• Steel and Composite Structures
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• v.21 no.6
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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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• v.22 no.2
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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.

• Proceedings of the KIEE Conference
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• 2005.07a
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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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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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.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.143-148
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• 2007

The many studies have been proceeding to express accurately the feature of a sudden signal and a uncertain system in the image processing field. It is well know that Fourier Transform is widely used for frequency analysis of any signal. However, The frequency transform domain is not used for expressing the sudden signal change and non-stationary signal at the time-axis by this method. This paper describes of image analysis by discrete wavelet transform. Wavelet modulus maxima on transformed plane gives the Lipschitz exponent expression, which is useful to examine the characteristics of signal or the edge of an image. It is possible to reconstruct the original image only using the few maxima points. The fractal analysis is applied as an examples. The visualized image of oil flow on a ship model is analyzed. The fractal variable is obtained by the maxima analysis and the good results on the exprement is obtained by the visualized image analysis.

• Progress in Superconductivity
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• v.5 no.1
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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.

• Proceedings of the KIEE Conference
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• 2005.07a
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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.

• Proceedings of the KIEE Conference
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• summer
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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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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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.

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.1008-1014
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• 2003

In this paper, we propose the image retrieval method based on object regions using bidirectional round filter in the wavelet transform domain. A conventional method that includes unnecessary background information reduce retrieval efficiency, because of the extraction of feature vectors from the whole region of subband. On proposed method, it extracts accurate feature vectors and keep certainly retrieval efficiency in case of reduced feature vectors, because of the extraction of feature vectors from the only extracted object region. Furthermore, it improve retrieval efficiency by removing unnecessary background information. Consequently, the retrieval efficiency is improved with 2.5%∼5.5% values, which have a little chances to vary according to characteristics of image.