A Modified Wavelet Transform Profilometry

2010 ◽  
Vol 136 ◽  
pp. 140-143 ◽  
Author(s):  
Chun Yuan Liu ◽  
Bo Zhou ◽  
Xiao Min Zhao
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Continuous Wavelet Transform ◽  
Reflection Spectrum ◽  
Experimental Results ◽  
Continuous Wavelet ◽  
Surface Reflection ◽  
Noisy Background ◽  
Flat Pattern

To overcome the noisy background comes from surface reflection, spectrum overlapping and dust noisy. In this paper, a novel modified continuous wavelet transform profilometry is proposed, which employs a fringe image and a flat image to eliminate the background. Both the fringe and flat pattern are projected onto the object by a projector. With the subtraction of the flat image from the fringe image, the background is completely removed and the spectrum overlapping in the frequency domain is prevented. Experimental results showed that the proposed method got a better result than the traditional CWT.

A continuous wavelet transform analysis of multiparticle emission data at SPS energies

2019 ◽  
Vol 28 (03) ◽  
pp. 1950016
Author(s):  
Gopa Bhoumik ◽  
Argha Deb ◽  
Swarnapratim Bhattacharyya ◽  
Dipak Ghosh
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Multiscale Analysis ◽  
Experimental Results ◽  
Rapidity Distribution ◽  
Continuous Wavelet ◽  
Emission Data ◽  
Wavelet Transform Analysis ◽  
Bose Einstein ◽  
Einstein Correlation

Continuous wavelet transform approach has been applied to the pseudo-rapidity distribution of shower tracks produced in [Formula: see text]O–AgBr interactions at 60[Formula: see text]AGeV and [Formula: see text]S–AgBr interactions at 200[Formula: see text]AGeV. Multiscale analysis of wavelet pseudo-rapidity spectra has been performed in order to find out the overabundance of produced tracks at some preferred pseudo-rapidity values, i.e., production of particle clusters. Presence of ring-like correlation is not confirmed from the analysis in pseudo-rapidity space only. The clusterization effect may be attributed to the presence of Bose–Einstein correlation among the produced tracks. Comparison of experimental results with that obtained from analyzing events generated by FRITIOF and UrQMD codes is not reproduced.

Diagnosis of rotor broken bars faults in squirrel cage induction motor using continuous wavelet transform

2019 ◽  
Vol 38 (1) ◽  
pp. 167-182 ◽  
Author(s):  
Omid Abdi Monfared ◽  
Aref Doroudi ◽  
Amin Darvishi
Keyword(s):  
Wavelet Transform ◽  
Induction Motor ◽  
Continuous Wavelet Transform ◽  
Experimental Results ◽  
Continuous Wavelet ◽  
Current Frequency ◽  
Content Type ◽  
Spectral Leakage ◽  
Squirrel Cage ◽  
High Flexibility

Purpose Squirrel cage induction motors suffer from several faults such as rotor broken bar. One of the powerful methods to detect induction motor faults is the line current signature analysis. This paper aims to present a novel algorithm based on continuous wavelet transform (CWT) to diagnose a rotor broken bar fault. Design/methodology/approach The proposed CWT has high flexibility in monitoring any frequency of interest in a waveform. Based on this transform, stator current frequency spectrum is analyzed to diagnose the rotor broken bar fault. The algorithm distinguishes the healthy motor from the faulted one based on a proper index. The method can be used in steady-state running time of induction motor and under different loading conditions. Experimental results are presented to show the validity of the proposed approach. Findings The proposed index considerably increases at the broken bars conditions compared to the healthy conditions. It can clearly diagnose the faulty conditions. The experimental results are found to be in good agreement with the theoretical and simulated results. The proposed method can reduce the noise and spectral leakage effects. Originality/value The main contribution of the paper are as follows: using CWT for detection of broken bar faults; introducing a proper index for diagnosing broken bars; and introducing a supplementary index to reduce the noise and spectral leakage effects.

Classification of Underwater Echo Based on Fractal Theory and Learning Vector Quantization Neural Network

2011 ◽  
Vol 148-149 ◽  
pp. 1365-1369
Author(s):  
Pu Hua Tang ◽  
Mu Rong Zhou ◽  
Ying Yong Bu
Keyword(s):  
Neural Network ◽  
Fractal Dimension ◽  
Wavelet Transform ◽  
Vector Quantization ◽  
Continuous Wavelet Transform ◽  
Fractal Theory ◽  
Learning Vector Quantization ◽  
Experimental Results ◽  
Continuous Wavelet

A classification method for underwater echo is introduced, which based on fractal theory and learning vector quantization (LVQ) neural network. The fractal dimension was extracted from the underwater echo by continuous wavelet transform. Combining with accumulative energy as input of a LVQ neural network, neural network was used to classify four kinds of underwater echo. The experimental results showed this method is effective and reliable.

scholarly journals Vibration Signature Analysis and Parameter Extractions on Damages in Gears and Rolling Element Bearings

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Chia-Hsuan Shen ◽  
Jie Wen ◽  
Pirapat Arunyanart ◽  
Fred K. Choy
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Continuous Wavelet Transform ◽  
Gear Tooth ◽  
Continuous Wavelet ◽  
Rolling Element Bearings ◽  
Damage Scenarios ◽  
Time Frequency ◽  
Rolling Element ◽  
Machine Health Monitoring

This paper is to analyze and identify damage in gear teeth and rolling element bearings by establishing pattern feature parameters from vibration signatures. In the present work, different damage scenarios involving different combinations of gear tooth damage, bearing damage are considered. Each of the damage scenarios are studied and compared in the time domain, the frequency domain, and the joint time-frequency domain using the FM0 technique, the Fourier Transform, the Wigner-Ville Transform, and the Continuous Wavelet Transform, respectively. Results obtained from the three different signal domains are analyzed to develop indicative parameters and visual presentations that measure the integrity and wellness of the bearing and gear components. The joint time-frequency domain obtained from the continuous wavelet transform has shown to be a superior technique for providing clear visual examination solution for different types of component damages as well as for feature extractions used for computer-based machine health monitoring solution.

scholarly journals Continuous Wavelet Transform of Schwartz Distributions in DL2′(Rn), n ≥ 1

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1106
Author(s):  
Jagdish N. Pandey
Keyword(s):  
Wavelet Transform ◽  
Function Space ◽  
Continuous Wavelet Transform ◽  
Inversion Formula ◽  
Continuous Wavelet ◽  
The Real ◽  
Schwartz Distributions ◽  
Distributional Sense ◽  
Wavelet Inversion

We define a testing function space DL2(Rn) consisting of a class of C∞ functions defined on Rn, n≥1 whose every derivtive is L2(Rn) integrable and equip it with a topology generated by a separating collection of seminorms {γk}|k|=0∞ on DL2(Rn), where |k|=0,1,2,… and γk(ϕ)=∥ϕ(k)∥2,ϕ∈DL2(Rn). We then extend the continuous wavelet transform to distributions in DL2′(Rn), n≥1 and derive the corresponding wavelet inversion formula interpreting convergence in the weak distributional sense. The kernel of our wavelet transform is defined by an element ψ(x) of DL2(Rn)∩DL1(Rn), n≥1 which, when integrated along each of the real axes X1,X2,…Xn vanishes, but none of its moments ∫Rnxmψ(x)dx is zero; here xm=x1m1x2m2⋯xnmn, dx=dx1dx2⋯dxn and m=(m1,m2,…mn) and each of m1,m2,…mn is ≥1. The set of such wavelets will be denoted by DM(Rn).

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