Papers by Keyword: Wavelets

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Authors: Yuan Hong Liu, Ming Zeng, Yan Sheng Zhang
Abstract: Additive Gauss white noise is one of the most commonly observed interferences in practical engineering applications. This paper proposed an algorithm for the adaptive determination of the optimal wavelet decomposition level based on Jarque-Bera test in efforts to solve the filtering problem of additive white Gaussian noise signal. By, The optimal decomposition level of wavelet is determined by testing the white noise which was realized by calculating skewness (S) and kurtosis (K) of the parameters. With signal-to-noise ratio (SNR) as the measurement index, simulation results show that the presented algorithm have higher accuracy, and better filtering effect on low SNR signals compared with nonparametric test methods.
Authors: Teuku Edisah Putra, Shahrum Abdullah, Dieter Schramm, Mohd Zaki Nuawi, Tobias Bruckmann
Abstract: This paper presents the ability of the wavelet transforms for compressing automobile strain data. The wavelet transforms identified and extracted higher amplitude segments and produced shorter edited signals. Based on the comparison of the edited signals resulted, it was found that the Morlet wavelet gave the shortest signals. It was able to summarize strain signals up to 77% and maintain more than 90% of the statistical parameters and the fatigue damage. Meanwhile the continuous and discrete Daubechies wavelet transforms summarized the signals below 60%. It proved that the Morlet wavelet was the best technique for fatigue data editing, especially for the automotive applications.
Authors: Raffaello Bartelletti, Gabriele Fiorentino, Giuseppe Lanzo, Davide Lavorato, Giuseppe Carlo Marano, Giorgio Monti, Camillo Nuti, Giuseppe Quaranta, Nunziante Squeglia
Abstract: Understanding the structural behavior of heritage buildings is usually a very complicated task because they typically present complex deterioration and damage patterns which cannot be fully evaluated by means of visual inspections. Moreover, the reliability of such constructions largely depends on different materials, structural components and details, the health of which is often unknown or affected by great uncertainties. In this regard, the experimental dynamic testing of heritage buildings and monuments subjected to ambient vibrations has become a valuable tool for their assessment because of the minimum interference with the structure. Traffic-induced vibrations are not always a feasible dynamic load for monumental buildings due to their very low intensity or owing to existing restrictions to road and rail traffic. On the other hand, the analysis of the experimental response under earthquakes can lead to more relevant information about the dynamic behavior of historic constructions, provided that the structure is equipped with a permanent sensor network. Within this framework, the present work illustrates preliminary results carried out from time and frequency domain analyses performed on the experimental dynamic response of the leaning tower of Pisa using seismic records. The main dynamic features of the monument have been identified, and then examined taking into account the seismic input and the soil-foundation-structure interaction.
Authors: Cherukuri Bhargav Sai, D. Mallikarjuna Reddy
Abstract: In this study, an effective method based on wavelet transform, for identification of damage on rotating shafts is proposed. The nodal displacement data of damaged rotor is processed to obtain wavelet coefficients to detect, localise and quantify damage severity. Because the wavelet coefficients are calculated with various scaled indices, local disturbances in the mode shape data can be found out in the finer scales that are positioned at local disturbances. In the present work the displacement data are extracted from the MATLAB model at a particular speed. Damage is represented as reduction in diameter of the shaft. The difference vectors between damaged and undamaged shafts are used as input vectors for wavelet analysis. The measure of damage severity is estimated using a parameter formulated from the distribution of wavelet coefficients with respect to the scales. Diagnosis results for different damage cases such as single and multiple damages are presented.
Authors: Okuwobi Idowu Paul, Yong Hua Lu
Abstract: Vibration is a mechanical phenomenon whereby oscillations occur about an equilibrium point. The oscillation may be periods such as the motion of a pendulum or random such as the movement of tire on a gravel road. Vibration causes waste of energy and creates unwanted sound-noise. Monitoring such process generally possess a big problem especially for a system. The present traditional single resolution techniques could not solve this problem, coupled with the Fourier transform which seems to be one of the best method in analyzing and monitoring vibration in machineries or machinery components.This paper present a new algorithm using wavelet- packet based feature in classification of vibration signals. This study explores the feasibility of the wavelet packet transform as a tool in search for features that may be used in the detection and classification of machinery vibration signals. By formulating a systematic method of determining wavelet packet based features that exploit class specific differences among interested signals, which avoid human interaction. This new algorithm provide more effective method to achieve robust classification than traditional single resolution techniques. The new algorithm in wavelet transform techniques proved to be more efficient, better analysis, and provides better results with minimum error than any existing method.
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