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Defect imaging curved surface based on flexible eddy current array sensor
Journal Pre-proofs Defect Imaging Curved Surface Based on Flexible Eddy Current Array Sensor Weipeng Zhang, Chenglong Wang, Fengqin Xie, Huayu Zhang PII: DOI: Reference:
S0263-2241(19)31144-3 https://doi.org/10.1016/j.measurement.2019.107280 MEASUR 107280
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Measurement
Received Date: Revised Date: Accepted Date:
2 July 2019 12 November 2019 17 November 2019
Please cite this article as: W. Zhang, C. Wang, F. Xie, H. Zhang, Defect Imaging Curved Surface Based on Flexible Eddy Current Array Sensor, Measurement (2019), doi: https://doi.org/10.1016/j.measurement.2019.107280
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Defect Imaging Curved Surface Based on Flexible Eddy Current Array Sensor Weipeng Zhang 1, Chenglong Wang2,Fengqin Xie 3 and Huayu Zhang 1,* 1College 2
of Mechanical and electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China;
Shandong Province Key Laboratory of Mine Mechanical Engineering，Shandong University of Science and Technology，Qingdao
266590，China; 3College
of Transportation, Shandong University of Science and Technology, Qingdao 266590, China
* Correspondence: [email protected]; Tel.: +86-136-0648-9180 Abstract: Due to it is difficult to visually reflect the size, distribution shape and location of the defects in surface quality
detection of complex surfaces, the flexible eddy current array (FECA) probe combined with the Cartesian coordinate robot is employed to scan the defects on last stage blades of steam turbine (LSBST) in this paper. The sensor probe is designed to parallel and stagger arrangement and weld onto a flexible printed circuit board (FPCB). The FECA sensor is thin and flexible, and thus it can conform to the surface geometries of the LSBST. To increase the inductance value of the single probe coil, Iron core is added in the middle of the cylindrical coil. In the process of detecting signal, the starting point compensation and lift-off effect compensation are carried out, which improves the resolution of defect detection and realizes the imaging of the natural defects on the surface of LSBST. Finally, the effect of defect imaging on steam turbine blades is verified through conducting experiments. Results demonstrate that the size, location and shape of defects are consistent with the actual situation which verifies the feasibility of the imaging algorithm. Keywords: flexible eddy current array; last stage blades of steam turbine; natural defects; defects imaging 1. Introduction Eddy current testing (ECT) refers to a nondestructive testing method using electromagnetic induction. Eddy current sensors can be used to measure the defect shape, conductivity and thickness of metal objects. In the application of industrial field, ECT sensor is frequently used for nondestructive evaluation (NDE) of metal work pieces. Significant features of ECT are metal surface quality inspection, high response speed of the sensor and non-contact measurement. The array distribution of the probe can greatly enhance the detection speed and efficiency, the detection accuracy and reliability of detection result [1-6]. Inductive thermography is another non-destructive technique for detecting surface cracks in electrically conductive materials. The induced eddy currents and the heat diffusion are disturbed by surface cracks, which make the defects visible in infrared images [7]. In order to measure and evaluate surface conditions of a freeform surface, D. Huang etc. [8] proposed a method for obtaining the thermal pattern of the entire freeform surface of a propeller by employing eddy current pulsed thermography (ECPT). Segment heating is applied using a line conductor type induction coil and the thermal image stitching is proposed for showing the entire thermal pattern on propeller surface. Therefore, the imaging and visualization of defect detection has become an important trend in the development of non-destructive testing instruments. If the location, shape and size of defects can be directly observed, it will greatly simplify the operation of instruments and reduce the workload of operators. To find out the location, length and depth of defects, it of necessity to collect more accurate defect signals. Defect imaging is realized by filtering all signals, and predictive maintenance scheme is discussed. Therefore, it is of great significance to study the imaging of natural defects. The low-pressure rotor blade of steam turbine is affected by high rotating centrifugal force, high temperature, corrosion, erosion and other complex working environments when it is in service. Defects on the surface can cause blade fracture, which can lead to accidents. Once the blades in use break and fall off, it will directly damage the turbine rotor, blade and separator, even endangering the operation safety of the unit [9-12]. For eddy current detection of LSBST surface, pen-type probe is commonly used to perform spot scanning at the position to be detected. The detection time is slow and the defect condition of turbine blade cannot be comprehensively analyzed. To improve the inductance of coil and the sensitivity of probe, it is necessary to print multi-layer coil, which is of high cost. In the Traditional eddy current array inspection method, each coil was scanned with time division multiplexing (TDM) technology, such as N.T Duong mentioned T-scan and U-scan [13, 14]. The excitation and acquisition of signals is complex, requiring more complicated signal processing method to realize the defect of image. In order to deal with the imaging problem of defects on curved surfaces, a detection system using a FECA sensor is adopted, which can simultaneously pick up the inductance signals of eight channels. The system is used to image the natural defects on the surface of LSBST. The Cartesian coordinate robot, which can accurately control the
scanning direction and speed, is used to scanning tool with the FECA sensor. The defect information of the target sample is extracted from the inductance data output by the FECA sensor. Through the combination of the collected inductance information with sampling frequency and experimental scanning time, the eight-channel inductance signal is integrated using MATLAB algorithm, and the two-dimensional and three-dimensional images of the defect are obtained. The system realizes the imaging of the natural defect on the surface of LSBST. The single probe in this study adopts the method of simultaneous excitation and reception to detect the defects on the blade surface, and the reasonable arrangement of coil spacing can avoid the mutual inductance interference between the probes and the decrease of signal sensitivity caused by mutual inductance. In the detection process, only one scan is needed to collect all the natural defect signals on the surface of LSBST. Fig.1 shows the schematic diagram of the sensor detection system of the FECA. The detection part mainly includes probe support, flexible substrate and array probe. The probe support of the FECA sensor is designed to be as close to the surface curvature of the specimen as possible. The array probes are all welded onto a flexible substrate and then fixed on a probe holder fitting the curve of the surface of the last stage turbine blade. The probe support is connected to the X-axis of the Cartesian coordinate robot. By controlling the Cartesian coordinate robot, the position and height of the FECA sensor are adjusted in Y and Z directions, and the surface defects of LSBST are scanned and detected in X direction to collect the changes of inductive signals caused by the defects.
LSBST with Natural Defects
Probe support
Flexible substrate
Array probe Z
Y X
Fig.1. Schematic diagram of the composition of the FECA sensor detection system
2. Inductive sensing realized with LDC1614
L
target
C
INA INB
R
Sensor
LDC1614
In detection system, the surface defects of LSBST are measured by detecting the parallel resonant inductance of the working coil, and the eddy current field changes caused by the surface defects of the turbine are detected by the coil designed on the principle of LC oscillator. According to Fig.2, a single probe of the array probe consists of a detection coil and a capacitor to form an LC oscillation circuit. L is the inductance of the detection coil and C is the resonant capacitor. During the sampling process of the resonant detection circuit of the probe, there exists a high-frequency oscillator in the LDC1614 inductance digital converter chip, which continuously outputs the frequency sweep signal. When the LC network of the detection probe resonates, the impedance of the LC resonant circuit is the maximum, and the output voltage of the detection circuit is the maximum. Through maintaining the frequency of the output signal, the maximum output voltage keeps constant, which can thus maintain the LC circuit in a resonant state.
Fig.2. Schematic diagram of a single probe detection circuit The sensor oscillation frequency is given by [15]:
f
1 2π LC
(1)
Where C dents the sensor capacitance, L is the sensor inductance. When the capacitance in parallel is constant, the resonant frequency corresponds to the inductance change of the coil. With the coil close to the measured object, the coil inductance changes, resulting in a change in the resonant frequency of the measurement system. The measurement of the resonant frequency is realized by the comparison and calibration of the internal clock signal of LDC1614. To reduce the experimental error, the system adopts the method of simultaneous excitation and sampling. Compared with time-sharing excitation scanning method, simultaneous excitation scanning method is simpler to extract signals. FECA sensor has eight array detection probes, each requiring a separate sampling channel. Due to LDC1614 inductance digital converter chip has only four channels, we use two conversion chips. Fig.3 presents the schematic diagram of the detection circuit of the FECA probe. As shown in the far left of Fig.3, there are eight FECA probes. Through the shielding wire, every 4 probes are connected with the pins of the FPCB circuit board of the flexible base of the FECA probe from IN1A/IN1B to IN4A/IN4B of the LDC1614 inductance digital conversion chip. The conversion chip will sample the inductance changes of each channel successively in the multi-channel mode and convert them into digital signals. Each channel has a separate setting for the IDrive current used to set the amplitude of the sensor oscillation. A high frequency reference clock can be connected at the CLKIN pin or use an internal reference oscillator. This reference clock is used to measure the sensor frequency. The front end of the detection circuit used in this paper is employed for the drive of the resonant circuit, followed by a multiplexer, which can connect the activated channels to the kernel module in turn as well as measure and digitize the frequency of the sensor. The kernel uses a reference frequency to measure the frequency of the sensor. The reference frequency comes from an internal reference clock (oscillator), or an externally supplied clock. Each LDC1614 needs to be equipped with an MSP430 host. The I2C interface of LDC1614 inductance digital conversion chip is used for device configuration. In addition, the digital inductance value is transmitted to MSP430 and stored in registers. INTB pins can be configured to actively MSP430 when system status changes. The upper computer software (LabVIEW) obtains the curve reflecting the real-time signal of the probe inductance by reading the register value in the MSP430 through USB port. L1 Sensor1 C1
IN1A Chan1 Drive IN1B
IN4A
C4 IN4B
Chan4 Drive
LDC1614
Clock DIVIDE 1
Clock DIVIDE 4
Vdd SD INTB
Multiplexer
L4 Sensor4
Drive
Internal clock Clock DIVIDE 4
SDA Multiplexer
Clock DIVIDE 1
GPIO
D+
DP
GPIO DM
USB
D-
MSP430
Detection core External clock
Vdd
SCL
Computer 1
I2C Peripheral
I2C
GND ADDR
L1 Sensor5 C1 L4 Sensor8
LDC1614
C4
SD INTB SDA SCL ADD R
Vdd DP DM msp430 GND
GPIO GPIO
USB
D+ D-
Computer 2
External clock 8 coils
Inductance to Digital Converter (LDC) for Inductive Sensing
Control chip
Fig.3. Schematic diagram of detection circuit of FECA probe
Data-processing systems
3. Design of detector probe for FECA sensor 3.1. Parameter selection of FECA sensor probe
For the detection coil of a single probe, in order to increase the inductance value with a small size, we add a ferrite core and increase the number of turns as much as possible. The schematic diagram of single detection coil model is shown in Fig.4.
Fig.4. Schematic diagram of detection coil model. The maximum inductance of the detection probe coil meeting this requirement is 47 μH by finished product. Therefore, we chose the standard inductor as our probe to detect the coil. To select the appropriate excitation frequency and capacitance value, we studied the relationship between the excitation frequency and the inductance variation, obtaining the curve of the relationship between the inductance variation and the excitation frequency, as shown in Fig.5. We can get that the excitation frequency is between 0.8 MHz and 1.1 MHz in Fig.5 and the inductance change the most, which indicates that the sensor sensitivity is the best in the excitation frequency from 0.8 MHz to 1.1 MHz. As a result, the capacitance value is 560 pF, and its no-load resonance frequency is 0.985 MHz. Specific parameters of FECA probe are shown in Table 1: 58
Inductance value（μH）
50 42 33 25 17 8 0 0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
frequency(MHz)
Fig.5. Relation curve between inductance change and excitation frequency
Table 1 Parameters of the FECA probe Values
Name
Unit
Number of turns of probe coil
80
turns
Inductance value of sub probe
47
μH
Resonant frequency (no load)
0.985
MHz
Capacitance of shunt capacitor
560
pF
8
-
2.13
mm
Height of a single probe
2.15
mm
Sampling number
1100
-
Sampling time
180
s
Sampling distance
110
mm
Number of sub probes Diameter
of a single probe
3.2. Arrangement of array coils
FECA sensor not only has the advantages of common eddy current array sensor, but also has better flexibility. It can be freely bent or even folded with flexible and diverse structure. Additionally, the sensors can also be randomly arranged according to the requirements of measuring conditions. In the meanwhile, it is very convenient to detect parts with complex surface shapes. Fig.6 (a) is the Position Map of FECA Sensor and Surface of LSBST, where L1 = 35 mm is cross section Arc Length of LSBST and L2 is Arc length of FECA sensor after bending, which is also the coverage area of the FECA sensor. d is the distance between the FECA Sensor and the surface of LSBST(Lift off). The Detection range of FECA will change with the FECA sensor mounted on the probe support, which can be found in Fig.1. In order to collect the surface quality detection signals of the LSBST in one scan, the detection range of the sensor should cover the width of the LSBST. With the Lift off height d=1.5, the Arc length of FECA sensor after bending is L2=26.4 mm, Based on [16], when center distance between the coils is greater than 2 times and less than 4 times of the outer radius of a single coil, the mutual inductance interference between coils decreases rapidly with increasing coil center distance. When center distance between the coils is greater than 4 times of the outer radius of a single coil, the mutual inductance interference between coils is negligible. In the present study, the distance between the coils is more than 4 times the outer radius of a single coil. In order to further eliminate the interference between coils and increase the scanning resolution, coils are arranged in two rows. The distance between coils in each row is 7.5 mm and the central distance between the two probes is 3.4 mm, as shown in Fig.6(c). Fig.6(b) is the Arrangement and composition of FECA coils.
L1
d L2
(a)
(b)
7.5
12.0
7.5
7.5
?Ф2,13 2.3
3.4 26.4 35.0 (c)
Fig.6. Arrangement and composition of FECA probe. (a) Position Map of FECA Sensor and Surface of LSBST (b) Physical drawing of flexible array probe (c) Arrangement and composition of FECA probe. 3.3. Finite Element Analysis of Flexible Eddy Current Array Sensor Probe
As shown in Fig.7, when an alternating current is passing through the coil, there is a changing magnetic field around the coil. When a conductor is close to the coil, its surface will generate eddy currents due to alternating magnetic fields.
Fig.7.Schematic diagram of eddy current effect According to [17], the impedance Z, inductance L and quality factor Q of the coil change with the mutual inductance M. In the meanwhile, since the eddy current in the conductor itself consumes energy, the equivalent resistance of the coil increases. Since the distance d between the probe and the target under test, the electromagnetic characteristics of the target under test (resistivity ρ and permeability μ), and the frequency f of alternating current all will change the coupling relationship between the conductor and the detection coil, The functional expression of coil inductance L can be obtained, as shown in Equation 2. The inductance of inspection coil is determined by the lift off d, the conductivity and the permeability μ of the detected material as well as the frequency f of the alternating current.
L F d，，μ，f
(2)
When the relative position of the probe and the blade remains unchanged during the detection process, there are erosion defects such as uplift and depression. According to Equation 2, local lift-off will occur on the surface of the probe and LSBST, and the inductance value of the probe’s detection coil will change. When the pitting and the tiny flaw of the defects occur, the conductivity and the magnetic permeability change to some extent, affecting the inductance of the probe. In order to analyze the eddy current distribution on the surface of turbine blades, finite element simulation was used to understand the magnetic field distribution among each probe. The parameters of each coil of the sensor are shown in Table 1. The conductivity of LSBST is 1.74 MS/m, and permeability of LSBST is 4000. Since FECA sensor will have assembly tolerance when it is installed on the probe support, the distance between the coil and the surface of LSBST is not uniform and remains the same. To more truly reflect the eddy current distribution on the surface of LSBST, the distance between the probe and the coil is set as shown in Fig.8(a) and 8(c), where the lift off of coil 5 and coil 4 is 2 mm, coil 3 and coil 6 is 1.5 mm, coil 2 and coil 7 is 1.2 mm, coil1 and coil 8 is 0.8 mm. Based on Fig.8(b) and 8(d), we can see that magnetic field distribution and intensity excited by coil1 and coil8 is the same, coil 2 and coil 7 is the same, and so on. The bottom of the single coil is flat and the surface of LSBST is irregularly curved. Therefore, the coil is not positively aligned to the detected surface of LSBST, and certainly magnetic field distribution excited by 8 coils is not uniform, which can be observed from Fig.8(b) and 8(d). Even so, with the sensor scans in the x-direction, the surface quality of the LSBST will be completely measured. From Fig.8(b) and 8(d), we can also see that there exists no interference between the 8 coils, proving that the coil arrangement scheme proposed in section 3.2 is feasible. 1
8 2
3
4
5
6
7 d=2mm
(a)
(b)
(c) (d) Fig.8. Finite element simulation of the probe of the FECA sensor. (a) Side view of Model (b) Side view of simulation results (c) Overlooking view of Model (d) Overlooking view of simulation results
4.Experiment 4.1 Introduction to the experiment table Fig.9(a) shows the sensor detection system of the FECA. The system consists of the Cartesian coordinate robot, Conversion chips, computers, FECA probes and Experiment Specimen (LSBST). The specimen is clamped by a three-jaw chuck. The Cartesian coordinate robot probe first moves along the Y-axis to find the test base point for LSBST (which is far from the blade root). Then, the optimal height of the sensor and the turbine blade surface is adjusted in the Z-axis direction to obtain the optimal detection height. Finally, the Cartesian coordinate robot sets the probe to scan along the X-axis to set the motion of the test bench at a constant speed. The detection time is 220 seconds, the moving distance is 110 mm, and the sampling points are 1100. Computer
Cartesian coordinate robot
Computer
FECA probe LSBST LDC1614
(a)
Probe support
Three-jaw chuck
FECA probe
LSBST
(b)
Fig.9. Detection system of LSBST surface quality. (a) Hardware composition of the detection system (b) Details of the probe and blade experimental positions According to the number of sampling points and sampling time, we calculated that the sampling interval was 0.2 s and thus the interval time swept the distance of 0.1 mm. The position of the FECA probe and the turbine end-stage blade is shown in Fig.9(b). 4.2 Specimen introduction There are many defects in the selected LSBST, and four typical defects are selected for research. As shown in Fig.10 (a), Defect 1 is multiple dense pitting pits, and Defect 2 is a larger corrosive pitting. Besides, the corrosion degree is deeper than that of defect 1. Defect 3 is an erosion area with ridges and valleys. Defect 4 is marginal missing area. In Fig.10 (b), we show a simplified figure of the defect and mark the distance between the defect and the turbine blade end face. The scan starts at A marked in the figure and ends at B, among which defect 1 is 5 mm away from the end face A, Defect 2 is 50 mm away from the end face A, Defect 3 is 80 mm away from the end face A, approximately 19 mm long, and defect 4 is 100 mm away from the end face. defect1
defect2
defect3
defect4
(a) 110mm 100mm 80mm 50mm 5mm
19mm
A
B
(b) Fig.10. Natural defects of turbine blade at the last stage. (a) Physical drawing of the specimen (b)Schematic diagram of defect distribution of specimens
5.Signal starting point compensation and plane imaging 5.1 Signal processing and compensation After a 3-minute scan, 1100 sample dots were collected from each channel, and 8 inductive signal curves were obtained, which can be found in Fig.11. From the experimental data, it can be seen that the inductance signal collected in the experiment contains obvious defect information, that is, the inductance value has obvious mutation at the defect.
At the same time, Fig.11 shows that there is inhomogeneous characteristics generation. The reasons for the inhomogeneous characteristics are presented as follows: (1) The lift-off distance of single probe from the blade surface is different; (2)The inductance and capacitance of single probe are slightly different due to the material; (3)Motor vibration and surrounding electromagnetic signal interference in the test bench. We compensate for the inhomogeneity and process the signal with the median filter. The collected acquisition signals form a matrix of 1160×8, and the average inductance of each channel is calculated, forming a matrix b.
Fig.11. Experimental data
a11 a12 a18 a a a 28 a 21 22 a n1 a n2 a n8 a11 a 21 a n1 n a a a 11 21 n1 b n a11 a 21 a n1 n
a12 a 22 a n2 n a12 a 22 a n2 n a12 a 22 a n2 n
a18 a 28 a n8 n a18 a 28 a n8 n a a 28 a n8 18 n
a11 a 21 a n1 a12 a 22 a n2 a18 a 28 a 8n a11 a12 a18 n n n a a a a11 a12 a1 n a12 a 22 a n2 a18 a 28 a 8n 28 a a - b 21 22 n n n a n1 a n2 a 8 n a11 a12 a1 n a12 a 22 a n2 a18 a 28 a 8n n n n Where n is the number of sampling points.
(3)
(4)
(5)
a11 a12 a18 8 a a 11 12 a18 a a - Δa a - 8 a11 a12 a18 8
a11 a12 a18 8 a11 a12 a18 8 a11 a12 a18 8
a11 a12 a18 8 a a a18 11 12 8 a a a18 11 12 8
(6)
In addition, median function movement is adopted. H=movement(A,X) Where X=10. According to the parameters of the probe sampling frequency 5 times/s, sampling time 3 minutes, sampling distance 110 mm, we replace the X-axis coordinates with the position information of the sampling points. The experimental data of Fig.12 (a) can be obtained. After resolving the inhomogeneity of the signal, the planar scanning imaging of the signal is completed. The location and distribution of the defects are determined, as shown in Fig.12 (b).
(a) defect1
defect3
defect2
defect4
(b)
Fig.12. (a) Image after signal processing (b) Plan of the scanned defects 5.2 Defect height and 3D inversion imaging
The difference in lift-off height exerts a great impact on the array probe in experiment. Therefore, it is necessary to compensate for the changes caused by different lifting lift-off height [18]. We used the lift-off data between the eddy current probe and turbine blade for fitting, obtaining the fitting curve to compensate the inhomogeneity caused by lifting lift-off height. According to the inductance parameters of sampling, we fitted the curve corresponding to its distance.
f x a e b x 0.3215
(7)
where a = -26.72(-40.46,-12.98), b = 2.566 ( 2.015, 3.116). Lift off fitting curve is shown in fig.13.
Fig.13. Lift off fitting curve Based on the Fig.13, the inductance value in plane scanning is introduced. The obtained height signal is taken as the z-axis, the distance value as the X-axis, and the number of channels as the Y-axis. The three-dimensional imaging figure of the turbine blade is obtained, which can be found in Fig.14. In comparison with the defects of LSBST, we can see that the system can estimate the location, shape and depth of the main defects on the blade surface. defect3
defect1
defect2
defect4
Fig.14. 3D imaging of defects 6. Conclusions In this paper, the detection and imaging of curved surface defects by FECA probe are studied. The detection sensitivity of eddy current array sensor is guaranteed by miniaturization and high inductance of array probe. Through unidirectional scanning of turbine blade surface, the inductance’s change of FECA sensor is extracted and analyzed. The three-dimensional image of turbine blade surface quality is obtained by filtering and compensation of signal processing and fitting equation of height and inductance change. In the current work, the coverage of the probe on the blade surface is demonstrated and the
signal data containing the defect is obtained through experiments. In addition, the defect inversion image is successfully obtained, which realizes the defect imaging of the blade surface erosion, pitting and edge defect. References [1] N. Yusa, H. Hashizume, R. Urayama, T. Uchimoto, T. Takagi, K. Sato, An arrayed uniform eddy current eprobe design for crack monitoring and sizing of surface breaking cracks with the aid of a computational inversion technique, Ndt & E International, 61 (2014) 29-34. [2] D. Caetano, F. Rabuske, D. Oliveira, T. Rabuske, J. Fernandes, M. Piedade, Ieee, Fast Settling VGA for Eddy Currents Non-Destructive Testing with an Array of Magneto Resistors, 2016 12th Conference on Ph.D. Research in Microelectronics and Electronics (Prime), (2016). [3] D. Cai, C. Zou, Z. Sun, Q. Chen, J. Wang, Ieee, Geometric Optimization of A Flexible Arrayed Eddy Current Sensor for Non-destructive Testing,
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Acknowledgments This work is financially supported by the Open Fund of Shandong Province Key Laboratory of Mine Mechanical Engineering NO. 2019KLMM201).
Biographies Zhang Weipeng receive B.Eng. degree in the School of Mechanical and Electronic Engineering from liaocheng university, shandong, China, in 2016, and he is studying for M.Eng. degree at the School of Mechanical and Electronic Engineering, Shandong University of Science and Technology. He is currently working on flexible eddy current array sensors. His research interests include simulation of electromagnetic field, novel sensor design, detection of fatigue cracks and defects of parts, signal processing. Chenglong WANG receive Dr. Eng degree in the College of Mechanical and Electronic Engineering from Shandong University of Science and Technology in 2009.He is currently working on servo control technology and intelligent equipment. His research interests include simulation of mechatronic system, intelligent shocking absorber, digital hydraulic servo system. Fengqin Xie received the Ph.D. degree in Machine Design Manufacturing and Their Automation from the Shandong University of Science and Technology, Qingdao, china, in 2013. Her research is focused on electromagnetic testing, thermography, and composite testing in the field of Non-destructive testing (NDT) and Structural health monitoring (SHM). Huayu Zhang received Ph.D degree in Mechanical and Electronic Engineering from the Shandong University of Science and Technology, Qingdao, china, in 2012.His research is focused on Non-destructive testing (NDT), Sensing and imaging, and Structural health monitoring (SHM). He has applied or holds about several patents in China. In recent 5 years, he has chaired or participated in several research programs funded by Ministry of Education and provincial government.
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
Highlights
Detect curved surface defects with flexible eddy current array sensors.
Reduce the effect of starting point and lift-off with compensation algorithm.
Completed the imaging experiment of natural defects on the surface of LSBST.
S0263-2241(19)31144-3 https://doi.org/10.1016/j.measurement.2019.107280 MEASUR 107280
To appear in:
Measurement
Received Date: Revised Date: Accepted Date:
2 July 2019 12 November 2019 17 November 2019
Please cite this article as: W. Zhang, C. Wang, F. Xie, H. Zhang, Defect Imaging Curved Surface Based on Flexible Eddy Current Array Sensor, Measurement (2019), doi: https://doi.org/10.1016/j.measurement.2019.107280
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Defect Imaging Curved Surface Based on Flexible Eddy Current Array Sensor Weipeng Zhang 1, Chenglong Wang2,Fengqin Xie 3 and Huayu Zhang 1,* 1College 2
of Mechanical and electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China;
Shandong Province Key Laboratory of Mine Mechanical Engineering，Shandong University of Science and Technology，Qingdao
266590，China; 3College
of Transportation, Shandong University of Science and Technology, Qingdao 266590, China
* Correspondence: [email protected]; Tel.: +86-136-0648-9180 Abstract: Due to it is difficult to visually reflect the size, distribution shape and location of the defects in surface quality
detection of complex surfaces, the flexible eddy current array (FECA) probe combined with the Cartesian coordinate robot is employed to scan the defects on last stage blades of steam turbine (LSBST) in this paper. The sensor probe is designed to parallel and stagger arrangement and weld onto a flexible printed circuit board (FPCB). The FECA sensor is thin and flexible, and thus it can conform to the surface geometries of the LSBST. To increase the inductance value of the single probe coil, Iron core is added in the middle of the cylindrical coil. In the process of detecting signal, the starting point compensation and lift-off effect compensation are carried out, which improves the resolution of defect detection and realizes the imaging of the natural defects on the surface of LSBST. Finally, the effect of defect imaging on steam turbine blades is verified through conducting experiments. Results demonstrate that the size, location and shape of defects are consistent with the actual situation which verifies the feasibility of the imaging algorithm. Keywords: flexible eddy current array; last stage blades of steam turbine; natural defects; defects imaging 1. Introduction Eddy current testing (ECT) refers to a nondestructive testing method using electromagnetic induction. Eddy current sensors can be used to measure the defect shape, conductivity and thickness of metal objects. In the application of industrial field, ECT sensor is frequently used for nondestructive evaluation (NDE) of metal work pieces. Significant features of ECT are metal surface quality inspection, high response speed of the sensor and non-contact measurement. The array distribution of the probe can greatly enhance the detection speed and efficiency, the detection accuracy and reliability of detection result [1-6]. Inductive thermography is another non-destructive technique for detecting surface cracks in electrically conductive materials. The induced eddy currents and the heat diffusion are disturbed by surface cracks, which make the defects visible in infrared images [7]. In order to measure and evaluate surface conditions of a freeform surface, D. Huang etc. [8] proposed a method for obtaining the thermal pattern of the entire freeform surface of a propeller by employing eddy current pulsed thermography (ECPT). Segment heating is applied using a line conductor type induction coil and the thermal image stitching is proposed for showing the entire thermal pattern on propeller surface. Therefore, the imaging and visualization of defect detection has become an important trend in the development of non-destructive testing instruments. If the location, shape and size of defects can be directly observed, it will greatly simplify the operation of instruments and reduce the workload of operators. To find out the location, length and depth of defects, it of necessity to collect more accurate defect signals. Defect imaging is realized by filtering all signals, and predictive maintenance scheme is discussed. Therefore, it is of great significance to study the imaging of natural defects. The low-pressure rotor blade of steam turbine is affected by high rotating centrifugal force, high temperature, corrosion, erosion and other complex working environments when it is in service. Defects on the surface can cause blade fracture, which can lead to accidents. Once the blades in use break and fall off, it will directly damage the turbine rotor, blade and separator, even endangering the operation safety of the unit [9-12]. For eddy current detection of LSBST surface, pen-type probe is commonly used to perform spot scanning at the position to be detected. The detection time is slow and the defect condition of turbine blade cannot be comprehensively analyzed. To improve the inductance of coil and the sensitivity of probe, it is necessary to print multi-layer coil, which is of high cost. In the Traditional eddy current array inspection method, each coil was scanned with time division multiplexing (TDM) technology, such as N.T Duong mentioned T-scan and U-scan [13, 14]. The excitation and acquisition of signals is complex, requiring more complicated signal processing method to realize the defect of image. In order to deal with the imaging problem of defects on curved surfaces, a detection system using a FECA sensor is adopted, which can simultaneously pick up the inductance signals of eight channels. The system is used to image the natural defects on the surface of LSBST. The Cartesian coordinate robot, which can accurately control the
scanning direction and speed, is used to scanning tool with the FECA sensor. The defect information of the target sample is extracted from the inductance data output by the FECA sensor. Through the combination of the collected inductance information with sampling frequency and experimental scanning time, the eight-channel inductance signal is integrated using MATLAB algorithm, and the two-dimensional and three-dimensional images of the defect are obtained. The system realizes the imaging of the natural defect on the surface of LSBST. The single probe in this study adopts the method of simultaneous excitation and reception to detect the defects on the blade surface, and the reasonable arrangement of coil spacing can avoid the mutual inductance interference between the probes and the decrease of signal sensitivity caused by mutual inductance. In the detection process, only one scan is needed to collect all the natural defect signals on the surface of LSBST. Fig.1 shows the schematic diagram of the sensor detection system of the FECA. The detection part mainly includes probe support, flexible substrate and array probe. The probe support of the FECA sensor is designed to be as close to the surface curvature of the specimen as possible. The array probes are all welded onto a flexible substrate and then fixed on a probe holder fitting the curve of the surface of the last stage turbine blade. The probe support is connected to the X-axis of the Cartesian coordinate robot. By controlling the Cartesian coordinate robot, the position and height of the FECA sensor are adjusted in Y and Z directions, and the surface defects of LSBST are scanned and detected in X direction to collect the changes of inductive signals caused by the defects.
LSBST with Natural Defects
Probe support
Flexible substrate
Array probe Z
Y X
Fig.1. Schematic diagram of the composition of the FECA sensor detection system
2. Inductive sensing realized with LDC1614
L
target
C
INA INB
R
Sensor
LDC1614
In detection system, the surface defects of LSBST are measured by detecting the parallel resonant inductance of the working coil, and the eddy current field changes caused by the surface defects of the turbine are detected by the coil designed on the principle of LC oscillator. According to Fig.2, a single probe of the array probe consists of a detection coil and a capacitor to form an LC oscillation circuit. L is the inductance of the detection coil and C is the resonant capacitor. During the sampling process of the resonant detection circuit of the probe, there exists a high-frequency oscillator in the LDC1614 inductance digital converter chip, which continuously outputs the frequency sweep signal. When the LC network of the detection probe resonates, the impedance of the LC resonant circuit is the maximum, and the output voltage of the detection circuit is the maximum. Through maintaining the frequency of the output signal, the maximum output voltage keeps constant, which can thus maintain the LC circuit in a resonant state.
Fig.2. Schematic diagram of a single probe detection circuit The sensor oscillation frequency is given by [15]:
f
1 2π LC
(1)
Where C dents the sensor capacitance, L is the sensor inductance. When the capacitance in parallel is constant, the resonant frequency corresponds to the inductance change of the coil. With the coil close to the measured object, the coil inductance changes, resulting in a change in the resonant frequency of the measurement system. The measurement of the resonant frequency is realized by the comparison and calibration of the internal clock signal of LDC1614. To reduce the experimental error, the system adopts the method of simultaneous excitation and sampling. Compared with time-sharing excitation scanning method, simultaneous excitation scanning method is simpler to extract signals. FECA sensor has eight array detection probes, each requiring a separate sampling channel. Due to LDC1614 inductance digital converter chip has only four channels, we use two conversion chips. Fig.3 presents the schematic diagram of the detection circuit of the FECA probe. As shown in the far left of Fig.3, there are eight FECA probes. Through the shielding wire, every 4 probes are connected with the pins of the FPCB circuit board of the flexible base of the FECA probe from IN1A/IN1B to IN4A/IN4B of the LDC1614 inductance digital conversion chip. The conversion chip will sample the inductance changes of each channel successively in the multi-channel mode and convert them into digital signals. Each channel has a separate setting for the IDrive current used to set the amplitude of the sensor oscillation. A high frequency reference clock can be connected at the CLKIN pin or use an internal reference oscillator. This reference clock is used to measure the sensor frequency. The front end of the detection circuit used in this paper is employed for the drive of the resonant circuit, followed by a multiplexer, which can connect the activated channels to the kernel module in turn as well as measure and digitize the frequency of the sensor. The kernel uses a reference frequency to measure the frequency of the sensor. The reference frequency comes from an internal reference clock (oscillator), or an externally supplied clock. Each LDC1614 needs to be equipped with an MSP430 host. The I2C interface of LDC1614 inductance digital conversion chip is used for device configuration. In addition, the digital inductance value is transmitted to MSP430 and stored in registers. INTB pins can be configured to actively MSP430 when system status changes. The upper computer software (LabVIEW) obtains the curve reflecting the real-time signal of the probe inductance by reading the register value in the MSP430 through USB port. L1 Sensor1 C1
IN1A Chan1 Drive IN1B
IN4A
C4 IN4B
Chan4 Drive
LDC1614
Clock DIVIDE 1
Clock DIVIDE 4
Vdd SD INTB
Multiplexer
L4 Sensor4
Drive
Internal clock Clock DIVIDE 4
SDA Multiplexer
Clock DIVIDE 1
GPIO
D+
DP
GPIO DM
USB
D-
MSP430
Detection core External clock
Vdd
SCL
Computer 1
I2C Peripheral
I2C
GND ADDR
L1 Sensor5 C1 L4 Sensor8
LDC1614
C4
SD INTB SDA SCL ADD R
Vdd DP DM msp430 GND
GPIO GPIO
USB
D+ D-
Computer 2
External clock 8 coils
Inductance to Digital Converter (LDC) for Inductive Sensing
Control chip
Fig.3. Schematic diagram of detection circuit of FECA probe
Data-processing systems
3. Design of detector probe for FECA sensor 3.1. Parameter selection of FECA sensor probe
For the detection coil of a single probe, in order to increase the inductance value with a small size, we add a ferrite core and increase the number of turns as much as possible. The schematic diagram of single detection coil model is shown in Fig.4.
Fig.4. Schematic diagram of detection coil model. The maximum inductance of the detection probe coil meeting this requirement is 47 μH by finished product. Therefore, we chose the standard inductor as our probe to detect the coil. To select the appropriate excitation frequency and capacitance value, we studied the relationship between the excitation frequency and the inductance variation, obtaining the curve of the relationship between the inductance variation and the excitation frequency, as shown in Fig.5. We can get that the excitation frequency is between 0.8 MHz and 1.1 MHz in Fig.5 and the inductance change the most, which indicates that the sensor sensitivity is the best in the excitation frequency from 0.8 MHz to 1.1 MHz. As a result, the capacitance value is 560 pF, and its no-load resonance frequency is 0.985 MHz. Specific parameters of FECA probe are shown in Table 1: 58
Inductance value（μH）
50 42 33 25 17 8 0 0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
frequency(MHz)
Fig.5. Relation curve between inductance change and excitation frequency
Table 1 Parameters of the FECA probe Values
Name
Unit
Number of turns of probe coil
80
turns
Inductance value of sub probe
47
μH
Resonant frequency (no load)
0.985
MHz
Capacitance of shunt capacitor
560
pF
8
-
2.13
mm
Height of a single probe
2.15
mm
Sampling number
1100
-
Sampling time
180
s
Sampling distance
110
mm
Number of sub probes Diameter
of a single probe
3.2. Arrangement of array coils
FECA sensor not only has the advantages of common eddy current array sensor, but also has better flexibility. It can be freely bent or even folded with flexible and diverse structure. Additionally, the sensors can also be randomly arranged according to the requirements of measuring conditions. In the meanwhile, it is very convenient to detect parts with complex surface shapes. Fig.6 (a) is the Position Map of FECA Sensor and Surface of LSBST, where L1 = 35 mm is cross section Arc Length of LSBST and L2 is Arc length of FECA sensor after bending, which is also the coverage area of the FECA sensor. d is the distance between the FECA Sensor and the surface of LSBST(Lift off). The Detection range of FECA will change with the FECA sensor mounted on the probe support, which can be found in Fig.1. In order to collect the surface quality detection signals of the LSBST in one scan, the detection range of the sensor should cover the width of the LSBST. With the Lift off height d=1.5, the Arc length of FECA sensor after bending is L2=26.4 mm, Based on [16], when center distance between the coils is greater than 2 times and less than 4 times of the outer radius of a single coil, the mutual inductance interference between coils decreases rapidly with increasing coil center distance. When center distance between the coils is greater than 4 times of the outer radius of a single coil, the mutual inductance interference between coils is negligible. In the present study, the distance between the coils is more than 4 times the outer radius of a single coil. In order to further eliminate the interference between coils and increase the scanning resolution, coils are arranged in two rows. The distance between coils in each row is 7.5 mm and the central distance between the two probes is 3.4 mm, as shown in Fig.6(c). Fig.6(b) is the Arrangement and composition of FECA coils.
L1
d L2
(a)
(b)
7.5
12.0
7.5
7.5
?Ф2,13 2.3
3.4 26.4 35.0 (c)
Fig.6. Arrangement and composition of FECA probe. (a) Position Map of FECA Sensor and Surface of LSBST (b) Physical drawing of flexible array probe (c) Arrangement and composition of FECA probe. 3.3. Finite Element Analysis of Flexible Eddy Current Array Sensor Probe
As shown in Fig.7, when an alternating current is passing through the coil, there is a changing magnetic field around the coil. When a conductor is close to the coil, its surface will generate eddy currents due to alternating magnetic fields.
Fig.7.Schematic diagram of eddy current effect According to [17], the impedance Z, inductance L and quality factor Q of the coil change with the mutual inductance M. In the meanwhile, since the eddy current in the conductor itself consumes energy, the equivalent resistance of the coil increases. Since the distance d between the probe and the target under test, the electromagnetic characteristics of the target under test (resistivity ρ and permeability μ), and the frequency f of alternating current all will change the coupling relationship between the conductor and the detection coil, The functional expression of coil inductance L can be obtained, as shown in Equation 2. The inductance of inspection coil is determined by the lift off d, the conductivity and the permeability μ of the detected material as well as the frequency f of the alternating current.
L F d，，μ，f
(2)
When the relative position of the probe and the blade remains unchanged during the detection process, there are erosion defects such as uplift and depression. According to Equation 2, local lift-off will occur on the surface of the probe and LSBST, and the inductance value of the probe’s detection coil will change. When the pitting and the tiny flaw of the defects occur, the conductivity and the magnetic permeability change to some extent, affecting the inductance of the probe. In order to analyze the eddy current distribution on the surface of turbine blades, finite element simulation was used to understand the magnetic field distribution among each probe. The parameters of each coil of the sensor are shown in Table 1. The conductivity of LSBST is 1.74 MS/m, and permeability of LSBST is 4000. Since FECA sensor will have assembly tolerance when it is installed on the probe support, the distance between the coil and the surface of LSBST is not uniform and remains the same. To more truly reflect the eddy current distribution on the surface of LSBST, the distance between the probe and the coil is set as shown in Fig.8(a) and 8(c), where the lift off of coil 5 and coil 4 is 2 mm, coil 3 and coil 6 is 1.5 mm, coil 2 and coil 7 is 1.2 mm, coil1 and coil 8 is 0.8 mm. Based on Fig.8(b) and 8(d), we can see that magnetic field distribution and intensity excited by coil1 and coil8 is the same, coil 2 and coil 7 is the same, and so on. The bottom of the single coil is flat and the surface of LSBST is irregularly curved. Therefore, the coil is not positively aligned to the detected surface of LSBST, and certainly magnetic field distribution excited by 8 coils is not uniform, which can be observed from Fig.8(b) and 8(d). Even so, with the sensor scans in the x-direction, the surface quality of the LSBST will be completely measured. From Fig.8(b) and 8(d), we can also see that there exists no interference between the 8 coils, proving that the coil arrangement scheme proposed in section 3.2 is feasible. 1
8 2
3
4
5
6
7 d=2mm
(a)
(b)
(c) (d) Fig.8. Finite element simulation of the probe of the FECA sensor. (a) Side view of Model (b) Side view of simulation results (c) Overlooking view of Model (d) Overlooking view of simulation results
4.Experiment 4.1 Introduction to the experiment table Fig.9(a) shows the sensor detection system of the FECA. The system consists of the Cartesian coordinate robot, Conversion chips, computers, FECA probes and Experiment Specimen (LSBST). The specimen is clamped by a three-jaw chuck. The Cartesian coordinate robot probe first moves along the Y-axis to find the test base point for LSBST (which is far from the blade root). Then, the optimal height of the sensor and the turbine blade surface is adjusted in the Z-axis direction to obtain the optimal detection height. Finally, the Cartesian coordinate robot sets the probe to scan along the X-axis to set the motion of the test bench at a constant speed. The detection time is 220 seconds, the moving distance is 110 mm, and the sampling points are 1100. Computer
Cartesian coordinate robot
Computer
FECA probe LSBST LDC1614
(a)
Probe support
Three-jaw chuck
FECA probe
LSBST
(b)
Fig.9. Detection system of LSBST surface quality. (a) Hardware composition of the detection system (b) Details of the probe and blade experimental positions According to the number of sampling points and sampling time, we calculated that the sampling interval was 0.2 s and thus the interval time swept the distance of 0.1 mm. The position of the FECA probe and the turbine end-stage blade is shown in Fig.9(b). 4.2 Specimen introduction There are many defects in the selected LSBST, and four typical defects are selected for research. As shown in Fig.10 (a), Defect 1 is multiple dense pitting pits, and Defect 2 is a larger corrosive pitting. Besides, the corrosion degree is deeper than that of defect 1. Defect 3 is an erosion area with ridges and valleys. Defect 4 is marginal missing area. In Fig.10 (b), we show a simplified figure of the defect and mark the distance between the defect and the turbine blade end face. The scan starts at A marked in the figure and ends at B, among which defect 1 is 5 mm away from the end face A, Defect 2 is 50 mm away from the end face A, Defect 3 is 80 mm away from the end face A, approximately 19 mm long, and defect 4 is 100 mm away from the end face. defect1
defect2
defect3
defect4
(a) 110mm 100mm 80mm 50mm 5mm
19mm
A
B
(b) Fig.10. Natural defects of turbine blade at the last stage. (a) Physical drawing of the specimen (b)Schematic diagram of defect distribution of specimens
5.Signal starting point compensation and plane imaging 5.1 Signal processing and compensation After a 3-minute scan, 1100 sample dots were collected from each channel, and 8 inductive signal curves were obtained, which can be found in Fig.11. From the experimental data, it can be seen that the inductance signal collected in the experiment contains obvious defect information, that is, the inductance value has obvious mutation at the defect.
At the same time, Fig.11 shows that there is inhomogeneous characteristics generation. The reasons for the inhomogeneous characteristics are presented as follows: (1) The lift-off distance of single probe from the blade surface is different; (2)The inductance and capacitance of single probe are slightly different due to the material; (3)Motor vibration and surrounding electromagnetic signal interference in the test bench. We compensate for the inhomogeneity and process the signal with the median filter. The collected acquisition signals form a matrix of 1160×8, and the average inductance of each channel is calculated, forming a matrix b.
Fig.11. Experimental data
a11 a12 a18 a a a 28 a 21 22 a n1 a n2 a n8 a11 a 21 a n1 n a a a 11 21 n1 b n a11 a 21 a n1 n
a12 a 22 a n2 n a12 a 22 a n2 n a12 a 22 a n2 n
a18 a 28 a n8 n a18 a 28 a n8 n a a 28 a n8 18 n
a11 a 21 a n1 a12 a 22 a n2 a18 a 28 a 8n a11 a12 a18 n n n a a a a11 a12 a1 n a12 a 22 a n2 a18 a 28 a 8n 28 a a - b 21 22 n n n a n1 a n2 a 8 n a11 a12 a1 n a12 a 22 a n2 a18 a 28 a 8n n n n Where n is the number of sampling points.
(3)
(4)
(5)
a11 a12 a18 8 a a 11 12 a18 a a - Δa a - 8 a11 a12 a18 8
a11 a12 a18 8 a11 a12 a18 8 a11 a12 a18 8
a11 a12 a18 8 a a a18 11 12 8 a a a18 11 12 8
(6)
In addition, median function movement is adopted. H=movement(A,X) Where X=10. According to the parameters of the probe sampling frequency 5 times/s, sampling time 3 minutes, sampling distance 110 mm, we replace the X-axis coordinates with the position information of the sampling points. The experimental data of Fig.12 (a) can be obtained. After resolving the inhomogeneity of the signal, the planar scanning imaging of the signal is completed. The location and distribution of the defects are determined, as shown in Fig.12 (b).
(a) defect1
defect3
defect2
defect4
(b)
Fig.12. (a) Image after signal processing (b) Plan of the scanned defects 5.2 Defect height and 3D inversion imaging
The difference in lift-off height exerts a great impact on the array probe in experiment. Therefore, it is necessary to compensate for the changes caused by different lifting lift-off height [18]. We used the lift-off data between the eddy current probe and turbine blade for fitting, obtaining the fitting curve to compensate the inhomogeneity caused by lifting lift-off height. According to the inductance parameters of sampling, we fitted the curve corresponding to its distance.
f x a e b x 0.3215
(7)
where a = -26.72(-40.46,-12.98), b = 2.566 ( 2.015, 3.116). Lift off fitting curve is shown in fig.13.
Fig.13. Lift off fitting curve Based on the Fig.13, the inductance value in plane scanning is introduced. The obtained height signal is taken as the z-axis, the distance value as the X-axis, and the number of channels as the Y-axis. The three-dimensional imaging figure of the turbine blade is obtained, which can be found in Fig.14. In comparison with the defects of LSBST, we can see that the system can estimate the location, shape and depth of the main defects on the blade surface. defect3
defect1
defect2
defect4
Fig.14. 3D imaging of defects 6. Conclusions In this paper, the detection and imaging of curved surface defects by FECA probe are studied. The detection sensitivity of eddy current array sensor is guaranteed by miniaturization and high inductance of array probe. Through unidirectional scanning of turbine blade surface, the inductance’s change of FECA sensor is extracted and analyzed. The three-dimensional image of turbine blade surface quality is obtained by filtering and compensation of signal processing and fitting equation of height and inductance change. In the current work, the coverage of the probe on the blade surface is demonstrated and the
signal data containing the defect is obtained through experiments. In addition, the defect inversion image is successfully obtained, which realizes the defect imaging of the blade surface erosion, pitting and edge defect. References [1] N. Yusa, H. Hashizume, R. Urayama, T. Uchimoto, T. Takagi, K. Sato, An arrayed uniform eddy current eprobe design for crack monitoring and sizing of surface breaking cracks with the aid of a computational inversion technique, Ndt & E International, 61 (2014) 29-34. [2] D. Caetano, F. Rabuske, D. Oliveira, T. Rabuske, J. Fernandes, M. Piedade, Ieee, Fast Settling VGA for Eddy Currents Non-Destructive Testing with an Array of Magneto Resistors, 2016 12th Conference on Ph.D. Research in Microelectronics and Electronics (Prime), (2016). [3] D. Cai, C. Zou, Z. Sun, Q. Chen, J. Wang, Ieee, Geometric Optimization of A Flexible Arrayed Eddy Current Sensor for Non-destructive Testing,
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[4] G.-H. Jang, J.-M. Kim, H.-J. Shin, J.-Y. Choi, Ieee, Optimal Design and Torque Analysis Considering Eddy-Current Reduction of Axial-Flux Permanent Magnet Couplings with Halbach Array Based on 3D-FEM, 2016 Ieee Conference on Electromagnetic Field Computation (Cefc), (2016). [5] R. Summan, G. Pierce, C. Macleod, C. Mineo, J. Riise, M. Morozov, G. Dobie, G. Bolton, A. Raude, C. Dalpe, J. Braumann, Conformable Eddy Current Array Delivery, in: D.E. Chimenti, L.J. Bond (Eds.) 42nd Annual Review of Progress in Quantitative Nondestructive Evaluation: Incorporating the 6th European-American Workshop on Reliability of Nde, Amer Inst Physics, Melville, 2016. [6] Shufang liu, Aurelien bustin, Pauline ferry, et al. A vectorized Levenberg-Marquardt model fitting algorithm for efficient post-processing of cardiac T 1 mapping MRI[J]. Elsevier Ltd, 2018, (96): 106-115. [7] B. Oswald-Tranta, Lock-in inductive thermography for surface crack detection in different metals, Quantitative Infrared Thermography Journal, (2019). [8] D. Huang, K. Li, G.Y. Tian, A. Imam Sunny, X. Chen, C. Tang, J. Wu, H. Zhang, A. Zhao, Thermal pattern reconstruction of surface condition on freeform-surface using eddy current pulsed thermography, Sensors and Actuators A: Physical, 251 (2016) 248-257. [9] Mazur Z , Garcia-Illescas R , Aguirre-Romano J , et al. Steam turbine blade failure analysis[J]. Engineering Failure Analysis, 2008, 15(1-2):129-141. [10] R.V lakshmi, A.K mondal, Shubhanjali, et al. Condition Assessment of Strain- Gauged Aero Engine Compressor Stator Vanes Using Eddy Current Testing[J]. Springer US, 2018, 18(3): 588-591. [11] Mohamed elmay, Nicolas saintier, Olivier devos,et al. Effect of Corrosion on the Low-cycle Fatigue Strength of Steels used in Frequent Start-up Power Generation Steam Turbine[J]. Elsevier Ltd, 2015, (133): 528-534. [12] D. N Adnyana, Corrosion Fatigue of a Low-Pressure Steam Turbine Blade[J] Fail. Anal. and Preven. 2018, 18:162-173. [13] Nguyen thanh duong, Fumio kojima. ECT Database and Greedy-Search Inverse Analysis for Crack Shape Recovery Arising in Eddy Current Testing[C] SICE- ICASE, 2006. International Joint Conference, 2006: 3537-3542. [14]Ji-wei sun, Fu-zhou feng, Qing-xu min, et al. Probability of Detection for Fatigue Crack in Pulsed Eddy Current Thermography[C]//IEEE BEIJING SECTION, Institute of Electrical and Electronics Engineers, 2017: 66-70. [15] Texas Instruments.http://www.ti.com/lit/ds/symlink/ldc1614.pdf, May 3, 2018. [16]Liu Bo, Research on Methods of Crack Parameter Estimation and Imaging in Eddy Current Array Nondestructive Testing, PhD Dissertation of National University of Defense Technology, 2011. [17] G.Y. Tian, Z.X. Zhao, R.W. Baines, The research of inhomogeneity in eddy current sensors, Sens. Actuat. A (Phys.) 69 (2) (1998) 148–151. [18] Dongdong Wen, Mengbao Fan, Binghua Cao, Bo Ye, and Guiyun Tian. Extraction of LOI Features From Spectral Pulsed Eddy Current Signals for Evaluation of Ferromagnetic [J] Samples- SENSORS JOURNAL, VOL. 19, NO. 1, JANUARY 1, 2019, 189-195.
Acknowledgments This work is financially supported by the Open Fund of Shandong Province Key Laboratory of Mine Mechanical Engineering NO. 2019KLMM201).
Biographies Zhang Weipeng receive B.Eng. degree in the School of Mechanical and Electronic Engineering from liaocheng university, shandong, China, in 2016, and he is studying for M.Eng. degree at the School of Mechanical and Electronic Engineering, Shandong University of Science and Technology. He is currently working on flexible eddy current array sensors. His research interests include simulation of electromagnetic field, novel sensor design, detection of fatigue cracks and defects of parts, signal processing. Chenglong WANG receive Dr. Eng degree in the College of Mechanical and Electronic Engineering from Shandong University of Science and Technology in 2009.He is currently working on servo control technology and intelligent equipment. His research interests include simulation of mechatronic system, intelligent shocking absorber, digital hydraulic servo system. Fengqin Xie received the Ph.D. degree in Machine Design Manufacturing and Their Automation from the Shandong University of Science and Technology, Qingdao, china, in 2013. Her research is focused on electromagnetic testing, thermography, and composite testing in the field of Non-destructive testing (NDT) and Structural health monitoring (SHM). Huayu Zhang received Ph.D degree in Mechanical and Electronic Engineering from the Shandong University of Science and Technology, Qingdao, china, in 2012.His research is focused on Non-destructive testing (NDT), Sensing and imaging, and Structural health monitoring (SHM). He has applied or holds about several patents in China. In recent 5 years, he has chaired or participated in several research programs funded by Ministry of Education and provincial government.
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
Highlights
Detect curved surface defects with flexible eddy current array sensors.
Reduce the effect of starting point and lift-off with compensation algorithm.
Completed the imaging experiment of natural defects on the surface of LSBST.