• Steel and Composite Structures
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• 제21권6호
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• pp.1183-1210
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• 2016

Viscoelastic sandwich structures (VSSs) are widely used in mechanical equipment, but in the service process, they always suffer from aging which affect the whole performance of equipment. Therefore, aging state recognition of VSSs is significant to monitor structural state and ensure the reliability of equipment. However, non-stationary vibration response signals and weak state change characteristics make this task challenging. This paper proposes a novel method for this task based on adaptive second generation wavelet packet transform (ASGWPT) and multiwavelet support vector machine (MWSVM). For obtaining sensitive feature parameters to different structural aging states, the ASGWPT, its wavelet function can adaptively match the frequency spectrum characteristics of inspected vibration response signal, is developed to process the vibration response signals for energy feature extraction. With the aim to improve the classification performance of SVM, based on the kernel method of SVM and multiwavelet theory, multiwavelet kernel functions are constructed, and then MWSVM is developed to classify the different aging states. In order to demonstrate the effectiveness of the proposed method, different aging states of a VSS are created through the hot oxygen accelerated aging of viscoelastic material. The application results show that the proposed method can accurately and automatically recognize the different structural aging states and act as a promising approach to aging state recognition of VSSs. Furthermore, the capability of ASGWPT in processing the vibration response signals for feature extraction is validated by the comparisons with conventional second generation wavelet packet transform, and the performance of MWSVM in classifying the structural aging states is validated by the comparisons with traditional wavelet support vector machine.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.123-130
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• 2022

During the past decades, detection of gear defects remains as a major problem, especially when the gears are subject to non-stationary phenomena. The idea of this paper is to mixture a multilevel wavelet transform with a fast EMD decomposition in order to early detect gear defects. The sensitivity of a kurtosis is used as an indicator of gears defect burn. When the gear is damaged, the appearance of a crack on the gear tooth disrupts the signal. This is due to the presence of periodic pulses. Nevertheless, the existence of background noise induced by the random excitation can have an impact on the values of these temporal indicators. The denoising of these signals by multilevel wavelet transform improves the sensitivity of these indicators and increases the reliability of the investigation. Finally, a defect diagnosis result can be obtained after the fast transformation of the EMD. The proposed approach consists in applying a multi-resolution wavelet analysis with variable decomposition levels related to the severity of gear faults, then a fast EMD is used to early detect faults. The proposed mixed methods are evaluated on vibratory signals from the test bench, CETIM. The obtained results have shown the occurrence of a teeth defect on gear on the 5th and 8th day. This result agrees with the report of the appraisal made on this gear system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.341-343
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• 2005

This paper introduces a compression algorithm for power quality disturbance signal via the discrete wavelet transform, DWT. Algorithm to estimate a time delay from the power quality disturbance signal is proposed. Pseudo-stationary signal is constructed from the estimated time delay. A difference signal or nonstationary signal is obtained by removing a pseudo-stationary signal from a disturbance signal. DWT is applied to a difference signal. The threshold is applied to reduce a number of coefficients. Simulation results show the resonable compression ratio while keep low signal distortion.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.895-902
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• 2007

Rotating machinery has two typical faults with clearance, one is partial rub and the other is looseness. Due to these faults, non-linear and non-stationary signals are occurred. Therefore, time-frequency analysis is necessary for exact fault diagnosis of rotating machinery. In this paper newly developed time-frequency analysis method, HHT(Hilbert-Huang Transform) is applied to fault diagnosis and compared with other method of FFT, SFFT and CWT. The results show that HHT can represent better resolution than any other method. Consequently, the faults of rotating machinery are diagnosed efficiently by using HHT.

• 융합신호처리학회논문지
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• 제8권3호
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• pp.143-148
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• 2007

영상처리에 있어서 갑작스러운 신호와 불확실한 시스템의 특징을 정확하게 표현하기 위하여 많은 연구가 수행되어 왔다. 많이 알려진 퓨리어 변환은 임의 신호의 주파수 해석에 폭넓게 사용되어 왔다. 그러나 이 방법은 시간 축에서 발생하는 갑작스러운 신호 변환과 비정상적인 신호를 주파수 변환 영역에서 나타낼 수 없으므로 유용하지 않다. 본 논문은 이산 웨이브렛을 이용한 영상해석을 하였다. 이는 웨이브렛 영역에서의 극대치는 Lipschitz 지수 표현이 가능하고, 또한 극대치만 사용하여 영상 데이터의 윤곽선 및 데이터 특성을 표현하는 유용함을 나타내었다. 더욱이 적은 극대치만을 사용하여 본래 영상을 재생하는 것도 가능하게 되었다. fractal 해석은 예로서 적용되었다. 그리고, 모형 배에서 기름 띠의 가시화 영상이 해석되었다. 극대치 해석으로 fractal 변수를 구하고, 가시화 영상 해석의 실험으로 양호한 결과를 얻었다.

• Progress in Superconductivity
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• 제5권1호
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• pp.26-30
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• 2003

Magneto-cardiogram(MCG) signals may be highly distorted by the environmental noise, such as power-line interference, broadband white noise, surrounding magnetic noise, and baseline wondering. Several kinds of digital filters and noise cancellation methods have been designed and realized by many researchers, but these methods gave some problems that the original signal may be distorted by digital filter due to the wideband characteristics of background noise. To eliminate noise effectively without distortion of MCG signals, we performed multi-level frequency decomposition using wavelet packets and local adaptive noise cancellation in each local frequency range. In addition to the proposed wavelet filter to eliminate these various non-stationary noise elements, the local adaptive filter using the least mean square(LMS) algorithm and the soft threshold do-noising method are introduced in this paper. The signal to noise ratio(SNR) and the reconstruction square error(RSE) are calculated to evaluate the performance of the proposed method and compared with the results of the conventional wavelet filter and adaptive filter. The experimental results show that the proposed local adaptive filtering method is better than the conventional methods.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.530-532
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• 2005

Actually, it's very difficult to discriminate the transient on underground power cable system because of the reflected signal including many noises. Therefore, in this paper, a solution based on multiple scales correlation of the transient using SWT(Stationary Wavelet Transform) is presented. It's quick and straightforward. For applying all algorithms, we just use the signal captured in single end.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.336-338
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• 2004

This paper introduces a compression algorithm for power quality disturbance signal via the discrete wavelet transform, DWT. Fundamental signal or stationary signal is estimated and then subtracted from a given signal to obtain a difference signal or nonstationary signal. DWT is applied to a difference signal to get coefficients that are thresholded to reduce a number of coefficients. Simulation results show the resonable compression ratio while keep low signal distortion.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 춘계학술대회논문집
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• pp.742-746
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• 2008

With the trend of factory automation, nowadays, much industrial machinery tends to be put into 24-hours operation a day. However, these trends in industrial equipments also increase the possibility of various mechanical problems and bring about innumerable maintenance cost. There is a strong need of the condition monitoring and diagnosis for industrial equipment, especially rotating machinery, since they are connected not only to the reduction in the maintenance costs but also connected to the enhancement of production efficiency. Generally, to evaluate the operating conditions in the machinery in the industrial field, various physical properties are monitored. Among them, vibration and Noise signals are the mist important indicator and it is effectively used in many diagnosis systems for machinery. Much previous research is based in the FFT (Fast Fourier Transform) method. The spectral analysis is assumed that the signal is stationary. However, almost random signals are non-stationary. The wavelet transform has been recognized an efficient Method. Most interesting sounds have time-varying features. Signal processing techniques for the analysis of transient sound have been not clearly given yet.

• 한국멀티미디어학회논문지
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• 제6권6호
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• pp.1008-1014
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• 2003

본 논문에서는 보다 강인하면서 화질의 열화를 최소화할 수 있는 워터 마크의 은닉을 위 해 9/7 쌍직교 웨이브릿 변환영역에서의 지각적 동조 (tuned)특성과 정상상태 일반화 가우시안 (stationary generalized Gaussian) 모델에서의 NVF (noise visibility function)를 적용하는 방법을 제안한다 이 방법은 웨이브릿 영역에서 각 부대역에 대한 문턱값을 결정한 다음 지각적 중요 계수를 찾은 후 이 지각적 중요계수에만 적응적인 삽입강도를 갖는 워터마크를 은닉한다. 워터마크는 3레벨로 분해된 웨이브릿 변환영역에서 영상의 인간시각 시스템에서 계산된 JND값과 통계적 다해상도 특성을 기반으로 정상상태 일반화 가우시안 모델에 따라 각 계수들의 값에 따라 은닉된다. 실험 결과 제안한 방법에서 WF를 이용함으로써 에지나 텍스쳐 영역에 더 강하게 삽입할 수 있고 각 부대역에 대한 지각특성을 이용할 수 있어 우수한 비가시성과 강인성을 확인하였다.