• Title/Summary/Keyword: Acoustic Diagnosis

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APPLICATION OF ACOUSTIC EMISSION FOR DIAGNOSIS OF QUENCH IN SUPER CONDUCTIVE MAGNET AT CRYOGENIC TEMPERATURE

  • Lee, Joon-Hyun;Lee, Min-Rae;Kwon, Young-Kin;Song, Bong-Min
    • Proceedings of the KSME Conference
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    • 2007.05a
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    • pp.160-165
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    • 2007
  • It is well recently recognized that quench is one of the serious problems for the integrity of superconducting magnets, which is mainly attribute to the rapid temperature rising in the magnet due to some extrinsic factors such as conductor motion, crack initiation etc. In order to apply acoustic emission(AE) technique effectively to monitor and diagnose superconducting magnets, it is essential to identify the sources of acoustic emission. In this paper, an acoustic emission technique has been used to monitor and diagnose quenching phenomenon in racetrack shaped superconducting magnets at cryogenic environment of 4.2K. For these purposes special attention was paid to detect AE signals associated with the quench of superconducting magnets. The characteristics of AE parameters have been analyzed by correlating with quench number, winding tension of superconducting coil and charge rate by transport current.

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Detection of onset of failure in prestressed strands by cluster analysis of acoustic emissions

  • Ercolino, Marianna;Farhidzadeh, Alireza;Salamone, Salvatore;Magliulo, Gennaro
    • Structural Monitoring and Maintenance
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    • v.2 no.4
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    • pp.339-355
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    • 2015
  • Corrosion of prestressed concrete structures is one of the main challenges that engineers face today. In response to this national need, this paper presents the results of a long-term project that aims at developing a structural health monitoring (SHM) technology for the nondestructive evaluation of prestressed structures. In this paper, the use of permanently installed low profile piezoelectric transducers (PZT) is proposed in order to record the acoustic emissions (AE) along the length of the strand. The results of an accelerated corrosion test are presented and k-means clustering is applied via principal component analysis (PCA) of AE features to provide an accurate diagnosis of the strand health. The proposed approach shows good correlation between acoustic emissions features and strand failure. Moreover, a clustering technique for the identification of false alarms is proposed.

A Review on Recent Study of Acoustic Analysis (음성분석의 최근 연구 동향에 관한 문헌적 고찰)

  • Park, Chan-Kyu;Park, Young-Jae;Park, Young-Bae
    • The Journal of the Society of Korean Medicine Diagnostics
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    • v.13 no.1
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    • pp.65-71
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    • 2009
  • Objective : To review studies about acoustic analysis and to study its medical application. Method : We investigate the research results through internet search engines, such as Pubmed. Conclusions : I would like to conclude that there should be continued study, along with the objectification and quantification of diagnosis and application of acoustic analysis in the wide variety of oriental medicine.

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Failure Forecast Diagnosis of Small Wind Turbine using Acoustic Emission Sensor

  • Bouno Toshio;Yuji Toshifumi;Hamada Tsugio;Hideaki Toya
    • KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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    • v.5B no.1
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    • pp.78-83
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    • 2005
  • Currently in Japan, the use of the small wind turbine is an upward trend. There are already many well established small wind turbine generators in use and their various failures have been reported. The most commonly sighted failure is blade damage. Thus the research purpose was set to develop a simple failure diagnostic system, where an Acoustic Emission (AE) signal was produced from the failure part of a blade which was measured by AE sensor. The failure diagnostic technique was thoroughly examined. Concurrently, the damage part of the blade was imitated, the AE signal was measured, and a FFT(Fast Fourier Transform) analysis was carried out, and was compared with the output characteristic. When one sheet of a blade was damaged 40mm or more, the level was computed at which failure could be diagnosed.

A Study on Diagnosis of Transformers Aging Sate Using Wavelet Transform and Neural Network (이산웨이블렛 변환과 신경망을 이용한 변압기 열화상태 진단에 관한 연구)

  • 박재준;송영철;전병훈
    • Journal of the Korean Institute of Electrical and Electronic Material Engineers
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    • v.14 no.1
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    • pp.84-92
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    • 2001
  • In this papers, we proposed the new method in order to diagnosis aging state of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion skewness, kurtosis) about each acoustic emission signal. Also, these coefficients are used to identify normal and fault signal of internal partial discharge in transformer. As improved method for classification use neural network. Extracted statistical parameters are input into an back-propagation neural network. The number of neurons of hidden layer are obtained through Result of Cross-Validation. The network, after training, can decide whether the test signal is early aging state, alst aging state or normal state. In quantity analysis, capability of proposed method is superior to compared that of classical method.

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A Study on the Aging Characteristics of Power Capacitors using Electrical Methods (전기적 방법을 이용한 전력용 콘덴서 열화특성 연구)

  • 김은식;김영욱;김종서;윤철섭;박대희
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • 1998.11a
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    • pp.199-202
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    • 1998
  • Power capacitors are highly reliable equipment due to their completely enclosed configuration. Aging diagnosis system using partial discharge(PD) and acoustic emission(AE) is being highlighted as a research area for degradation of power capacitors. Their dielectric strength can be however reduced due to some stresses such as over-voltage or thermal degradation of the insulation material during their long period of operation. In this paper, it has been developed to express the AE of the EVENT(average amplitude) and HIT(pulse count) according to the source location. And real time measurement of PD signals for aging diagnosis of power capacitors.

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The Abnormal Condition Diagnosis of Compressor Parts using Multi-signal Sensing (복합신호 검출에 의한 압축기 부품의 상태 진단)

  • Lee, Kam-Gyu;Kim, Jeon-Ha;Kang, Ik-Su;Kang, Myung-Chang;Kim, Jeong-Suk
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.3 no.3
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    • pp.11-16
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    • 2004
  • In this study, the characteristics of signals such as acoustic emission, vibration amplitude and noise level which are derived from the abnormal condition of compressor are investigated. The normal condition, vane stick sound and roller defect condition are chosen to analyze the signal in each cases. From the feature extraction of each signals, the dominant parameters of each signals which can identify the abnormal condition are suggested.

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Characteristic Analysis and Origin Positioning of Acoustic Signals Produced by Partial Discharges in Insulation Oil

  • Park, Dae-Won;Jo, Hyang-Eun;Kim, Sun-Jae;Kil, Gyung-Suk
    • Journal of Electrical Engineering and Technology
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    • v.8 no.6
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    • pp.1468-1473
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    • 2013
  • This paper dealt with the propagation characteristics of acoustic signals produced by partial discharges and the positioning of PD origin in insulation oil to develop insulation diagnostic techniques of oil-immerged transformers. Electrode systems such as needle to plane, plane to plane, and particle electrodes were fabricated to simulate some defects of power transformers. In addition, the frequency spectrum and propagation characteristics of acoustic signals with partial discharge (PD) in insulation oil were analyzed. Although there were differences based on the type of defect, the frequency spectra of the acoustic signals measured by wide and narrow band acoustic emission (AE) sensors were distributed in the range of 50 kHz-400 kHz. Therefore, a narrowband AE sensor is suitable for the diagnosis of oil-immersed power transformers. We could find the position of the PD source with an error margin of 10% in the experiments by calculating the position of the PD occurrence using the time difference of arrival measured by five AE sensors.

A Fault Detection Scheme in Acoustic Sensor Systems Using Multiple Acoustic Sensors (다중 센서를 이용한 음향 센서 시스템의 고장 진단)

  • Oh, Won-Geun
    • The Journal of the Korea institute of electronic communication sciences
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    • v.11 no.2
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    • pp.203-208
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    • 2016
  • This paper presents a fault detection and data processing algorithm for acoustic sensor systems using the multiple sensor algorithm that has originally developed for the wireless sensor nodes. The multiple sensor algorithm can increase the reliability of the sensor systems by utilizing and comparing the measurements of the multiple sensors. In the acoustic sensor system, the equivalent sound level($L_{eq}$) is used to detect the faulty sensor. The experiment was conducted to demonstrate the feasibility of the multiple acoustic sensor algorithm, and the results show that the algorithm can detect the faulty sensor and validate the data.