Detection of Multiple Ultrasonic Echoes Reflected

Transcription

Detection of Multiple Ultrasonic Echoes Reflected from Internal Flaws in
Structures Using Advanced Signal Processing Techniques
1
S. Haddad1, M. Grimes, T. Benkedidah, A. Boufersada
NDT Lab, Faculty of Sciences and Technology, Jijel University, Algeria
E-Mail: [email protected], [email protected]
Tel: +213(0)661217995
Abstract
In order to improve the detection accuracy of multiple ultrasonic echoes reflected from nonhomogeneous 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.
3ème 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
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 (t )) / 2 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
n
x (t ) =
å
c i (t ) + rn (t )
i =1
with c i (t ) being the ith IMF and rn (t ) 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
y j ,k (t ) generated from a kernel function y (t ) by dilation j and translation k
y j ,k (t ) = 2-
j /2
y (2- j t - k )
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:
a j ,k = x (t ), y j ,k (t ) =
ò x (t ).y
j ,k
(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
23
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-tonoise 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
1V
0.8 V
0.6 V
Frequency
1 MHz
0.5 MHz
0.25 MHz
0.125 MHz
Simulated signal
6
1.5
2
x 10
STFT of the original signal
1.8
1
1.6
0.5
Frequecy [Hz]
Amplitude [V]
1.4
0
1.2
1
0.8
0.6
-0.5
0.4
0.2
-1
0
1
Time [s]
2
-4
x 10
0
0
1
Time [s]
2
-4
x 10
Figure 1: Simulated signal -left, STFT of the simulated signal -right
24
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).
6
IMF1
1
2
0.8
1.8
0.6
Echo#1
1.6
Echo#2
1.4
0.2
Frequecy [Hz]
Amplitude [V]
0.4
0
-0.2
1.2
0.6
-0.6
0.4
-0.8
0.2
0
1
Time [s]
0
2
0.4
1.8
STFT of the IMF2
1.4
Frequecy [Hz]
Amplitude [V]
x 10
2
-4
x 10
1.6
Echo#3
0.2
0.1
0
-0.1
1.2
1
0.8
-0.2
0.6
-0.3
0.4
-0.4
0.2
0
1
Time [s]
0
2
E3
0
1
Time [s]
-4
x 10
6
IMF4
0.5
2
0.4
1.8
x 10
2
-4
x 10
STFT of the IMF4
1.6
0.3
Echo#4
1.4
Frequecy [Hz]
0.2
Amplitude [V]
1
Time [s]
6
2
0.1
0
-0.1
1.2
1
0.8
0.6
-0.2
0.4
-0.3
-0.4
0
-4
IMF2
-0.5
E2
x 10
0.5
0.3
E1
1
0.8
-0.4
-1
STFT of the IMF1
x 10
0.2
0
1
Time [s]
2
-4
x 10
0
E4
0
1
Time [s]
2
-4
x 10
Figure 2 : IMFs of the simulated signal -left, STFT of the IMFs -right
25
1.5
1.5
Simulated signal
IMF1
Simulated signal
IMF2
1
1
E2
E3
0.5
Amplitude [V]
Amplitude [V]
E1
0
-0.5
-1
0.5
0
-0.5
0
1
Time [s]
-1
2
0
-4
x 10
1.5
1
Time [s]
1.5
Simulated signal
IMF4
1
1
0.5
0.5
Amplitude [V]
Amplitude [V]
Simulated signal
IMF3
0
-0.5
-1
2
-4
x 10
E4
0
-0.5
0
1
Time [s]
2
-4
x 10
-1
0
1
Time [s]
2
-4
x 10
Figure 3: Position of the detected echoes in the simulated signal
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.
26
Simulated signal + Gaussian noise
IMF1
1.5
0.8
0.6
1
0.4
0.2
Amplitude [V]
Amplitude [V]
0.5
0
-0.5
0
-0.2
-0.4
-0.6
-1
-0.8
-1.5
0
1
Time [s]
-1
2
0
-4
x 10
1
Time [s]
2
-4
x 10
IMF3
IMF2
1
0.6
0.8
Echo#1
Echo#2
0.4
0.6
0.2
0.2
Amplitude [V]
Amplitude [V]
0.4
0
-0.2
-0.4
0
-0.2
-0.4
-0.6
-0.6
-0.8
-1
0
1
Time [s]
-0.8
2
0
-4
x 10
IMF4
1
Time [s]
2
-4
x 10
IMF5
0.6
0.5
Echo#3
0.4
Echo#4
0.4
0.3
0.2
Amplitude [V]
Amplitude [V]
0.2
0
-0.2
0.1
0
-0.1
-0.2
-0.4
-0.3
-0.6
-0.4
-0.8
0
1
Time [s]
2
-4
x 10
-0.5
0
1
Time [s]
2
-4
x 10
Figure 4: Noisy simulated signal and the IMFs with detected echoes.
27
1.5
1.5
Noisy simulated signal
A2 of IMF2
E1
1
1
0.5
Amplitude [V]
Amplitude [V]
0.5
0
-0.5
-1
-1
0
1
Time [s]
-1.5
2
1
Time [s]
2
-4
x 10
1.5
A3 of IMF4
E3
1
A3 of IMF5
E4
1
0.5
Amplitude [V]
0.5
0
0
-0.5
-0.5
-1
-1
-1.5
0
-4
x 10
1.5
Amplitude [V]
0
-0.5
-1.5
A3 of IMF3
E2
0
1
Time [s]
2
-4
x 10
-1.5
0
1
Time [s]
2
-4
x 10
Figure 5: Noisy simulated signal and the IMFs with detected echoes.
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 pulseecho mode in the longitudinal direction using immersion transducer with 2.25 MHz center frequency
and 0.5 inch diameter [Fig. 6.].
28
3.5x5 cm2
5x5 cm2
Figure 6. Studied specimen –left, thin sheet of Aluminum –middle, picture of the measuring
system -right.
Window size : 32, Window type: Hamming
Original signal
30
100
20
50
Amplitude [V]
10
0
0
-50
6
-10
4
-20
6
x 10
-30
0
1
2
3
4
5
Time [s]
6
7
8
9
2
Frequency (Hz)
0
0
1
3
2
Time (sec)
-5
x 10
4
14
4
5
6
7
9
8
-5
x 10
Received Signal Spectrum
x 10
12
FFT Amplitude
10
8
6
4
2
0
0
0.5
1
1.5
2
2.5
3
Frequency [Hz]
3.5
4
4.5
5
6
x 10
Figure 7. Ultrasonic received signal –left, 3D plot of STFT –right, Received signal
spectrum -billow middle.
29
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.
SSP output using frequency compoundig
60
100
40
E#1
E#1
50
Amplitude [V]
20
E#2
E#3
E#2
0
0
E#3
-20
-50
6
-40
4
6
x 10
-60
0
1
2
3
4
Time [s]
5
6
7
8
2
0
Frequency (Hz)
7
6
5
4
3
2
1
0
-5
x 10
9
8
-5
x 10
Time (sec)
Fig. 8. SSP based frequency compounding algorithm –left, 3D plot of its STFT –right
Approximation 2
50
10
E#1
40
E#2
5
E#3
30
E#1
0
Amplitude [V]
20
-5
10
E#2
-10
0
-15
-10
6
4
-20
6
x 10
-25
E#3
0
1
2
3
4
Time [s]
5
6
7
8
-5
x 10
Frequency (Hz)
2
0
0
1
2
3
4
Time (sec)
5
6
7
9
8
-5
x 10
Fig. 9. Approximation 2 of the received signal using Daubechies wavelet –left, 3D plot of its STFT –right
30
IMF2
10
30
8
25
E#1
6
20
Amplitude [V]
4
E#2
2
E#1
15
10
E#3
0
5
-2
0
-4
-5
6
E#2
E#3
-6
4
6
-8
-10
x 10
0
1
2
3
4
Time [s]
5
6
7
8
2
0
Frequency (Hz)
8
6
4
2
0
-5
x 10
Time (sec)
-5
x 10
Figure 10: Selected IMF (IMF2) of the received signal –left, 3D plot of its STFT –
right
SSP output using PT
IMF2&IMF3 using PT
35
10
9
30
8
25
6
Amplitude [V]
Amplitude [V]
7
E#1
5
4
E#2
3
20
E#1
15
10
E#2
2
E#3
1
0
5
0
1
2
3
4
5
Time [s]
6
7
8
9
0
E#3
0
1
2
3
-5
x 10
4
Time [s]
5
6
7
8
-5
x 10
Figure 11: Recombined selected IMFs (IMF1&IMF2) using PT algorithm–left,SSP
output of the received signal using PT algorithm –right
Table 2- Position detection of echoes, ultrasonic velocity in mortar and SNR of the processed original
received ultrasonic signal.
Echo#1
Echo#2
Echo#3
Velocity
SNR
SSP
4.64x10-5(s)
5.43x10-5(s)
6.28x10-5(s)
4268 (m/s)
0.3199 dB
EMD
4.61x10-5(s)
5.44x10-5(s)
6.04x10-5(s)
4895 (m/s)
0.3796 dB
WA
4.64x10-5(s)
5.44x10-5(s)
6.20x10-5(s)
4487 (m/s)
1.0125 dB
31
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.
References
[1] C.-C. Lin et al., Application of empirical mode decomposition in the impact-echo test NDT&E
International 42 (2009) 589–598
[2] V. Matz et al., Signal-to-noise ratio enhancement based on wavelet filtering in ultrasonic testing,
Ultrasonics 49 (2009) 752–759
[3] TIAN ef al., multiple target detection using split spectrum processing, IEEE transactions on
ultrasonics, ferroelectrics, and frequency control, vol. 42, (1995), no. 6
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Detection, IEEE transactions on ultrasonics, ferroelectrics, and frequency control, vol. 43, (1996). no.
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[5] Qi Tian et al., Statistical Analysis of Split Spectrum Processing for Multiple Target Detection,
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Ultrasonics 52 (2012) 351–363
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[11] N. E. Huang et al., The Empirical Mode Decomposition and Hilbert Spectrum for Nonlinear and
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[12] S. HADDAD et al., Ultrasonic Signal Processing based on the combined use of Empirical Mode
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32

Detection of Multiple Ultrasonic Echoes Reflected

Transcription

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