fundamental waves and higher harmonics in power transformers. We showed the fault discriminating functions that are made at each case ; normal state and fault state. These functions are tested by the detected vibration signals, and we showed that the proposed method can discriminate the state of power transformers.

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Microphone Array와 열화상 카메라를 이용한 고압설비 고장검출 (Malfunction Detection of High Voltage Equipment Using Microphone Array and Infrared Thermal Imaging Camera)

  • 한순신;최재영;이장명
    • 제어로봇시스템학회논문지
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    • 제16권1호
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    • pp.25-32
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    • 2010
  • The paper proposes a hierarchical fault detection method for the high voltage equipment using a microphone array which detects the location of fault and the thermal imaging and CCD cameras which verifies the fault and stores the image, respectively. There are partial arc discharges on the faulty insulators, which generates a specific pattern of sound. Detecting the signal using the microphone array, the location of the faulty insulator can be estimated. The 6th band-pass filter was applied to remove noise signal from wind or external influence. When the mobile robot carries the thermal and CCD cameras to the possible place of the fault insulator, the fault insulators or power transmission wires can be detected by the thermal images, which are caused by the aging or natural erosion. Finally, the CCD camera captures the image of the fault insulator for the record. The detection scheme of fault location using the microphone array and the thermal images have been proved to be effective through the real experiments. As a result of this research, it becomes possible to use a mobile robot with the integrated sensors to detect the fault insulators instead of a human being.

소음 신호의 웨이블렛 변환 및 상호상관 함수를 이용한 고장 검출 및 위치 판별 (Fault Detection and Localization using Wavelet Transform and Cross-correlation of Audio Signal)

  • 지효근;김정현
    • 한국정밀공학회지
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    • 제31권4호
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    • pp.327-334
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    • 2014
  • This paper presents a method of fault detection and fault localization from acoustic noise measurements. In order to detect the presence of noise sources wavelet transform is applied to acoustic signal. In addition, a cross correlation based method is proposed to calculate the exact location of the noise allowing the user to quickly diagnose and resolve the source of the noise. The fault detection system is implemented using two microphones and a computer system. Experimental results show that the system can detect faults due to artifacts accidentally inserted during the manufacturing process and estimate the location of the fault with approximately 1 cm precision.

유전 알고리즘기반 퍼지 모델을 이용한 모터 고장 진단 자동화 시스템의 구현 (Implementation of Automated Motor Fault Diagnosis System Using GA-based Fuzzy Model)

  • 박태근;곽기석;윤태성;박진배
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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    • pp.24-26
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    • 2005
  • At present, KS-1000 which is one of a commercial measurement instrument for motor fault diagnosis has been used in industrial field. The measurement system of KS-1000 is composed of three part : harmonic acquisition, signal processing by KS-1000 algorithm, diagnosis for motor fault. First of all, voltage signal taken from harmonic sensor is analysed for frequency by KS-1000 algorithm. Then, based on the result values of analysis skilled expert makes a judgment about whether motor system is the abnormality or degradation state. But the expert system such a motor fault diagnosis is very difficult to bring the expectable results by mathematical modeling due to the complexity of judgment process. In this reason, we propose an automation system using fuzzy model based on genetic algorithm(GA) that builded a qualitative model of a system without priori knowledge about a system provided numerical input output data.

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뉴럴네트웍을 이용한 디젤기관의 데이터 이상감지 시스템에 관한 연구 (A study on the data fault detection system for diesel engine using neural network.)

  • 천행춘;김영일;김경엽;안순영;오현경;유영호
    • 한국마린엔지니어링학회:학술대회논문집
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    • 한국마린엔지니어링학회 2002년도 춘계학술대회논문집
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    • pp.245-250
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    • 2002
  • The operational data of diesel generator engine is two kind of discrete signal and analog signal. We can find the fault information from analog data measured for every sampling time if it is invested the changing rate or direction of data. This paper propose the Malfunction Diagnosis Engine(MDE) using the commercial data mining tool and show the data Process and fault finding method with the data collected from generator engine of the ship.

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은닉 마르코프 모형을 이용한 회전체 결함신호의 패턴 인식 (Pattern Recognition of Rotor Fault Signal Using Bidden Markov Model)

  • 이종민;김승종;황요하;송창섭
    • 대한기계학회논문집A
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    • 제27권11호
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    • pp.1864-1872
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    • 2003
  • Hidden Markov Model(HMM) has been widely used in speech recognition, however, its use in machine condition monitoring has been very limited despite its good potential. In this paper, HMM is used to recognize rotor fault pattern. First, we set up rotor kit under unbalance and oil whirl conditions. Time signals of two failure conditions were sampled and translated to auto power spectrums. Using filter bank, feature vectors were calculated from these auto power spectrums. Next, continuous HMM and discrete HMM were trained with scaled forward/backward variables and diagonal covariance matrix. Finally, each HMM was applied to all sampled data to prove fault recognition ability. It was found that HMM has good recognition ability despite of small number of training data set in rotor fault pattern recognition.

A Fault Detection System Design for Uncertain Fuzzy Systems

  • Yoo, Seog-Hwan;Choi, Byung-Jae
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제6권1호
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    • pp.1-5
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    • 2006
  • This paper deals with a fault detection system design for uncertain nonlinear systems modelled as T-S fuzzy systems with the integral quadratic constraints. In order to generate a residual signal, we used a left coprime factorization of the T-S fuzzy system. From the filtered signal of the residual generator, the fault occurence can be detected effectively. A simulation study with nuclear steam generator level control system shows that the suggested method can be applied to detect the fault in actual applications.

Fault Classification in Phase-Locked Loops Using Back Propagation Neural Networks

  • Ramesh, Jayabalan;Vanathi, Ponnusamy Thangapandian;Gunavathi, Kandasamy
    • ETRI Journal
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    • 제30권4호
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    • pp.546-554
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    • 2008
  • Phase-locked loops (PLLs) are among the most important mixed-signal building blocks of modern communication and control circuits, where they are used for frequency and phase synchronization, modulation, and demodulation as well as frequency synthesis. The growing popularity of PLLs has increased the need to test these devices during prototyping and production. The problem of distinguishing and classifying the responses of analog integrated circuits containing catastrophic faults has aroused recent interest. This is because most analog and mixed signal circuits are tested by their functionality, which is both time consuming and expensive. The problem is made more difficult when parametric variations are taken into account. Hence, statistical methods and techniques can be employed to automate fault classification. As a possible solution, we use the back propagation neural network (BPNN) to classify the faults in the designed charge-pump PLL. In order to classify the faults, the BPNN was trained with various training algorithms and their performance for the test structure was analyzed. The proposed method of fault classification gave fault coverage of 99.58%.

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1D CNN 알고리즘 기반의 가속도 데이터를 이용한 머시닝 센터의 고장 분류 기법 연구 (A Study on Fault Classification of Machining Center using Acceleration Data Based on 1D CNN Algorithm)

  • 김지욱;장진석;양민석;강지헌;김건우;조용재;이재욱
    • 한국기계가공학회지
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    • 제18권9호
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    • pp.29-35
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    • 2019
  • The structure of the machinery industry due to the 4th industrial revolution is changing from precision and durability to intelligent and smart machinery through sensing and interconnection(IoT). There is a growing need for research on prognostics and health management(PHM) that can prevent abnormalities in processing machines and accurately predict and diagnose conditions. PHM is a technology that monitors the condition of a mechanical system, diagnoses signs of failure, and predicts the remaining life of the object. In this study, the vibration generated during machining is measured and a classification algorithm for normal and fault signals is developed. Arbitrary fault signal is collected by changing the conditions of un stable supply cutting oil and fixing jig. The signal processing is performed to apply the measured signal to the learning model. The sampling rate is changed for high speed operation and performed machine learning using raw signal without FFT. The fault classification algorithm for 1D convolution neural network composed of 2 convolution layers is developed.