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Investigation of temperature effect of stress detection based on Barkhausen noise
Sensors and Actuators A 194 (2013) 232–239
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Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna
Investigation of temperature effect of stress detection based on Barkhausen noise Ping Wang a,∗ , Xiaoli Ji a , Xiaoming Yan a , Lei Zhu a , Haitao Wang a , Guiyun Tian a,b , Entao Yao a a b
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China School of Electrical and Electronic Engineering, Merz Court, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
a r t i c l e
i n f o
Article history: Received 24 September 2012 Received in revised form 11 January 2013 Accepted 11 January 2013 Available online 29 January 2013 Keywords: Barkhausen noise Temperature Stress Feature value
a b s t r a c t The temperature of rail tracks differs throughout the year because of seasonal climate change. Exposure to the elements causes thermal stress, which can be measured using magnetic Barkhausen noise (MBN). However, changes in temperature have an effect on the stress and the MBN signal itself. In order to extract feature values which reflect the state of stress, this paper introduces a design of an MBN stress measurement system for use at different temperatures. To analyze the relationship between the signal features and temperature, we measured the MBN signals at different temperatures when the stress was constant in experiment. It is found that within the elastic range of the steel specimen, the average value, RMS value, counts value, peak value, and peak–width ratio value decrease with increasing temperature. However, the variation rate of different feature values with changes in temperature is different. Finally, we found the feature values which have a smaller change with temperature, have larger change with stress. From the theory, we explain the phenomenon that temperature change has effect on the rail stress and analyze the impact of residual stress and uneven distribution of temperature in the detection. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Exposed to the elements, the temperature of rail tracks is different throughout the year. In the height of the summer when the ambient temperature is up to 40 ◦ C, the rail temperature can be up to 20 ◦ C higher than the ambient temperature. With the trend of high-speed and heavy-loads, more and more seamless rail routes are used in China. The seamless rail line which is very long can only be stretched at both ends of the rail, and the thermal expansion and cold contraction in the middle of the rail due to thermal stress would not be released freely. So a larger temperature stress is accumulated within the rail, which is extremely prone to lateral deformation of the track. When a train passes, it will increase the chance of bending and swelling of the track [1]. The domestic and foreign railway operation practice shows that the seamless line is the development trend in the future, however bending of track has been seen in seamless rail lines, so extensive research of the temperature and stress testing on the seamless rail should be undertaken in order to help prevent the possibility of bending rails. Among several non-destructive techniques used for material evaluation [2–6] (e.g. X-ray, blind hole drilling, and eddy current [7]), Barkhausen noise (BN) [8,9] offers exceptional material
∗ Corresponding author. Tel.: +86 13815863471. E-mail address: [email protected] (P. Wang). 0924-4247/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.sna.2013.01.027
and stress characterization capabilities. Now the widely methods used to detect stress are magnetic Barkhausen noise, magnetic acoustic emission, stress detection based on inverse magnetostrictive, metal magnetic memory and eddy detection etc. However, most of them are more complex because they need coupling agent or can only detect the stress or crack in the surface. They are not very effective in the detection of stress of ferromagnetic material. Barkhausen noise is measured by the microscopic theory that the magnetic domains turn over during the magnetization of the ferromagnetic materials. We can pick up the signal using a detection coil placed on the surface of the ferromagnetic material. This method is fast, convenient, and mature, and more importantly it is fit for stress testing of ferromagnetic materials so can be used to detect the temperature stress of the rail. 2. MBN principle and features 2.1. The principle of MBN detection In ferromagnetic materials, there are many small and different orientated magnetic domains. In the absence of external factors, each magnetic domain direction is the same as its direction of polarizable crystallization, and its overall magnetization effect is zero. If coupled with an alternating magnetic field or stress, the magnetic domain will flip 90◦ or 180◦ or the magnetic domain wall will move, which in turn leads to certain rule orientation of the magnetic domain. This kind of magnetic domain change process enables
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peak time. They can reflect the variation relationship of MBN signal with the variation of stress and microstructure.
B
- Hs
- Hc
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+Hc
+Hs
H
Fig. 1. The generated diagram of Barkhausen noise [11].
the material inside to develop a series of transiliently pulsed signals, i.e. the Barkhausen signals. This transilient phenomenon of the magnetization process was discovered by Heinrich Barkhausen [10] in 1919 and was called the Barkhausen effect. Every Barkhausen that jumps in the detection coil on the ferromagnetic materials surface will form induction electric pulse signals, known as magnetic Barkhausen noise (MBN). There will be magnetization in the ferromagnetic material under the effect of external magnetic field. When the saturation occurs in the ferromagnetic materials, the magnetic state is not restored to its original state after removing the magnetic field. When the magnetic field changes back and forth in the positive and negative directions, the magnetization of the media is in a cycle, thus forming a hysteresis loop. By observing the hysteresis loop of the ferromagnetic materials we find that the curve displays a step-jitter state (see Fig. 1) in the irreversible magnetization phase, rather than a smooth and continuous curve. The coil placed on the surface of the specimen produces a noise pulse in the form of voltage indicating that the magnetization process of the ferromagnetic materials is not continuous. After years of research, work [12] has shown that the factor which directly affects the MBN signal is the microstructure of the material, whereas the microstructure of material is affected by many factors, such as material composition, stress, deformation, heat treatment, and ambient temperature. These factors will directly or indirectly decide or change the material microstructure. Therefore, it will affect the MBN signal, so that the stress and microstructure of the materials can be detected by the method of MBN. 2.2. The effect of temperature on the MBN signal The temperature stress within the rail was caused by changes of temperature of the rail. The changes in temperature will cause deformation due to thermal expansion and cold contraction. This distortion is restrained between the various parts of the rail and on the boundary constraints. In the experiment, the value of the applied compressive stress caused by the platform of loading stress decreases with decreasing temperature of the specimen, and also increases with increasing temperature. The changed value is the temperature stress, which is caused by the changing temperature. Studies [13] have shown that not only the thermal stress which is produced by the temperature can affect the MBN signal, but also the temperature itself has an impact on the MBN signal, therefore, temperature compensation must be applied.
2.3.1. RMS value The noise intensity usually uses the RMS value to indicate the noise energy. While the MBN is a high-frequency noise, so we use RMS to study the corresponding relationship between the stress and MBN signals [14]. 2.3.2. Average value Average is the envelope of the peak-to-peak value voltage in a magnetization cycle after the MBN signal has been filtered. 2.3.3. Ring number Ring number is the number of sampling points whose amplitude is greater than the threshold at a saturated magnetization cycle. Even in the same test, if the threshold is different, the values are different. Generally the threshold value should be as small as possible, however, it should be larger than the noise amplitude of the detection system. From microscopic theory it should be explained that the MBN signal is generated in the rising and falling area of excitation signal. The MBN signal is made up of myriad turns of 180◦ magnetic domain walls, and its external manifestation is a ring [15] (as shown in Fig. 2). The voltage signal above an appropriate threshold is called a ring. The ring number of MBN signals is closely related to the intensity of the signal overflow, reflecting the stress, microstructure and other information of the material. 2.3.4. Envelope Envelope refers to the outline of an MBN signal in the rising and falling cycle of excitation signals. We can assess the stress and microstructure of materials by extracting the envelope of the MBN signal. The MBN signal envelope contains peak, peak time, full width at half maximum (FWHM) and other information. It is accurate and reliable to assess the internal microstructure of materials, such as tiny cracks inside the material, corrosion, strain, creep and other information. 2.3.5. Peak value Full width at half maximum refers to the full width of the envelope at the corresponding half maximum peak value. Peak value refers to the peak value of the envelope. Fig. 3 shows [16] the method of extraction of the peak value and full width of half maximum value. The peak time refers to the time when the envelope
2.3. Feature extraction In the stress detection using MBN, the method of feature extraction is very important. In this paper, the feature values extracted are RMS (root mean square), average, ring numbers, envelope, and
Fig. 2. Ring number diagram of MBN.
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Fig. 5. Stress loading model diagram.
(8) Temperature measurement devices: Infrared thermometer (ipook PK51 Series), ranging of −32 ◦ C to 350 ◦ C and the accuracy is ±1.5%. 3.2. Methods of the equipment Fig. 3. Peak value and FWHM of MBN.
of MBN signal reaches the maximum value, here we use sample points to indicate time. 3. Experimental study 3.1. Experiment setup The block diagram of the experimental system is shown in Fig. 4, which is divided into seven parts: (1) Magnetizer: A CALTEK CA1640-02 function generator is used to generate a 10 Hz, 3 V sine wave. The sine wave signal is amplified by a Power Amplifier (Newtons4th LPA05B). The magnetizer is made up of a U-shaped core wound with 600 turns of 0.21 mm diameter enameled wire. (2) Receiver: High-frequency cores wound with 5000 turns of 0.07 mm diameter enameled wire and shielded by copper foil. (3) Amplifying circuit. (4) Data acquisition and signal processing: A DAQ-2010 data acquisition card is used to acquire data, which is then processed in MATLAB. The methods of signal processing are band-pass filtering and wavelet denoising. (5) Stress loading device: The rail stress load model is shown in Fig. 5 and rail stress testing diagram is shown in Fig. 6. (6) Heating equipment: electric stove. (7) Loading specimen: A3 steel block, whose size is 660 mm × 30 mm × 10 mm.
Fig. 4. Block diagram.
In the experiment, we selected a piece of A3-steel (see Fig. 6) to simulate a rail track, and then detected the MBN signal in the same stress with different temperature at the surface of the assembly. The low and high frequency noise was filtered from the signal using a band-pass filter whose lower cut-off frequency is 4 kHz and upper cut-off frequency is 21 kHz. Then we use db5 wavelet with six layers to decompose the MBN signal and reconstruct after de-noised. Finally, the features of the MBN signal were extracted. The specimen was heated using a stove to more than 100 ◦ C, then load stress was applied on the specimen from 15 MPa to 150 MPa in 15 MPa steps. We completed 10 experiments that measured the MBN signal at a fixed pressure with different temperatures. Measuring the temperature of test points by using infrared thermometers, the MBN signal is recorded. During the experiment, we found that the compressive stress values showed by the stress loading platform decreases as the temperature of the steel decreases, then the feature value of MBN signal will also have a corresponding change. This indicates that the changing temperature of the steel would lead to stress concentration and thermal stress. So in order to ensure the constant stress in the experiment, we adjusted the stress load equipment over time. From the results of experiment we can see that, under the condition of a constant compressive stress, the feature values of the MBN signal increase as temperature decreases also decrease with increasing temperature. Thus, we can see that the temperature not only produces temperature stress effect on MBN signal, but also the temperature itself has some
Fig. 6. Stress loading instrument.
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Fig. 7. Relationship between average and temperature.
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Fig. 9. Relationship between ring numbers and temperature.
influence on the MBN signal, so we need to apply temperature compensation. 4. Experimental results We used MATLAB tools to analyze and organize the experimental results and plot the features of MBN signal, temperature and pressure. 4.1. Three-dimensional map Depending on the experiment results of different stresses, we can draw three-dimensional diagrams among the feature values of MBN, temperature and stress. This is shown in Figs. 7–11.
Fig. 10. Relationship between peak and temperature.
(1) Average and RMS Fig. 7 shows the three-dimensional diagram between the average of MBN signal and temperature change at different pressures. It can be seen from the figure that the average of MBN decreases as the temperature increases at different pressures, and further validation of the relationship that the MBN signal decreases with the pressure stress increase also fit for the case of high temperatures. Fig. 8 shows the relationship between the RMS of MBN signal (strength of MBN) and the temperature at different
pressures. In the figure, the RMS increases with decreasing temperature at different pressures. There is a good linear relationship between 50 ◦ C and 100 ◦ C, however, poor linearity appeared below 50 ◦ C. We analyzed the specific reasons below. (2) Ring numbers Fig. 9 shows the three-dimensional diagram of the ring numbers of MBN with temperature and stress. It can be seen that
Fig. 8. Relationship between the RMS and temperature.
Fig. 11. Relationship between peak–width ratio and temperature.
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Table 1 Comparison among the variation of peak, temperature and stress. Stress (MPa)
Temperature 25 ◦ C
15 30 45 60 75 90 105 120 135
0.0115 0.0019 0.0034 0.0061 0.0043 0.0388 0.0200 0.0173 0.0118
35 ◦ C 0.2344 0.1289 0.0480 0.0342 0.0165 0.0083 0.0086 0.0071 0.0005
0.0180 0.0023 0.0107 0.0077 0.0123 0.0449 0.0174 0.0207 0.0293
45 ◦ C 0.2441 0.1274 0.0575 0.0238 0.0596 0.0105 0.0059 0.0126 0.0216
0.0175 0.0246 0.0286 0.0379 0.0265 0.0128 0.0130 0.0371 0.0188
55 ◦ C 0.2284 0.1357 0.0545 0.0284 0.0922 0.0380 0.0091 0.0040 0.0220
0.0337 0.1107 0.0195 0.0136 0.0309 0.0199 0.0462 0.0257 0.0430
65 ◦ C 0.2356 0.1398 0.0638 0.0170 0.0785 0.0378 0.0332 0.0222 0.0095
0.0268 0.0076 0.0363 0.0406 0.0599 0.0329 0.0747 0.0544 0.0393
75 ◦ C 0.3125 0.0486 0.0579 0.0343 0.0675 0.0115 0.0127 0.0050 0.0043
0.0647 0.0341 0.0112 0.0404 0.0649 0.0636 0.0606 0.0786 0.0373
85 ◦ C 0.2933 0.0772 0.0623 0.0536 0.0404 0.0304 0.0076 0.0202 0.0138
0.0754 0.0423 0.0246 0.0326 0.0653 0.0497 0.0382 0.0464 0.0093
0.2627 0.0544 0.0915 0.0780 0.0391 0.0274 0.0105 0.0615 0.0051
Table 2 Comparison among the variation of average, temperature and stress. Stress (MPa)
Temperature 25 ◦ C
15 30 45 60 75 90 105 120 135
0.0044 0.0016 0.0074 0.0030 7e−05 0.0138 0.0014 0.0034 0.0080
35 ◦ C 0.0147 0.0146 0.0084 0.0110 0.0122 0.0040 0.0163 0.0014 0.0089
0.0101 0.0003 0.0014 0.0008 0.0018 0.0014 0.0005 0.0037 0.0071
45 ◦ C 0.0206 0.0236 0.0188 0.0079 0.0261 0.0084 0.0183 0.0032 0.0002
0.0005 0.0029 0.0050 0.0067 0.0022 0.0007 0.0035 0.0086 0.0044
55 ◦ C 0.0103 0.0247 0.0193 0.0106 0.0257 0.0104 0.0225 0.0067 0.0047
the ring numbers of MBN also decrease with increasing temperature. MBN ring numbers are characterized by the number of 180◦ magnetic domain reversals. The number of magnetic domain reversal is closely related to the number of magnetic domains and geometry of the material, but the most important factor is the size of threshold. As we select the different thresholds in the data for processing, we can see in the figure that there are not any rules to follow that the ring numbers changing with stress. However, in the actual experiment, when the choice of threshold should not exceed one-third of the maximum value, the ring numbers decrease when the compressive stress increases. (3) Peak and peak–width ratio of the envelope Fig. 10 shows the peak of MBN, which characterizes the maximization of MBN energy. In the figure we can see, like RMS and average, the peak also decreases with increasing temperature at different pressures, but the measured error of the peak value is minimal. Fig. 11 shows how the three-dimensional diagram of the peak–width ratio changes with temperature and stress. In the figure we can see that the changing ratio of peak value is smaller as the temperature changes, and larger as the pressure stress changes compared with other feature values within the stress of 90 MPa.
0.0078 0.0038 0.0026 0.0018 0.0065 0.0056 0.0114 0.0057 0.0082
65 ◦ C 0.0126 0.0225 0.0210 0.0061 0.0242 0.0076 0.0276 0.0025 0.0068
0.0048 0.0070 0.0119 0.0065 0.0142 0.0095 0.0144 0.0136 0.0103
75 ◦ C 0.0086 0.0237 0.0202 0.0108 0.0233 0.0018 0.0220 0.0050 0.0062
0.0087 0.0010 0.0208 0.0081 0.0142 0.0154 0.0127 0.0220 0.0142
85 ◦ C 0.0108 0.0188 0.0147 0.0185 0.0186 0.0031 0.0212 0.0016 0.0068
0.0079 0.0113 0.0255 0.0381 0.0257 0.0164 0.0236 0.0092 0.0054
0.0032 0.0011 0.0020 0.0246 0.0198 0.0004 0.0305 0.0061 0.0059
4.2. Comparison between the changing ratio of feature values A further comparison is made between the feature values of MBN to distinguish which feature changes faster with temperature and which feature changes faster with stress. This paper compared the average, RMS and peak by the table. The ring number was meanless because the values of threshold are different. The peak–width ratio is a small value itself and changes little so it is hard to compare. As is shown in the figures, the comparison among the variation of peak, average and RMS which change with temperature and stress in Tables 1–3. The abscissa represents temperature, whose unit is ◦ C. The Ordinate represents stress, whose unit is MPa. As is shown in the table, the left column data represents the variation of feature value when the temperature changes 1 ◦ C at the condition of maintain the stress constant. The right column data represents the variation of feature value when the stress changes by 1.5 MPa when maintaining the temperature constant. Comparing the data in Tables 1–3, we find that the peak, average and RMS which changes with stress value faster than temperature within 75 MPa, and the variation of peak with stress change is about 20 times than with temperature change. After temperature compensation, the variation of peak with stress change can also be achieved about 13 times than with temperature change.
Table 3 Comparison among the variation of RMS, temperature and stress. Stress (MPa)
Temperature 25 ◦ C
15 30 45 60 75 90 105 120 135
0.0073 0.0009 0.0096 0.0054 0.0017 0.0276 0.0019 0.0057 0.0102
35 ◦ C 0.0679 0.0083 0.0361 0.0326 0.0234 0.0135 0.0204 0.0037 0.0092
0.0185 0.0019 0.0013 0.0005 0.0046 0.0047 0.0012 0.0114 0.0140
45 ◦ C 0.0761 0.0189 0.0512 0.0289 0.0493 0.0122 0.0242 0.0008 0.0038
0.0014 0.0092 0.0127 0.0180 0.0075 0.0052 0.0067 0.0119 0.0086
55 ◦ C 0.0557 0.0183 0.0531 0.0330 0.0493 0.0156 0.0345 0.0034 0.0116
0.0145 0.0084 0.0073 0.0027 0.0149 0.0131 0.0256 0.0158 0.0194
65 ◦ C 0.0635 0.0149 0.0584 0.0225 0.0470 0.0142 0.0397 0.0001 0.0136
0.0106 0.0155 0.0269 0.0200 0.0303 0.0195 0.0310 0.0279 0.0176
75 ◦ C 0.0575 0.0160 0.0538 0.0346 0.0452 0.0017 0.0299 0.0037 0.0134
0.0367 0.0474 0.0064 0.0145 0.0291 0.0303 0.0226 0.0416 0.0272
85 ◦ C 0.0623 0.0045 0.0469 0.0450 0.0345 0.0098 0.0268 0.0067 0.0111
0.0824 0.0499 0.0951 0.0546 0.0379 0.0303 0.0336 0.0184 0.0168
0.0730 0.0454 0.0549 0.0597 0.0356 0.0021 0.0458 0.0210 0.0121
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Fig. 12. Normalized at compress stress of 900 N.
The variation decreases in the higher temperature (75 ◦ C and 85 ◦ C) within 75 MPa. When the temperature is above 60 ◦ C, every characteristic value’s variation which change with temperature is larger compared with the stress above 90 MPa; the peak’s variation which changes with temperature is about 4 times compared with stress. In the previous three-dimensional diagram (Fig. 10), we also find that the peak’s change is significantly better than other characteristics, regardless of temperature changes or pressure, so we can test the peak’s change to detect the rail stress. 4.3. The normalized curve between the feature values and temperature We can get five feature values which are average, RMS, ring numbers, peak and peak–width ratio in the processing of the Barkhausen signal. These values all change as temperature and stress change and can reflect the Barkhausen signal’s change with temperature, stress and microstructure changes. We normalize a set of experimental data at 900 N applied pressure. As shown in Fig. 12, the feature values of MBN, signal all decrease with increasing temperature, however, the variation rate of relative P–W ratio with changes in temperature is the largest. The
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Fig. 14. Time’s effect on average at 600 N.
variation rate of relative ring number and peak value with changes in temperature are similar and they are smaller than the P–W ratio. Furthermore the variation rate of relative average and RMS value with changes in temperature are similar and they are the smallest. 4.4. Analysis of the experiment error During the data processing, we found that there was a stronger linear relationship in the case of higher temperatures for each feature, when it is below 50 ◦ C, the rate of change of each feature value is much lower than when over 50 ◦ C, sometimes it even brings on negative growth (see Fig. 13(a) and (b)). Similarly, there are still time effects existing in the experiment. When compared with the result that we obtained when measuring the sample’s MBN signal at room temperature overnight, there is a big difference when we measure it immediately and the temperature down to room temperature directly. Take the relationship between MBN’s average value and temperature changes under the pressure of 600 N, as shown in Fig. 14, solid lines represent the average values of MBN signal vs. temperature curve which is directly tested non-overnight, dashed is the average MBN–temperature curve measured overnight.
Fig. 13. (a) Amplified. (b) Normalized at 1000 N.
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It is true that environmental conditions cannot be ignored, however, the main reason is that the uneven distribution of the sample’s temperature. In the experiment, we first heat the sample, and then the signal and temperature are recorded at the same time during the process of cooling of the sample. The recorded temperature values are sample’s surface temperature, while the MBN is characterized by the inside of the sample. At the situation of high temperatures, the temperature distribution in the sample is uniform. However, in the case of low temperatures, heat emission is from the surface initially, which leads to uneven temperature distribution of materials, so the measurement results of the temperature do not fully reflect the internal state of the material. After one night, there is sufficient time to ensure that the temperature of the sample has a completely uniform distribution, so the data of the test are more accurate. 4.5. Theoretical interpretation of experimental results From the various features of MBN signal variation with temperature, we can conclude that the MBN signal increases with decreasing temperature at a fixed pressure, and the MBN signal increases with decreasing stress at a fixed temperature. According to the theory of thermodynamics we know that the role of mechanical and thermal is equivalent in some extent effect. The MBN signal is inversely proportional to pressure stress and proportional to the tensile stress. Correspondingly, the magnetic permeability will increase as tensile stress increases, and decrease as compressive stress increases. When the temperature changes, the phenomenon of thermal expansion and contraction will occur in the ferromagnetic specimen. As the temperature increased, the magnetostriction and the magnetic domain wall’s deflection phenomena have intensified. Thus the magnetoresistance will be stretched and the magnetostriction constant will be increased. According to the energy minimum principle and energy transition theory, stress is inversely proportional to the magnetic permeability, resulting in the release of internal stress. The four-point bending test shows that pressure on the upper surface of the ferromagnetic specimen, so in the same load, the permeability will decrease in the surface as the temperature increases. According to the energy transition theory, we know that the energy of compressive stress in the surface will increase with temperature increases. Because the MBN signal is mainly contributed by the magnetic domains 180◦ irreversible flip, it cannot overcome the energy of the barrier which in the same conditions of strong external magnetic field from one state to another, that will also cause the decrease of the MBN signal. Based on these two factors, it can be concluded that the MBN signal in the surface will decrease with increasing temperature in the platform of four-point bending. 5. Conclusion and outlook This paper examined the relationship between the MBN of an A3-steel specimen and the changing temperature at different pressures. We can make the following conclusions: (1) The features of MBN all decrease with increasing temperature at different pressures. (2) The peak, average and RMS which changes with stress value faster than temperature within 75 MPa, and the variation of peak with stress change is about 20 times than with temperature change. After temperature compensation, the
variation of peak with stress change can also be achieved about 13 times than with temperature change. The variation decreases in the higher temperature (75 ◦ C and 85 ◦ C) within 75 MPa. (3) When the temperature is above 60 ◦ C, every characteristic value’s variation which change with temperature is larger compared with the stress above 90 MPa; the peak’s variation which changes with temperature is about 4 times compared with stress. By extracting the feature values of different characteristics, we analyze the relationship between the features, temperature and compressive stress, thus we can test the temperature stress of rail by calibrating the MBN amplitude and temperature curve, as the evaluation of the presence excess stress about the rail. To solve this uneven distribution of the temperature, we can use the thermostat or electric heat tracing as the heating equipment. Because we did not have such experimental conditions before, we will take this method to solve this uneven distribution of the temperature in future experiments. Acknowledgements The research of this paper is supported by FP7 Health monitoring of offshore wind farms (HEMOW) project, National Science Foundation of China (50907032/E070104), “simulation and realization of integration of electromagnetic NDT methods for online high speed railway inspection”, Key project of Technology of Jiangsu (SBE200900338), the Aeronautical Science Foundation of China (2010ZD52) and the Ph.D. Programs Foundation of Ministry of Education of China (20093218120019). References [1] X. Wang, H. Liu, X. Qi, B. Dong, J. Di, The development and application of Barkhausen noise CWR stress testing instrument, Beijing University of Chemical Technology Journal 37 (3) (2010) 123–126. [2] V.S. Pisarev, V.V. Balalov, V.S. Aistov, M.M. Bondarenko, M.G. Yustus, Reflection hologram interferometry combined with hole drilling technique as an effective tool for residual stresses fields investigation in thin-walled structures, Optics and Lasers in Engineering 36 (2001) 551–597. [3] T. Murotani, T. Yano, H. Hirose, A. Ikenaga, Applications of X-ray stress measurement for interface area of Ni3 Al system intermetallic compound coating, International Centre for Diffraction Data 2004, Advances in X-ray Analysis 47 (2004) 385–389. [4] J.W. Wilson, G.Y. Tian, Pulsed electromagnetic methods for defect detection and characterization, NDT&E International 40 (2007) 275–283. [5] G.Y. Tian, A. Sophian, D. Taylor, J. Rudlin, Wavelet-based PCA defect classification and quantification for pulsed eddy current NDT, IEE Proceedings Science, Measurement & Technology 152 (2005) 141–148. [6] Y. He, M. Pan, F. Luo, G. Tian, Reduction of lift-off effects in pulsed eddy current for defect classification, IEEE Transactions on Magnetics 47 (2011) 4753–4760. [7] G. Tian, Y. He, I. Adewale, A. Simm, Research on spectral response of pulsed eddy current and NDE applications, Sensors and Actuators A: Physical 189 (2013) 313–320. [8] A. Dhar, D.L. Atherton, Effects of magnetic flux density and tensile stress on the magnetic Barkhausen noise in pipeline steel, Nondestructive Testing And Evaluation 10 (1993) 287–294. [9] T.W. Krause, L. Clapham, A. Pattantyus, D.L. Atherton, Investigation of the stress-dependent magnetic easy axis in steel using magnetic Barkhausen noise, Journal of Applied Physics 79 (1996) 4242–4252. [10] J.W. Wilson, G.Y. Tian, V. Moorthy, B.A. Shaw, Magneto-acoustic emission and magnetic Barkhausen emission for case depth measurement in En36 gear steel, IEEE Transactions on Magnetics 45 (1) (2009) 177–183. [11] M. Liu, C. Yan, Z. Yu, X. Qi, Barkhausen noise application in the detection of stress and fatigue damage, Journal of Harbin University of Science and Technology 6 (1) (2001) 73–76. [12] P. Wang, S. Zhu, G. Tian, H. Wang, J. Wilson, X. Wang, Stress measurement using magnetic Barkhausen noise and metal magnetic memory testing, Measurement Science & Technology 21 (5) (2010) 055703.
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Biographies Ping Wang obtained his doctor’s degree from Southeast University (Nanjing, China) in 2004. Currently, he is Associate Professor and Tutor for graduates in Measurement and Sensor Technologies at Nanjing University of Aeronautics and Astronautics. His research interests are focused on electromagnetic non-destructive testing and sensor technologies. Xiaoli Ji received her bachelor’s degree from Yangzhou University in 2010. Currently, she is a Postgraduate student at Nanjing University of Aeronautics and Astronautics. Her research interest is electromagnetic non-destructive testing.
Contents lists available at SciVerse ScienceDirect
Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna
Investigation of temperature effect of stress detection based on Barkhausen noise Ping Wang a,∗ , Xiaoli Ji a , Xiaoming Yan a , Lei Zhu a , Haitao Wang a , Guiyun Tian a,b , Entao Yao a a b
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China School of Electrical and Electronic Engineering, Merz Court, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
a r t i c l e
i n f o
Article history: Received 24 September 2012 Received in revised form 11 January 2013 Accepted 11 January 2013 Available online 29 January 2013 Keywords: Barkhausen noise Temperature Stress Feature value
a b s t r a c t The temperature of rail tracks differs throughout the year because of seasonal climate change. Exposure to the elements causes thermal stress, which can be measured using magnetic Barkhausen noise (MBN). However, changes in temperature have an effect on the stress and the MBN signal itself. In order to extract feature values which reflect the state of stress, this paper introduces a design of an MBN stress measurement system for use at different temperatures. To analyze the relationship between the signal features and temperature, we measured the MBN signals at different temperatures when the stress was constant in experiment. It is found that within the elastic range of the steel specimen, the average value, RMS value, counts value, peak value, and peak–width ratio value decrease with increasing temperature. However, the variation rate of different feature values with changes in temperature is different. Finally, we found the feature values which have a smaller change with temperature, have larger change with stress. From the theory, we explain the phenomenon that temperature change has effect on the rail stress and analyze the impact of residual stress and uneven distribution of temperature in the detection. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Exposed to the elements, the temperature of rail tracks is different throughout the year. In the height of the summer when the ambient temperature is up to 40 ◦ C, the rail temperature can be up to 20 ◦ C higher than the ambient temperature. With the trend of high-speed and heavy-loads, more and more seamless rail routes are used in China. The seamless rail line which is very long can only be stretched at both ends of the rail, and the thermal expansion and cold contraction in the middle of the rail due to thermal stress would not be released freely. So a larger temperature stress is accumulated within the rail, which is extremely prone to lateral deformation of the track. When a train passes, it will increase the chance of bending and swelling of the track [1]. The domestic and foreign railway operation practice shows that the seamless line is the development trend in the future, however bending of track has been seen in seamless rail lines, so extensive research of the temperature and stress testing on the seamless rail should be undertaken in order to help prevent the possibility of bending rails. Among several non-destructive techniques used for material evaluation [2–6] (e.g. X-ray, blind hole drilling, and eddy current [7]), Barkhausen noise (BN) [8,9] offers exceptional material
∗ Corresponding author. Tel.: +86 13815863471. E-mail address: [email protected] (P. Wang). 0924-4247/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.sna.2013.01.027
and stress characterization capabilities. Now the widely methods used to detect stress are magnetic Barkhausen noise, magnetic acoustic emission, stress detection based on inverse magnetostrictive, metal magnetic memory and eddy detection etc. However, most of them are more complex because they need coupling agent or can only detect the stress or crack in the surface. They are not very effective in the detection of stress of ferromagnetic material. Barkhausen noise is measured by the microscopic theory that the magnetic domains turn over during the magnetization of the ferromagnetic materials. We can pick up the signal using a detection coil placed on the surface of the ferromagnetic material. This method is fast, convenient, and mature, and more importantly it is fit for stress testing of ferromagnetic materials so can be used to detect the temperature stress of the rail. 2. MBN principle and features 2.1. The principle of MBN detection In ferromagnetic materials, there are many small and different orientated magnetic domains. In the absence of external factors, each magnetic domain direction is the same as its direction of polarizable crystallization, and its overall magnetization effect is zero. If coupled with an alternating magnetic field or stress, the magnetic domain will flip 90◦ or 180◦ or the magnetic domain wall will move, which in turn leads to certain rule orientation of the magnetic domain. This kind of magnetic domain change process enables
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peak time. They can reflect the variation relationship of MBN signal with the variation of stress and microstructure.
B
- Hs
- Hc
233
+Hc
+Hs
H
Fig. 1. The generated diagram of Barkhausen noise [11].
the material inside to develop a series of transiliently pulsed signals, i.e. the Barkhausen signals. This transilient phenomenon of the magnetization process was discovered by Heinrich Barkhausen [10] in 1919 and was called the Barkhausen effect. Every Barkhausen that jumps in the detection coil on the ferromagnetic materials surface will form induction electric pulse signals, known as magnetic Barkhausen noise (MBN). There will be magnetization in the ferromagnetic material under the effect of external magnetic field. When the saturation occurs in the ferromagnetic materials, the magnetic state is not restored to its original state after removing the magnetic field. When the magnetic field changes back and forth in the positive and negative directions, the magnetization of the media is in a cycle, thus forming a hysteresis loop. By observing the hysteresis loop of the ferromagnetic materials we find that the curve displays a step-jitter state (see Fig. 1) in the irreversible magnetization phase, rather than a smooth and continuous curve. The coil placed on the surface of the specimen produces a noise pulse in the form of voltage indicating that the magnetization process of the ferromagnetic materials is not continuous. After years of research, work [12] has shown that the factor which directly affects the MBN signal is the microstructure of the material, whereas the microstructure of material is affected by many factors, such as material composition, stress, deformation, heat treatment, and ambient temperature. These factors will directly or indirectly decide or change the material microstructure. Therefore, it will affect the MBN signal, so that the stress and microstructure of the materials can be detected by the method of MBN. 2.2. The effect of temperature on the MBN signal The temperature stress within the rail was caused by changes of temperature of the rail. The changes in temperature will cause deformation due to thermal expansion and cold contraction. This distortion is restrained between the various parts of the rail and on the boundary constraints. In the experiment, the value of the applied compressive stress caused by the platform of loading stress decreases with decreasing temperature of the specimen, and also increases with increasing temperature. The changed value is the temperature stress, which is caused by the changing temperature. Studies [13] have shown that not only the thermal stress which is produced by the temperature can affect the MBN signal, but also the temperature itself has an impact on the MBN signal, therefore, temperature compensation must be applied.
2.3.1. RMS value The noise intensity usually uses the RMS value to indicate the noise energy. While the MBN is a high-frequency noise, so we use RMS to study the corresponding relationship between the stress and MBN signals [14]. 2.3.2. Average value Average is the envelope of the peak-to-peak value voltage in a magnetization cycle after the MBN signal has been filtered. 2.3.3. Ring number Ring number is the number of sampling points whose amplitude is greater than the threshold at a saturated magnetization cycle. Even in the same test, if the threshold is different, the values are different. Generally the threshold value should be as small as possible, however, it should be larger than the noise amplitude of the detection system. From microscopic theory it should be explained that the MBN signal is generated in the rising and falling area of excitation signal. The MBN signal is made up of myriad turns of 180◦ magnetic domain walls, and its external manifestation is a ring [15] (as shown in Fig. 2). The voltage signal above an appropriate threshold is called a ring. The ring number of MBN signals is closely related to the intensity of the signal overflow, reflecting the stress, microstructure and other information of the material. 2.3.4. Envelope Envelope refers to the outline of an MBN signal in the rising and falling cycle of excitation signals. We can assess the stress and microstructure of materials by extracting the envelope of the MBN signal. The MBN signal envelope contains peak, peak time, full width at half maximum (FWHM) and other information. It is accurate and reliable to assess the internal microstructure of materials, such as tiny cracks inside the material, corrosion, strain, creep and other information. 2.3.5. Peak value Full width at half maximum refers to the full width of the envelope at the corresponding half maximum peak value. Peak value refers to the peak value of the envelope. Fig. 3 shows [16] the method of extraction of the peak value and full width of half maximum value. The peak time refers to the time when the envelope
2.3. Feature extraction In the stress detection using MBN, the method of feature extraction is very important. In this paper, the feature values extracted are RMS (root mean square), average, ring numbers, envelope, and
Fig. 2. Ring number diagram of MBN.
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Fig. 5. Stress loading model diagram.
(8) Temperature measurement devices: Infrared thermometer (ipook PK51 Series), ranging of −32 ◦ C to 350 ◦ C and the accuracy is ±1.5%. 3.2. Methods of the equipment Fig. 3. Peak value and FWHM of MBN.
of MBN signal reaches the maximum value, here we use sample points to indicate time. 3. Experimental study 3.1. Experiment setup The block diagram of the experimental system is shown in Fig. 4, which is divided into seven parts: (1) Magnetizer: A CALTEK CA1640-02 function generator is used to generate a 10 Hz, 3 V sine wave. The sine wave signal is amplified by a Power Amplifier (Newtons4th LPA05B). The magnetizer is made up of a U-shaped core wound with 600 turns of 0.21 mm diameter enameled wire. (2) Receiver: High-frequency cores wound with 5000 turns of 0.07 mm diameter enameled wire and shielded by copper foil. (3) Amplifying circuit. (4) Data acquisition and signal processing: A DAQ-2010 data acquisition card is used to acquire data, which is then processed in MATLAB. The methods of signal processing are band-pass filtering and wavelet denoising. (5) Stress loading device: The rail stress load model is shown in Fig. 5 and rail stress testing diagram is shown in Fig. 6. (6) Heating equipment: electric stove. (7) Loading specimen: A3 steel block, whose size is 660 mm × 30 mm × 10 mm.
Fig. 4. Block diagram.
In the experiment, we selected a piece of A3-steel (see Fig. 6) to simulate a rail track, and then detected the MBN signal in the same stress with different temperature at the surface of the assembly. The low and high frequency noise was filtered from the signal using a band-pass filter whose lower cut-off frequency is 4 kHz and upper cut-off frequency is 21 kHz. Then we use db5 wavelet with six layers to decompose the MBN signal and reconstruct after de-noised. Finally, the features of the MBN signal were extracted. The specimen was heated using a stove to more than 100 ◦ C, then load stress was applied on the specimen from 15 MPa to 150 MPa in 15 MPa steps. We completed 10 experiments that measured the MBN signal at a fixed pressure with different temperatures. Measuring the temperature of test points by using infrared thermometers, the MBN signal is recorded. During the experiment, we found that the compressive stress values showed by the stress loading platform decreases as the temperature of the steel decreases, then the feature value of MBN signal will also have a corresponding change. This indicates that the changing temperature of the steel would lead to stress concentration and thermal stress. So in order to ensure the constant stress in the experiment, we adjusted the stress load equipment over time. From the results of experiment we can see that, under the condition of a constant compressive stress, the feature values of the MBN signal increase as temperature decreases also decrease with increasing temperature. Thus, we can see that the temperature not only produces temperature stress effect on MBN signal, but also the temperature itself has some
Fig. 6. Stress loading instrument.
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Fig. 7. Relationship between average and temperature.
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Fig. 9. Relationship between ring numbers and temperature.
influence on the MBN signal, so we need to apply temperature compensation. 4. Experimental results We used MATLAB tools to analyze and organize the experimental results and plot the features of MBN signal, temperature and pressure. 4.1. Three-dimensional map Depending on the experiment results of different stresses, we can draw three-dimensional diagrams among the feature values of MBN, temperature and stress. This is shown in Figs. 7–11.
Fig. 10. Relationship between peak and temperature.
(1) Average and RMS Fig. 7 shows the three-dimensional diagram between the average of MBN signal and temperature change at different pressures. It can be seen from the figure that the average of MBN decreases as the temperature increases at different pressures, and further validation of the relationship that the MBN signal decreases with the pressure stress increase also fit for the case of high temperatures. Fig. 8 shows the relationship between the RMS of MBN signal (strength of MBN) and the temperature at different
pressures. In the figure, the RMS increases with decreasing temperature at different pressures. There is a good linear relationship between 50 ◦ C and 100 ◦ C, however, poor linearity appeared below 50 ◦ C. We analyzed the specific reasons below. (2) Ring numbers Fig. 9 shows the three-dimensional diagram of the ring numbers of MBN with temperature and stress. It can be seen that
Fig. 8. Relationship between the RMS and temperature.
Fig. 11. Relationship between peak–width ratio and temperature.
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Table 1 Comparison among the variation of peak, temperature and stress. Stress (MPa)
Temperature 25 ◦ C
15 30 45 60 75 90 105 120 135
0.0115 0.0019 0.0034 0.0061 0.0043 0.0388 0.0200 0.0173 0.0118
35 ◦ C 0.2344 0.1289 0.0480 0.0342 0.0165 0.0083 0.0086 0.0071 0.0005
0.0180 0.0023 0.0107 0.0077 0.0123 0.0449 0.0174 0.0207 0.0293
45 ◦ C 0.2441 0.1274 0.0575 0.0238 0.0596 0.0105 0.0059 0.0126 0.0216
0.0175 0.0246 0.0286 0.0379 0.0265 0.0128 0.0130 0.0371 0.0188
55 ◦ C 0.2284 0.1357 0.0545 0.0284 0.0922 0.0380 0.0091 0.0040 0.0220
0.0337 0.1107 0.0195 0.0136 0.0309 0.0199 0.0462 0.0257 0.0430
65 ◦ C 0.2356 0.1398 0.0638 0.0170 0.0785 0.0378 0.0332 0.0222 0.0095
0.0268 0.0076 0.0363 0.0406 0.0599 0.0329 0.0747 0.0544 0.0393
75 ◦ C 0.3125 0.0486 0.0579 0.0343 0.0675 0.0115 0.0127 0.0050 0.0043
0.0647 0.0341 0.0112 0.0404 0.0649 0.0636 0.0606 0.0786 0.0373
85 ◦ C 0.2933 0.0772 0.0623 0.0536 0.0404 0.0304 0.0076 0.0202 0.0138
0.0754 0.0423 0.0246 0.0326 0.0653 0.0497 0.0382 0.0464 0.0093
0.2627 0.0544 0.0915 0.0780 0.0391 0.0274 0.0105 0.0615 0.0051
Table 2 Comparison among the variation of average, temperature and stress. Stress (MPa)
Temperature 25 ◦ C
15 30 45 60 75 90 105 120 135
0.0044 0.0016 0.0074 0.0030 7e−05 0.0138 0.0014 0.0034 0.0080
35 ◦ C 0.0147 0.0146 0.0084 0.0110 0.0122 0.0040 0.0163 0.0014 0.0089
0.0101 0.0003 0.0014 0.0008 0.0018 0.0014 0.0005 0.0037 0.0071
45 ◦ C 0.0206 0.0236 0.0188 0.0079 0.0261 0.0084 0.0183 0.0032 0.0002
0.0005 0.0029 0.0050 0.0067 0.0022 0.0007 0.0035 0.0086 0.0044
55 ◦ C 0.0103 0.0247 0.0193 0.0106 0.0257 0.0104 0.0225 0.0067 0.0047
the ring numbers of MBN also decrease with increasing temperature. MBN ring numbers are characterized by the number of 180◦ magnetic domain reversals. The number of magnetic domain reversal is closely related to the number of magnetic domains and geometry of the material, but the most important factor is the size of threshold. As we select the different thresholds in the data for processing, we can see in the figure that there are not any rules to follow that the ring numbers changing with stress. However, in the actual experiment, when the choice of threshold should not exceed one-third of the maximum value, the ring numbers decrease when the compressive stress increases. (3) Peak and peak–width ratio of the envelope Fig. 10 shows the peak of MBN, which characterizes the maximization of MBN energy. In the figure we can see, like RMS and average, the peak also decreases with increasing temperature at different pressures, but the measured error of the peak value is minimal. Fig. 11 shows how the three-dimensional diagram of the peak–width ratio changes with temperature and stress. In the figure we can see that the changing ratio of peak value is smaller as the temperature changes, and larger as the pressure stress changes compared with other feature values within the stress of 90 MPa.
0.0078 0.0038 0.0026 0.0018 0.0065 0.0056 0.0114 0.0057 0.0082
65 ◦ C 0.0126 0.0225 0.0210 0.0061 0.0242 0.0076 0.0276 0.0025 0.0068
0.0048 0.0070 0.0119 0.0065 0.0142 0.0095 0.0144 0.0136 0.0103
75 ◦ C 0.0086 0.0237 0.0202 0.0108 0.0233 0.0018 0.0220 0.0050 0.0062
0.0087 0.0010 0.0208 0.0081 0.0142 0.0154 0.0127 0.0220 0.0142
85 ◦ C 0.0108 0.0188 0.0147 0.0185 0.0186 0.0031 0.0212 0.0016 0.0068
0.0079 0.0113 0.0255 0.0381 0.0257 0.0164 0.0236 0.0092 0.0054
0.0032 0.0011 0.0020 0.0246 0.0198 0.0004 0.0305 0.0061 0.0059
4.2. Comparison between the changing ratio of feature values A further comparison is made between the feature values of MBN to distinguish which feature changes faster with temperature and which feature changes faster with stress. This paper compared the average, RMS and peak by the table. The ring number was meanless because the values of threshold are different. The peak–width ratio is a small value itself and changes little so it is hard to compare. As is shown in the figures, the comparison among the variation of peak, average and RMS which change with temperature and stress in Tables 1–3. The abscissa represents temperature, whose unit is ◦ C. The Ordinate represents stress, whose unit is MPa. As is shown in the table, the left column data represents the variation of feature value when the temperature changes 1 ◦ C at the condition of maintain the stress constant. The right column data represents the variation of feature value when the stress changes by 1.5 MPa when maintaining the temperature constant. Comparing the data in Tables 1–3, we find that the peak, average and RMS which changes with stress value faster than temperature within 75 MPa, and the variation of peak with stress change is about 20 times than with temperature change. After temperature compensation, the variation of peak with stress change can also be achieved about 13 times than with temperature change.
Table 3 Comparison among the variation of RMS, temperature and stress. Stress (MPa)
Temperature 25 ◦ C
15 30 45 60 75 90 105 120 135
0.0073 0.0009 0.0096 0.0054 0.0017 0.0276 0.0019 0.0057 0.0102
35 ◦ C 0.0679 0.0083 0.0361 0.0326 0.0234 0.0135 0.0204 0.0037 0.0092
0.0185 0.0019 0.0013 0.0005 0.0046 0.0047 0.0012 0.0114 0.0140
45 ◦ C 0.0761 0.0189 0.0512 0.0289 0.0493 0.0122 0.0242 0.0008 0.0038
0.0014 0.0092 0.0127 0.0180 0.0075 0.0052 0.0067 0.0119 0.0086
55 ◦ C 0.0557 0.0183 0.0531 0.0330 0.0493 0.0156 0.0345 0.0034 0.0116
0.0145 0.0084 0.0073 0.0027 0.0149 0.0131 0.0256 0.0158 0.0194
65 ◦ C 0.0635 0.0149 0.0584 0.0225 0.0470 0.0142 0.0397 0.0001 0.0136
0.0106 0.0155 0.0269 0.0200 0.0303 0.0195 0.0310 0.0279 0.0176
75 ◦ C 0.0575 0.0160 0.0538 0.0346 0.0452 0.0017 0.0299 0.0037 0.0134
0.0367 0.0474 0.0064 0.0145 0.0291 0.0303 0.0226 0.0416 0.0272
85 ◦ C 0.0623 0.0045 0.0469 0.0450 0.0345 0.0098 0.0268 0.0067 0.0111
0.0824 0.0499 0.0951 0.0546 0.0379 0.0303 0.0336 0.0184 0.0168
0.0730 0.0454 0.0549 0.0597 0.0356 0.0021 0.0458 0.0210 0.0121
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Fig. 12. Normalized at compress stress of 900 N.
The variation decreases in the higher temperature (75 ◦ C and 85 ◦ C) within 75 MPa. When the temperature is above 60 ◦ C, every characteristic value’s variation which change with temperature is larger compared with the stress above 90 MPa; the peak’s variation which changes with temperature is about 4 times compared with stress. In the previous three-dimensional diagram (Fig. 10), we also find that the peak’s change is significantly better than other characteristics, regardless of temperature changes or pressure, so we can test the peak’s change to detect the rail stress. 4.3. The normalized curve between the feature values and temperature We can get five feature values which are average, RMS, ring numbers, peak and peak–width ratio in the processing of the Barkhausen signal. These values all change as temperature and stress change and can reflect the Barkhausen signal’s change with temperature, stress and microstructure changes. We normalize a set of experimental data at 900 N applied pressure. As shown in Fig. 12, the feature values of MBN, signal all decrease with increasing temperature, however, the variation rate of relative P–W ratio with changes in temperature is the largest. The
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Fig. 14. Time’s effect on average at 600 N.
variation rate of relative ring number and peak value with changes in temperature are similar and they are smaller than the P–W ratio. Furthermore the variation rate of relative average and RMS value with changes in temperature are similar and they are the smallest. 4.4. Analysis of the experiment error During the data processing, we found that there was a stronger linear relationship in the case of higher temperatures for each feature, when it is below 50 ◦ C, the rate of change of each feature value is much lower than when over 50 ◦ C, sometimes it even brings on negative growth (see Fig. 13(a) and (b)). Similarly, there are still time effects existing in the experiment. When compared with the result that we obtained when measuring the sample’s MBN signal at room temperature overnight, there is a big difference when we measure it immediately and the temperature down to room temperature directly. Take the relationship between MBN’s average value and temperature changes under the pressure of 600 N, as shown in Fig. 14, solid lines represent the average values of MBN signal vs. temperature curve which is directly tested non-overnight, dashed is the average MBN–temperature curve measured overnight.
Fig. 13. (a) Amplified. (b) Normalized at 1000 N.
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It is true that environmental conditions cannot be ignored, however, the main reason is that the uneven distribution of the sample’s temperature. In the experiment, we first heat the sample, and then the signal and temperature are recorded at the same time during the process of cooling of the sample. The recorded temperature values are sample’s surface temperature, while the MBN is characterized by the inside of the sample. At the situation of high temperatures, the temperature distribution in the sample is uniform. However, in the case of low temperatures, heat emission is from the surface initially, which leads to uneven temperature distribution of materials, so the measurement results of the temperature do not fully reflect the internal state of the material. After one night, there is sufficient time to ensure that the temperature of the sample has a completely uniform distribution, so the data of the test are more accurate. 4.5. Theoretical interpretation of experimental results From the various features of MBN signal variation with temperature, we can conclude that the MBN signal increases with decreasing temperature at a fixed pressure, and the MBN signal increases with decreasing stress at a fixed temperature. According to the theory of thermodynamics we know that the role of mechanical and thermal is equivalent in some extent effect. The MBN signal is inversely proportional to pressure stress and proportional to the tensile stress. Correspondingly, the magnetic permeability will increase as tensile stress increases, and decrease as compressive stress increases. When the temperature changes, the phenomenon of thermal expansion and contraction will occur in the ferromagnetic specimen. As the temperature increased, the magnetostriction and the magnetic domain wall’s deflection phenomena have intensified. Thus the magnetoresistance will be stretched and the magnetostriction constant will be increased. According to the energy minimum principle and energy transition theory, stress is inversely proportional to the magnetic permeability, resulting in the release of internal stress. The four-point bending test shows that pressure on the upper surface of the ferromagnetic specimen, so in the same load, the permeability will decrease in the surface as the temperature increases. According to the energy transition theory, we know that the energy of compressive stress in the surface will increase with temperature increases. Because the MBN signal is mainly contributed by the magnetic domains 180◦ irreversible flip, it cannot overcome the energy of the barrier which in the same conditions of strong external magnetic field from one state to another, that will also cause the decrease of the MBN signal. Based on these two factors, it can be concluded that the MBN signal in the surface will decrease with increasing temperature in the platform of four-point bending. 5. Conclusion and outlook This paper examined the relationship between the MBN of an A3-steel specimen and the changing temperature at different pressures. We can make the following conclusions: (1) The features of MBN all decrease with increasing temperature at different pressures. (2) The peak, average and RMS which changes with stress value faster than temperature within 75 MPa, and the variation of peak with stress change is about 20 times than with temperature change. After temperature compensation, the
variation of peak with stress change can also be achieved about 13 times than with temperature change. The variation decreases in the higher temperature (75 ◦ C and 85 ◦ C) within 75 MPa. (3) When the temperature is above 60 ◦ C, every characteristic value’s variation which change with temperature is larger compared with the stress above 90 MPa; the peak’s variation which changes with temperature is about 4 times compared with stress. By extracting the feature values of different characteristics, we analyze the relationship between the features, temperature and compressive stress, thus we can test the temperature stress of rail by calibrating the MBN amplitude and temperature curve, as the evaluation of the presence excess stress about the rail. To solve this uneven distribution of the temperature, we can use the thermostat or electric heat tracing as the heating equipment. Because we did not have such experimental conditions before, we will take this method to solve this uneven distribution of the temperature in future experiments. Acknowledgements The research of this paper is supported by FP7 Health monitoring of offshore wind farms (HEMOW) project, National Science Foundation of China (50907032/E070104), “simulation and realization of integration of electromagnetic NDT methods for online high speed railway inspection”, Key project of Technology of Jiangsu (SBE200900338), the Aeronautical Science Foundation of China (2010ZD52) and the Ph.D. Programs Foundation of Ministry of Education of China (20093218120019). References [1] X. Wang, H. Liu, X. Qi, B. Dong, J. Di, The development and application of Barkhausen noise CWR stress testing instrument, Beijing University of Chemical Technology Journal 37 (3) (2010) 123–126. [2] V.S. Pisarev, V.V. Balalov, V.S. Aistov, M.M. Bondarenko, M.G. Yustus, Reflection hologram interferometry combined with hole drilling technique as an effective tool for residual stresses fields investigation in thin-walled structures, Optics and Lasers in Engineering 36 (2001) 551–597. [3] T. Murotani, T. Yano, H. Hirose, A. Ikenaga, Applications of X-ray stress measurement for interface area of Ni3 Al system intermetallic compound coating, International Centre for Diffraction Data 2004, Advances in X-ray Analysis 47 (2004) 385–389. [4] J.W. Wilson, G.Y. Tian, Pulsed electromagnetic methods for defect detection and characterization, NDT&E International 40 (2007) 275–283. [5] G.Y. Tian, A. Sophian, D. Taylor, J. Rudlin, Wavelet-based PCA defect classification and quantification for pulsed eddy current NDT, IEE Proceedings Science, Measurement & Technology 152 (2005) 141–148. [6] Y. He, M. Pan, F. Luo, G. Tian, Reduction of lift-off effects in pulsed eddy current for defect classification, IEEE Transactions on Magnetics 47 (2011) 4753–4760. [7] G. Tian, Y. He, I. Adewale, A. Simm, Research on spectral response of pulsed eddy current and NDE applications, Sensors and Actuators A: Physical 189 (2013) 313–320. [8] A. Dhar, D.L. Atherton, Effects of magnetic flux density and tensile stress on the magnetic Barkhausen noise in pipeline steel, Nondestructive Testing And Evaluation 10 (1993) 287–294. [9] T.W. Krause, L. Clapham, A. Pattantyus, D.L. Atherton, Investigation of the stress-dependent magnetic easy axis in steel using magnetic Barkhausen noise, Journal of Applied Physics 79 (1996) 4242–4252. [10] J.W. Wilson, G.Y. Tian, V. Moorthy, B.A. Shaw, Magneto-acoustic emission and magnetic Barkhausen emission for case depth measurement in En36 gear steel, IEEE Transactions on Magnetics 45 (1) (2009) 177–183. [11] M. Liu, C. Yan, Z. Yu, X. Qi, Barkhausen noise application in the detection of stress and fatigue damage, Journal of Harbin University of Science and Technology 6 (1) (2001) 73–76. [12] P. Wang, S. Zhu, G. Tian, H. Wang, J. Wilson, X. Wang, Stress measurement using magnetic Barkhausen noise and metal magnetic memory testing, Measurement Science & Technology 21 (5) (2010) 055703.
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Biographies Ping Wang obtained his doctor’s degree from Southeast University (Nanjing, China) in 2004. Currently, he is Associate Professor and Tutor for graduates in Measurement and Sensor Technologies at Nanjing University of Aeronautics and Astronautics. His research interests are focused on electromagnetic non-destructive testing and sensor technologies. Xiaoli Ji received her bachelor’s degree from Yangzhou University in 2010. Currently, she is a Postgraduate student at Nanjing University of Aeronautics and Astronautics. Her research interest is electromagnetic non-destructive testing.