• Journal of Korean Society of Water and Wastewater
• /
• v.35 no.1
• /
• pp.15-25
• /
• 2021

In this study, a method of leakage detection was proposed to locate leak position for a reservoir pipeline valve system using wavelet coherence analysis for an injected pressure wave. An unsteady flow analyzer handled nonlinear valve maneuver and corresponding experimental result were compared. Time series of pressure head were analyzed through wavelet coherence analysis both for no leak and leak conditions. The leak information can be obtained through either time domain reflectometry or the difference in wavelet coherence level, which provide predictions in terms of leak location. The reconstructed pressure signal facilitates the identification of leak presence comparing with existing wavelet coherence analysis.

• Journal of Korean Society on Water Environment
• /
• v.22 no.5
• /
• pp.831-837
• /
• 2006

The present study applies wavelet transform for the extraction of various periodicities which are included in TOC and pH time series of water quality and rainfall data. The primary objective of the present study is to detect the relationships between the respective data through the correlation analysis using the approximation components which are decomposed by wavelet transform. The results reveal the approximation components of TOC and pH in the 5th level of wavelet transform can explain more than 99% of the whole energy for the raw data respectively and there are considerably high correlation between the approximation components of the respective data used for the study even through no significant correlation between the raw data has been detected.

• Computers and Concrete
• /
• v.2 no.6
• /
• pp.481-498
• /
• 2005

A wavelet based approach is proposed for structural damage detection in beams, plate and delamination of composite plates. Wavelet theory is applied here for crack identification of a beam element with a transverse on edge non-propagating open crack. Finite difference method was used for generating a general displacement equation for the cracked beam in the first example. In the second and third example, damage is detected from the deformed shape of a loaded simply supported plate applying the wavelet theory. Delamination in composite plate is identified using wavelet theory in the fourth example. The main concept used is the breaking down of the dynamic signal of a structural response into a series of local basis function called wavelets, so as to detect the special characteristics of the structure by scaling and transformation property of wavelets. In the light of the results obtained, limitations of the proposed method as well as suggestions for future work are presented. Results show great promise of wavelet approach for damage detection and structural health monitoring.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.16 no.1
• /
• pp.26-32
• /
• 2007

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform(WFT or STFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform(WT) is used to decompose the acoustic emission(AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• Journal of the Korean Society of Safety
• /
• v.15 no.2
• /
• pp.1-7
• /
• 2000

This paper presents the approach involving fault detection and diagnosis of gears using pattern recognition and Wavelet transform. It describes result of the comparison between KDI (Kullback Discrimination Information) with the nearest neighbor classification rule as one of pattern recognition methods and Wavelet transform to know a way to detect and diagnosis of gear damages experimentally. To model the damages 1) Normal (no defect), 2) one tooth is worn out, 3) All teeth faces are worn out 4) One tooth is broken. The vibration sensor was attached on the bearing housing. This produced the total time history data that is 20 pieces of each condition. We chose the standard data and measure distance between standard and tested data. In Wavelet transform analysis method, the time series data of magnitude in specified frequency (rotary and mesh frequency) were earned. As a result, the monitoring system using Wavelet transform method and KDI with nearest neighbor classification rule successfully detected and classified the damages from the experimental data.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2005.05a
• /
• pp.342-348
• /
• 2005

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform (WFT or SIFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform (WT) is used to decompose the acoustic emission (AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• Journal of the Korea Society for Simulation
• /
• v.19 no.2
• /
• pp.127-135
• /
• 2010

The validation of a simulation model is a key to demonstrate that the simulation model is reliable. However, among various validation methods have been introduced, it is very poor to research the specific techniques for the time series data. Therefore, this paper suggests the methodology to verify the simulation using the time series data by Wavelet Transform, Power Spectrum and Coherence. This method performs 2 steps as followed. Firstly, we get spectrum using the Wavelet transform available for non-periodic signal separation. Secondly, we compare 2 patterns of output data from simulation model and actual system by Coherence Analysis. As a result of comparing it with other validation techniques, the suggested way can judge simulation model accuracy more clearly. By this way, we can make it possible to perform the simulation validation test under various situations using detailed sectional validation method, which has been impossible using a single statistics for the whole model.

• Journal of the Korean Society for Precision Engineering
• /
• v.30 no.1
• /
• pp.67-76
• /
• 2013

We present an innovative real-time laser welding monitoring technique employing the correlation analysis of the plasma plume optical emission generated during the process. The plasma optical radiation emitted during Nd:YAG laser welding of S45C steel samples has detected with a Photodiode and analyzed under different process conditions. The discrete DC voltage difference, filter methods and wavelet transform has been used to decompose the optical signal into various discrete series of sequences over different frequency bands. Considering that wavelet analysis can decompose the optical signals, extract the characteristic information of the signals and define the defects location accurately, it can be used to implement process-control of laser welding.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.20 no.6
• /
• pp.583-588
• /
• 2008

Swell data observed in the East Sea in February, 2008 were analyzed using wavelet method. The wavelet analyzed results show detailed time series variation of wave group, peak frequency and spectrum. The comparison of time averaged wavelet spectrum with fourier spectrum turn out to be very similar in terms of spectrum shape and peak frequency evolution but the peak frequency wave energy and the significant wave height show discrepancies. Wavelet analysis can detect the change of spectrum in time as well as in frequency and very efficient to study transient and irregular phenomena such as freak waves and abnormal swells in the ocean. More analysis with more wave data are needed for future application.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.6
• /
• pp.1812-1821
• /
• 2014

Wind has been a rapidly growing renewable power source for the last twenty years. Since wind behavior is chaotic in nature, its forecasting is not easy. At the same time, developing an accurate forecasting method is essential when wind farms are integrated into the power grid. In fact, wind speed forecasting tools can solve issues related to grid stability and reserve allocation. In this paper 30 hours ahead wind speed profile forecast is proposed using Adaptive Wavelet Neural Network (AWNN). The implemented AWNN uses a Mexican hat mother Wavelet, and Morlet Mother Wavelet for seven, eight and nine levels decompositions. For wind speed forecasting, the time series data on wind speed has been gathered from the National Renewable Energy Laboratory (NREL) website. In this work, hourly averaged 10-min wind speed data sets for the year 2004 in the Midwest ISO region (site number 7263) is taken for analysis. Data sets are normalized in the range of [-1, 1] to improve the training performance of forecasting models. Total 8760 samples were taken for this forecasting analysis. After the forecasting phase, statistical parameters are calculated to evaluate system accuracy, comparing different configurations.