• Tribology and Lubricants
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• 제35권5호
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• pp.317-322
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• 2019

In spite of progress in tribological research, machine component failure due to friction and wear has been reported frequently. This failure may lead to secondary damage that can cause huge expense for maintenance and repair. To prevent economic loss, it is important to detect and predict the initial failure point. In this sense, various researchers have been tried to develop Condition Monitoring (CM) method using Acoustic Emission (AE) generated while the materials undergo failure. In this study, effect of particles on friction and wear was investigated using the pin-on-plate friction test and AE signal was recorded with a band-width type AE sensor. The experiments were performed in dry and lubricant conditions using steel and glass as specimens. After the experiment, 3D laser microscope image was captured to evaluate the wear behavior quantitatively. The AE signal was analyzed in time-domain and frequency-domain. The amplitude was compared with the frictional results. The results of this study showed that particle generation accelerate wear, generate high magnitude AE signal and change the frequency characteristics of the signal. Also, lubricant condition test results showed low coefficient of friction, low wear rate, and low magnitude of AE signal compared to the dry condition. It is expected that the results of this study will aid in better assessment of wear in CM technology

• International Journal of Precision Engineering and Manufacturing
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• 제1권1호
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• pp.106-110
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• 2000

The researches classifying the artificial flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including user defined function is developed and the total procedure is made up the digital signal processing, feature extraction, feature selection, classfier design. Specially it is composed with and discussed using the ststistical classfier such as the linear discriminant function classfier, the empirical Bayesian classfier.

• Composites Research
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• 제35권1호
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• pp.8-12
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• 2022

테라헤르츠(THz)파의 디컨벌루션을 통한 유리섬유 복합재 내부 결함 신호 분석 기법을 연구하였다. 결함이 존재하는 유리섬유 복합재료 시편을 제작하였고, Terahertz Time-Domain Spectroscopy (THz-TDS) 시스템의 반사모드를 통해 THz 신호를 측정하였다. 디컨벌루션을 위해 THz 입사 신호와 검출 신호의 Normalized Cross Correlation (NCC) 계산을 통해 THz 신호의 피크 위치를 증폭하였다. 증폭된 피크의 위치 및 세기를 임펄스로 추출하였고 THz 원신호에서 추출된 임펄스 위치의 신호를 제거하였다. 해당 과정을 반복함으로써 시편의 경계면을 나타내는 THz 검출 신호의 주요 임펄스를 도출하였다. 주요 임펄스와 기준 신호의 컨벌루션을 통해 노이즈가 제거된 THz 원신호의 복원이 가능한 것을 확인함으로써 디컨벌루션 과정을 검증하였다. 결과적으로 주요 임펄스들의 검출 시간을 통해 15 ㎛ 이내의 정확도로 유리섬유 복합재 내부 결함의 두께 판별이 가능하였다.

• 드라이브 ㆍ 컨트롤
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• 제12권3호
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• pp.44-51
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• 2015

In this study, a metering cylinder was constructed, and the velocity obtained from the linear velocity transducer (LVT) of the cylinder piston was used to evaluate the dynamic performance of an electro-hydraulic servovalve. Frequency response experiments involving the spool displacement and piston velocity (LVT signal) were conducted with different input signal amplitudes, hydraulic pipe diameters, and supply pressures. The spool displacement signal accurately reflected the performance of the servovalve. Meanwhile, the -3 dB bandwidth frequency of the LVT signal was similar to the spool displacement signal, except for a small-amplitude input signal, and the $-90^{\circ}$ phase lag bandwidth frequency showed some differences.

• Smart Structures and Systems
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• 제23권1호
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• pp.71-79
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• 2019

In general, it may be advantageous to measure the pressure pulsation near a valve to detect a valve defect in a linear compressor. However, the acceleration signals are more advantageous for rapid classification in a mass-production line. This paper deals with the performance improvement of fault classification using only the compressor-shell acceleration signal based on the relation between the refrigerant pressure pulsation and the shell acceleration of the compressor. A transfer function was estimated experimentally to take into account the signal noise ratio between the pressure pulsation of the refrigerant in the suction pipe and the shell acceleration. The shell acceleration signal of the compressor was modified using this transfer function to improve the defect classification performance. The defect classification of the modified signal was evaluated in the acceleration signal in the frequency domain using Fisher's discriminant ratio (FDR). The defect classification method was validated by experimental data. By using the method presented, the classification of valve defects can be performed rapidly and efficiently during mass production.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.895-896
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• 2006

This paper proposes a new method to measure the ECG signal from the driver. The ECG signal is often measured in the room. But it is mixed with many kinds of noise when we measure it during the vehicle moving. We classified noise occupied most many parts as the experimental among them. And we designed one suitable filter for each noise. It used ALE(Adaptive Line Enhancement) to remove the noise occurred to electromagnetic wave in vehicle. To remove the noise occurred to steering or vibration of vehicle, we used Wavelet transformation after ALE(preprocessing filter).

• 한국정밀공학회지
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• 제28권6호
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• pp.670-677
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• 2011

This study is to estimate the joint torques without torque sensor using the EMG (Electromyogram) signal of agonist/antagonist muscle with Neural Network Back Propagation Algorithm during the elbow motion. Command Signal can be guessed by EMG signal. But it cannot calculate the joint torque. There are many kinds of field utilizing Back Propagation Learning Method. It is generally used as a virtual sensor estimated physical information in the system functioning through the sensor. In this study applied the algorithm to obtain the virtual senor values estimated joint torque. During various elbow movement (Biceps isometric contraction, Biceps/Triceps Concentric Contraction (isotonic), Biceps/Triceps Concentric Contraction/Eccentric Contraction (isokinetic)), exact joint torque was measured by KINCOM equipment. It is input to the (BP)algorithm with EMG signal simultaneously and have trained in a variety of situations. As a result, Only using the EMG sensor, this study distinguished a variety of elbow motion and verified a virtual torque value which is approximately(about 90%) the same as joint torque measured by KINCOM equipment.

• 대한기계학회논문집A
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• 제21권7호
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• pp.1013-1020
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• 1997

Among various gyroscopes, the vibrating gyroscope has advantages such as compact size, mass-productivity and low cost. However this kind of gyroscope usually suffer from low linearity and low signal to noise ratio. Therefore, it is necessary to study on vibrating gyroscope to improve performance. In this paper, triangular cross section si selected in consideration for several points. The existing designs of exciting and sensing have some problems such that small signal size and low linearity. This paper proposes new design of exciting and sensing which is named one period exciting and the other period sensing. This design improves signal size, but it cannot improve linearity. This is because above two designs are the same open-loop type. So, another new design is proposed, which is named rebalancing, is applied to one period exciting and the other period sensing. This design are closed-loop type. It feedbacks the output signal. According to control theory, it can improve linearity. The circuits of each design are realized and used to calibration test. Calibration results show that new design of rebalancing improves linearity and signal size.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1254-1258
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• 2003

One of the major factors that effect sensor performance is analog noise that added in a sensor signal such as voltage. In multi-axis force sensors, error sources may be classified mainly in two groups. One is structural error due to inaccuracy of sensor body. The other error source is noise signals existing in the sensed information. This paper presents a brief review about the principle of multi-axis force sensors, and then proposes a method that can reduce the effect of noise signal to sensor performance. The method is to convert analog voltage signal to digital numbers near sensor body and then to read these digital signals and conduct signal processing in the computer. By this way, we can eliminate a bad effect of electromagnetic wave emitted from computer and of 60 Hz noise emitted from AC source. The proposed method is investigated through experimental demonstration. The experimental results show that it improves S/N ratio of the sensor about 40 times in our experimental setup.

• Smart Structures and Systems
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• 제3권1호
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• pp.1-22
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• 2007

The energy efficiency of a self-powered wireless sensing system for pressure monitoring in injection molding is analyzed using Bond graph models. The sensing system, located within the mold cavity, consists of an energy converter, an energy modulator, and a ultrasonic signal transmitter. Pressure variation in the mold cavity is extracted by the energy converter and transmitted through the mold steel to a signal receiver located outside of the mold, in the form of ultrasound pulse trains. Through Bond graph models, the energy efficiency of the sensing system is characterized as a function of the configuration of a piezoceramic stack within the energy converter, the pulsing cycle of the energy modulator, and the thicknesses of the various layers that make up the ultrasonic signal transmitter. The obtained energy models are subsequently utilized to identify the minimum level of signal intensity required to ensure successful detection of the ultrasound pulse trains by the signal receiver. The Bond graph models established have shown to be useful in optimizing the design of the various constituent components within the sensing system to achieve high energy conversion efficiency under a compact size, which are critical to successful embedment within the mold structure.