- Email: [email protected]
Design of a pulsed eddy current sensor for detection of defects in aircraft lap-joints
Sensors and Actuators A 101 (2002) 92–98
Design of a pulsed eddy current sensor for detection of defects in aircraft lap-joints Ali Sophiana, Gui Yun Tiana,*, David Taylora, John Rudlinb a
School of Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK b Structural Integrity Department, TWI, Granta Park, Great Abington, Cambridge CB1 6AL, UK Received 23 May 2002; accepted 31 May 2002
Abstract This paper presents a new type of pulsed eddy current (PEC) sensor that has been designed for defect detection in aircraft lap-joint structures. The sensor employs a new excitation circuit that requires no additional signal amplification and the paper also reports compensation techniques that improve the sensing resolution and stability. A new hybrid feature of the peak value in time domain and the maximum frequency magnitude in frequency domain has been investigated. A test rig has been built and some results from aircraft samples are presented. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Pulsed eddy current (PEC) sensor; Transient response; Magnetic sensor; Feature extraction; Digital compensation; Aircraft NDT
The constitutive equations in a nonmagnetic conductor are given by
1. Introduction The detection and characterisation of sub-surface flaws in conductive materials still pose major challenges to the NDE community and pulsed eddy current (PEC) sensing has emerged as one possible solution [1–3]. In contrast to conventional sinusoidal eddy current technique, where the excitation is limited to one frequency component, PEC techniques excite the induction coil with a rectangular stimulus. This frequency rich stimulus has been shown to be particularly useful for detecting deeply hidden sub-surface defects [4]. It is widely understood that the penetration depth of an eddy current sensor depends on the excitation frequency, and consequently, PEC sensors are capable of detecting both surface-breaking and buried defects. Faraday’s law and the Ampere’s law are, respectively, expressed as follows: r E ¼ B
(1)
rH ¼J
(2)
B ¼ m0 H
(3)
J ¼ sE
(4)
where m0 and s are the magnetic permeability of vacuum and the electrical conductivity of a material sample. The above equations are used to derive the governing equation for eddy current sensing [5] and to model and design new probes [6]. Throughout this development, computer modelling has been used to understand the principles behind the technique [5] and to refine the methodology. The computer models have also been used to design new probes, optimising their size and shape, to allow a wide variety of physical situations to be tested [6]. The frequency components of a pulse waveform can be represented using Fourier series. If the excitation waveform is defined as f ðtÞ ¼
*
Corresponding author. Tel.: þ44-1484-472319; fax: þ44-1484-451883. E-mail addresses: [email protected] (A. Sophian), [email protected] (G.Y. Tian), [email protected] (D. Taylor), [email protected] (J. Rudlin).
Vmax
nT t < ðn þ GÞT;
n ¼ 0; 1; 2; 3; . . .
0
ðn þ GÞT t < ðn þ 1ÞT;
n ¼ 0; 1; 2; 3; . . . (5)
The coil’s effective impedance is monitored, as it changes each time a defect is detected. In our approach the above
0924-4247/02/$ – see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 4 - 4 2 4 7 ( 0 2 ) 0 0 1 9 5 - 4
A. Sophian et al. / Sensors and Actuators A 101 (2002) 92–98
pulsed voltage f(t) is applied to give the discrete time series. The discrete Fourier transform Vk is then calculated: Vk ¼
N1 X
vk eið2pkr=NÞ
(6)
r¼0
By comparing the resulting frequency response it is possible to detect the presence of defects. In analysing the electromagnetic field distribution, three regions can be identified as follows [7–9]: region 1 is the eddy current region containing conducting, current carrying materials. Region 2 contains nonconducting materials and region 3 contains nonconductive materials, which may be ferromagnetic, but do not contain any source currents. This electromagnetic modelling and simulation, which is out of the scope of this paper, has informed our sensor design and sensor signal identification. PEC systems have recently been developed for commercial applications. For aircraft inspection, among others, two distinguished works have been published by Lebrun et al. and DERA. Lebrun et. al. [4] have developed a PEC system and a technique which uses peak value, time to peak, and characteristic frequency to detect defects under fasteners in aircraft structures. The characteristic frequency is defined as the frequency at which the highest magnitude spectrum of the pulse response is seen. In this technique, both time and frequency spectral analyses are used. They are able to detect and locate 1:4 mm 1:5 mm defect at 5 mm under the surface in AU4G structures with a coating layer. A centering technique has also been developed and employed. DERA has developed a PEC system called TRESCAN [5]. Rather than using Bz (air) as reference, they use Bz on the defect-free surface as reference and the time to the peak of the transient is used to find the depth of the defect within the structure. A two-dimensional image is obtained by scanning the probe over the aircraft sample surface and information from different depths is obtained by using the transient values at a particular time point. Metal loss of 0.9 mm could be detected at depths down to 7 mm and cracks of 6 mm 1:5 mm could be detected at a 6 mm depth. However, there are still some important technical and economic issues to address in the PEC world. It is the aim of this paper to develop a new simplified PEC system with novel signal conditioning, which will detect and characterise flaws under the surface of conductive materials
93
by using advanced signal processing. The system block diagram can be seen in Fig. 1. The rest of the paper is organized as follows. Section 2 provides an introduction to the new PEC sensor system, then Section 3 presents our novel approach for signal compensation. Section 4 illustrates the use of the proposed techniques in aircraft NDT and the paper concludes with a brief discussion in Section 5.
2. A new PEC system 2.1. System design The new PEC system consists of a waveform generator, a coil driver, a probe, an A/D capture card and a PC with signal processing software and display as shown in Fig. 1. The waveform generator outputs rectangular signals of variable frequency and duty cycle. The waveform is fed to a coil driver circuit, which excites the induction coil in the probe. The pick-up sensors measure the effective magnetic field, which consists of the one generated by the excitation coil and the one generated by the induced eddy current in the sample. An A/D card converts this signal into digital data ready to be processed by software in the PC, which includes data acquisition, feature extraction and defect detection. Finally the results are displayed on the monitor for interpretation by the user. 2.2. Excitation circuit design The important parameters in the excitation are the asymptotic value of the current and the rate of change of the rising edge. The larger the current, the larger the generated magnetic field will be and hence, the resulting eddy current in the specimen. This will increase the sensitivity and the signal to noise ratio. However, a current that is too large can cause local heating and easily introduce temperature drift into the system. The experiment reported in this paper has used a peak current of 300 mA. The rate of change of the rising edge of the current pulse is crucial as it determines the frequency components contained in the excitation. The higher the rate of change, the more high frequency components generated and hence, more diagnostic information can be expected. It is also important to stabilise the excitation voltage or current sources for a
Fig. 1. System design.
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A. Sophian et al. / Sensors and Actuators A 101 (2002) 92–98
wide range of measurement objects and for various measurement conditions such as lift-up, target inhomogeniety and surface geometrical effects [10]. In this system, software compensation techniques, described in Section 3, are used. Our new simplified excitation circuit uses a pair of MOSFETs as a switch that is turned on and off by the generator. The supply voltage is variable in order to be able to control the peak excitation current. Fig. 2. Sensor probe structure.
2.3. Sensor probe design 3. Signal compensation and feature extraction The sensor probe consists of an inductive flat coil and a magnetic field sensor. The coil is used to generate the varying magnetic field, and the magnetic field sensor to pick-up the perturbed magnetic field. The design of the coil is adapted to the particular application. It determines the depth of penetration and the spatial resolution. Smaller diameter and longer length is suitable for applications that demand high spatial resolution with sacrificed penetration depth. Several types of magnetic sensor are available for sensing the resulting magnetic field strength including Hall effect devices, anisotropic magnetoresistive and giant magnetoresisitve (GMR) devices. These sensors perform better than pickup coils for sub-surface defect detection, as they are more sensitive to low frequencies. From the three types of sensor that we have used, the Hall effect device has been shown to possess a much wider magnetic field range and the anisotropic magnetoresistive device has the smallest range. The sensitive layer of the device lays parallel to the surface of the testing sample and measures the magnetic field in a direction perpendicular to the surface. The sensor structure is shown in Fig. 2.
A 20 MHz analogue to digital conversion PCI card has been used in the data acquisition. Importantly, the requirement to amplify the response signal has been negated, which saves some cost and complexity. The digitised output data is captured and ready for advanced signal analysis and diagnosis. To minimise the noise generated at the output, averaging of multisamples for consecutive cycles and a low-pass Gaussian filters are applied. The averaging is carried out on 10 consecutive responses, before the application of the Gaussian filter, which reduces spurious noise in the signal and smoothes it. For the signal analysis we use the response signal when the probe is in air as a reference, rather than a defect-free sample, in order to get differential signals with higher signal to noise ratios. A differential signal is generated by subtracting the response signal when the probe is on the specimen from the reference signal. The peak value of the differential and its time of arrival are used to characterise defect features. In the measurement system, the repeatability of the excitation current is crucial, as it will determine the magnetic field that induces the eddy current in the sample. However, the
Fig. 3. Sensor responses and coil currents.
A. Sophian et al. / Sensors and Actuators A 101 (2002) 92–98
95
Fig. 4. Compensated differential response signals.
inductance of the coil will be subject to variations due to liftoff, target material properties and structure. This change in inductance will lead to a change in the excitation current and a compensation technique is required. To design and evaluate a compensation technique, the sensor has been used to test different some layered samples, where each layer comprises a 1.5 mm Al plate. If fair is the response in air, fn is the response of an n-layered sample, cair is the coil current in the air, and cn is the coil current for an nlayered sample, the differential signals can be calculated by dn ¼ fn fair
(7)
dcn ¼ cn cair
(8)
GðxÞ ¼ x G
(9)
where G is a Gaussian filter function and x is the discrete response data; n will be in the range of 1–5. Fig. 3 illustrates some test sample differential signals, where the upper diagram is the differential response from the
Hall effect device and the lower is the current variation in the excitation coil. As indicated in the Fig. 3, different samples have different response, where the coil current is varied. In contrast to the blind signal separation and compensation approach [11], we apply simple linear compensation algorithms for real time measurement: compn ¼ Gðdn Þ þ KGðdcn Þ
(10)
where K maximises the measured feature differences between different samples by compensating for current change. By optimisation the discrimination and the stability of the defect detection, the recommendation for K is 1.5, where a local maxima point is obtained by least mean square (LMS) algorithms. Fig. 4 illustrates the compensated signals, where the differences in peak values have been maximised. In other words, the compensation has increased measurement resolution and stability. As indicated in Fig. 4, different thickness of Al samples will affect the features of the compensated differential
Fig. 5. Aircraft lap-joint sample.
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A. Sophian et al. / Sensors and Actuators A 101 (2002) 92–98
Fig. 6. (a) Model of surface defect and (b) sub-surface defect in second layer.
signals from the sensor. These changes will include the peak value and the signature of the signals. Therefore, by observing these two quantities, we can obtain some information about the specimen. Peak values in the time domain can provide simple information about defects. However, they are sensitive to noise and a single parameter is not good enough to quantify complex cracks. A new feature extraction technique has,
therefore, been devised. In addition to the peak value feature, the technique employs spectral analysis using the fast Fourier transform (FFT). To improve discrimination between surface and sub-surface defects, two frequency components have been selected. As widely understood in eddy current NDT, low frequency components are suitable for deep penetration and hence, detection of deeply buried defects, and high frequency components are suitable for surface
Fig. 7. Scanning result of second row.
A. Sophian et al. / Sensors and Actuators A 101 (2002) 92–98
breaking defects. Following this principle and some experiments, the two selected frequencies are the fundamental frequency, which always has the maximum magnitude and a frequency component around 10 kHz. However, this pair of components should be adjusted to the sample to be tested. By integrating these new features in the frequency domain and the peak values in time domain, we can obtain comprehensive information about any cracks. Section 4 will report the application of this sensor and compensation techniques to the testing of industrial samples.
97
presented in the paper requires no amplification circuitry and uses pure undistorted data. The software compensation techniques employed have improved the measurement resolution and stability. The initial results show that the current system is capable of detecting surface and sub-surface defects using hybrid features. By integration of both time and frequency domain analyses, the new feature extraction techniques achieve good discrimination of defects by their location and are less prone to noise. Further work attempting to quantify three-dimensional shape of the defects is ongoing.
4. Preliminary tests and analysis Acknowledgements 4.1. Aircraft lap-joint specimen A preliminary test was carried out in order to evaluate these techniques. Two plates of aluminium were joined together using rivets to simulate aircraft lap-joint structures. Slots were manufactured on the top side of the top plate and on the bottom side of the bottom plate. The surface and sub-surface defects are illustrated in Fig. 5 with the thick lines inside the illustration representing surface defects and the thin lines representing sub-surface defects. The details of the surface and sub-surface defects under the rivets can be seen in Fig. 6. 4.2. Scanning testing Whilst using these discriminating features of the peak value and the maximum frequency magnitude for the aircraft lap-joint samples, a scanning technique was employed. Fig. 7 shows the result of scanning the rows of rivets. From top to bottom, the graphs in the figure show peak value, maximum frequency component magnitude, and the magnitude of frequency component around 10 kHz. The graphs show that the surface defects with length of 10 mm or greater are detected as indicated by high peak values and fundamental magnitudes. Some shorter defects are also detected. The magnitudes of both features, in addition, provide indication of the length of the defects. By comparison of frequency magnitude distribution along frequencies, sub-surface/surface will be detected. Further work on the extraction of depth information for sub-surface crack is under-investigation. The results show that the low frequency component highlights the sub-surface defects better than the peak value whilst the high frequency component highlights the surface defects and not the sub-surface ones. This is useful for the discrimination and classification of types of defects and automatic classification and pattern signature extraction are current areas of work.
5. Conclusions and further work The paper has reported a simple PEC system for surface and sub-surface crack detection. Uniquely, the system
The authors would like to thank TWI Ltd. for their financial support and samples. The authors thank Mr. W. Hussain for his contribution to the work.
References [1] A. Sophian, G.Y. Tian, D. Taylor, J. Rudlin, Eddy current and electromagnetic NDT: a review, insight, J. Br. Inst. NDT 43 (2001). [2] J.R. Rudlin, A beginner’s guide to—eddy current testing, Br. J. NDT 31 (6) (1989) 308–313. [3] Y. Bar-Cohen, Emerging NDE technologies and challenges at the beginning of the 3rd milennium. Part II, Mater. Eval. 58 (2) (2000) 141–150. [4] B. Lebrun, Y. Jayet, J.C. Baboux, Pulsed eddy current signal analysis: application to the experimental detection and characterization of deep flaws in highly conductive materials, NDT & E Int. 30 (3) (1997) 163–170. [5] R.A. Smith, G.R. Hugo, Transient eddy current NDE for ageing aircraft—capabilities and limitations, Insight 43 (1) (2001) 14–25. [6] K. Miya, M. Uesaka, Y. Yoshia, Applied electromagnetics research and application, Prog. Nucl. Energy 32 (1/2) (1998) 174–194. [7] U. Patel, D. Rodger, Finite element modelling of pulsed eddy currents for nondestructive testing, IEEE Trans. Magn. 32 (3) (1996) 1593–1596. [8] O. Bomin, J. Cahouet, P. Giordano, Eddy current nondestructive testing-experiment and numerical model for the conception and optimisation of probes, J. de Phys. III 3 (3) (1993) 485–494. [9] G.Y. Tian, Z.X. Zhao, R.W. Baines, Computational algorithms for linear variable differential transformers (LVDTs), IEE Proc. Sci. Measure. Technol. 144 (4) (1997) 189–193. [10] G.Y. Tian, Z.X. Zhao, R.W. Baines, The research of inhomogeniety in eddy current sensors, Sens. Actuators A 58 (1998) 153–156. [11] G.Y. Tian, Design and implementation of distributed measurement systems by fieldbus-based intelligent sensors, IEEE Trans. Instrum. Measure. 50 (5) (2001) 1197–1202.
Biographies Ali Sophian obtained his BEng in electronic and information engineering at the University of Huddersfield in 1998. Currently, he is pursuing his PhD in the area of pulsed eddy current NDT at the same university. His research interest includes eddy current NDT and digital signal processing. Gui Yun Tian obtained his BSc in metrology and instrumentation and MSc in precision engineering at the University of Sichuan (Chengdu, PR China)
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A. Sophian et al. / Sensors and Actuators A 101 (2002) 92–98
in 1985 and 1988, respectively. After working in the University of Sichuan for several years as a member of the academic staff, he was awarded his degree at the University of Derby, UK in 1998. Currently, he is a senior lecturer in engineering at the School of Engineering, University of Huddersfield. Dr Tian has joint background in engineering and computer science. His research interests are broadly in the areas of sensor and instrumentation, signal processing, computer vision and graphics. David Taylor obtained his BSc in electronic and electrical engineering from Huddersfield Polytechnic, UK, in 1983 as a scholarship student from
the NCB. After working in the mining industry for several years he returned to academia and was awarded a PhD in 1990 for research into IC testing strategies. Currently, he is a professor in the School of Engineering, University of Huddersfield and leads the Electronics and Communications Research Group, working primarily in transient testing of analogue ICs and sensors. John Rudlin is principal consultant NDT in Structural Integrity Department, The Welding Institute (TWI). After serving in the University College of London, now he works in industrial applications of NDE in TWI.
Design of a pulsed eddy current sensor for detection of defects in aircraft lap-joints Ali Sophiana, Gui Yun Tiana,*, David Taylora, John Rudlinb a
School of Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK b Structural Integrity Department, TWI, Granta Park, Great Abington, Cambridge CB1 6AL, UK Received 23 May 2002; accepted 31 May 2002
Abstract This paper presents a new type of pulsed eddy current (PEC) sensor that has been designed for defect detection in aircraft lap-joint structures. The sensor employs a new excitation circuit that requires no additional signal amplification and the paper also reports compensation techniques that improve the sensing resolution and stability. A new hybrid feature of the peak value in time domain and the maximum frequency magnitude in frequency domain has been investigated. A test rig has been built and some results from aircraft samples are presented. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Pulsed eddy current (PEC) sensor; Transient response; Magnetic sensor; Feature extraction; Digital compensation; Aircraft NDT
The constitutive equations in a nonmagnetic conductor are given by
1. Introduction The detection and characterisation of sub-surface flaws in conductive materials still pose major challenges to the NDE community and pulsed eddy current (PEC) sensing has emerged as one possible solution [1–3]. In contrast to conventional sinusoidal eddy current technique, where the excitation is limited to one frequency component, PEC techniques excite the induction coil with a rectangular stimulus. This frequency rich stimulus has been shown to be particularly useful for detecting deeply hidden sub-surface defects [4]. It is widely understood that the penetration depth of an eddy current sensor depends on the excitation frequency, and consequently, PEC sensors are capable of detecting both surface-breaking and buried defects. Faraday’s law and the Ampere’s law are, respectively, expressed as follows: r E ¼ B
(1)
rH ¼J
(2)
B ¼ m0 H
(3)
J ¼ sE
(4)
where m0 and s are the magnetic permeability of vacuum and the electrical conductivity of a material sample. The above equations are used to derive the governing equation for eddy current sensing [5] and to model and design new probes [6]. Throughout this development, computer modelling has been used to understand the principles behind the technique [5] and to refine the methodology. The computer models have also been used to design new probes, optimising their size and shape, to allow a wide variety of physical situations to be tested [6]. The frequency components of a pulse waveform can be represented using Fourier series. If the excitation waveform is defined as f ðtÞ ¼
*
Corresponding author. Tel.: þ44-1484-472319; fax: þ44-1484-451883. E-mail addresses: [email protected] (A. Sophian), [email protected] (G.Y. Tian), [email protected] (D. Taylor), [email protected] (J. Rudlin).
Vmax
nT t < ðn þ GÞT;
n ¼ 0; 1; 2; 3; . . .
0
ðn þ GÞT t < ðn þ 1ÞT;
n ¼ 0; 1; 2; 3; . . . (5)
The coil’s effective impedance is monitored, as it changes each time a defect is detected. In our approach the above
0924-4247/02/$ – see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 4 - 4 2 4 7 ( 0 2 ) 0 0 1 9 5 - 4
A. Sophian et al. / Sensors and Actuators A 101 (2002) 92–98
pulsed voltage f(t) is applied to give the discrete time series. The discrete Fourier transform Vk is then calculated: Vk ¼
N1 X
vk eið2pkr=NÞ
(6)
r¼0
By comparing the resulting frequency response it is possible to detect the presence of defects. In analysing the electromagnetic field distribution, three regions can be identified as follows [7–9]: region 1 is the eddy current region containing conducting, current carrying materials. Region 2 contains nonconducting materials and region 3 contains nonconductive materials, which may be ferromagnetic, but do not contain any source currents. This electromagnetic modelling and simulation, which is out of the scope of this paper, has informed our sensor design and sensor signal identification. PEC systems have recently been developed for commercial applications. For aircraft inspection, among others, two distinguished works have been published by Lebrun et al. and DERA. Lebrun et. al. [4] have developed a PEC system and a technique which uses peak value, time to peak, and characteristic frequency to detect defects under fasteners in aircraft structures. The characteristic frequency is defined as the frequency at which the highest magnitude spectrum of the pulse response is seen. In this technique, both time and frequency spectral analyses are used. They are able to detect and locate 1:4 mm 1:5 mm defect at 5 mm under the surface in AU4G structures with a coating layer. A centering technique has also been developed and employed. DERA has developed a PEC system called TRESCAN [5]. Rather than using Bz (air) as reference, they use Bz on the defect-free surface as reference and the time to the peak of the transient is used to find the depth of the defect within the structure. A two-dimensional image is obtained by scanning the probe over the aircraft sample surface and information from different depths is obtained by using the transient values at a particular time point. Metal loss of 0.9 mm could be detected at depths down to 7 mm and cracks of 6 mm 1:5 mm could be detected at a 6 mm depth. However, there are still some important technical and economic issues to address in the PEC world. It is the aim of this paper to develop a new simplified PEC system with novel signal conditioning, which will detect and characterise flaws under the surface of conductive materials
93
by using advanced signal processing. The system block diagram can be seen in Fig. 1. The rest of the paper is organized as follows. Section 2 provides an introduction to the new PEC sensor system, then Section 3 presents our novel approach for signal compensation. Section 4 illustrates the use of the proposed techniques in aircraft NDT and the paper concludes with a brief discussion in Section 5.
2. A new PEC system 2.1. System design The new PEC system consists of a waveform generator, a coil driver, a probe, an A/D capture card and a PC with signal processing software and display as shown in Fig. 1. The waveform generator outputs rectangular signals of variable frequency and duty cycle. The waveform is fed to a coil driver circuit, which excites the induction coil in the probe. The pick-up sensors measure the effective magnetic field, which consists of the one generated by the excitation coil and the one generated by the induced eddy current in the sample. An A/D card converts this signal into digital data ready to be processed by software in the PC, which includes data acquisition, feature extraction and defect detection. Finally the results are displayed on the monitor for interpretation by the user. 2.2. Excitation circuit design The important parameters in the excitation are the asymptotic value of the current and the rate of change of the rising edge. The larger the current, the larger the generated magnetic field will be and hence, the resulting eddy current in the specimen. This will increase the sensitivity and the signal to noise ratio. However, a current that is too large can cause local heating and easily introduce temperature drift into the system. The experiment reported in this paper has used a peak current of 300 mA. The rate of change of the rising edge of the current pulse is crucial as it determines the frequency components contained in the excitation. The higher the rate of change, the more high frequency components generated and hence, more diagnostic information can be expected. It is also important to stabilise the excitation voltage or current sources for a
Fig. 1. System design.
94
A. Sophian et al. / Sensors and Actuators A 101 (2002) 92–98
wide range of measurement objects and for various measurement conditions such as lift-up, target inhomogeniety and surface geometrical effects [10]. In this system, software compensation techniques, described in Section 3, are used. Our new simplified excitation circuit uses a pair of MOSFETs as a switch that is turned on and off by the generator. The supply voltage is variable in order to be able to control the peak excitation current. Fig. 2. Sensor probe structure.
2.3. Sensor probe design 3. Signal compensation and feature extraction The sensor probe consists of an inductive flat coil and a magnetic field sensor. The coil is used to generate the varying magnetic field, and the magnetic field sensor to pick-up the perturbed magnetic field. The design of the coil is adapted to the particular application. It determines the depth of penetration and the spatial resolution. Smaller diameter and longer length is suitable for applications that demand high spatial resolution with sacrificed penetration depth. Several types of magnetic sensor are available for sensing the resulting magnetic field strength including Hall effect devices, anisotropic magnetoresistive and giant magnetoresisitve (GMR) devices. These sensors perform better than pickup coils for sub-surface defect detection, as they are more sensitive to low frequencies. From the three types of sensor that we have used, the Hall effect device has been shown to possess a much wider magnetic field range and the anisotropic magnetoresistive device has the smallest range. The sensitive layer of the device lays parallel to the surface of the testing sample and measures the magnetic field in a direction perpendicular to the surface. The sensor structure is shown in Fig. 2.
A 20 MHz analogue to digital conversion PCI card has been used in the data acquisition. Importantly, the requirement to amplify the response signal has been negated, which saves some cost and complexity. The digitised output data is captured and ready for advanced signal analysis and diagnosis. To minimise the noise generated at the output, averaging of multisamples for consecutive cycles and a low-pass Gaussian filters are applied. The averaging is carried out on 10 consecutive responses, before the application of the Gaussian filter, which reduces spurious noise in the signal and smoothes it. For the signal analysis we use the response signal when the probe is in air as a reference, rather than a defect-free sample, in order to get differential signals with higher signal to noise ratios. A differential signal is generated by subtracting the response signal when the probe is on the specimen from the reference signal. The peak value of the differential and its time of arrival are used to characterise defect features. In the measurement system, the repeatability of the excitation current is crucial, as it will determine the magnetic field that induces the eddy current in the sample. However, the
Fig. 3. Sensor responses and coil currents.
A. Sophian et al. / Sensors and Actuators A 101 (2002) 92–98
95
Fig. 4. Compensated differential response signals.
inductance of the coil will be subject to variations due to liftoff, target material properties and structure. This change in inductance will lead to a change in the excitation current and a compensation technique is required. To design and evaluate a compensation technique, the sensor has been used to test different some layered samples, where each layer comprises a 1.5 mm Al plate. If fair is the response in air, fn is the response of an n-layered sample, cair is the coil current in the air, and cn is the coil current for an nlayered sample, the differential signals can be calculated by dn ¼ fn fair
(7)
dcn ¼ cn cair
(8)
GðxÞ ¼ x G
(9)
where G is a Gaussian filter function and x is the discrete response data; n will be in the range of 1–5. Fig. 3 illustrates some test sample differential signals, where the upper diagram is the differential response from the
Hall effect device and the lower is the current variation in the excitation coil. As indicated in the Fig. 3, different samples have different response, where the coil current is varied. In contrast to the blind signal separation and compensation approach [11], we apply simple linear compensation algorithms for real time measurement: compn ¼ Gðdn Þ þ KGðdcn Þ
(10)
where K maximises the measured feature differences between different samples by compensating for current change. By optimisation the discrimination and the stability of the defect detection, the recommendation for K is 1.5, where a local maxima point is obtained by least mean square (LMS) algorithms. Fig. 4 illustrates the compensated signals, where the differences in peak values have been maximised. In other words, the compensation has increased measurement resolution and stability. As indicated in Fig. 4, different thickness of Al samples will affect the features of the compensated differential
Fig. 5. Aircraft lap-joint sample.
96
A. Sophian et al. / Sensors and Actuators A 101 (2002) 92–98
Fig. 6. (a) Model of surface defect and (b) sub-surface defect in second layer.
signals from the sensor. These changes will include the peak value and the signature of the signals. Therefore, by observing these two quantities, we can obtain some information about the specimen. Peak values in the time domain can provide simple information about defects. However, they are sensitive to noise and a single parameter is not good enough to quantify complex cracks. A new feature extraction technique has,
therefore, been devised. In addition to the peak value feature, the technique employs spectral analysis using the fast Fourier transform (FFT). To improve discrimination between surface and sub-surface defects, two frequency components have been selected. As widely understood in eddy current NDT, low frequency components are suitable for deep penetration and hence, detection of deeply buried defects, and high frequency components are suitable for surface
Fig. 7. Scanning result of second row.
A. Sophian et al. / Sensors and Actuators A 101 (2002) 92–98
breaking defects. Following this principle and some experiments, the two selected frequencies are the fundamental frequency, which always has the maximum magnitude and a frequency component around 10 kHz. However, this pair of components should be adjusted to the sample to be tested. By integrating these new features in the frequency domain and the peak values in time domain, we can obtain comprehensive information about any cracks. Section 4 will report the application of this sensor and compensation techniques to the testing of industrial samples.
97
presented in the paper requires no amplification circuitry and uses pure undistorted data. The software compensation techniques employed have improved the measurement resolution and stability. The initial results show that the current system is capable of detecting surface and sub-surface defects using hybrid features. By integration of both time and frequency domain analyses, the new feature extraction techniques achieve good discrimination of defects by their location and are less prone to noise. Further work attempting to quantify three-dimensional shape of the defects is ongoing.
4. Preliminary tests and analysis Acknowledgements 4.1. Aircraft lap-joint specimen A preliminary test was carried out in order to evaluate these techniques. Two plates of aluminium were joined together using rivets to simulate aircraft lap-joint structures. Slots were manufactured on the top side of the top plate and on the bottom side of the bottom plate. The surface and sub-surface defects are illustrated in Fig. 5 with the thick lines inside the illustration representing surface defects and the thin lines representing sub-surface defects. The details of the surface and sub-surface defects under the rivets can be seen in Fig. 6. 4.2. Scanning testing Whilst using these discriminating features of the peak value and the maximum frequency magnitude for the aircraft lap-joint samples, a scanning technique was employed. Fig. 7 shows the result of scanning the rows of rivets. From top to bottom, the graphs in the figure show peak value, maximum frequency component magnitude, and the magnitude of frequency component around 10 kHz. The graphs show that the surface defects with length of 10 mm or greater are detected as indicated by high peak values and fundamental magnitudes. Some shorter defects are also detected. The magnitudes of both features, in addition, provide indication of the length of the defects. By comparison of frequency magnitude distribution along frequencies, sub-surface/surface will be detected. Further work on the extraction of depth information for sub-surface crack is under-investigation. The results show that the low frequency component highlights the sub-surface defects better than the peak value whilst the high frequency component highlights the surface defects and not the sub-surface ones. This is useful for the discrimination and classification of types of defects and automatic classification and pattern signature extraction are current areas of work.
5. Conclusions and further work The paper has reported a simple PEC system for surface and sub-surface crack detection. Uniquely, the system
The authors would like to thank TWI Ltd. for their financial support and samples. The authors thank Mr. W. Hussain for his contribution to the work.
References [1] A. Sophian, G.Y. Tian, D. Taylor, J. Rudlin, Eddy current and electromagnetic NDT: a review, insight, J. Br. Inst. NDT 43 (2001). [2] J.R. Rudlin, A beginner’s guide to—eddy current testing, Br. J. NDT 31 (6) (1989) 308–313. [3] Y. Bar-Cohen, Emerging NDE technologies and challenges at the beginning of the 3rd milennium. Part II, Mater. Eval. 58 (2) (2000) 141–150. [4] B. Lebrun, Y. Jayet, J.C. Baboux, Pulsed eddy current signal analysis: application to the experimental detection and characterization of deep flaws in highly conductive materials, NDT & E Int. 30 (3) (1997) 163–170. [5] R.A. Smith, G.R. Hugo, Transient eddy current NDE for ageing aircraft—capabilities and limitations, Insight 43 (1) (2001) 14–25. [6] K. Miya, M. Uesaka, Y. Yoshia, Applied electromagnetics research and application, Prog. Nucl. Energy 32 (1/2) (1998) 174–194. [7] U. Patel, D. Rodger, Finite element modelling of pulsed eddy currents for nondestructive testing, IEEE Trans. Magn. 32 (3) (1996) 1593–1596. [8] O. Bomin, J. Cahouet, P. Giordano, Eddy current nondestructive testing-experiment and numerical model for the conception and optimisation of probes, J. de Phys. III 3 (3) (1993) 485–494. [9] G.Y. Tian, Z.X. Zhao, R.W. Baines, Computational algorithms for linear variable differential transformers (LVDTs), IEE Proc. Sci. Measure. Technol. 144 (4) (1997) 189–193. [10] G.Y. Tian, Z.X. Zhao, R.W. Baines, The research of inhomogeniety in eddy current sensors, Sens. Actuators A 58 (1998) 153–156. [11] G.Y. Tian, Design and implementation of distributed measurement systems by fieldbus-based intelligent sensors, IEEE Trans. Instrum. Measure. 50 (5) (2001) 1197–1202.
Biographies Ali Sophian obtained his BEng in electronic and information engineering at the University of Huddersfield in 1998. Currently, he is pursuing his PhD in the area of pulsed eddy current NDT at the same university. His research interest includes eddy current NDT and digital signal processing. Gui Yun Tian obtained his BSc in metrology and instrumentation and MSc in precision engineering at the University of Sichuan (Chengdu, PR China)
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in 1985 and 1988, respectively. After working in the University of Sichuan for several years as a member of the academic staff, he was awarded his degree at the University of Derby, UK in 1998. Currently, he is a senior lecturer in engineering at the School of Engineering, University of Huddersfield. Dr Tian has joint background in engineering and computer science. His research interests are broadly in the areas of sensor and instrumentation, signal processing, computer vision and graphics. David Taylor obtained his BSc in electronic and electrical engineering from Huddersfield Polytechnic, UK, in 1983 as a scholarship student from
the NCB. After working in the mining industry for several years he returned to academia and was awarded a PhD in 1990 for research into IC testing strategies. Currently, he is a professor in the School of Engineering, University of Huddersfield and leads the Electronics and Communications Research Group, working primarily in transient testing of analogue ICs and sensors. John Rudlin is principal consultant NDT in Structural Integrity Department, The Welding Institute (TWI). After serving in the University College of London, now he works in industrial applications of NDE in TWI.