Application of continuous wavelet transform to the impact location estimation of the Loose Parts Monitoring System (LPMS)

2006 ◽  
Vol 2 (3) ◽  
pp. 233
Author(s):  
Jin Ho Park ◽  
Jeong Han Lee
Keyword(s):  
Wavelet Transform ◽  
Monitoring System ◽  
Continuous Wavelet Transform ◽  
Location Estimation ◽  
Continuous Wavelet ◽  
Impact Location ◽  
Loose Parts ◽  
The Impact

Development of Novel Optical Fiber AE Sensor with Multi-Sensing Function

2006 ◽  
Vol 321-323 ◽  
pp. 71-76 ◽  
Author(s):  
Hideo Cho ◽  
Takashi Naruse ◽  
Takuma Matsuo ◽  
Mikio Takemoto
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Optical Fiber ◽  
Monitoring System ◽  
Continuous Wavelet Transform ◽  
Steel Plate ◽  
Continuous Wavelet ◽  
Arrival Times ◽  
Resonance Frequencies ◽  
Different Diameters

A novel optical fiber acoustic emission (AE) system with multi-sensing function in single long fiber was developed and utilized for the estimation of AE sources of model steel plate and jointed pipes. Multi-sensing function was achieved by dividing the single sensing fiber into several sensor portions with different resonance frequencies. The resonance frequencies were provided by winding the sensing fiber around the solid rods (sensor holders) with different diameters. The monitoring system with three sensors in a 10 m long fiber was demonstrated to detect three wave packets with different frequencies and correctly estimate the source locations of AEs from artificial (Nelson-Sue) sources on a 0.9 wide x 1.8 m long steel plate. Here the arrival times of AEs for the source location were determined by the continuous wavelet transform. Source locations on the steel plate were determined within a distance error of 53 mm. The system also makes the location of the pipe with damage possible.

Detecting Rebars and Tubes Inside Concrete Slabs Using Continuous Wavelet Transform of Elastic Waves

2004 ◽  
Vol 20 (4) ◽  
pp. 297-302 ◽  
Author(s):  
C. H. Chiang ◽  
C. C. Cheng
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Concrete Slab ◽  
Amplitude Spectrum ◽  
Concrete Slabs ◽  
Continuous Wavelet ◽  
Steel Tubes ◽  
Impact Echo ◽  
The Impact ◽  
Impact Echo Method

AbstractA typical problem of elastic wave methods, such as the impact echo method, is due to peak detection based solely on amplitude spectrum. Current study aims to improve the feature identification of impact-echo signals obtained from buried objects in concrete slabs. Steel rebar, steel tubes, and PVC tubes embedded in a concrete slab are tested. Numerical simulations are carried out based on models constructed using the finite element method. The received signals, both experimental and simulated, are analyzed using both fast Fourier transform and continuous wavelet transform (CWT). The amplitude spectra can only provide global information and lose some important local effects of frequency components. This can be resolved by continuous wavelet transform for preserving the transient effects in the frequency domain. Localized spectral contents are analyzed and thus better understanding is achieved for the impulse responses due to different objects below the surface of the concrete slab. Features related to steel rebar, PVC and steel tubes are readily identified in the coefficient plot of wavelet coefficients. Multiple reflections and vibration modes related to various characteristics of wave propagation in the concrete slab can now be decomposed into distinctive frequency bands with different time durations. The result of CWT provides more information and is easier to interpret than that of the spectral analysis. The same peak frequency found in the amplitude spectrum is now distinguishable between PVC and steel tubes at a resolution of 0.1kHz or better. Such findings provide a more effective way to pick up true rebar signals using the impact-echo method.

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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