• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.3 s.108
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• pp.255-262
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• 2006

Frequency spectrum using the conventional Fourier analysis gives adequate information about the dynamic characteristics of the slider air bearing for the linear and stationary cases. The intermittent contacts for the extremely low flying height, however, generate nonlinear and nonstationary vibration at the instant of contact. Nonlinear dynamic model should be developed to simulate the impulse response of the air bearing during slider-disk contact. Time-frequency analysis is widely used to investigate the nonstationary signal. Several time-frequency analysis methods are employed and compared for the slider vibration signal caused by the impact against an artificially induced scratch on the disk. The representative Wigner-Ville distribution leads to the severe interference problem by cross terms even though it gives good resolution both in time and frequency. The smoothing process improves the interference problem at the expense of resolution. In order to get the results with good resolution and little interference, the reassignment method is proposed. Among others the reassigned Gabor spectrogram shows the best resolution and readability with negligible interference.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.429-434
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• 2012

This paper presents a technology for manipulating external devices by Brain Computer Interface (BCI) system. Recently, BCI based rehabilitation and assistance system for disabled people, such as patient of Spinal Cord Injury (SCI), general paralysis, and so on, is attracting tremendous interest. Especially, electroencephalogram (EEG) signal is used to organize the BCI system by analyzing the signals, such as evoked potential. The general findings of neurophysiology support an availability of the EEG-based BCI system. We concentrate on the event-related synchronization of motor imagery EEG signal, which have an affinity with an intention for moving control of external device. To analyze the brain activity, short-time Fourier transform and particle swarm optimization are used to optimal feature selection from the preprocessed EEG signals. In our experiment, we can verify that the power spectral density correspond to range mu-rhythm(${\mu}8$~12Hz) have maximum amplitude among the raw signals and most of particles are concentrated in the corresponding region. Result shows accuracy of subject left hand 40% and right hand 38%.

• Composites Research
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• v.16 no.3
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• pp.58-67
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• 2003

To perform the realtime strain and fracture monitoring of the smart composite structures, two optical fiber sensor systems are proposed. The two types of the coherent sources were used for fracture signal detection - EDFA with FBG and EDFA with Fabry-Perot filter. These sources were coupled to EFPI sensors imbedded in composite specimens. To understand the characteristics of matrix crack signals, at first, we performed tensile tests using surface attached PZT sensors by changing the thickness and width of the specimens. This paper describes the implementation of time-frequency analysis such as short time Fourier transform (STFT) and wavelet transform (WT) for the quantitative evaluation of fracture signals. The experimental result shows the distinctive signal features in frequency domain due to the different specimen shapes. And, from the test of tensile load monitoring using optical fiber sensor systems, measured strain agreed with the value of electric strain gage and the fracture detection system could detect the moment of damage with high sensitivity to recognize the onset of micro-crack fracture signal.

• Composites Research
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• v.13 no.1
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• pp.40-49
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• 2000

The objective of this research is to develop the impact monitoring techniques providing impact identification and damage diagnostics of smart composite laminates susceptible to impacts. This can be implemented simultaneously by using the acoustic waves by the impact loads and the acoustic emission waves from damage. In the previous research, we have discussed the impact location detection process in which impact generated acoustic waves are detected by PZT using the improved neural network paradigm. This paper describes the implementation of time-frequency analysis such as the Short-Time Fourier Transform (STFT) and the Wavelet Transform (WT) on the determination of the occurrence and the estimation of damage.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.05a
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• pp.39-43
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• 2002

As the concept of the smart structure, monitoring of acoustic emission (AE) can be applied to inspect the fracture of the entire structure in operating condition using built-in sensors. The objective of this study is to find the characteristics of matrix crack signals in composites due to the different specimen shapes. To detect matrix crack signals, we performed tensile tests by changing the thickness, width and length of the specimen. For the quantitative evaluation, time frequency analysis such as short-time Fourier transform (STFT) was used to characterize the matrix crack signals from PZT sensor. The experimental result shows the distinctive signal features in frequency domain due to the different specimen shapes.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.113-116
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• 1999

신호 대 잡음비가 낮은 환경에서 센서 어레이에 입사된 입력 신호 사이의 지연시간 추정(Time Delay Estimation, 이하 TDE)은 높은 분해능이 요구된다. 본 연구에서는 높은 분해능의 TDE를 구하기 위해 상호상관함수(Cross-Correlation)에 평균 차 함수(Average Magnitude Difference function, AMDF)의 역수를 가중한 MCC (Modified Cross-Correlation)알고리즘을 제안한다. 모의신호를 사용한 수치 시뮬레이션 실험으로 종래의 AMDF, Cross-Correlation 알고리즘과 본 연구에서 제안한 MCC 알고리즘의 분해능을 비교 분석하였다 각 알고리즘의 TDE 결과에 대해 STFT(Short Time fourier Transform)에 의한 시간 주파수 해석을 한 결과 MCC알고리즘을 사용하여 TDE 분해능이 향상되었음을 보고한다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.10 no.2
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• pp.105-118
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• 2008

Shotcrete is one of the main support materials in tunnelling. Its bonding state on excavated rock surfaces controls the safety of the tunnel: De-bonding of shotcrete from an excavated surface decreases the safety of the tunnel. Meanwhile, the bonding state of shotcrete is affected by blasting during excavation at tunnel face as well as bench cut. Generally, the bonding state of shotcrete can be classified as void, de-bonded, or fully bonded. In this study, the state of the back-surface of shotcrete is investigated using impact-echo (IE) techniques. Numerical simulation of IE technique is performed with ABAQUS. Signals obtained from the IE simulations were analyzed at time, frequency, and time-frequency domains, respectively. Using an integrated active signal processing technique coupled with a Short-Time Fourier Transform (STFT) analysis, the bonding state of the shotcrete can be evaluated accurately. As the bonding state worsens, the amplitude of the first peak past the maximum amplitude in the time domain waveform and the maximum energy of the autospectral density are increasing. The resonance frequency becomes detectable and calculable and the contour in time-frequency domain has a long tail parallel to the time axis. Signal characteristics with respect to ground condition were obtained in case of fully bonded condition. As the ground condition worsens, the length of a long tail parallel to the time axis is lengthened and the contour is located in low frequency range under 10 kHz.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.243-245
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• 2005

Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. In this paper, a model-based method using neural network modeling of induction noter in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals to continuous spectra so that the neural network model can be trained with vibration spectra. And the faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results.

• Journal of Mechanical Science and Technology
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• v.20 no.7
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• pp.929-940
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• 2006

Early detection and diagnosis of incipient induction machine faults increases machinery availability, reduces consequential damage, and improves operational efficiency. However, fault detection using analytical methods is not always possible because it requires perfect knowledge of a process model. This paper proposes a neural network based expert system for diagnosing problems with induction motors using vibration analysis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals, and the neural network is trained and tested using the vibration spectra. The efficiency of the developed neural network expert system is evaluated. The results show that a neural network expert system can be developed based on vibration measurements acquired on-line from the machine.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.11
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• pp.723-730
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• 2015

This paper investigated the noise generation mechanism of a rotary compressor using experimental method. The measurement was carried out for primary parameters which influence noise characteristics. By using STFT(short time Fourier transform), noise sources of a rotary compressor were identified and vibrating modes that increase the noise are verified. Also, it was studied that the correlation between operating speed and noise. Main factors that affect the variation of noise level were considered by the comparison of the experimental results. In addition, a dynamic characteristic of crank shaft was studied and the critical speed was analyzed.