Mathematical Problems in Engineering

Volume 2015, Article ID 312620, 7 pages

http://dx.doi.org/10.1155/2015/312620

## Study on the Thick-Walled Pipe Ultrasonic Signal Enhancement of Modified S-Transform and Singular Value Decomposition

^{1}Key Laboratory of Fundamental Science for Advanced Machining, Beijing Institute of Technology, Beijing 100081, China^{2}School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471000, China

Received 29 August 2014; Revised 16 December 2014; Accepted 25 December 2014

Academic Editor: Giuseppe Rega

Copyright © 2015 Haichao Cai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

When detecting the ultrasonic flaw of thick-walled pipe, the flaw echo signals are often interrupted by scanning system frequency and background noise. In particular when the thick-walled pipe defect is small, echo signal amplitude is often drowned in noise signal and affects the extraction of defect signal and the position determination accuracy. This paper presents the modified S-transform domain singular value decomposition method for the analysis of ultrasonic flaw echo signals. By changing the scale rule of Gaussian window functions with S-transform to improve the time-frequency resolution. And the paper tries to decompose the singular value decomposition of time-frequency matrix after the S-transform to determine the singular entropy of effective echo signal and realize the adaptive filter. Experiments show that, using this method can not only remove high frequency noise but also remove the low frequency noise and improve the signal-to-noise ratio of echo signal.

#### 1. Introduction

In petroleum, electric power, nuclear industry department, there are a large number of pipelines. In order to ensure the efficiency and benefit of pipeline operation, which can withstand high pressure and temperature, these pipelines tend to have larger pipe wall thickness. Practice shows that the pipes often have flaws such as crack, corrosion in the long run, along with the change of material properties. Seriously, it can endanger the safety of the pipeline [1, 2]. Therefore, testing the pipeline condition timely is important. Commonly used pipeline detection methods, such as magnetic powder, penetrant, radiography, and ultrasonic, are commonly used for pipe detection, but ultrasonic test method is widely used for its simple principle and harmlessness to human body [3]. However, when detecting the ultrasonic flaw for the thick-walled pipes, due to the scattering and attenuation, echo signal is a nonstationary random signal, which contains information related to the defect. By extracting the information of pipeline defect, it can judge whether the pipelines have flaws or not. But when scanning the power frequency interference of the system, various doping factors such as environmental noise mixed in the echo signal, it causes great influence to the defect judgment of the gate. Therefore, noise suppression and the effective extraction of echo to realize the accurate recognition are a problem worthy of further research.

Ultrasonic signal processing technology has made great progress. The correlated analysis, Fourier transform, short-time Fourier transform (STFT), wavelet transform (WT), and pseudo Wigner transform method have been used for ultrasonic signal analysis and processing. These methods of the ultrasonic signal processing show the corresponding advantages [4–8]. But the correlated analysis is just the time domain analysis; and Fourier transform is just frequency domain analysis; the signal short-time steady states, which STFT need, cannot make it effectively distinguish the multiple echo signal submerged in noise. Moreover, STFT uses the constant sliding window, and it cannot accurately decompose low frequency signal whose cycle is bigger than the time window; although WT can effectively reflect the signal of low frequency and high frequency components, it looks for suitable wavelet basis function which is not close connected with the Fourier spectrum item; the pseudo Wigner distribution is not linear transformation; the response signal spectrum will appear harmful cross terms [9–12].

S-transform is proposed by Stockwell et al. [13] which is an important tool to study the distribution of nonstationary signal’s time frequency. The following scholars promote it to the generalized S-transform. It is the product of STFT and WT joint development, which not only overcomes the fixed STFT window frequency and the absence of WT signal phase information, but also absorbs the characteristic of wavelet multiresolution, for example, the characteristics of nondestructivity, no cross interference of Cohen. It does not need signal and measuring system and does not have to satisfy admissibility conditions and so forth [14, 15]. This paper applies the modified S-transform and singular value decomposition with the signal analysis of thick-walled pipe, through controlling the noise to get better extraction of ultrasonic echo signal time-frequency information of purposes, to achieve effective testing.

This paper is organized as follows. Section 2 describes the basic principle of S-transform. In Section 3, we elaborate a mathematical model of ultrasonic signals and then present an enhancement algorithm based on modified S-transform and singular values decomposition. Section 4 utilizes ultrasonic simulation signal with different noise level and experiment signal to validate the performance of our presented algorithm and gives results and analysis. Conclusions are presented in Section 5.

#### 2. S-Transform Theories

##### 2.1. S-Transform

The known frequency domain of is expressed as the Fourier transform: where is represented as frequency, because Fourier transforms can only reflect the signal frequency domain information. The frequency can both reflect the signal frequency domain information and reflect the signal time-frequency representation as follows: is the Gaussian window function: Equation (2) is the STFT; is frequency scale regulation factor; is time shift factor; if substituting in (3), then it can get Equation (4) is substituted in (5); it can get Equation (5) is the S-transform definition given by Pinnegar and Mansinha [16], the window function of S-transform should satisfy

Therefore, by the time integral on the S-transform, we can get From (7) it can be got that the inverse transformation of S-transform [17] can be obtained from inverse Fourier transform

##### 2.2. Modified S-Transform

If introducing two regulatory factors , in S-transform Gauss function to control time and frequency resolution, then window function can be changed into Equation (9) is substituted in (5); it can get modified S-transform [18]: Based on Fourier transform properties, (10) time domain can be changed into frequency domain:

#### 3. Modified S-Transform and Singular Value Decomposition

Set the original signal sampled discrete time series , noise discrete time series , mathematical model for detecting the actual signal as follows:

Improving the discrete time sequence , S-transform can get , ; is the transform coefficients after modified S-transform; for coefficient , it can use matrix

The coefficient matrix contains not only the original time series information but also the noise time sequence information. Because the noise signal energy distribution is scattered, the adjustable time-frequency factors make the signal energy concentrated and then decompose the modified S-transform matrix by using the singular value. The decomposed singular embodies the useful signal and noise. The large singular values mainly reflect the useful signal; the rest of singular values reflect noise and analyze the singular entropy to determine the optimal order number and reconstruct the original signal. The algorithm steps of modified S-transform and singular value decompositions are as follows.

Change the thick-walled pipes ultrasonic signal by using the modified S-transform, in order to determine the value of time-frequency regulator.

Decompose the time-frequency matrix by using singular value decomposition; the definition of singular value decomposition can be got from [19] and are feature vector and indicate signal time frequency and frequency information The diagonal element of (16) satisfies ; all the diagonal elements of the matrix are called singular value of matrix . After the singular value decomposition, the singular value number of zeros is bigger and bigger, which shows the signal-to-noise ratio is good.

Compute (17) by using the signal singular entropy increment , , is the order of singular entropy, and is the total number of singular value.

Compute the ratio between and singular entropy . Taking increment ratio of singular entropy as the threshold for the trade-offs of singular value, in terms of filter accuracy, while signal of interest is easily filtered if the chosen ratio is too small, it is hard to be effectively filtered if the ratio is too large. Figure 1 shows the variation tendency of singular entropy increment for ultrasonic signal of different SNR with the variation of the order. It can be seen that at low order, with the increase of signal-to-noise ratio (SNR), the singular entropy increment also increases. At high order, however, the singular entropy increment decreases with the increase of SNR. Regardless of SNR, with the order increasing, singular entropy increment is decreasing and approaching saturation. This means that increment ratio of singular entropy is also approaching saturation; when the increment ratio of singular entropy is equal to 0.9, the signal mainly contains useful signal and most of the noise signals are removed. Therefore, the singular entropy increment ratio 0.9 can be chosen as a threshold. Figure 2 is the arithmetic flowchart. If the ratio of singular entropy is greater than 0.9, then make singular value which is greater than or equal to order equal zero and make up the novelty spectrum matrix , according to formula (15) to obtain new data matrix. The new data matrix inverses S-transform to obtain the denoising ultrasonic signal of thick-walled pipe.