• Journal of Sensor Science and Technology
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• v.5 no.6
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• pp.15-24
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• 1996

In this study, as a part of the basic study for the application of optical fiber sensors to smart composite structures, the integrity of optical fiber sensors embedded within the composite structures was examined and then the laser signal transmitted through optical fiber sensors during the deformation of host structures was investigated. Firstly, it was found that bending test could be substituted for tensile test by comparing cumulative failure distribution based on weakest link theory and introducing the correction factor. Weibull parameters were obtained through the experiments and the correction factor was found to be applied to cumulative failure distribution derived from bending test. The integrity of embedded optical fiber sensors due to the thermal effect was evaluated by the comparison of the mean tensile strengths of cured and uncured optical fibers. Secondly, relationships between stress-strain curve obtained in tensile test of composite laminate and the intensity of laser signal transmitted through embedded optical fibers were examined and the possibility of the effective damage detection using optical fiber sensors was studied.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.6
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• pp.972-979
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• 2001

Tool fracture index(TFI) was developed in order not only to detect tool fracture but also to predict the amount of tool fracture in face milling. TFI is calculated by using peak-to-valley values of cutting force acting on teeth and their ratio between the adjacent teeth. When the tool fractures, a large value of TFI proportional to the amount of tool fracture was obtained periodically and decreased gradually. It was found that TFI is independent of cutter runout and it almost does not vary during transient cutting such as cutting condition change during machining. The threshold of tool fracture can be analytically determined by TFI developed in this paper, because the magnitude of TFI was shown to be dependent on the ratio of the amount of tool fracture to feed per tooth and immersion ratio. It was possible to predict the amount of tool fracture in experiments by using the proposed TFI.

• The Journal of the Acoustical Society of Korea
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• v.16 no.1
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• pp.5-9
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• 1997

If small cracks in stud bolts are not detected early enough, they grow rapidly and cause total fracture. It is difficult to detect, prior to failure, flaws such as stress-corrosion cracking in thread roots and corrosion wastages using conventional ultrasonic testing methods during inservice inspection. This study show a method of detecting a small crack by digital signal processing. When ultrasonic beams travels into threads in parallel way, the echoes from each successive threads has almost the same intervals between any two signals. We can estimate the next thread signal based on previous thread signal by the predictive distance. The optimized operator is used to remove the predicted successive thread signals so that a small crack signal can be detected.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.6
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• pp.1-6
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• 2015

Tool condition monitoring plays one of the most important roles in the improvement of both machining quality and productivity. In this regard, various process signals and monitoring methods have been developed. However, most of the existing studies used cutting force or acoustic emission signals, which posed risks of interference with the machining system in dynamics, fixturing, and machining configuration. In this study, a feed motor current signal is used as a process signal representing process and tool states in tool breakage monitoring based on an adaptive autoregressive model and unsupervised neural network. From the experimental results using various cases of tool breakage, it is shown that the developed system can successfully detect tool breakage before two revolutions of the spindle after tool breakage.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.1063-1066
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• 2002

The wavelet transform is a popular tool for studying intermittent and localized phenomena in signals. In this study the wavelet transform of cutting force signals was conducted for the detection of a tool failure in turning process. We used the Daubechies wavelet analyzing function to detect a sudden change in cutting signal level. A preliminary stepped workpiece which had intentionally a hard condition was cut by the inserted cermet tool and a tool dynamometer obtained cutting force signals. From the results of the wavelet transform, the obtained signals were divided into approximation terms and detailed terms. At tool failure, the approximation signals were suddenly increased and the detailed signals were extremely oscillated just before tool failure.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.698_699
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• 2009

본 논문은 정적 편심과 회전자 도체 봉 파손에 대해서 각각 특성 해석을 한 후, 두 고장이 복합된 상태를 특성 해석하였다. 해석 대상 전동기는 coupled magnetic circuits 방법으로 모델링 하고, 시뮬레이션하였다. 특성 해석에 필요한 중요 파라미터인 인덕턴스는 winding function 이론으로 계산하였다. 시뮬레이션 결과 중에서 고정자 전류를 전류 신호 분석 기술로 특성 해석을 하였다. 특성 해석 결과에서 고장 상태가 변하면 특정 주파수의 크기가 변하는 것을 알 수 있었다. 복합 고장에서는 정적 편심은 아래쪽 측 대역 성분의 크기를 증가시키고, 회전자 도체 봉 파손은 기본 특이 주파수 주변에 기생 성분을 만드는 것을 알 수 있었다.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.9
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• pp.31-37
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• 1996

This Paper is concerned with effective signal identification method for tool breakage and micro chipping using discrete wavelet transform of feed motor current in milling operations. The wavelet transform uses an analyzing waveletfunction which is localized in both frequency and time domain to detect subtle time localized changes in input signals. The changing pattern of wavelet coefficient is continuously compared to detect tool breakage and micro chipping over one spindle revolution. The results indicate that the wavelet transform can identify tool failure with much greater sensi- tivity than the time domain monitoring and frequency domain monitoring such as FFT. Experimental results are presented to support the proposed scheme.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.1
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• pp.132-137
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• 2006

The second part of paper related rotor failure is to evaluate that the axial magnetic flux measurement could be used as a tool of the condition monitoring system for the induction motor and to develope the diagnostic algorithm for the various fault in the electric motors. The magnetic leakage flux signal is captured by the flux coil located at the end of motor without the disturbance of the operation. And the signal is analyzed both time and frequency domain to detect the failure of the motor. Specific signature can be described in tin and frequency domain for each fault of the motor. The experimental test found that the rotor failures - broken rotor bar, broken end ing and rotor eccentricity, could be detected from the spectrum with high resolution. The method of detecting the rotor fault was found by analysing the specific frequency and the sideband of the rotor bar pass frequency from axial leakage flux spectrum. In addition the optimal flux coil and measuring equipment for the axial leakage flux measurement was verified and the diagnostic method for the detection of the rotor related failure was developed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.04a
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• pp.216-222
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• 1992

현재의 가공 시스템에 있어서 가공 작업자체는 NC 공작기계, 머시닝 센터등에의해 자동적으로이루어 지고있으나, 작업 상태에 대한 감시 및 공구교환 시기의 결정은 주로 숙련된 작업자에 의해이루어 지고 있으므로완전 자동화, 무 인화에 큰장애가 되고 있다. 특히 공구 파손 및 공구 마멸에 대한 감시는 공구 교환 시점의 자동결정 뿐 아니라 가공 시스템의 무인 운전을 위해서 필수적인 것으로, 기계 정지 시간(down time)을 줄일 수 있고, 제품의 정밀도를 높일 수 있다. 본 연구에서는 이를 위한 기초 연구로, 가공상태를 나타내는 감시신호로 AC 주축 모터 전류와 주축대진동 신호 를 선정하여 엔드밀 공구와 드릴 공구를 이용한 작업에서의 공구 상태변화에 따른 감시신호의 성능을 조사하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.429-432
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• 2010

주차장에서는 다양한 형태의 사건 사고가 발생하는데, 기존 DVR(CCTV)는 단순 영상녹화 기능만 지원하므로, 이를 효과적으로 분석하는데는 한계가 있다. 따라서, DVR의 영상카메라와 마이크를 통해서 입력되는 영상과 사운드 신호를 대상으로, 해당 영상이 발생하는 음향 신호의 종류를 파악하여, 특정 음향이 발생한 영상구간을 저장하여 이를 검색할 수 있다면, 주차장 관리자가 효과적으로 사건 사고를 대처할 수 있게 된다. 본 연구에서는 주차장에서 발생하는 차량관련 음향(충돌음, 과속음, 경적음, 유리파손, 비명)을 분류하기 위해 효과적인 특징벡터를 제안하고, 제안한 특징벡터를 이용하여 신경망 차량음향분류기를 설계하여 성능을 평가함으로써, 효과적으로 차량음향을 분류하기 위한 방법을 제안하였다. 또한, 신경망 차량음향분류기를 DVR시스템과 연동하여, 마이크로부터 입력되는 음향신호를 실시간 분석하고, 특정 소리가 발생한 영상구간을 기록함으로써, 음향 키워드에 의해서 해당 사고영상을 검색 및 디스플레이하는 시스템을 개발하였다.