Noise Reduction Using Wavelet Transform in Ultrasonic Flaw Detection of Small-Diameter Steel Pipe with Thick Wall

2011 ◽  
Vol 383-390 ◽  
pp. 4755-4761
Author(s):  
Shao Jiang Wang ◽  
Li Hou ◽  
Yu Lin Wang ◽  
Jian Quan Zhang
Keyword(s):  
Wavelet Transform ◽  
Noise Reduction ◽  
Flaw Detection ◽  
Small Diameter ◽  
Internal Noise ◽  
Thick Wall ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Ultrasonic Signals ◽  
Steel Pipes

In order to ensure that small diameter steel pipes with thick wall have high intensity and high quality, ultrasonic immersion method with focusing probe was used to detect the flaw of the small-diameter steel pipes with thick wall. In practice, the echoes are often corrupted with external noise or internal noise, therefore, it is necessary to reduce the noise and to enhance the SNR of ultrasonic signals. A technique for improving the SNR of ultrasonic signals using wavelet transform is presented. In this method, WT, consider as one band-pass filter, is used to remove the noises. The performance of this technique has been verified by experimental, which is done by using a series of flaw ultrasonic echoes obtained from a specimen of the small-diameter steel pipes with thick wall. In particular we have found the processing of the ultrasonic signals using wavelet transform extremely useful for noise reduction. After processing, the SNR of ultrasonic signals are enhanced substantially. All experimental results show that this technique is effective for removing the white noise from the ultrasonic signals.

Noise reduction in seismic data using Fourier correction coefficient filtering

Geophysics ◽  
1997 ◽  
Vol 62 (5) ◽  
pp. 1617-1627 ◽  
Author(s):  
Douglas Alsdorf
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Seismic Data ◽  
Wave Number ◽  
Correction Coefficient ◽  
Band Pass Filter ◽  
Data Set ◽  
Pass Filter ◽  
Phase Angle Difference ◽  
Reduction Methods

The correlation coefficient between two frequency (or two wave number) componets equals the cosine of their phase‐angle difference. This relation can be exploited to build a filter that separates noise from signal in seismic data in either the F‐X or F-K domain (termed “correlation coefficient filtering”). To implement this filter, seismic data are first divided to form two subsets that are then compared using the cosine function. Signal is defined as the correlative frequencies (or wavelengths) while noncorrelative energy is attributed to noise. Depending on the application, appropriate subsets may consist of (1) groups of adjacent traces or (2) low‐fold stacks created from differing shot gathers. When comparing adjacent traces [i.e., (1)], the correlation coefficient filter combines both phase and dip information and assumes that reflections advance relatively little in time across traces and less than the noise. Correlation coefficient filtering of low‐fold stacks [i.e., (2)] does not depend on dip. Reflections are assumed to be present in both subsets whereas the noise is found only in one data set. Hence, the reflections are correlative and the noise is noncorrelative. In either case, the filter reduces linearly dipping coherent energy, ground roll, and randomly occurring noise bursts while generally maintaining signal integrity. A primary advantage of this filter is its simplicity. It is implemented much like a simple band‐pass filter, thus requiring much less parameterization than alternative noise‐reduction methods.

scholarly journals Detect the live after eartquake with CW radar

2009 ◽  
Author(s):  
Seyed Javad Javadi Moghaddam
Keyword(s):  
Wavelet Transform ◽  
Digital Signal ◽  
Radar System ◽  
Analog Signal ◽  
Pass Band ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Portable System ◽  
Band Pass ◽  
Cw Radar

In this paper, new algorithm for Detecting the Live after Earthquake is presented. Here the application of the CW Radar with frequency 2.45GHz in a portable system for detecting the live below a mass of concrete or trash is introduced. The characters of radar hardware are shown too. The software which is used for computer process is LabVIEW, that some part of it is presented. Output of the Radar system which is analog convert to digital signal and enters into PC then for using of continues filtering by a section of the program digital signal is converted to an analog signal again. Now a software band-pass filter with variable pass band is applied, which change the quantity of the system. For the mathematic analyze a special wavelet transform (in-place kind) is applied that its algorithm and its mathematic debate are existed.

A novel variable filter band discrete wavelet transform: Theory and principle

2014 ◽  
Vol 12 (04) ◽  
pp. 1460006
Author(s):  
Zhong Zhang ◽  
Hiroshi Toda ◽  
Takashi Imamura ◽  
Tetsuo Miyake
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Perfect Reconstruction ◽  
Discrete Wavelet ◽  
Band Pass Filter ◽  
Mother Wavelet ◽  
Pass Filter ◽  
Signal Characteristics ◽  
Filter Characteristic ◽  
Band Pass

It is well-known that a mother wavelet for the discrete wavelet transform (DWT) has the band-pass filter characteristic with octave width in the frequency domain and can be used for octave analysis. However, it is possible that the octave analysis is not necessarily the most suitable to match the analysis signal. In this study, in order to construct the most suitable basis to match the analysis signal, a novel variable-filter band discrete wavelet transform (VFB-DWT) is proposed. It is achieved by using variable-band filters instead of conventional decomposition and reconstruction sequences, which are designed in consideration of the real signal characteristics. Additionally, it is proven that perfect reconstruction of the analysis signal by VFB-DWT is guaranteed using the perfect shift invariant theorem that underlies the theory of the PTI-CDWT having base DWT.

scholarly journals Extraction of Quasi-Monochromatic Gravity Waves from an Airglow Imager Network

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 615
Author(s):  
Chang Lai ◽  
Wei Li ◽  
Jiyao Xu ◽  
Xiao Liu ◽  
Wei Yuan ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Gravity Waves ◽  
Basis Vector ◽  
Discrete Wavelet ◽  
Band Pass Filter ◽  
Monochromatic Wave ◽  
Pass Filter ◽  
Wave Patterns ◽  
Airglow Image ◽  
Band Pass

An algorithm has been developed to isolate the gravity waves (GWs) of different scales from airglow images. Based on the discrete wavelet transform, the images are decomposed and then reconstructed in a series of mutually orthogonal spaces, each of which takes a Daubechies (db) wavelet of a certain scale as a basis vector. The GWs in the original airglow image are stripped to the peeled image reconstructed in each space, and the scale of wave patterns in a peeled image corresponds to the scale of the db wavelet as a basis vector. In each reconstructed image, the extracted GW is quasi-monochromatic. An adaptive band-pass filter is applied to enhance the GW structures. From an ensembled airglow image with a coverage of 2100 km × 1200 km using an all-sky airglow imager (ASAI) network, the quasi-monochromatic wave patterns are extracted using this algorithm. GWs range from ripples with short wavelength of 20 km to medium-scale GWs with a wavelength of 590 km. The images are denoised, and the propagating characteristics of GWs with different wavelengths are derived separately.

scholarly journals Phased Array Ultrasonic Test  Signal Enhancement and Classification Using Empirical Wavelet Transform And Deep Convolution Neural Network

2021 ◽  
Author(s):  
Jayasudha J.C ◽  
Lalithakumari S
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Phased Array ◽  
Ultrasonic Test ◽  
Convolution Neural Network ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Empirical Wavelet Transform ◽  
Deep Convolution Neural Network

Abstract In the recent past, Non-Destructive Testing (NDT) has become a most popular technology due to its efficiency and accuracy without destroying the object and maintains its original structure and gathering while examining external and internal welding defects. Generally, the NDT environment is harmful which is distinguished by huge volatile fields of electromagnetic, elevated radiation emission instability and elevated heat. Therefore, a suitable NDT approach could be recognized and practiced. In this paper, a novel algorithm is proposed based on Phased array ultrasonic test (PAUT) for NDT in order to attain the proper test attributes. In the proposed methodology, carbon steel welding section is synthetically produced with various defects and tested using PAUT method. The signals acquired from PAUT device is found with noise interference. The Adaptive Least Mean Square (ALMS) filter is proposed to filter PAUT signal in order to eliminate random noise and Gaussian noise. The ALMS filter is the combination of low pass filter (LPF), high pass filter (HPF) and band pass filter (BPF). The time domain PAUT signal is converted into frequency domain signal in order to extract more number of features by applying Empirical Wavelet Transform (EWT) algorithm. In the frequency domain signal, 1st order and 2nd order features extraction techniques are applied to extract various features for further classification. The Deep Learning methodology is proposed for classification PAUT signals. Based on the PAUT signal features, the Deep Convolution Neural Network (DCNN) is applied for further classification. The DCNN will classify the welding signal is whether it is defective or non-defective. The Confusion Matrix (CM) is used for estimation of measurement of performance of classification as calculating accuracy, sensitivity and specificity. The experiments prove that out proposed methodology for PAUT testing for welding defect classification is obtained more accurately and efficiently across existing methodologies by providing numerical and graphical results.

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