Rail damage detection method and device based on acfm and mbn signals

Abstract

The application discloses a rail damage detection method and device based on ACFM and MBN signals, and is applied to the field of electromagnetic nondestructive detection, and comprises the following steps: acquiring ACFM and MBN detection signals of a rail through ACFM and MBN arc-shaped probes respectively; establishing a space coordinate system, and converting the ACFM and MBN detection signals from a curved surface to a plane; setting a delay display time between the ACFM and MBN detection signals according to the interval between the ACFM and MBN arc-shaped probes and the speed of a running device carrying the ACFM and MBN arc-shaped probes; fitting the ACFM detection signals into a plane by using a linear interpolation function, forming a signal cloud chart, and detecting rail surface defects; and detecting the surface state of a plastic deformation layer and a white layer thickness of the rail surface according to the peak-to-peak value, the root mean square value and the envelope line of the MBN detection signals. The application combines the advantages of two nondestructive detection technologies of ACFM and MBN, and improves the rail damage detection efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the field of electromagnetic nondestructive testing, and in particular to a rail damage detection method and device based on ACFM and MBN signals. Background Art

[0002] 60kg / m heavy-duty rails, made of U71Mn or U75V, are widely used on my country's mainline and high-speed railways. The rail surface profiles consist of three types of arcs: R13, R80, and R300. During routine service, cyclic wheel-rail loads can cause numerous rolling fatigue contact cracks at the gauge angles, seriously impacting train safety. With the rapid development of high-speed railways, the demand for rail crack detection is increasing.

[0003] AC electromagnetic field measurement (ACFM) and magnetic Barkhausen noise (MBN) are two emerging nondestructive testing technologies. ACFM is an electromagnetic nondestructive testing method based on the principle of electromagnetic induction, and has an accurate electromagnetic theoretical model. ACFM technology is developed based on the principle of AC voltage drop measurement. It overcomes the disadvantage of AC potential drop technology requiring direct contact with the workpiece, and retains the advantage of eddy current nondestructive testing technology in achieving quantitative detection. It is suitable for nondestructive testing of defects in ferromagnetic and non-ferromagnetic conductive materials, and can detect surface and shallow surface cracks in materials. As an emerging nondestructive testing technology, MBN can quickly and effectively evaluate the plastic deformation layer, white layer, stress state, microstructure, fatigue service condition, etc. at a certain depth of the rail head. It is an important and promising testing method in the field of nondestructive testing.

[0004] In the current research stage, the two technologies have not been combined to detect materials. For AC electromagnetic field technology, it can detect small cracks and spalling pit defects on the rail surface. For magnetic Barkhausen technology, it can detect the plastic deformation layer, white layer, stress state, microstructure, fatigue service condition, etc. of the rail surface.

[0005] Therefore, how to provide a rail damage detection method and device based on ACFM and MBN signals that can combine the advantages of two non-destructive testing technologies, ACFM and magnetic Barkhausen noise MBN, to improve the efficiency and accuracy of rail damage detection is an urgent problem that needs to be solved by technical personnel in this field. Summary of the Invention

[0006] In view of this, the present invention proposes a rail damage detection method and device based on ACFM and MBN signals.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] The rail damage detection method based on ACFM and MBN signals includes:

[0009] Step (1): obtaining the ACFM detection signal and the MBN detection signal of the rail through the ACFM arc probe and the MBN arc probe respectively;

[0010] Step (2): Establish a spatial coordinate system and convert the ACFM detection signal and the MBN detection signal from a curved surface to a flat surface;

[0011] Step (3): setting a delay display time between the ACFM detection signal and the MBN detection signal according to the distance between the ACFM arc probe and the MBN arc probe and the speed of the running device carrying the ACFM arc probe and the MBN arc probe;

[0012] Step (4): Use the linear interpolation function to fit the ACFM detection signal into a surface to form a signal cloud map to detect rail surface defects; detect the surface conditions of the plastic deformation layer and the white layer thickness of the rail surface based on the peak-to-peak value, root mean square value and envelope of the MBN detection signal.

[0013] Optionally, both the ACFM detection signal and the MBN detection signal are multi-point array curved surface signals.

[0014] Optionally, in step (2), a spatial coordinate system is established, specifically:

[0015] Flatten the rail surface perpendicular to the rail direction as the X-axis of the spatial coordinate system;

[0016] The moving distance of the multi-point array probe along the rail is used as the Y axis of the spatial coordinate system;

[0017] The multi-point array surface signal is used as the Z axis of the spatial coordinate system.

[0018] Optionally, the rail surface flattening method is to establish interpolation coordinate points by using the intersection point of the unit circle and the rail surface.

[0019] Optionally, in step (3), the delay display time between the ACFM detection signal and the MBN detection signal is set according to the distance between the ACFM arc probe and the MBN arc probe and the speed of the running device carrying the ACFM arc probe and the MBN arc probe, specifically:

[0020] T = L / V;

[0021] Wherein, T is the delayed display time; L is the distance between the ACFM arc probe and the MBN arc probe; V is the speed of the traveling device carrying the ACFM arc probe and the MBN arc probe.

[0022] Optionally, in step (4), a linear interpolation function is used to fit the ACFM detection signal into a surface to form a signal cloud map, specifically:

[0023] Determine the multi-point array probe position of the ACFM arc probe;

[0024] According to the multi-point array probe detection signal of the ACFM arc probe, linear interpolation processing is performed with the interpolation coordinate points in the spatial coordinate system;

[0025] Fit the cloud plot to the data by linear interpolation.

[0026] Optionally, the peak-to-peak value of the MBN detection signal is the difference between the maximum and minimum voltage values ​​in the MBN signal generated when the ferromagnet reaches a magnetic saturation state under the action of a magnetic field, indicating the amplitude range of the signal.

[0027] Optionally, the root mean square value of the MBN detection signal is the signal intensity, which represents the statistical analysis result of the MBN signal during the magnetization process, and is calculated as follows:

[0028]

[0029] Where n is the number of MBN noises, V i The peak value after fitting the noise curve for each MBN.

[0030] The present invention also provides a rail damage detection device based on ACFM and MBN signals, comprising:

[0031] ACFM arc probe: used to obtain ACFM detection signals of rails;

[0032] MBN arc probe: used to obtain MBN detection signals of rails;

[0033] Traveling device: used to carry ACFM arc probe and MBN arc probe;

[0034] Upper computer: used to convert ACFM detection signals and MBN detection signals from curved surfaces into flat surfaces, set the delay display time between ACFM detection signals and MBN detection signals according to the distance between the ACFM arc probe and the MBN arc probe and the speed of the running device carrying the ACFM arc probe and the MBN arc probe, use the linear interpolation function to fit the ACFM detection signal into a surface to form a signal cloud map, detect rail surface defects, and detect the surface conditions of the plastic deformation layer and the white layer thickness of the rail surface according to the peak-to-peak value, root mean square value and envelope of the MBN detection signal.

[0035] Optionally, both the ACFM arc probe and the MBN arc probe are multi-point array probes.

[0036] It can be seen from the above technical solutions that, compared with the prior art, the present invention proposes a rail damage detection method and device based on ACFM and MBN signals. The present invention converts the ACFM detection signal (ACFM multi-point array curved surface signal) and the MBN detection signal (MBN multi-point array curved surface signal) from a curved surface to a plane by establishing a spatial coordinate system, which is conducive to intuitive signal analysis. According to the different arc radii of the rail surface, it is divided into different areas, and a detection space coordinate system is established to achieve rapid positioning of rail surface defects; by setting the delay display time between the ACFM detection signal and the MBN detection signal according to the distance between the ACFM arc probe and the MBN arc probe and the speed of the running device carrying the ACFM arc probe and the MBN arc probe, the position of the rail at the same point can be quickly located. The ACFM detection signal and MBN detection signal are placed in the same point rail spatial coordinate system. The characteristics of the two signals can be combined for analysis at any position on the rail, realizing the complementary advantages of the two detection technologies and detecting the rail surface condition. By using the multi-channel ACFM distorted magnetic field signals collected by the ACFM arc probe, the linear interpolation function is used to fit the collected multi-channel data into a surface to form a signal cloud map, and three-dimensional visualization technology is used to detect rail surface defects. In MBN detection, signal processing methods such as peak-to-peak value, root mean square value, and envelope are used to study the state of the rail surface deformation layer, thereby improving the efficiency and accuracy of rail damage detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0038] Figure 1 Schematic diagram of the method of the present invention.

[0039] Figure 2 This is a schematic diagram of the overall structure of the 60kg / m rail array probe detection space coordinate system established by the present invention.

[0040] Figure 3 The figure is a flow chart of the method for implementing the cloud diagram fitting algorithm of the AC electromagnetic field signal collected by the ACFM arc probe in the present invention.

[0041] Figure 4 This is a schematic diagram of the peak-to-peak value and envelope structure of the signal detected by the MBN arc probe of the present invention.

[0042] Figure 5 The figure is a flow chart of the method for executing the characteristic value analysis program of the magnetic Barkhausen signal collected by the MBN arc probe in the present invention.

[0043] Figure 6 This is a schematic diagram of ACFM signals and MBN signals collected when performing rail defect detection in the present invention.

[0044] Figure 7 Schematic diagram of the device structure of the present invention.

[0045] Figure 8 Schematic diagram of the structure of the ACFM arc probe device of the present invention.

[0046] Figure 9 It is a structural schematic diagram of the MBN arc probe device of the present invention.

[0047] Figure 10 This is a schematic diagram of the device structure of the rail in contact with the ACFM arc probe and the MBN arc probe according to the present invention.

[0048] Figure 11 This is a schematic diagram of the overall structure of the ACFM arc probe of the present invention when performing rail damage detection.

[0049] Figure 12 This is a schematic diagram of the overall structure of the MBN arc probe of the present invention when performing rail damage detection.

[0050] Markings in the accompanying drawings: 1-TMR linear sensor circuit board, 2-manganese zinc ferrite core, 3-MBN detection coil, 4-excitation coil, 5-rail, 6-bearing base, 7-traveling wheel, 8-bearing, 9-U-clamp, 10-ACFM arc probe, 11-base plate, 12-MBN arc probe, 13-guide wheel. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] Example 1:

[0053] Example 1 of the present invention discloses a rail damage detection method based on ACFM and MBN signals, such as Figure 1 As shown, including:

[0054] Step (1): obtaining the ACFM detection signal (ACFM multi-point array curved surface signal) and the MBN detection signal (MBN multi-point array curved surface signal) of the rail through the ACFM arc probe and the MBN arc probe respectively;

[0055] Step (2): Establish a spatial coordinate system and convert the ACFM detection signal and the MBN detection signal from a curved surface to a flat surface, specifically:

[0056] The interpolation coordinate points are established by using the intersection point of the unit circle and the rail surface, and the rail surface is flattened along the perpendicular direction of the rail as the X-axis of the spatial coordinate system, specifically:

[0057] The 60kg / m rail surface shape is composed of three types of arcs: R13, R80, and R300. Considering the need to interpolate the data collected by the multi-point array probe, the rail surface needs to be flattened along the perpendicular direction of the rail as the X-axis to establish a spatial coordinate system;

[0058] Secondly, the rail surface is flattened by using the intersection point of the unit circle and the rail surface to establish the interpolation coordinate points, such as Figure 2 As shown in the figure, the plane coordinate system of the 60kg / m rail section is established with the left end point of the R13L area as the center of the circle, and the rail surface arc function G can be obtained. (x,y) ,as follows:

[0059]

[0060] The method for establishing the detection space coordinate system is as follows: the left end point of the 60kg / m rail surface is used as the center of the unit circle, which is recorded as point R 13L1 , this point is used as the first interpolation coordinate point of the X-axis of the spatial coordinate system. According to the arc interval, the track surface arc function and the unit circle function are combined:

[0061]

[0062] The intersection point R13L1(x1,y1) is obtained as the second interpolation coordinate point, and then the combined functions are:

[0063]

[0064] By analogy, the i-th interpolation coordinate point of the arc segment R13L is the simultaneous function:

[0065]

[0066] In R 13L Set R on the arc segment 13L1 、R 13L2 、R 13L3 、R 13L4 、……R 13Lnn interpolation coordinate points, and the points are arranged in the unit length on the X-axis of the space coordinate system. Finally, for the rail surface arc connection, if the last arc is exactly divided by n unit circles, the interpolation coordinate points are sequentially taken from the end point of the next arc; if the last arc cannot be exactly divided by n unit circles, the remaining arc length s can be supplemented to the next rail surface arc, that is, the distance between the center of the next unit circle and the end point of the arc is the difference between the unit circle and s, so as to obtain the X-axis of the space coordinate system.

[0067] The moving distance of the multi-point array probe along the steel rail is taken as the Y-axis of the space coordinate system.

[0068] The multi-point array curved surface signal is taken as the Z-axis of the space coordinate system.

[0069] Step (3): Since the excitation frequencies of the ACFM arc-shaped probe and the MBN arc-shaped probe are quite different, in order to prevent interference between the two signals, the two probes need to be arranged separately, and the distance between the two probes is set to 500 mm in this embodiment. The ACFM signal and the MBN signal collected by real-time detection reflect the state of different positions of the steel rail, so the data detected by the probe in front are displayed with a delay, and the signal collected by the probe behind is processed at the same time. In this way, the ACFM and MBN signals of the same point of the steel rail can be placed in the same point of the steel rail space coordinate system, which facilitates the use of the characteristics of the two signals to distinguish the state of the same point of the steel rail. The advantages of the two detection technologies can be complementary to analyze any position on the steel rail in combination with the signal characteristics of the two, so as to detect the state of the rail surface. The delay display time between the ACFM detection signal and the MBN detection signal is set according to the distance between the ACFM arc-shaped probe and the MBN arc-shaped probe and the speed of the traveling device carrying the ACFM arc-shaped probe and the MBN arc-shaped probe, and specifically:

[0070] T=L / V;

[0071] Wherein, T is the delay display time; L is the distance between the ACFM arc-shaped probe and the MBN arc-shaped probe; V is the speed of the traveling device carrying the ACFM arc-shaped probe and the MBN arc-shaped probe.

[0072] Step (4): The ACFM detection signal (multi-channel ACFM distorted magnetic field signal) is fitted into a surface by using a linear interpolation function to form a signal cloud chart, and the rail surface defects are detected, and specifically:

[0073] First, the positions of the multi-point array probes of the ACFM arc-shaped probe are determined for interpolation processing. Each arc-shaped probe includes a plurality of multi-point array probes, and in this embodiment, there are 7 multi-point array probes arranged symmetrically on the rail surface.

[0074] Secondly, since rail fatigue cracks often concentrate at the gauge angle, the multi-point array probes are also relatively concentrated at the gauge angle. Multi-point array probes 1 and 7 are located at the R13 arc segment, 1 / 3 of the way from the R80 arc. Probes 2 and 6 are located at the midpoint of the R80 arc. Probes 3 and 5 are located at the junction of R80 and R300. Probe 4 is located at the center of the R300 arc segment. Based on the multi-point array probe detection signal of the ACFM arc probe, linear interpolation processing is performed using the interpolation coordinate points in the spatial coordinate system, as follows:

[0075] The data collected by probe 1 is recorded as The data collected by probe 2 is recorded as f (x2) , according to linear interpolation, the data interpolation formula between the two probes is as follows:

[0076]

[0077] Similarly, the interpolation coordinates between the probes are interpolated using the collected data of the two probes. Finally, for the parts on both sides of probes 1 and 7, since they are only adjacent to one sensor, the interpolation can be used (taking the left side of probe 1 as an example):

[0078]

[0079] in It is the average value of the seven sensor probe signals collected last time, that is:

[0080]

[0081] Where i=1,2,3···n,n=7.

[0082] Finally, the cloud map is fitted by linear interpolation data, such as Figure 3 As shown in the figure, after linear interpolation of the original data points, the target matrix can be obtained. Each element in the target matrix can be regarded as a point in the three-dimensional coordinate system. The row and column numbers of the element are the horizontal and vertical coordinates of the corresponding point respectively, and the value of the element is the Z coordinate of the corresponding point. They are output to the one-dimensional arrays X(1,i), Y(1,j) and Z(1,i×j) respectively. When the sensor responds to the defect signal waveform, it can quickly locate the area, which is convenient for subsequent rail repair work.

[0083] The surface conditions of the plastic deformation layer and the white layer thickness of the rail surface are detected based on the peak-to-peak value, root mean square value and envelope of the MBN detection signal, specifically:

[0084] MBN detects the peak-to-peak value and envelope of the signal, such as Figure 4 shown.

[0085] The peak-to-peak value of the MBN arc probe detection signal is the difference between the maximum and minimum voltage values ​​in the MBN signal generated when the ferromagnetic body reaches the magnetic saturation state under the action of the magnetic field, which represents the amplitude range of the signal.

[0086] The root mean square value of the MBN arc probe detection signal is the signal strength, which represents the statistical analysis result of the MBN signal during the magnetization process. The calculation formula is as follows:

[0087]

[0088] Where n is the number of MBN noises, V i The peak value after fitting the noise curve for each MBN.

[0089] like Figure 5 As shown, during the magnetization process of ferromagnetic materials, the deflection of magnetic domains is highly random. To more accurately reflect the thickness of the plastic deformation layer and white layer on the rail surface through the MBN signal, the method of averaging multiple MBN signal eigenvalues ​​is usually used to obtain more accurate eigenvalue analysis results. The data step size of the MBN signal analysis is calculated and set based on the sampling frequency and sampling time. Since MBN reaches its peak when the excitation reaches the coercive field strength, the point where the sinusoidal excitation amplitude is zero is used as the starting point for data analysis and the signal eigenvalue is calculated. The eigenvalue calculation results are temporarily stored as a one-dimensional numerical value. Finally, the average value is taken based on the total number of MBN signal analyses, which is used as the final result of a single measurement.

[0090] like Figure 6 As shown, the running device moves above the rail, and the sensors on the array ACFM probe and MBN probe mounted above the running device collect data. After passing through the operational amplifier and filtering circuit, the data is transmitted to the host computer software to achieve the purpose of real-time detection of the rail surface. The ACFM and MBN detection technologies are used to detect the rail surface status.

[0091] Example 2:

[0092] Example 2 of the present invention discloses a rail damage detection device based on ACFM and MBN signals, such as Figure 7 As shown, including:

[0093] ACFM arc probe (ACFM multi-point array probe): used to obtain ACFM detection signals of rails;

[0094] MBN arc probe (MBN multi-point array probe): used to obtain MBN detection signals of rails;

[0095] ACFM arc probe 10, such as Figure 8 As shown, it includes a TMR linear sensor circuit board 1, a manganese-zinc ferrite core 2, and an excitation coil 4.

[0096] MBN curved probe 12, such as Figure 9 As shown, it includes: a manganese-zinc ferrite core 2, an MBN detection coil 3 and an excitation coil 4.

[0097] The rail 5 fitted with the ACFM arc probe 10 and the MBN arc probe 12 is as follows: Figure 10 shown.

[0098] Seven TMR linear sensor circuit boards 1 are evenly distributed under the manganese-zinc ferrite core 2 of the ACFM arc probe 10, which are used to collect Bx and Bz signals above the rail (distorted magnetic field signals caused by defects above the rail surface). Multiple MBN detection coils 3, which are wound with wires and the manganese-zinc ferrite core 2, are evenly distributed under the manganese-zinc ferrite core 2 of the MBN arc probe 12, which are used to collect magnetic Barkhausen signals (magnetic field changes caused by magnetic domain reversal on the rail head and rail surface). The bottom of the manganese-zinc ferrite core 2 is composed of three types of arcs: R300, R80, and R13, which are consistent with the surface of a 60kg / m rail. The total height and width of the internal space are 20mm and 34mm, and the top arc radius is 64mm.

[0099] like Figure 11 、 Figure 12 As shown, both the ACFM arc probe 10 and the MBN arc probe 12 need to pass an AC sinusoidal excitation signal into the excitation coil 4 wound on the manganese-zinc ferrite core 2, and then the TMR linear sensor circuit board 1 and the MBN detection coil 3 arranged under the manganese-zinc ferrite core 2 collect the magnetic field signal, and then the collected magnetic field signal is subjected to operational amplifier filtering, A / D conversion, and finally analyzed in the host computer software.

[0100] Traveling device: used to carry ACFM arc probe and MBN arc probe, including: bearing base 6, traveling wheel 7, bearing 8, U-shaped clamp 9, bottom plate 11, guide wheel 13;

[0101] The entire device moves on the rail via the running wheels 7, and the guide wheels 13 ensure that the running device does not move laterally on the rail. The U-shaped clamp 9 is welded to the running device, and the ACFM arc probe 10 and MBN arc probe 12 are fixed to the running device via the bolts on the U-shaped clamp 9. The lift-off height of the probe can be fine-tuned by turning the threads on the U-shaped clamp, which prevents the probe and the rail from moving stably while increasing the sensitivity of the device.

[0102] Upper computer: used to convert ACFM detection signals and MBN detection signals from curved surfaces into flat surfaces, set the delay display time between ACFM detection signals and MBN detection signals according to the distance between the ACFM arc probe and the MBN arc probe and the speed of the running device carrying the ACFM arc probe and the MBN arc probe, use the linear interpolation function to fit the ACFM detection signal into a surface to form a signal cloud map, detect rail surface defects, and detect the surface conditions of the plastic deformation layer and the white layer thickness of the rail surface according to the peak-to-peak value, root mean square value and envelope of the MBN detection signal.

[0103] The embodiment of the present invention discloses a rail damage detection method and device based on ACFM and MBN signals. The present invention converts ACFM detection signals (ACFM multi-point array curved surface signals) and MBN detection signals (MBN multi-point array curved surface signals) from curved surfaces to planes by establishing a spatial coordinate system, which is conducive to intuitive signal analysis. According to different arc radii of the rail surface, it is divided into different areas, and a detection spatial coordinate system is established to achieve rapid positioning of rail surface defects; by setting the delay display time between ACFM detection signals and MBN detection signals according to the distance between the ACFM arc probe and the MBN arc probe and the speed of the running device carrying the ACFM arc probe and the MBN arc probe, the rail position at the same point can be quickly positioned. The ACFM detection signal and MBN detection signal are placed in the same point rail spatial coordinate system. The characteristics of the two signals can be combined for analysis at any position on the rail, realizing the complementary advantages of the two detection technologies and detecting the rail surface condition. By using the multi-channel ACFM distorted magnetic field signals collected by the ACFM arc probe, the linear interpolation function is used to fit the collected multi-channel data into a surface to form a signal cloud map, and three-dimensional visualization technology is used to detect rail surface defects. In MBN detection, signal processing methods such as peak-to-peak value, root mean square value, and envelope are used to study the state of the rail surface deformation layer, thereby improving the efficiency and accuracy of rail damage detection.

[0104] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0105] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A rail damage detection method based on ACFM and MBN signals, characterized in that: include: Step (1): obtaining the ACFM detection signal and the MBN detection signal of the rail through the ACFM arc probe and the MBN arc probe respectively; Step (2): establishing a spatial coordinate system, and converting the ACFM detection signal and the MBN detection signal from a curved surface to a flat surface; Step (3): setting a delay display time between the ACFM detection signal and the MBN detection signal according to the distance between the ACFM arc probe and the MBN arc probe and the speed of the running device carrying the ACFM arc probe and the MBN arc probe; Step (4): Use a linear interpolation function to fit the ACFM detection signal into a surface to form a signal cloud diagram to detect rail surface defects; detect the surface conditions of the plastic deformation layer and the white layer thickness of the rail surface based on the peak-to-peak value, root mean square value and envelope of the MBN detection signal.

2. The rail damage detection method based on ACFM and MBN signals according to claim 1, characterized in that: The ACFM detection signal and the MBN detection signal are both multi-point array curved surface signals.

3. The rail damage detection method based on ACFM and MBN signals according to claim 2 is characterized in that: In step (2), a spatial coordinate system is established, specifically: Flatten the rail surface along the direction perpendicular to the rail as the X-axis of the spatial coordinate system; The moving distance of the multi-point array probe along the rail is used as the Y axis of the spatial coordinate system; The multi-point array curved surface signal is used as the Z axis of the spatial coordinate system.

4. The rail damage detection method based on ACFM and MBN signals according to claim 3 is characterized in that: The rail surface flattening method is to establish interpolation coordinate points by using the intersection point of the unit circle and the rail surface.

5. The rail damage detection method based on ACFM and MBN signals according to claim 1, characterized in that: In step (3), the delay display time between the ACFM detection signal and the MBN detection signal is set according to the distance between the ACFM arc probe and the MBN arc probe and the speed of the running device carrying the ACFM arc probe and the MBN arc probe, specifically: T = L / V; Wherein, T is the delayed display time; L is the distance between the ACFM arc probe and the MBN arc probe; V is the speed of the running device carrying the ACFM arc probe and the MBN arc probe.

6. The rail damage detection method based on ACFM and MBN signals according to claim 1, characterized in that: In step (4), a linear interpolation function is used to fit the ACFM detection signal into a surface to form a signal cloud map, specifically: Determining the multi-point array probe position of the ACFM arc probe; Performing linear interpolation processing using interpolation coordinate points in the spatial coordinate system according to the multi-point array probe detection signal of the ACFM arc probe; Fit the cloud plot to the data by linear interpolation.

7. The rail damage detection device based on ACFM and MBN signals according to claim 1, characterized in that: The peak-to-peak value of the MBN detection signal is the difference between the maximum and minimum voltage values ​​in the MBN signal generated when the ferromagnetic body reaches a magnetic saturation state under the action of a magnetic field, indicating the amplitude range of the signal.

8. The rail damage detection device based on ACFM and MBN signals according to claim 1, characterized in that: The root mean square value of the MBN detection signal is the signal intensity, which represents the statistical analysis result of the MBN signal during the magnetization process. The calculation formula is as follows: Where n is the number of MBN noises, V i The peak value after fitting the noise curve for each MBN.

9. The rail damage detection device based on ACFM and MBN signals is characterized by: include: ACFM arc probe: used to obtain ACFM detection signals of rails; MBN arc probe: used to obtain MBN detection signals of rails; Traveling device: used to carry the ACFM arc probe and the MBN arc probe; Host computer: used to convert the ACFM detection signal and the MBN detection signal from a curved surface into a flat surface, set the delay display time between the ACFM detection signal and the MBN detection signal according to the distance between the ACFM arc probe and the MBN arc probe and the speed of the running device carrying the ACFM arc probe and the MBN arc probe, and use a linear interpolation function to fit the ACFM detection signal into a surface to form a signal cloud map to detect rail surface defects; detect the surface conditions of the plastic deformation layer and the white layer thickness of the rail surface according to the peak-to-peak value, root mean square value and envelope of the MBN detection signal.

10. The rail damage detection device based on ACFM and MBN signals according to claim 9, characterized in that: The ACFM arc probe and the MBN arc probe are both multi-point array probes.

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