International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 8 - May 2014
Acoustic Emission Signal Analysis using Data
Acquisition System
Gaurav Vijay Yeole#1, Sagar Ramchandra Shinde#2
1
PG Student, Electrical Engineering Department, VJTI, Mumbai, India
PG Student, Electrical Engineering Department, VJTI, Mumbai, India
2
Abstract— Data acquisition system acquires and stores the data.
USB data acquisition (DAQ) system provides the choice and
flexibility for creating solutions that evolve and expand as per the
changing measurement needs. Anyone can quickly and easily
acquire, measure and analyze data from electrical, mechanical
and physical phenomena. In this paper, acoustic emission (AE)
sensor is used to acquire the data of physical phenomenon (like
pencil lead break or crack in beam or leakage in pipeline), which
is very small (in mV). This has been further transformed
approximately into the higher range (in volts) by using a
preamplifier of suitable gain. The amplified signal is filtered
using nonlinear filtering or wavelet filtering. Wavelet filtering is
better but the problem is to select proper wavelet and deciding
the threshold. Filtered signal is transformed into digital signal by
using 6 channel USB data acquisition module. This digitized
signal is connected to computer using USB and processing is
done by using software. Here AE signal is generated using pencil
lead break on metal sheet. To know the behavior of the physical
phenomenon taking place, AE sensor signal is analyzed & found
parameters of that signal such as peak, number of cycles above
threshold, duration of signal, rise time, etc. From these
parameters, the nature of event taking place can be predicted
and also it is possible to find the approximate location at which
the event occurs.
from real time system may contain noise. So capturing such a
real time signal to know the behavior of the physical
phenomenon taking place is not that much easy. The problem
is that the signal to be stored is very small & is ranging from
20 kHz to 1MHz depending upon sensor used.
Previously concrete beam specimen is used for experiment
[1]. In that, types of cracks can be detected. AE signal
parameters are measured for different materials and concluded
on behavior of signal in different materials [2]. Using TOA
(time of arrival method) approximate location of leakage can
be found [3]. Importance and design of signal conditioning
circuitry is explained in [4]. In this paper, limited pass-band
sensors are used so appropriate band pass filters are required.
Comparison for different types of sources of acoustic emission
had done [5]. The components of USB data acquisition system
includes:
Keywords— USB DAQ System, AE sensor, Wavelet filtering, 6channel DT9816 USB data acquisition module, Preamplifier.
2) Signal conditioning circuit: Signal conditioning
circuitry convert sensor signals into a form that is suitable to
be converted into digital values. Signal conditioning circuitry
may be amplifier, filter or attenuator depending upon the
sensor signal. Here preamplifier is used as signal conditioning
circuitry. It convert smaller AE signal into higher value so that
analysis can be easily done.
I. INTRODUCTION
Nowadays most of the applications require portable data
acquisition system. In this paper, USB DAQ system is to be
developed using acoustic emission applications. Acoustic
emissions (AE) are defined as transient elastic waves
generated from a rapid release of strain energy caused by a
deformation or damage within or on the surface of a material
[2]. Another definition of the same phenomenon is that
Acoustic emission is a phenomenon of stress wave generation
resulting from a local displacement in a material. Based on
Kaiser effect and Felicity effect, AE technology developed
fast [2]. Acoustic emission frequencies are usually in the
range of 150-300 kHz. AE technology has lots of applications.
AE technique is used in leakage detection of pipeline [3],
structural health monitoring of Aircraft, crack detection in
concrete beam [1]. Though many applications are using AE
technology, many properties of AE are unknown to us. In
practical applications, there is too much noise and the nature
of AE is not clear, so it could not be used in factory
environment only in laboratory condition. Thus it is difficult
to know the behavior of real time system. Signals generated
ISSN: 2231-5381
1) Sensor: Sensors convert physical parameters (like
vibration, pressure, temperature, etc) to electrical signals.
Acoustic emission (AE) sensor is used in crack/leakage
detection. Parameters to be measured from AE signal are peak
amplitude, counts, frequency, rise time. Output of sensor is
voltage signal.
Preamplifier
Signal
conditioni
ng
circuitry
DSO
DT9816
Module
Comput
er
AE sensor
Specimen
Fig. 1 Block diagram of USB DAQ system
3) ADC: Analog to digital converter converts the
conditioned analog sensor signal to digital values. 6-channel
DT9816 USB data acquisition module is used as ADC.
http://www.ijettjournal.org
Page 384
International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 8 - May 2014
4) Storage system: Storage system is computer or DSO.
The digitized signal is stored in computer through the 6channel DT9816 USB data acquisition module.
AE process begins with stress. When material deforms
under loading, then energy is released and the amplitude of
resulting wave depends on the size of specimen and speed of
the event. A stronger event creates greater signal than weak
event. Noise is the main issue in acoustic technology [7].
Factors considered for performance evaluation of AE system
are attenuation and wave velocity. AE signal travels inside
material in form of waves. These waves are of following
types.
II. RELATED WORK
A. Experimental Setup for Acoustic Emission Testing
DSO
28V DC
Power supply
VS75V
1) Tensile waves: These waves are produced during initial
stages of event. These waves are having high peak amplitude
and less duration.
2) Shear waves: These waves are produced at the failure of
specimen. These waves are having less peak amplitude and
more duration.
Metal sheet
Fig. 3 Experimental setup for acoustic emission testing
B. Specifications of Material & Sensors used
1)
Metal sheet: Metal sheet is of mild steel. Its
dimensions are: Length = 88cm, Breadth = 12.2cm,
Thickness = 1mm.
2)
Sensors: Sensors used are manufactured by Vallen
Systeme. Each sensor is having particular pass-band
in which it gives proper response. Both sensors
require 28V dc power supply. The distance between
two sensors is 37.5cm. Sensors are placed on metal
sheet by coupling them using grease so that event
can be easily sensed with less noise.
Different parameters of AE signal measured for analysis are:
1) Peak amplitude: Maximum positive amplitude of signal
is called peak amplitude.
2) Rise time: Time between the first cycle above threshold
and peak is called rise time.
3) Duration: Time between the first and last cycle above
threshold is called duration of signal.
4) Counts: Number of cycles measured over threshold
level is called count.
Amplitude
(V)
Duration (us)
RT
Threshold
VS150-RIC
TABLE I shows the specifications of each sensor. Each
sensor gives response in particular pass-band. Use of
wideband sensor is generally avoided because it will result in
more noise along with signal.
Many acoustic emission sources are available. AE signal
can be generated by metal impact, pencil lead break, friction,
electrodes for EMG signal recording [5], etc. In this paper,
pencil lead break is used to generate acoustic signals. Pencil
lead break is the best source of AE in most of the AE testing
applications. The energy released after the lead break is
sensed by the AE sensors placed on the metal sheet. Signal
conditioning circuitry for VS75 is preamplifier of gain 40 dB
& VS150-RIC is having integral preamplifier. Sensor outputs
are stored in DSO.
Peak
ampli
tude
Time (us)
TABLE I
SPECIFICATIONS OF SENSORS
Fig. 2 Typical acoustic emission signal
Rise amplitude and average frequency value analysis is
used to find the nature of AE signal in metal sheet [1].
Average frequency =
(1)
Rise Amplitude =
ISSN: 2231-5381
(2)
Sensors
Peak
frequency
Pass-band
Type
Distance of
sensor from left
end of metal
sheet
http://www.ijettjournal.org
Sensor 1
VS75V
Sensor 2
VS150-RIC
75kHz
150kHz
30-120kHz
Non-integral
100-450kHz
Integral
40.5cm
78cm
Page 385
International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 8 - May 2014
C. Flowchart
TABLE II
VARIOUS PARAMETERS OF SENSOR 1 AFTER EVENT AT DIFFERENT P OSITIONS
Sensor 1 (VS75V)
Start
X
First make pencil lead break on metal sheet at 10
cm from left end
Set trigger for both channels and capture sensor
signal on DSO
Measure peak for each sensor signal and set
threshold at 40% of peak obtained
Measure counts, duration and rise time for both
sensor signals
30-120kHz, f peak = 75kHz
Vpp(V) T (us) RT (us) N RA (us/V) AF(kHz)
10
6.4
107
68
4
10.62
37.38
20
8.16
96.8
59.2
3
7.25
30.99
30
11.4
76.8
48.8
3
4.28
39.06
40
13.6
50.4
12.8
2
0.94
39.68
50
10
54
23.2
3
2.32
55.55
60
9.6
68
33.6
3
3.5
44.11
70
6.08
84
47.2
3
7.76
35.71
80
5.4
102
50
4
9.25
39.21
TABLE III
VARIOUS PARAMETERS OF SENSOR 2 AFTER EVENT AT DIFFERENT P OSITIONS
Sensor 2 (VS150-RIC)
Measure the delay between the arrival times of
both sensor signals
Repeat same procedure for pencil lead break at
20cm, 30cm, 40cm, 50cm, 60cm, 70cm, 80cm
Store each sensor signal for every pencil lead
break in DSO and then process it in Matlab
Find frequency of sensor signal using FFT in Matlab
and filter the signal using different techniques
Stop
III. RESULTS AND DISCUSSION
Rise amplitude (RA) analysis is done for finding the nature
of waves travelling in material. The different parameters like
amplitude, rise time, counts, duration are measured for each
sensor output.
Let,
X = Distance of pencil lead break from the left end of the
metal sheet in centimetres.
ISSN: 2231-5381
100-450kHz, fpeak = 150kHz
X
Vpp (V) T (us) RT (us) N
RA
AF (kHz)
(us/V)
Delay
(us)
10
1.32
42.4
18.4
4
13.93
94.33
108
20
1.56
38.2
15.2
3
9.74
78.53
120
30
2.36
36
12.8
2
5.42
55.55
127
40
2.64
27.2
12
2
4.54
73.52
126
50
3.12
23
10.4
2
3.33
86.95
56
60
4.32
20.7
8
2
1.85
96.61
36.8
70
5.92
16
7.4
2
1.25
125
101
80
6.48
12.8
6.4
1
0.98
78.12
220
Where,
T = Duration of signal in microseconds
RT = Rise time in microseconds
N = Counts
RA = Rise amplitude in volts per microseconds
AF = Average frequency in kHz
TABLE II and TABLE III show the readings for each
sensor signal, taken on digital storage oscilloscope (DSO)
when pencil lead break is made at different locations on metal
sheet. Rise amplitude and average frequency calculated using
(1) and (2). Then average frequency is plotted against rise
amplitude for both sensors.
http://www.ijettjournal.org
Page 386
International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 8 - May 2014
Fig. 6 shows the plot of amplitude against distance at which
pencil lead break is made. It shows the response of sensor with
different locations of pencil lead break. It can be observed that
the signal attenuates as the pencil lead break is made away
from sensor. Attenuation for each sensor is calculated.
Attenuation for VS75V is around 0.22 V/cm and attenuation
for VS150-RIC is around 0.074 V/cm.
Fig. 4 Average frequency versus RA plot for VS75V
Fig. 5 Average frequency versus RA plot for VS150
Fig. 4 and Fig. 5 show the plot of average frequency
against rise amplitude. Every point in plot defines the nature of
waves at different locations on metal sheet. The points on the
left side of plot for both sensors indicate that pencil lead break
at corresponding locations made with less force i.e. waves are
tensile in nature and points on right side indicate that pencil
lead break at corresponding locations made with more force
i.e. waves are shear in nature. At initial stage more tensile
waves are generated than shear waves, while at the later stage
more shear waves are generated than tensile waves.
The intensity of an AE signal detected by a sensor is
considerably lower than the intensity that would have been
observed in the close proximity of the source. This is due to
attenuation.
IV. CONCLUSION
Points showing high average frequency & less RA value
implies tensile wave means at those locations event occurred
is stronger, while points showing low average frequency &
high RA value implies shear wave means at those locations
event occurred is weaker and it lasts for more time. Delay
between the arrival times of AE signals gives the approximate
location of pencil lead break. Signal amplitude is inversely
proportional to the distance at which event occurs.
AE signal obtained is not typical signal. It contains the
noise. Hence appropriate filter have to be designed to remove
noise. By using traditional methods of filtering, noise is not
reduced as required and also attenuation is more. Thus
filtering using wavelet [6] is preferable.
ACKNOWLEDGMENT
The authors Gaurav Vijay Yeole and Sagar Ramchandra
Shinde would like to thank all staff members and authorities
of Veermata Jijabai Technological Institute, Matunga,
Mumbai, India for their support and motivation.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
S. Shahidan, N. Muhamad Bunnori, S. Mohd, N. Md Nor, M A. Megat
Johari, “Analysis of the AE signals parameter at the critical area on the
concrete beam”, in ISIEA, September 2012, pp.386-391.
S. Boukhenous, N. Meziane, M. Attari, Y. Remram, “A USB based
data acquisition system for EMG signal recording”, 8th International
Workshop on Systems, Signal Processing and their Applications
(WoSSPA), 2013, pp. 230-232.
Yu Yang..Yang Ping, “Research of acoustic emission characteristics
based on the signal parameters”, ICIECS, 2009, pp.1-4.
Athanisios Anastasopoulos, Dimtrios Kourousis, Konstantinos Bollas,
“Acoustic Emission Leak Detection of Liquid Filled Buried Pipeline”,
Journal of Acoustic Emission, pp. 27-39, 2009.
Kaphle, Manindra R. and Tan, Andy, “Source location of acoustic
emission waves for structural health monitoring of bridges”,
Infrastructure Research Theme Postgraduate Student Conference,
2009, pp. 1-7.
Su Weijun, Zhou Ying, “Wavelet Transform Threshold Noise
Reduction Methods in the Oil Pipeline Leakage Monitoring and
Positioning System”, ICMTMA, 2010, vol. 3, pp. 1091-1094.
(2013) The non-destructive testing for AE website. [Online].
Available: http://www.ndt-ed.org/
Fig. 6 Plot of amplitude versus distance at which pencil lead break made
ISSN: 2231-5381
http://www.ijettjournal.org
Page 387