Detection of Multiple Ultrasonic Echoes Reflected from Internal Flaws in Structures Using Advanced Signal Processing Techniques

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3^ème Conférence Internationale sur

le Soudage, le CND et l’Industrie des Matériaux et Alliages (IC-WNDT-MI’12) Oran du 26 au 28 Novembre 2012,

http://www.csc.dz/ic-wndt-mi12/index.php 22

Detection of Multiple Ultrasonic Echoes Reflected from Internal Flaws in Structures Using Advanced Signal Processing Techniques

S. Haddad¹, M. Grimes, T. Benkedidah, A. Boufersada

1NDT Lab, Faculty of Sciences and Technology, Jijel University, Algeria E-Mail: s_haddad@univ-jijel.dz, sof_had@yahoo.fr

Tel: +213(0)661217995

Abstract

In order to improve the detection accuracy of multiple ultrasonic echoes reflected from non- homogeneous structure, we have used three advanced signal processing techniques, namely, empirical mode decomposition, wavelet analysis and split spectrum processing. Simulation results to detect multiple ultrasonic overlapping echoes contaminated by white Gaussian additive noise are presented, which demonstrate the feasibility of the proposed processing techniques for detecting multiple targets in such materials. An experiment technique was used to study the proposed processing schemes, in which a cubic shape mortar specimen was processed.

Keywords: Non-destructive testing, Empirical Mode Decomposition, Wavelet analysis, Split Spectrum Processing, multiple ultrasonic overlapping echoes.

1. Introduction

In ultrasonic non-destructive testing of complex structures, the extraction of targets from signals is a complicate task due to the high level of background grain noise present in the measured signals. In large-grained materials significant backscattering occurs at the grain boundaries especially as the wavelength approaches the grain size, which makes grain noise an important factor in limiting layers and flaw detection capability.

The acquisition system is non-linear and the backscattered signal information is non-stationary due to frequency dependent scattering, attenuation and dispersion. The standard spectral analysis cannot determine the time of arrival of different frequency components in the signal. To overcome this problem, three approaches of signal processing are proposed in this work. With EMD method, any signal can be decomposed into a finite and often small number of “intrinsic mode functions” (IMF) that satisfy the following two conditions: (1) in the whole data set, the number of extrema and the number of zero crossings must either equal or differ at most by one; and (2) at any point, the mean value of the envelope defined by the local maxima and the envelope defined by the local minima is zero [1]. Wavelet Analysis (WA) is an advanced technique in signal processing field. Its prominent capability in feature extraction and detail detection has been proven and used in various application fields. In traditional signal processing fields such as ultrasonic signal processing, WA played an important role in denoising, signal recognizing and classification, as well as feature extraction [2].

A suitable solution for detecting multiple echoes masked by high intensity grain scattering echoes is to employ Empirical Mode Decomposition combined with Split Spectrum Processing or Wavelet Analysis. EMD is used to extract temporal and frequency information using different IMFs and SSP or WA are used to discriminate target echoes from the undesired grain echoes and enhance detection and position determination.

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http://www.csc.dz/ic-wndt-mi12/index.php 23 The split spectrum processing technique obtains a frequency-diverse ensemble of narrowband signals through a filter bank then recombines them nonlinearly to improve target visibility. Although split spectrum processing is an effective method for suppressing grain noise in ultrasonic non-destructive testing, its application was mainly limited to the detection of single targets or multiple targets having similar spectra1 characteristics [3,4,5].

The results of signal processing methods applied in ultrasonic echoes detection are presented in various forms, so that a direct comparison is very difficult [6,7,8,9,10]. The received experimental signals examined in this paper contain three echoes from closely spaced targets embedded in background grain noise.

2. Signal processing methods 2.1. EMD based decomposition

EMD is an adaptive method that decomposes the signal into a sum of frequency-modulated oscillations called intrinsic mode functions (IMFs). IMFs are not built on an a priori basis, but are directly constructed from the signal itself. The numerical procedure used to obtain the different IMFs is described in [11]. Briefly, the local maxima and minima of x t

( )

are interpolated, which gives the envelopes e_max

( )

t and e_min

( )

t . Then, the mean envelope m t

( ) = (

e_max

( )

t

+

e_min

( )) / 2

t is computed, and the difference d t

( ) =

x t

( ) -

m t

( )

is defined as the first IMF. The whole procedure is then repeated on m t

( )

to extract the following IMFs. The signal x t

( )

can then be expressed as

1

( ) ( ) ( )

n

i n

i

x t c t r t

=

å

+

with c t_i

( )

being the ith IMF and r t_n

( )

the residue.

2.2. Wavelet based decomposition

The wavelet transform, a powerful tool for localized frequency analysis, decomposes an input signal into smooth and detailed parts with low-pass and high-pass filters on multiresolution levels. The 1-D wavelet transform is defined as a decomposition of a signal x t

( )

with a family of orthonormal bases

, ( )

j k t

y generated from a kernel function y

( )

t by dilation j and translation k

/ 2

, ( ) 2 ^j (2 ^j )

j k t t k

y = ^- y ^- -

Since y_{j k}_, ( )t forms an orthonormal set, the wavelet coefficients a_{j k}_, of the signal x t

( )

can be calculated by the inner product:

, ( ), , ( ) ( ). , ( ) ( )

j k j k j k

a = x t y t =

ò

x t y t d t

2.3. Split Spectrum Processing

The SSP algorithm can be described as follows: The first step involves fast Fourier transform (FFT) which gives the frequency spectrum of the received echo signal. In the second step, several filters split the signal spectrum into different narrow frequency bands. Next step, inverse FFT gives the time

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http://www.csc.dz/ic-wndt-mi12/index.php 24 domain signal of each individual frequency band. Observations from each channel cover the bandwidth of the frequency spectrum of the transducer and each observation contributes to signal-to- noise improvement. Therefore, at any given time, the outputs of band pass filters can be represented as a random feature vector that contains information related to flaw and grain echoes. The signals from each individual frequency band (SSP channel) are passed into a post-detection processor. This processor can employ different techniques such as frequency compounding, minimization, maximization, etc.

3. Simulation results

In order to verify the effectiveness of the signal processing methods, we take an example of an ultrasonic simulated signal consisted of four echoes, the last three of them are overlapped; Frequency, amplitude and location in table I. Our aim through this simulation, is to separate and detect the echoes first without noise then in the presence of noise, the simulated signal is embedded in a Gaussian white noise (signal to noise ratio SNR = 10 dB) in a way where the four echoes are totally masked.

Table 1- Location, Amplitude and Frequency of the simulated ultrasonic signal

Echoes# #1 #2 #3 #4

Location 1x10^-5s 11.5x10^-5s 12x10^-5s 13x10^-5s

Amplitude 1.2 V 1 V 0.8 V 0.6 V

Frequency 1 MHz 0.5 MHz 0.25 MHz 0.125 MHz

0 1 2

x 10^-4 -1

-0.5 0 0.5 1 1.5

Time [s]

Amplitude [V]

Simulated signal

Time [s]

Frequecy [Hz]

STFT of the original signal

0 1 2

x 10^-4 0

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

2x 10⁶

Figure 1: Simulated signal -left, STFT of the simulated signal -right

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http://www.csc.dz/ic-wndt-mi12/index.php 25 Figure 1 shows an example of the simulated data based on the parameters presented in table 1, which reflects an example of the potential variations in the spectral characteristics of multiple overlapping targets in non-homogeneous materials. Figure 2 illustrates the result of the processed data using EMD, where we can see clearly the capability of the EMD to detect and separate the overlapping echoes in such case. In figure 3 we present the position of each detected echo based on the selected IMFs of the original simulated echoes. Based on the temporal position and the spectrogram in figure 2 the separated last three echoes are presented in IMF1 (Echo1 and Echo2), IMF2 (Echo3) and IMF4 (Echo4).

0 1 2

x 10^-4 -1

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

Time [s]

Amplitude [V]

IMF1

0 1 2

x 10^-4 -0.5

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

Time [s]

Amplitude [V]

IMF2

Time [s]

Frequecy [Hz]

STFT of the IMF1

0 1 2

x 10^-4 0

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

2x 10⁶

Time [s]

Frequecy [Hz]

STFT of the IMF2

0 1 2

x 10^-4 0

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

2x 10⁶

Time [s]

Frequecy [Hz]

STFT of the IMF4

0 1 2

x 10^-4 0

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

2x 10⁶

Figure 2 : IMFs of the simulated signal -left, STFT of the IMFs -right Echo#2

Echo#3

0 1 2

x 10^-4 -0.4

-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

Time [s]

Amplitude [V]

IMF4

Echo#4 Echo#1

E1

E2

E3

E4

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http://www.csc.dz/ic-wndt-mi12/index.php 26 In order to evaluate the same scheme of processing, we have applied the EMD to the simulated signal embedded in a Gaussian white noise (signal to noise ratio SNR = 10 dB) in a way where the four echoes are totally masked. The result is shown in fig. 4. the four echoes are extracted from the noisy signal and the last three of them are successfully separated. Fig. 5. shows the enhancement in signal filtering processing using wavelet based decomposition. We present the position of the separated and the detected echoes in the noisy simulated signal, which demonstrates a very large improvement.

0 1 2

x 10^-4 -1

-0.5 0 0.5 1 1.5

Time [s]

Amplitude [V]

Simulated signal IMF1

0 1 2

x 10^-4 -1

-0.5 0 0.5 1 1.5

Time [s]

Amplitude [V]

0 1 2

x 10^-4 -1

-0.5 0 0.5 1 1.5

Time [s]

Amplitude [V]

0 1 2

x 10^-4 -1

-0.5 0 0.5 1 1.5

Time [s]

Amplitude [V]

Figure 3: Position of the detected echoes in the simulated signal

E1 E3

E4 E2

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0 1 2

x 10^-4 -1.5

-1 -0.5 0 0.5 1 1.5

Time [s]

Amplitude [V]

Simulated signal + Gaussian noise

0 1 2

x 10^-4 -1

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

Time [s]

Amplitude [V]

IMF1

0 1 2

x 10^-4 -1

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

Time [s]

Amplitude [V]

IMF2

0 1 2

x 10^-4 -0.8

-0.6 -0.4 -0.2 0 0.2 0.4 0.6

Time [s]

Amplitude [V]

IMF3

0 1 2

x 10^-4 -0.8

-0.6 -0.4 -0.2 0 0.2 0.4 0.6

Time [s]

Amplitude [V]

IMF4

0 1 2

x 10^-4 -0.5

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

Time [s]

Amplitude [V]

IMF5

Figure 4: Noisy simulated signal and the IMFs with detected echoes.

Echo#1 Echo#2

Echo#3

Echo#4

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4. Experimental Results

The studied material is a cube of mortar with rectangular sides, having dimensions of 3.5x5x5cm3, made of Portland cement and sea sand. The water/cement ratio was 0.5 and the cement/sand ratio 0.5 too. During the preparation phase we made a crack with a thin sheet of aluminum at a depth 1.6cm of the specimen (see Fig 6.). The specimen is about 1 year old. Measurements were made in the pulse- echo mode in the longitudinal direction using immersion transducer with 2.25 MHz center frequency and 0.5 inch diameter [Fig. 6.].

0 1 2

x 10^-4 -1.5

-1 -0.5 0 0.5 1 1.5

Time [s]

Amplitude [V]

Noisy simulated signal A3 of IMF3

0 1 2

x 10^-4 -1.5

-1 -0.5 0 0.5 1 1.5

Time [s]

Amplitude [V]

0 1 2

x 10^-4 -1.5

-1 -0.5 0 0.5 1 1.5

Time [s]

Amplitude [V]

0 1 2

x 10^-4 -1.5

-1 -0.5 0 0.5 1 1.5

Time [s]

Amplitude [V]

Figure 5: Noisy simulated signal and the IMFs with detected echoes.

E3 E4

E1 E2

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http://www.csc.dz/ic-wndt-mi12/index.php 29 Figure 6. Studied specimen –left, thin sheet of Aluminum –middle, picture of the measuring

system -right.

5x5 cm

²

3.5x5 cm

²

0 1 2 3 4 5 6 7 8 9

x 10^-5 0

2 4 6

x 10⁶ -50

0 50 100

Time (sec) Window size : 32, Window type: Hamming

Frequency (Hz)

0 1 2 3 4 5 6 7 8 9

x 10^-5 -30

-20 -10 0 10 20 30

Time [s]

Amplitude [V]

Original signal

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

x 10⁶ 0

2 4 6 8 10 12

14x 10⁴ Received Signal Spectrum

Frequency [Hz]

FFT Amplitude

Figure 7. Ultrasonic received signal –left, 3D plot of STFT –right, Received signal spectrum -billow middle.

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http://www.csc.dz/ic-wndt-mi12/index.php 30 Fig. 7. shows the received signal before processing and indicate its spectrum. In fig. 8. we present SSP results with frequency compounding algorithm, the indicated time in table. 2. corresponds to the position of the crack and the thickness of the specimen. Results using wavelet based decomposition and EMD based decomposition are presented in fig. 9 and fig. 10. respectively. A multi-step method is developed as in [12,13] which consists of iteratively identifying the separate frequency regions for processing to detect and locate multiple ultrasonic echoes, the result is shown in fig. 11. As expected, the scattering noise is cancelled out during the recombination of filtered signal while the echoes from boundary and crack do not. This can be attributed to the fact that the phase coherence is maintained as frequencies is shifted for target echoes that are of larger dimensions, while smaller background reflections which result in random phase with frequency shifts are eliminated or reduced significantly.

The thickness of the specimen, the crack position and the velocity in mortar specimen can now obtained accurately from the corresponding time of flight measurement (Table 2). This process suppresses the scattering noise from aggregates, while the back surface and the crack echoes get stronger.

0 1 2 3 4 5 6 7 8

x 10^-5 -60

-40 -20 0 20 40 60

Time [s]

Amplitude [V]

SSP output using frequency compoundig

0 1 2 3 4 5 6 7 8 9

x 10^-5 0

2 4 6

x 10⁶ -50

0 50 100

Frequency (Hz)

E#1 E#2

E#3

E#1

E#2 E#3

Fig. 8. SSP based frequency compounding algorithm –left, 3D plot of its STFT –right

0 1 2 3 4 5 6 7 8

x 10^-5 -25

-20 -15 -10 -5 0 5 10

Time [s]

Amplitude [V]

Approximation 2

0 1 2 3 4 5 6 7 8 9

x 10^-5 0

2 4 6

x 10⁶ -10

0 10 20 30 40 50

Window size : 32, Window type: Hamming

Time (sec) Frequency (Hz)

E#1 E#2

E#3

E#1

E#2 E#3

Fig. 9. Approximation 2 of the received signal using Daubechies wavelet –left, 3D plot of its STFT –right

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http://www.csc.dz/ic-wndt-mi12/index.php 31 Table 2- Position detection of echoes, ultrasonic velocity in mortar and SNR of the processed original

received ultrasonic signal.

SSP EMD WA

Echo#1 4.64x10-5(s) 4.61x10-5(s) 4.64x10-5(s) Echo#2 5.43x10-5(s) 5.44x10-5(s) 5.44x10-5(s) Echo#3 6.28x10-5(s) 6.04x10-5(s) 6.20x10-5(s)

Velocity 4268 (m/s) 4895 (m/s) 4487 (m/s)

SNR 0.3199 dB 0.3796 dB 1.0125 dB

0 1 2 3 4 5 6 7 8

x 10^-5 -10

-8 -6 -4 -2 0 2 4 6 8 10

Time [s]

Amplitude [V]

IMF2

0 2 4 6 8

x 10^-5 0

2 4 6

x 10⁶ -5

0 5 10 15 20 25 30

Frequency (Hz)

E#1

E#2 E#3 E#1

E#2 E#3

Figure 10: Selected IMF (IMF2) of the received signal –left, 3D plot of its STFT – right

0 1 2 3 4 5 6 7 8 9

x 10^-5 0

1 2 3 4 5 6 7 8 9 10

IMF2&IMF3 using PT

Time [s]

Amplitude [V]

0 1 2 3 4 5 6 7 8

x 10^-5 0

5 10 15 20 25 30 35

Time [s]

Amplitude [V]

SSP output using PT

E#1

E#2 E#3 E#1

E#2

E#3

Figure 11: Recombined selected IMFs (IMF1&IMF2) using PT algorithm–left,SSP output of the received signal using PT algorithm –right

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5. Conclusion

Extraction of useful information from ultrasonic signals reflected from non-homogeneous materials is always one of challenging tasks in signal processing fields. The main objective of this work was to improve the nondestructive testing of non-homogeneous materials using signal processing methods.

Despite the presence of significant scattered noise, the EMD, WA and SSP based polarity thresholding and frequency compounding algorithms reduce the noise level and successfully identify the ultrasonic echoes reflected from the backsurface and targets of interest. This improvement can be related to the decorrelation of grain echoes resulting from frequency shifts between the transmitted signals. These results not only illustrate the capability of these methods in detecting multiple ultrasonic echoes simultaneously, but also the ability of separating multiple overlapping echoes that are not readily visible in the unprocessed received signals. These results lead us to conclude that a good combination between these three signal processing methods will be successful for thickness and defect determination in thin non-homogeneous materials.

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