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A lab-scale digital acoustic emission system for source location

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(1)

by

M.-H.

Yang and

C.-P.

Chou

A LAB-SCALE DIGITAL ACOUSTIC EMISSION SYSTEM

FOR SOURCE LOCATION

coustic emission

(AE)

techniques have been used *

of arrival-time differences (delta-T’s) for each mesh point. To in the study of crack location, cracking mecha- ‘ calculate the source position for a set of burst-type

AE

sig- nisms, deformation of metals and leak detection.

:

nals, a straightforward “shift and a d d method was used.

A

In these techniques, AE sensors hit by transient

.

First, assuming the AE source is at a mesh point inside the stress waves generate

AE

signals, and then the

AE

signals

.

circle, the signal waveform of each channel was shifted for- are analyzed with various methods. Conventional AE sys-

.

ward in the time domain, with the corresponding delta-T tems only record some parameters of the

AE

signals, such * value. Each time-shifted waveform was added to that of the

as peak amplitude, ringdown counts, duration, risetime, and * first-hit channel, and then the resultant additive waveform

so on. On the other hand, a fully digital

AE

system can dig- ’ was squared to give the signal power from that mesh point.

itize the waveforms of

AE

signals and perform various anal- ’ When these operations have been performed for all mesh yses. Since many versatile and relatively cheap personal

:

points, a power map can be created, and the peak of power computers (PC), analog-to-digital (A/D) cards, and digital

.

map is considered the closest point to the AE source. Figures signal processing (DSP) software packages for PC are avail-

.

2 shows a set of simulated signals, with the

AE

from (1, 21, able, a fully digital and PC-based lab-scale

AE

system for 3-

.

propagating at 1480 m/s, of 10 kHz carrier frequency and

D source location can be set up. With the results of DSP for

.

sampled at 100 kHz. With this sampling rate, a series of simulated and real

AE

signals, features of this

AE

system

.

location results for various carrier frequencies of signal (10, and critical factors involved are presented.

.

20, and 50 kHz) are shown in Figure 3-5. It was found that

when the sampling rate was

CONFIGURATION OF 5 times the carrier frequency

AE SYSTEM or higher, source location re-

sults were good no matter where the postulated source The configuration of our

AE

was.

As

the sampling rate system is shown in Fig. 1.

lowered, other peaks began Appropriate

AE

sensors and

to emerge on the power map. amplifiers can be employed,

When the sampling rate was depending on the purpose of

2 times the carrier frequency, testing. Low-frequency (30

marginally meeting the Ny- kHz)

AE

sensors were used

quist criterion, the location in our leak testing. The A/D

result was incorrect. The cards were two Nicolet

per channel for a total number of 8 channels. This means an

:

approximate 7-times sampling of signals and a time resolu-

.

tion of 5 microseconds. Digitized

AE

signals were stored in

.

If a signal appears with infinite duration and excellent rep- an Ordinary WINTEL ”, and IUTLAB was wed for data

.

etition, as those examples often shown in textbooks when software, so this system was highly flexible.

MAIN AMPLIFIER

Fig. I: The configuration of AE system

BE493 A/D cards, working at a sampling rate of 200 kHz ’ Same result was obtained when evaluating with cross-

method.

processing. Not only the

AE

SenSorS and amplifiers were ex- ’ the Nyquist criterion is stated, a sampling rate of two times

changeable, but also the PC, AID cards and the analysis ’ the carrier frequency is sufficient. In AF, testing or our sim-

SAMPLING RATE

For a digital AE system to perform properly, sampling at a rate high enough is a prerequisite. Because of the nature of irreproducibility of

AE

phenomena,

AE

signals simulated by software were used to evaluate the influence of sampling rate. Assume a circle with a diameter of 12 meters is on the

XY plane and centered at the origin.

Six

AE

sensors were assumed to have been mounted, equally spaced along the circumference of this circle. Then, an imaginary square mesh with an edge of 12 meters, 0.5-meter interval, also centered at the origin, is placed on the XY plane. This results in 25 X 25 mesh points. Since the propagation speed of

AE

burst is known, the time needed for an

AE

burst from any mesh point to reach each sensor is known. That is, there is a set

M.-H. Yang is a Research Scientist at Materials Research Lab, Hsinehu, Taiwan. C.-P Chou is a professor in the Department of Mechanical Engineeringat National

cn 1 CH 2 cn 3 cn 4 cn 5 CH 6 (1111” 0 200 400 600 800 to00 Data polntr Chiao-7Lng Llniuersity.

32

EXPERIMENTAL TECHNIQUES JulylAugust 1999

(2)

x lo‘

Power

Carrier Freq. = 10 Iblr

Sampling Rate = 100 kt-k Peak at ( 1 , 2 ) 1 5 1 0 5 0 6 I

Fig. 3: Source location result when carrier frequency is 10 kHz ‘ ~~ ~ Carrier Freq. = 20 kHz Sampling Rate = 100 W 10‘ 3, Power

5i

Peak at ( 1, 2 ) 2 1 5 1 n s n 6 J

Fig. 4: Source location result when carrier frequency is 20 kHz Carrier Freq. = 50 kHz Sampling Rate = 100 kHz x lo‘ 2 5 2 1 5 1 0 5 0 6 Peak at ( -0.5, 1 ) Power 6 -6 -6

Fig. 5: Source location result when carrier frequency is 50

kHz

ulation where signals are of finite duration and consist of

noise, a sampling rate of Nyquist frequency seems ins&% cient. As a rough approximation, an AE signal h(t) can be simulated by a sinusoidal function with an exponential de- cay envelope along with noise, that is, h(t) = Ae-“’cos(2.rrft)

+

noise, where A and a are constants, t is time, and f i s the carrier frequency. The frequency spectrum of this signal ex- hibits a bell-shape (due to the exponential decay), and is shifted and centered at f (due to modulation). As the signal becomes shorter (greater a), plus the effect of noise, the bell- shaped curve spreads. This means more frequency (and time) information will be lost if the sampling rate is fked at Nyquist frequency, then the result of data processing will tend to be incorrect.

DIGITAL SIGNAL PROCESSING

To pinpoint the position of an

AE

source, the propagation speed of

AE

burst, the hit-sequence, and the delta-T’s must be known. Conventionally, the “threshold-crossing method” is used: when the

AE

signal crosses a preset threshold of a channel, this channel is considered “hit” by the

AE

burst, and the time of crossing is defined as the arrival time. Then the hit-sequence and delta-”% are deduced thereafter. Threshold setting is user-dependent and somewhat subjec- tive, but usually it is set at 6 dB (two times) higher than background noise. Because of the random nature of noise, in practice the threshold-crossing method is susceptible to

noise, therefore leading to erroneous location results.

To make our system more robust to noise, a series of digital signal processing techniques were used. First, a digital band- pass (BPI filtering was applied to each digitized waveform of

AE signal, according to the nominal frequency of AE sensors used. For instance, a BP filter of 20-40

k H z

is used for AE

sensors of 30 kHz. Second, filtered

AE

signals were trans- formed to their corresponding analytic signals’. With an an- alytic signal, the information of amplitude vs. time is kept but the oscillation term is suppressed. Third, the threshold- crossing method was applied to determine arrivals. Finally, the cross-correlation between two analytic signals was per- formed to give the relative arrival-time between two chan- nels, then the hit-sequence and delta-T’s were determined. Since the maximal value of cross-correlation indicates the relative time-shift between two main energy packets, correct hit-sequence and delta-T’s can be deduced even in the pres- ence of noises. Figure 6 shows a set of

AE

signals due to the leakage through a drilled hole in a leak experiment.

As

shown in Figure 6, in source location calculation, it is a com- mon practice to set a certain time window which at least is equal to the maximal possible arrival-time difference be- tween

AE

sensors, and then analyze the signals within the time window only. This also reduces calculation time signif- icantly. The corresponding normalized analytic signals within the time window are shown in Figure

7.

The noise was rather low, so the main energy packets (designated by

“P)

can be easily identified. Figure 8 shows another set of

AE

signals that are weaker and severely “contaminated by noise. Since there are many “noise packets” (designated by “n” in Figure 91, conventional threshold-crossing method

will

lead to incorrect result. However, by using cross-correlation the correct result still can be obtained.

(3)

I I 0 cilculilcd AE source X sensor + drlUed hole/ / 0 2 - /

1

~ 0 1 - 0 - (melera) - 0 1 - 0 -0 2 - \ / /' - 0 3 - 0 2 -01 0 0 1 0 2 0 3

Fig. 6 A set of AE signals obtained in a leak experiment.

The partial loss of couplant resulted in the lower signal amplitude of channel 6.

I

cH4L

P

P

f l \

1

C H0 6 h20 _ _40 _ _ .60 . - 880 100 120 140 160 ' ' 180

data points ( 1 point = 5 microseconds)

Fig. 7: The normalized analytic signals of AE signals within

the time window in Figure 6. The main energy packets (C'P")

can be easily identified.

1 0 ma 0 1 1 N I . .I I am* a l l 1 1.1 1 mma a, " I 1.1 1 mom U 5 " * 11. 1 **a Ma " t (*a t 57.

Fig. 8: Another set of AE signals obtained in a leak

experiment

P

n n /-,\,

CH 6 _i - 8 - 8 -

0 20 40 60 80 100 120 140 160 180 data points ( 1 point = 5 microseconds)

I

'

Fig. 9: The normalized analytic signals of AE signals within

, the time window in Figure 8. Many noises (C'n") are present.

Fig. 10 Some location results of the AE signals in the leak

experiment

:

3-D SOURCE LOCATION

.

When hit-sequence and delta-T data are available, calcula-

'

tions can be conducted to determine the location of an

AE

'

source. Various algorithms of AE source calculation have be-

:

ing developed, but the Spherical Interpolation 61) method

.

for 3-D location by Smith and Abe12 was adopted. The SI

.

method consists of a series of matrix computations, and is

.

implemented with MATLAB. Some location results of the

AE

.

signals from leakage are shown in Figure 10, which is the

.

projection of 3-D locations on the =-plane. The calculated * positions show good consistency and are very close to the

' leak hole.

q CONCLUSIONS

:

A PC-based, fully digital and lab-scale acoustic emission sys-

.

tem for 3-D source location was set up. To get good results,

.

a sampling rate higher than the Nyquist frequency is pre-

(4)

ferred. and is a t least five times the carrier freauencv of sia- ‘ References

nal for our experiment. With digital filtering, aialytical sig- 1, Gammell, P.M., “Improved Ultrasonic Detection using the

nals, and cross-correlation technique, the source location op-

.

Analytic Signal Amplitude, 7) Ultrasonics, 73-76 (Mar. 1981). eration can be made more robust to noise. Versatile 3-D

.

source location calculation can be implemented with numer-

.

2. Smith, J.O., Abel, J.S., “Closed-Form Least-Squares Source

.

Location Estimation from Range-Difference Measurements, ” IEEE

ical and DSP software packages.

.

Transactions on Acoustics, Speech, and Signal Processing, ASSP-35

.

(121, 1661-1669 (Dec. 1987).

ACKNOWLEDGMENT

This work was supported by Chinese Petroleum Corporation,

.

Taipei, Taiwan.

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Fig.  I:  The configuration of  AE  system
Fig. 3:  Source location result when carrier frequency is  10  kHz  ‘  ~~  ~  Carrier Freq
Fig.  6  A  set of  AE signals obtained in a leak experiment.

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