Backing layers on electroacoustic properties of the acoustic emission sensors

Applied Acoustics 156 (2019) 387–393

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Backing layers on electroacoustic properties of the acoustic emission sensors Wenkang Zhang, Hongyu Jia, Guangpeng Gao, Xin Cheng, Peng Du, Dongyu Xu ⇑ Shandong Provincial Key Lab. of Preparation and Measurement of Building Materials, University of Jinan, Jinan 250022, PR China

a r t i c l e

i n f o

Article history: Received 16 March 2019 Received in revised form 26 July 2019 Accepted 27 July 2019 Available online 3 August 2019 Keywords: Acoustic emission Sensor Acoustic impedance Backing layer Bandwidth

a b s t r a c t The acoustic emission sensors were tailored by using Piezoelectric Lead Zirconate Titanate (PZT) ceramic as sensing element and different materials as backing layers. The influences of backing layers on electroacoustic properties of the sensors were investigated based on the pencil lead break test and simulated noise experiments. The results show that compared with PZT ceramic, the resonant frequency of the acoustic emission sensors decreases due to damping effects from backing layer and the metal shell. The backing layer of air has the largest acoustic reflection coefficient, and epoxy resin/tungsten powder has the largest acoustic attenuation performance, which is benefit to improve the bandwidth of the acoustic emission sensor. The acoustic emission sensors made of different backing layers own good reception ability to acoustic emission signals, and the characteristic parameters analysis results prove their good identifying ability to effective acoustic emission signals and noise signals. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction Acoustic emission (AE) is a phenomenon of occurrence of transient elastic waves caused by the rapid release of local energy in materials. Acoustic emission detection technique is a kind of nondestructive inspection method by employing acoustic emission sensors to catch the acoustic emission signals emitted from the dynamic damage process of materials. In 1950s, Kaiser discovered the famous ‘‘Kaiser effect” by studying the deformation process of metal and alloy and proposed the concept of continuous and burst emission signals [1]. Subsequently, Schofield studied the plastic deformation of metal and found that acoustic emission was mainly caused by a large number of dislocation movements, which was a kind of volume effect rather than surface effect [2]. In early 1970s, Dunegan et al. performed the fracture analysis of metal materials by using acoustic emission technique, and developed the commercialized acoustic emission device [3–5]. In recent thirty years, as a kind of dynamic nondestructive inspection method, acoustic emission technology has been widely used in many fields, such as civil engineering, petrochemical, electric power, aeronautics and machinery [6–11]. It is known that acoustic emission sensor is the core element to receive acoustic emission signals, which plays an important role in acoustic emission detection technology. Usually, acoustic emission ⇑ Corresponding author. E-mail address: [email protected] (D. Xu). https://doi.org/10.1016/j.apacoust.2019.07.038 0003-682X/Ó 2019 Elsevier Ltd. All rights reserved.

sensors are made up of four components, that is, sensing element, matching layer, backing layer and shell. The function of sensing element is to realize the energy conversion between mechanical and electrical signals, and the piezoelectric materials are often regarded as the optimal candidate of the sensing element of acoustic emission sensors due to their excellent direct and inverse piezoelectric effects. The matching layer plays a role of protecting the piezoelectric sensing element, improving the acoustical transmission coefficient, and realizing the acoustic impedance matching between sensors and the medium, which is usually made up of Al2O3 ceramics or organic glass. As for the backing layer, it is mainly used to absorb the backward acoustic wave excited by the sensing element, thus improving the resolution and frequency response range of the sensor. Presently, the research referring to acoustic emission technique has been widely reported, especially in aspects of acoustic emission mechanism, features correlation and location analysis etc. Hamel et al. investigated the acoustic emission mechanisms of steel during high-cycle fatigue in terms of several models [12]. Stone et al. discussed the potential and limitations of frequency and amplitude analysis methods in analyzing acoustic emission signals and described theoretical relationships between various electrical and acoustic parameters [13]. Tobias investigated the acoustic emission source location in two dimensions from the arrival times at the sensors by using an array of three sensors [14]. Ebrahimkhanlou et al. carried out the location research of acoustic emission source, and developed a three-step probabilistic framework to quantify the uncertainties associated

W. Zhang et al. / Applied Acoustics 156 (2019) 387–393

with single-sensor localizations [15,16]. Kundu et al. presented a non-iterative source location algorithm employing four AE sensors in application of insulation failure location of transformers [17]. Although there have been many reports about the acoustic emission detection methods, the design and performance of acoustic emission sensors (AE sensors) is less reported. It is known that the piezoelectric materials can produce induction charge under effect of stress wave due to direct piezoelectric effect, therefore, they are the good candidate of the AE sensors. In 1994, Turner et al. described the piezoelectric and ferroelectric materials for high temperature acoustic and vibration sensors in a review article [18]. Barbezat et al. used active fibre composite elements as acoustic emission sensors, and compared their performance with that of the commercial AE sensors [19]. However, when the stress wave excited by the piezoelectric sensing element encounters the shell wall of the AE sensor during propagation, part of it will be reflected to the piezoelectric element, which accordingly increases the response time of acoustic emission pulse signal and reduces the bandwidth of the acoustic emission sensors. In ultrasonic fields, tungsten power which possesses large density and velocity were usually used as the acoustic attenuation materials, therefore, in order to attenuate the backward stress wave, we developed the AE sensors by adding different backing layers, such as polymer, mixture tungsten power/polymer, to one side of the piezoelectric sensing element. The influence of backing layers on acoustic performance of the sensors was also investigated through pencil lead breaking and simulated noise experiments.

12 PZTa PZTb PZTc PZTd PZTe

10 8 6 4

G/(mS)

388

2 0 PZTa PZTb PZTc PZTd PZTe 0 50 100 150 200 250 300 350 400

Frequency(kHz) Fig. 1. Conductance frequency spectra of PZT ceramics.

Fig. 2. Structural schematic of the acoustic emission sensor.

2. Preparation of acoustic emission sensors 2.1. Preparation method The commercial Piezoelectric Lead Zirconate Titanate (PZT) ceramic (Yu Hai Electronic Ceramics Co., Ltd., China) with a dimension of 8
8
8 mm was used as sensing element of the acoustic emission sensor, whose performance parameters are listed in Table 1. The conductance spectra of PZT ceramics of the same size (termed as PZTa, PZTb, PZTc, PZTd, PZTe) as a function of frequency were obtained by using the impedance analyzer (Agilent E4990A, USA), as shown in Fig. 1. It can be seen that the conductance spectra of the three PZT ceramics are basically in coincidence, indicating a good consistency of electromechanical property. There exist several obvious resonant peaks in the spectra, and the dominant resonant peak appears around 160 kHz, which also determines the working frequency of the acoustic emission sensors. The structural schematic of the acoustic emission sensors was illustrated in Fig. 2. It is known that tungsten owns properties of large density and velocity, therefore, the addition of tungsten powder in epoxy resin can effectively enhance the attenuation of acoustic wave. Here, the PZT and Al2O3 ceramics were used as sensing element and matching layer of the AE sensors, respectively, and air, epoxy, mixture of epoxy resin and tungsten powder according to different ratios were used as backing layers, respectively. In this research, AE sensors made of backing layers of air and epoxy were described as sensor A and sensor B, and that made of backing layers of different epoxy resin/tungsten powder mixing ratio were termed as sensor C, sensor D and sensor E, respectively.

The mass percentage of tungsten powder was 4% for sensor C, 10% for sensor D and 20% for sensor E. The detailed preparation process is as follows. A connecter was welded to a wire and screwed into the reserved hole of a stainlesssteel outer shell, and the other end of the wire was welded to the positive electrode of the PZT ceramic. Then, the negative electrode surface of PZT ceramic was bonded to the Al2O3 matching layer. Finally, the matching layer was installed on the bottom of the steel shell. The photo of the fabricated acoustic emission sensor with different backing layers is shown in Fig. 3. 2.2. Performance testing The conductance spectra of the acoustic emission sensors with different backing layers as a function of frequency were tested. The acoustic emission performance of the AE sensors was experimentally verified by using a four-channel digital AE system (PAC Micro-II, USA) and preamplifier (PAC Model:2/4/6, USA), and a commercial acoustic emission sensor (PAC, R3a SNBK57, USA) was used as the reference sensor. The schematic diagram of the acoustic emission test and experimental setup were illustrated in Fig. 4. The acoustic emission sensors of different backing layers and the reference sensor were placed together on a piece of aluminum plate with a dimension of 500
500
2 mm by using vaseline as the coupling agent, as shown in Fig. 4(a). Then, the preamplifier was set to 40 dB, and a pencil lead break test was carried out according to the Nielsen-Hsu method [20,21]. The acoustic

Table 1 Main performance parameters of PZT ceramics. Density (103 kgm3)

Relative Permittivity

Piezo Strain Constant/(pCN1)

Piezo Voltage Constant /(103VmN1)

Curie Temperature Tc/°C

7.5

2500

426

19.2

340

389

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0.8

Sensor A Sensor B Sensor C Sensor D Sensor E

G/(mS)

0.6 0.4 0.2 Fig. 3. Photos of acoustic emission sensors and different backing layers.

emission source was produced by broking the pencil lead of 0.5 mm in diameter and 2.5 mm in length on the aluminum plate at an angle of 30°. The whole acoustic emission process was recorded by the digital AE system at a threshold value of 40 dB.

0.0 50

100

150 200 250 Frequency(kHz)

300

Fig. 5. The conductance versus frequency spectra of AE sensors of different backing layers.

3.2. Acoustic impedance 3. Results discussion and analysis

Acoustic impedance means the resistance when acoustic wave propagates in medium. The acoustic impedance of different backing layers can be calculated according to following formula.

3.1. Conductance-frequency spectra Fig. 5 illustrates the conductance vs. frequency spectra of acoustic emission sensors of different backing layers. It can be found that the dominant resonant frequency of the acoustic emission sensors decreases compared with that of the PZT ceramics in Fig. 1 as well as the corresponding conductance value. The backing layer has obvious influence on conductance spectra of the sensors. With increasing the acoustic loading of sensors, the resonant frequency shift towards low frequency direction. Sensor A of air backing layer has the largest resonant frequency and conductance amplitude, however, those are minimum for sensor E with epoxy resin/tungsten powder backing layer. It is well known that, the resonant frequency of the acoustic sensor is directly proportional to stiffness and inversely proportional to loading mass. After PZT ceramic was fabricated to acoustic emission sensor, the acoustic loading increased due to effects of metal shell and backing layer, thus the resonant frequency decreases. The air is the lightest among all backing materials, so sensor A has the lowest loading mass and the largest resonance frequency. The epoxy resin/tungsten powder is the heaviest of all backing materials, so sensor E has the largest loading mass and the least resonance frequency. In addition, due to damping effect of the backing layers, the high order resonance of the sensors is also greatly inhibited, and the stronger the damping effect, the more obvious the inhibition phenomenon is. Therefore, it also can be observed from the spectra that the high order resonance peaks of sensors B-E are very weak.

Z ¼ q
vC

ð1Þ

where Z, q and vc are acoustic impedance, density and sound velocity, respectively. Cylinder shape samples of U17
20 mm made of different backing materials were prepared, and their density was calculated. The propagation time of sound wave in the samples was measured based on acoustic transmission method, as shown in Fig. 6. It is well known that there exist various acoustic phenomena such as reflection, refraction and transmission at the boundary of different media when acoustic wave propagates in the medium. The reflection coefficient at interface can be calculated based on following formula.

R1 ¼

Z2  Z1 Z 1 þZ 2

ð2Þ

where R1, Z1 and Z2 are reflection coefficient, acoustic impedance of backing layer and PZT ceramics. Table 2 lists the physical properties of different materials. It can be seen that the sound propagation characteristics has distinct difference at boundary of PZT ceramics and different backing layers due to the obvious acoustic impedance difference of PZT ceramics, air, epoxy resin and epoxy resin/tungsten powder. There is the largest reflection coefficient at the interface of PZT ceramic and air backing layer. The reflected backward sound wave is benefit to improve the receiving sensitivity of sensor A, however, which also

Fig. 4. (a) Schematic diagram of acoustic emission test; (b) experimental setup.

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A-C. Because of the dimension limitation of aluminum plate, the acoustic emission wave would reflect after encountering the boundary of the aluminum plate, accordingly results in coupling of the forward acoustic emission wave and reflection wave. Therefore, the time domain spectra of head 150 ls were employed here for analysis to reduce the interference of reflection wave, as shown in Fig. 7(b). The receiving sensitivity of acoustic emission sensors can usually be reflected by the amplitude of acoustic emission wave. In Fig. 7(b), it can be observed that the peak-to-peak value of the acoustic emission signals received by sensors A-C decreases gradually. In addition, the acoustic attenuation of sensor A is also more remarkable than that of the other sensors. It is known that the acoustic attenuation property is correlated to the acoustic absorption, reflection and scatting ability of the backing layers. Because the reflection coefficient of epoxy resin and epoxy resin/tungsten powder backing layers is larger than that of the air, and the acoustic impedance is obviously larger than that of the air, the backing layers made of epoxy resin and epoxy resin/tungsten powder own better acoustic attenuation ability than the air backing layer. This is useful to decrease the acoustic coupling interference and improve the bandwidth of the acoustic emission sensors. In order to investigate the effects of tungsten powder content on propagation property of the acoustic emission waves, the pencil broken experiment was carried out again, and the time domain spectra of the acoustic emission signals received by sensors D-E are shown in Fig. 8(a). Also, the time domain spectra of head 150 ls was cut out for analysis, as shown in Fig. 8(b). It can be seen that the peak-to-peak value of the acoustic emission signals received by sensors C-E decreases slightly, and the acoustic attenuation of the late arrival wave shows a decreasing trend. The acoustic attenuation capability of the sensors could be improved

Fig. 6. Schematic diagram of acoustic transmission method.

increases the pulse duration time of the arrived acoustic emission signal, thus reduces its bandwidth. The acoustic reflection coefficient of other materials is obviously smaller than that of sensor A. With increasing the mass fraction of tungsten powder, the acoustic reflection coefficient of different materials decreases gradually and acoustic impedance increases greatly.

3.3. Spectra of time and frequency domains Fig. 7(a) is the time domain spectra of acoustic emission wave which propagated on the aluminum plate and received by sensors

Table 2 Physical performance parameters of different materials. Materials

Density (103 kgm3)

Acoustic velocity (kms1)

Acoustic impedance (M Rayl)

Reflection coefficient

PZT ceramics Air Epoxy resin Epoxy resin/tungsten powder (4:1:0.2) Epoxy resin/tungsten powder (4:1:0.5) Epoxy resin/tungsten powder (4:1:1)

7.6 0.0012 1.15 1.21 1.26 1.31

4.00 0.34 1.82 1.82 1.95 2.20

30.40 0.00041 2.09 2.21 2.46 2.89

– 1.00 0.87 0.86 0.85 0.83

10

10 5

Sensor C

0

0

-5

-5

-10 10

-10 10

Sensor B

5 0 -5 -10 10 Sensor A

5

Voltage(mV)

Voltage(mV)

5

0 -5 -10 10

0 -5 200

400 600 Time(µs)

800

1000

Sensor A

5

-5 0

Sensor B

5

0

-10

Sensor C

-10

0

25

50 75 100 Time(µs)

125

150

Fig. 7. (a) Full time domain spectra of acoustic emission signals caught by sensors A and B; (b) time domain spectra at head 150 ls.

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10

10

Sensor E

0

0

-5

-5

-10 10

-10 10

Sensor D

5 0 -5 -10 10 Sensor C

5

Voltage(mV)

Voltage(mV)

Sensor E

5

5

Sensor D

5 0 -5 -10 10 Sensor C

5 0

0 -5

-5

-10

-10

0

200

400 600 Time(µs)

800

1000

0

25

50 75 Time(µs)

100

125

150

Fig. 8. (a) Full time domain spectra of acoustic emission signals caught by sensors C–E; (b) time domain spectra at head 150 ls.

by increasing tungsten powder content in the backing layer, but is at expense of sensor sensitivity. Fourier transform method is used to obtain the frequency domain spectra of the acoustic emission signal at head 150 ls, and the spectra of acoustic emission signals received by sensors A-C are shown in Fig. 9. It can be seen that the acoustic emission signal produced by pencil lead break test mainly occurs in the frequency range of 0–200 kHz, and the sensors also has better acoustic response in this frequency range. It is known that the transmission modes of acoustic emission wave are complicated, which therefore results in complicated frequency components of the acoustic emission signals. In Fig. (9), the sensor A has a largest frequency response around 25 kHz, and a weaker response in other frequency range. As for sensors B and C, the high frequency response ability is obviously enhanced, and meanwhile the response peaks around 25 kHz decrease a little bit. Because the attenuation effects of the backing layers, it also can be found that the largest amplitude in the spectra decreases from sensor A to C, however, the 6 dB bandwidth increases. Sensor A has a minimum bandwidth of 4.2 kHz, and the bandwidth of sensors B and C is 131 kHz and 147 kHz. This indicates that the bandwidth of the prepared acoustic emission sensor can be obviously broadened by inducing the heavy backing layers. Also, the spectra of acoustic emission signals received by sensors C-E are shown in Fig. 10. It can also be seen that the acoustic emission signal caused by pencil lead break occurs in the frequency range of 0–200 kHz, and sensors C, D and E have better acoustic

Sensor C

0.2

Voltage(mV)

0.1 0.0 0.2

Sensor B

0.1 0.0 0.6 0.4 0.2 0.0

Sensor A

0

50

100

150

200

250

300

Frequency(kHz) Fig. 9. Frequency domain spectra of acoustic emission signals caught by sensors A–C.

Sensor E

0.2 0.1 0.0 0.2

Sensor D

0.1 0.0 0.2

Sensor C

0.1 0.0 0

50

100 150 200 Frequency(kHz)

250

300

Fig. 10. Frequency domain spectra of acoustic emission signals caught by sensors C–E.

response in this frequency range. With increasing the tungsten powder content, the frequency response around 25 kHz become weakened gradually, and the largest amplitude also decreases as the increase of the content of tungsten powder. The 6 dB bandwidth of sensors C, D and E is 142 kHz, 155 kHz, and 157 kHz, respectively. It is known based on the acoustic theory that, the slower the pulse attenuation of acoustic wave, the longer the pulse duration time, and the larger the amplitude of dominant frequency. The acoustic energy and pulse duration time of sensor A are larger than other sensors due to effect of reflection acoustic wave, therefore, its amplitude of dominant frequency is the largest, and the frequency response range is the smallest. However, because the attenuation ability of epoxy resin and epoxy resin/tungsten powder back layers is stronger than that of the air backing layer, their amplitude of dominant frequency decreases and frequency response range increases, which also indicates that backing layer is benefit to increase attenuation of acoustic waves and improve bandwidth of the sensors, but is at the expense of reducing the sensitivity of the acoustic emission sensor. 3.4. Acoustic emission characteristic parameters The steel ball drop experiment was carried out to further investigate the acoustic emission characteristic of the different acoustic emission sensors. Here, sensors A, B and E and a reference sensor

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(model SNBK 57) were used. The testing setup is shown in Fig. 11 (a). A steel ball of 11 mm in diameter was freely dropped from a height of 20 cm above the center of the aluminum plate, and the stress wave produced by the impact of steel ball on the aluminum plate was simultaneously received by the acoustic emission sensors. In addition, another experiment was also performed to explore the influence of environmental noise on the acoustic emission

sensors, as shown in Fig. 12(a). In Fig. 12(a), when the steel ball hit the aluminum plate, a brush was simultaneously used to rub the aluminum plate to produce environmental noise, then the sensors caught various acoustic emission signals during this process. The acoustic emission characteristic parameters of the sensors of different backing layers were analyzed by comparison with the reference sensor. Here, the acoustic emission parameter of hit

1.2

(a)

(b)

Hit count

0.8 0.6

SNBK57 Sensor A Sensor B Sensor C

Rolling hit

Second hit

First hit

1.0

0.4 0.2 0.0 0.0

MARSE×10

Amplitude(dB )

60

20

SNBK57 Sensor A 0 Sensor B Sensor C

40 20

0.4

2.5

Rolling hit

40

30

2.0

Second hit

60

Rolling hit

Second hit

80

(d)

First hit

100

80

SNBK57 Sensor A Sensor B Sensor C

First hit

F

1.0 1.5 Time(s)

3

120

0.5

0.6

0.8

1.0

0

0 0.0

0.5

1.0 1.5 Time(s)

2.0

0.0

2.5

0.5

1.0 1.5 Time(s)

2.0

2.5

Fig. 11. Experimental setup of impact and acoustic emission characteristic parameters.

3

Hit Count

(a)

(b)

SNBK57 Sensor A Sensor B Sensor C

2

1

0 0.0

0.5

1.0

1.5

2.0

2.5

Time(s)

60 40 20 0.0

3

(d)

60 40

SNBK57 Sensor A Sensor B Sensor E

2

First hit

80

SNBK57 Sensor A Sensor B Sensor E

First hit

100 Amplitude(dB)

80

(c)

3 MARSE ×10

120

1 0 0.5

1.0

1.5

2.0

2.5

20 0

0.5

1.0

1.5

Time(s)

2.0

2.5

0.0

0.5

1.0

1.5

2.0

Time(s)

Fig. 12. Experimental setup of impact and noise and acoustic emission characteristic parameters.

2.5

W. Zhang et al. / Applied Acoustics 156 (2019) 387–393

refers to any signal that exceeds the threshold of the AE system and enables it to get data, which reflects the amounts and frequency of acoustic emission events. It can be observed from Fig. 11 (b) that there exist three hits in 2.5 s, that is, the first hit between the steel ball and aluminum plate at 0.2 s, the second hit at 0.4 s due to rebound of the steel ball on the aluminum plate, and the rolling hit from the steel ball on aluminum plate at 0.8 s. The testing result of hit parameter indicates that all the sensors effectively record the acoustic emission signal induced by impact. The acoustic parameter of amplitude in Fig. 11(c) is the maximum waveform amplitude of the acoustic emission signal, which is related the intensity and attenuation of acoustic emission signal. It can be seen that amplitude of all sensors in the first hit at 0.2 s is highly consistent, however, the amplitude of the second and third hits decreases obviously, furthermore, a weak difference of amplitude for different sensors can also be found. The reason is that when the steel ball first hit the aluminum plate, the propagation routine of stress wave induced by the impact to each sensor is the same, that is, the acoustic energy received by all sensors is the same, so the amplitude at 0.2 s for all sensors keeps the same. However, the propagation distance of stress wave induced by rebound and rolling to different sensors is different due to irregular movement of a stainless steel ball, which leads to different energy loss of the stress wave in the aluminum plate, thus there exists a weak difference of amplitude for different sensors at the second and third hits. In addition, acoustic emission energy count (MARSE) is also an important acoustic emission parameter, which means the relative energy or intensity of acoustic emission events. As shown in Fig. 11 (d), the curves of MARSE vs. time of all sensors are basically consistent. The energy of the first hit is the strongest and the impact energy caused by rebound and rolling is very weak, which agrees well with the phenomena in Fig. 11(c). Above acoustic emission characteristic parameters analysis indicates that the acoustic emission sensors of different backing layers have good responding ability to emission events induced by impact. In Fig. 12(b), because the acoustic emission sensors record the acoustic emission events caused by both hit, rebound and rolling of the steel ball on aluminum plate and the friction between brush and aluminum plate, it is hard to distinguish the acoustic emission signals induced by impact or environmental noise. However, it can be found in Fig. 12(c) that, the amplitude is the strongest at 0.2 s when the steel ball first hit the aluminum plate, and the amplitude induced by other acoustic emission events decreases. This phenomenon is especially obvious in Fig. 12(d), the MARSE of first hit is far larger than other acoustic emission events, therefore, noise and effective signals can be identified accurately through parameters of amplitude and MARSE. 4. Conclusion The acoustic emission sensors with different backing layers were fabricated by using PZT ceramics as sensing elements. Comparing with PZT ceramic, the resonant frequency of all sensors decreases due to effects of backing layer and the metal shell. The high order resonance peak of the sensors with epoxy resin and epoxy resin/tungsten powder backing layer decreases greatly due to damping effect of the backing layer. The air backing layer has the largest acoustic reflection coefficient, and epoxy resin/tungsten powder backing layer has the largest acoustic attenuation performance, which is benefit to improve bandwidth of the acoustic emission sensor. The acoustic emission experiments results show that the acoustic emission sensors of different backing layers can effectively receive the acoustic emission signals caused by impact and accurately identify the effective signals and noise signals,

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which is suitable for application of acoustic emission inspection and monitoring. Although the developed acoustic emission sensors exhibit excellent performance, this paper do not discuss the influence of tungsten powder content in backing layer on properties of the sensors. In addition, the metal shell is easily to be corroded when embedded in concrete structure. Therefore, the acoustic emission sensors with non-metallic shell should also be paid more attention in fields of structural health monitoring. Acknowledgements This work was supported by National Key Research and Development Program of China (Grant No.2017YFE0120900), Natural Science Outstanding Youth Foundation of Shandong Province (Grant no. ZR2017JL023), National Natural Science Foundation of China (51632003) and Taishan Scholars Program. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.apacoust.2019.07.038. References [1] Kaiser J. Investigation of Acoustic Emission in Tensile Testing. Munich, Germany: Technische Hochschule München; 1950. [2] Schofield BH. Acoustic Emission from Metals, its Detection, Characteristics and Source. San Antonio, Texas: Research Institute; 1963. p. 63–91. [3] Dunegan HL, Harris DO, Tatro CA. Fracture analysis by use of acoustic emission. Eng Fract Mech 1968;1:105–10. [4] Harris DO, Dunegan HL. Continuous monitoring of fatigue-crack growth by acoustic-emission techniques. Exp Mech 1974;14:71–81. [5] Dunegan HL, Harris DO. Acoustic emission-a new nondestructive testing tool. Ultrasonics 1969;7(3):160–6. [6] Vidya Sagar R, Raghu Prasad BK. A review of recent developments in parametric based acoustic emission techniques applied to concrete structures. Nondestruct Test Eval 2012;27:47–68. [7] Heiple CR, Carpenter SH. Acoustic emission produced by deformation of metals and alloys-A review. J Acoust Emission 1987;6:177–204. [8] Ebrahimkhanlou A, Salvatore S. Single-sensor acoustic emission source localization in plate-like structures using deep learning. Aerospace 2018;5:50. [9] Hamel M, Addali A, Mba D. Investigation of the influence of oil film thickness on helical gear defect detection using acoustic emission. Appl Acoust 2014;79:42–6. [10] Posada-Roman J, Garcia-Souto JA, Rubio-Serrano J. Fiber optic sensor for acoustic detection of partial discharges in oil-paper insulated electrical systems. Sensors 2012;12(4):4793–802. [11] Mao W, Aoyama S, Towhata I. Feasibility study of using acoustic emission signals for investigation of pile spacing effect on group pile behavior. Appl Acoust 2018;139:189–202. [12] Hamel F, Bailon JP, Bassim MN. Acoustic emission mechanisms during highcycle fatigue. Eng Fract Mech 1981;14:853–60. [13] Stone DEW, Dingwall PF. Acoustic emission parameters and their interpretation. NDT&E Int 1977;10:51–62. [14] Tobias A. Acoustic-emission source location in two dimensions by an array of three sensors. Non-Destr Test 1976;9:9–12. [15] Ebrahimkhanlou A, Salamone S. Acoustic emission source localization in thin metallic plates: a single-sensor approach based on multimodal edge reflections. Ultrasonics 2017;78:134–45. [16] Ebrahimkhanlou A, Salamone S. A probabilistic framework for single-sensor acoustic emission source localization in thin metallic plates. Smart Mater Struct 2017;26(9):095026. [17] Kundu P, Kishore NK, Sinha AK. A non-iterative partial discharge source location method for transformers employing acoustic emission techniques. Appl Acoust 2009;70:1378–83. [18] Turner RC, Fuierer PA, Newnham RE, Shrout TR. Materials for high temperature acoustic and vibration sensors: a review. Appl Acoust 1994;41:229–324. [19] Barbezat M, Brunner AJ, Flüeler P, Huber C, Kornmann X. Acoustic emission sensor properties of active fibre composite elements compared with commercial acoustic emission sensors. Sens Actuators A-Phys 2004;114:13–20. [20] Jemielniak K. Some aspects of acoustic emission signal pre-processing. J Mater Process Tech 2001;109:242–7. [21] Gorman MR. Plate wave acoustic emission. J Acoust Soc Am 1991;90:358–64.