High-speed railway RCF crack size characterization method based on By signal of ACFM technology

Abstract

The invention discloses a high-speed railway RCF crack size characterization method based on an ACFM technology By signal. The method comprises the steps that a finite element model of a high-speed railway steel rail is established; adopting a control variable method, taking one of the crack length, the crack pocket depth and the crack inclination angle as a variable and taking the other two of the crack length, the crack pocket depth and the crack inclination angle as a fixed quantity, carrying out ACFM detection simulation, detecting the crack to obtain a By signal, and extracting characteristic parameters of the By signal; obtaining a quantitative relationship between the By signal characteristic parameter and the crack geometric parameter; based on the quantitative relationship, establishing a By signal-crack size mapping model through a polynomial regression algorithm or a support vector regression algorithm; inputting a to-be-measured By signal into the By signal-crack size mapping model, and outputting three quantitative results of the crack length, the crack pocket depth and the crack inclination angle. The method not only provides a theoretical basis for developing a more comprehensive ACFM quantization algorithm, but also lays a technical foundation for online intelligent monitoring and maintenance of the high-speed railway steel rail.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing technology, and in particular to a method for characterizing the size of RCF cracks in high-speed railways based on ACFM technology By signals. Background Technology

[0002] Rolling contact fatigue (RCF) cracking is one of the main causes affecting the operational safety of high-speed railway rails. Currently, the main methods for detecting rolling contact fatigue cracks in rails include ultrasonic testing and eddy current testing. Ultrasonic testing utilizes the propagation and reflection characteristics of ultrasonic waves in the rail. When a crack is encountered, a reflected wave is generated, and the location, size, and shape of the crack are determined by receiving and analyzing the reflected wave signal. This technology has advantages such as fast detection speed, strong penetration, and the ability to detect internal defects, but it requires a high degree of surface condition and the influence of coupling agent must be considered. Eddy current testing is based on the principle of electromagnetic induction, detecting cracks by detecting changes in eddy currents on the rail surface. When the probe moves across the rail surface, the eddy current testing instrument determines the presence of cracks based on changes in the impedance plane diagram. It can quickly detect surface cracks, is sensitive to surface defects, and the detection process does not require a coupling agent. However, the detection depth is limited, and it is easily affected by the material surface condition and near-surface defects.

[0003] Alternating Current Field Measurement (ACFM) is a novel electromagnetic technology based on the principle of electromagnetic induction. By introducing a uniform alternating current magnetic field onto the surface of a conductive material through an excitation coil, when a crack is present, the current flowing around it generates significant magnetic field disturbances at the crack tip and bottom. A probe equipped with an induction coil (or magnetic sensor) captures the three-dimensional magnetic field components (Bx, By, Bz) in real time along a preset measurement path. Through analysis of peak and valley characteristics, the location, size, and geometry of the crack can be determined with high precision.

[0004] Currently, RCF crack characterization methods based on Bx and Bz signals in ACFM technology have been extensively studied and applied. The Bx signal is primarily used to determine crack length, while the Bz signal is often used to locate the crack center. However, these two types of signals are generally not sensitive enough to changes in crack tilt angle, and when faced with changes in crack pocket depth, the signal response suffers from nonlinear drift and amplitude instability, limiting their characterization accuracy and robustness in identifying complex crack morphologies. In contrast, the magnetic field response of the AC electromagnetic field in the Y direction (By component) is theoretically more sensitive to current path deflection caused by crack disturbances, thus possessing potentially high responsiveness and recognition capability for the spatial morphological features of cracks (including surface length, pocket depth, and tilt angle). However, the systematic utilization of the By component in existing research is extremely limited, and its role in the quantitative assessment of RCF crack size has not been thoroughly explored, with related applications almost nonexistent. Therefore, in response to this technological and theoretical gap, this invention innovatively proposes an RCF crack characterization method based on By signals. By extracting the structural features of the By waveform, it achieves high-precision estimation of crack length, depth, and tilt angle, opening up a new direction for ACFM crack size characterization. Summary of the Invention

[0005] The purpose of this invention is to provide a method for characterizing the size of RCF cracks in high-speed railways based on ACFM technology By signal, filling the research gap in the application of By component signals in RCF crack characterization, and providing theoretical support and technological innovation for the rapid identification and intelligent evaluation of RCF cracks in high-speed railways.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] S1. Establish a finite element model of high-speed railway rails;

[0008] S2. Using the controlled variable method, one of the crack length, crack pocket depth and crack inclination angle is used as a variable, and the other two are quantitative. ACFM detection simulation is carried out. Cracks are detected on the surface of high-speed railway rails along a fixed angle detection line to obtain By signal and extract the characteristic parameters of By signal.

[0009] S3. Establish the correlation between crack length, crack pocket depth, crack inclination angle and By signal characteristic parameters respectively, and obtain the quantitative relationship between By signal characteristic parameters and crack geometric parameters; the crack geometric parameters include crack length, crack pocket depth and crack inclination angle.

[0010] S4. Based on the above quantitative relationship, establish a By signal-crack size mapping model using a multinomial regression algorithm or a support vector regression algorithm, and form a simulation database.

[0011] S5. Input the By signal to be measured into the By signal-crack size mapping model, and output three quantitative results: crack length, crack pocket depth, and crack tilt angle.

[0012] Preferably, in S1, the finite element model of the high-speed railway rail is established based on COMSOL Multiphysics finite element software.

[0013] Preferably, in S2, cracks are detected along a fixed-angle detection line on the surface of the high-speed railway rail to obtain a By signal. The feature parameters of the By signal are extracted specifically including:

[0014] Cracks are detected on the surface of high-speed railway rails along a 45° detection line to obtain By signals. Feature parameters of the By signals are extracted, including characteristic valley values, horizontal valley spacing, and valley ratio.

[0015] Preferably, in S3, the quantitative relationship between the obtained By signal characteristic parameters and crack geometric parameters includes:

[0016] Quantitative relationship between horizontal spacing of troughs and crack length: Crack length is calculated by measuring the horizontal spacing of troughs; Quantitative relationship between trough ratio and crack inclination angle: The trough ratio is defined as follows: Normalization was performed to characterize the crack inclination angle, where By trough max The trough value where the By signal value is the largest. trough min The valley value with the smallest By signal value; quantitative relationship between characteristic valley value and crack pocket depth: the crack pocket depth is calculated by measuring the characteristic valley value.

[0017] Preferably, the quantitative relationship between the horizontal spacing of the troughs and the crack length is as follows:

[0018] Y = 0.00111X + 0.00531, R 2 =0.99803

[0019] Where X is the crack length and Y is the horizontal spacing between the troughs.

[0020] Preferably, S4-S5 also includes:

[0021] Develop a graphical software platform in the MATLAB environment to automatically import By signal time-domain data in CSV format, extract feature parameters in real time, call the embedded By signal-crack size mapping model, and output three quantitative results—crack length, crack pocket depth, and crack tilt angle—with one click.

[0022] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a method for characterizing the RCF crack size of high-speed railway based on ACFM technology By signal as described above.

[0023] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0024] (1) This invention innovatively proposes a new method for crack morphology estimation based on By signals. Unlike current research on quantitative characterization of RCF crack size based on ACFM Bx and Bz signals, the characterization method described in this invention shows that By signals exhibit higher sensitivity and accuracy in RCF crack geometry characterization than traditional Bx / Bz signals. For the first time, By signals are used to quantitatively evaluate crack length, pocket depth, and tilt angle.

[0025] (2) This invention not only provides a theoretical basis for the development of a more comprehensive ACFM quantization algorithm, but also lays a technical foundation for online intelligent monitoring and maintenance of high-speed railway rails. The method proposed in this paper, through By signal detection and characterization, constructs a database, which greatly improves the detection efficiency and accuracy of cracks, provides a new method for studying the geometric change law of cracks, and is of great significance in ensuring the safety performance of railway rails. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 The contour plot and schematic diagram show the magnetic flux density Y component of a single crack in this invention.

[0028] Figure 2 The horizontal spacing between the troughs of the By signal extracted in this embodiment of the invention;

[0029] Figure 3 The first characteristic trough of the By signal in this embodiment of the invention is By. troughD ;

[0030] Figure 4 This is a diagram showing the influence of the detection direction and crack tilt angle on the By trough value in an embodiment of the present invention.

[0031] Figure 5 This is a modeling diagram of the ACFM finite element model in an embodiment of the present invention;

[0032] Among them, 1-refined mesh domain, 2-single RCF crack, 3-rail domain, 4-air domain;

[0033] Figure 6 This is a schematic diagram of the crack inclination angle in an embodiment of the present invention;

[0034] Figure 7 This is a diagram illustrating the influence of crack length variation on the spacing between horizontal valleys of the By signal in an embodiment of the present invention.

[0035] Figure 8 This is a graph of the fitted curve function representing the crack length using the horizontal spacing of the troughs in an embodiment of the present invention;

[0036] Figure 9 This is a control group diagram of five groups of By signal values ​​in an embodiment of the present invention, with a crack length of 7.5 mm, varying depths from 2 to 20 mm, and crack inclination angles gradually increasing from 10° to 20° to 90°.

[0037] Among them, (a) is By signal data with a crack inclination angle of 10°, crack length of 7.5mm, and varying depths of 2-20mm; (b) is By signal data with a crack inclination angle of 30°, crack length of 7.5mm, and varying depths of 2-20mm; (c) is By signal data with a crack inclination angle of 50°, crack length of 7.5mm, and varying depths of 2-20mm; (d) is By signal data with a crack inclination angle of 70°, crack length of 7.5mm, and varying depths of 2-20mm; and (e) is By signal data with a crack inclination angle of 90°, crack length of 7.5mm, and varying depths of 2-20mm.

[0038] Figure 10 The following are five control group diagrams for the crack length of 7.5 mm in this embodiment of the invention, with the crack pocket depth changed from 1 to 9 mm, corresponding to the actual crack depth, and the crack inclination angle gradually increased from 10° to 20° to 90° by the characteristic wave estimation signal.

[0039] (a) shows the variation of the By characteristic valley value with the crack pocket depth when the inclination angle is the same, representing the crack length. (b) shows the scatter plot and fitting function plot of the characteristic valley value corresponding to the actual vertical depth.

[0040] Figure 11 This is a diagram showing the relationship between the depth of the cracked pocket and the actual vertical depth in an embodiment of the present invention.

[0041] Figure 12 This is a simulation result diagram of a crack surface length of 7.5 mm and a pocket depth of 6 mm in an embodiment of the present invention, with the crack inclination angle gradually increased from 10° to 170°.

[0042] Among them, (a) is the simulation result of the tilt angle gradually increasing from 10° to 90°, and (b) is the simulation result of the tilt angle gradually increasing from 90° to 170°;

[0043] Figure 13 This is a response diagram of the crack in the By direction when the crack inclination angles are complementary in an embodiment of the present invention.

[0044] Among them, (a) is the simulated By signal with crack inclination angles of 10° and 170°, (b) is the simulated By signal with crack inclination angles of 30° and 150°, (c) is the simulated By signal with crack inclination angles of 50° and 130°, (d) is the simulated By signal with crack inclination angles of 70° and 110°, and (e) is the simulated By signal with crack inclination angles of 90° and 90°.

[0045] Figure 14 This is a curve showing the fitting function of the crack tilt angle and the ratio of characteristic troughs in an embodiment of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] This invention provides a method for characterizing the RCF crack size of high-speed railway based on ACFM technology By signal, comprising the following steps:

[0049] S1. Establish a finite element model of high-speed railway rails;

[0050] S2. Using the controlled variable method, one of the crack length, crack pocket depth and crack inclination angle is used as a variable, and the other two are quantitative. ACFM detection simulation is carried out. Cracks are detected on the surface of high-speed railway rails along a fixed angle detection line to obtain By signal and extract the characteristic parameters of By signal.

[0051] S3. Establish the crack length, crack pocket depth, and crack inclination angle respectively, as follows: Figure 6 The correlation between the By signal characteristic parameters and the crack geometric parameters is shown to obtain the quantitative relationship between the By signal characteristic parameters and the crack geometric parameters, including crack length, crack pocket depth and crack inclination angle.

[0052] S4. Based on the above quantitative relationship, establish a By signal-crack size mapping model using a multinomial regression algorithm or a support vector regression algorithm, and form a simulation database.

[0053] S5. Input the By signal to be measured into the By signal-crack size mapping model, and output three quantitative results: crack length, crack pocket depth, and crack tilt angle.

[0054] Furthermore, in S1, the finite element model of the high-speed railway rail is established based on the COMSOL Multiphysics finite element software.

[0055] Furthermore, in S2, cracks are detected along a fixed-angle detection line on the surface of the high-speed railway rail to obtain the By signal. The specific feature parameters extracted from the By signal include:

[0056] Cracks are detected on the surface of high-speed railway rails along a 45° detection line to obtain By signals. Feature parameters of the By signals are extracted, including characteristic valley values, horizontal valley spacing, and valley ratio.

[0057] Furthermore, the quantitative relationship between the obtained By signal characteristic parameters and crack geometric parameters includes:

[0058] Quantitative relationship between horizontal spacing of troughs and crack length: Crack length is calculated by measuring the horizontal spacing of troughs; Quantitative relationship between trough ratio and crack inclination angle: The trough ratio is defined as follows: Normalization was performed to characterize the crack inclination angle, where By troughmax The trough value where the By signal value is the largest. troughmin The valley value with the smallest By signal value; quantitative relationship between characteristic valley value and crack pocket depth: the crack pocket depth is calculated by measuring the characteristic valley value.

[0059] Furthermore, the quantitative relationship between the horizontal spacing of the troughs and the crack length is as follows:

[0060] Y = 0.00111X + 0.00531, R 2 =0.99803

[0061] Where X is the crack length and Y is the horizontal spacing between the troughs.

[0062] Furthermore, S4-S5 also include:

[0063] Develop a graphical software platform in the MATLAB environment to automatically import By signal time-domain data in CSV format, extract feature parameters in real time, call the embedded By signal-crack size mapping model, and output three quantitative results—crack length, crack pocket depth, and crack tilt angle—with one click.

[0064] Specifically, the magnetic flux density of the By signal generated by ACFM technology in the By direction is centrally symmetrically distributed. When the current flow lines are disturbed by the crack and there is a current component flowing parallel to the Y-axis, a Y component of the magnetic flux density is generated, such as... Figure 1 As shown, the contour plot of the magnetic flux density Y component of a single crack is displayed. When a 45° detection line is used to pass through a single RCF crack, the By component signal exhibits a distinct characteristic response in the crack center region: along the measurement path, the By signal shows a positive peak sandwiched between two negative troughs. The grayscale values ​​in the figure represent the distribution of magnetic flux density, with darker areas corresponding to lower magnetic flux density (forming troughs) and lighter areas corresponding to higher magnetic flux density (forming peaks). The results indicate that the By signal of a single crack measured along the 45° line consists of one peak and two troughs. The By signal exhibits strong sensitivity to the crack tip region and can clearly reflect the spatial distribution characteristics of the crack along the detection path.

[0065] The workflow of this invention is as follows: First, a finite element theoretical model of U71Mn rail is established in COMSOL Multiphysics. Then, using the controlled variable method, the crack length is simulated from 2.5mm to 20mm (6 groups) while keeping the crack pocket depth and tilt angle constant; the pocket depth is simulated from 1mm to 9mm (5 groups) while keeping the crack length and tilt angle constant; and the crack tilt angle is simulated from 10° to 170° (9 groups) while keeping the crack length and pocket depth constant. The above characteristic quantities and corresponding geometric parameters are mapped to three sets of models using multinomial regression or support vector regression, forming a "By feature → crack size" database. Subsequently, a graphical software platform is developed in the MATLAB environment to automatically import By data in CSV format, extract characteristic valleys in real time, and call the embedded regression model to convert the input signal into three quantitative results: crack length, pocket depth, and tilt angle with one click. Finally, based on the research results, it is concluded that the By signal has higher sensitivity to crack surface length, pocket depth, and tilt angle, and can more efficiently and accurately reflect the geometric change law of the crack.

[0066] The specific implementation steps of the method of the present invention are as follows:

[0067] 1. Based on the widely used U71Mn steel rail, a dedicated AC flux method (ACFM) finite element model was developed using the AC / DC module of COMSOL Multiphysics finite element software. This model is specifically designed for detecting complex rolling contact fatigue (RCF) cracks under realistic working conditions and for quantitatively describing these cracks. Figure 5 As shown, it includes four parts: refined mesh domain 1, single RCF crack 2, rail domain 3, and air domain 4.

[0068] 2. Detect the crack along the 45° detection line to obtain the By signal, and extract the valley value and horizontal spacing of the By.

[0069] 3. Using the controlled variable method, analyze the following: 6 groups of simulated crack lengths ranging from 2.5mm to 20mm while keeping the crack pocket depth and tilt angle constant; 5 groups of simulated crack lengths ranging from 1mm to 9mm while keeping the crack length and tilt angle constant; and 5 groups of simulated crack lengths ranging from 10° to 90° while keeping the length 7.5mm and depth 6mm constant. Figure 9 As shown in (a)-(e) in the embodiments of the present invention, there are five groups of By signal value control groups with crack length of 7.5 mm, depth of 2-20 mm, and crack inclination angle gradually increased from 10° to 20° to 90°.

[0070] 4. This study investigates and analyzes the quantitative relationship between By signal and crack geometric parameters, and proposes a characterization method for rolling contact fatigue cracks in high-speed railways based on By signal ACFM technology: by measuring the horizontal spacing of By wave troughs (e.g., ... Figure 2 As shown, high-precision estimation of crack length is achieved. The change in crack length affects the spacing of the horizontal wave troughs of the By signal as follows: Figure 7 As shown, the fitted curve function representing the crack length using the horizontal spacing of the troughs is as follows: Figure 8 As shown: Y = 0.00111X + 0.00531, R 2 =0.99803; The trough of the By signal formed when a crack obstructs the direction of current is defined as the characteristic trough BytroughD, such as Figure 3 As shown, by measuring the characteristic valley ΔBytroughD value, a high-precision estimation of the crack pocket depth is achieved. When the crack length and tilt angle are the same, the variation of the By characteristic valley value with the crack pocket depth is as follows: Figure 10 As shown in (a), the scatter plot and fitting function graph of the actual vertical depth corresponding to the characteristic valley values ​​are plotted as follows. Figure 10 As shown in (b), simulation results indicate that the By signal is sensitive to the crack profile, and the By signal trough value can be used to characterize the crack inclination angle. Further, it is proposed to use... The normalized ratio is used to characterize the crack inclination angle, such as Figure 4 As shown in (a)-(b), a functional relationship between the crack inclination angle and the crack tilt angle is established to achieve effective quantification of the crack tilt angle.

[0071] 5. Based on the feature quantities and corresponding geometric parameters extracted in steps 2–4, three By signal-crack size mapping models are constructed using multinomial regression or support vector regression methods to form a simulation database. A graphical software platform is developed in the MATLAB environment to automatically import By time-domain data in CSV format, extract feature valleys in real time, call the embedded regression model, and output three quantitative results of crack length, pocket depth, and tilt angle with one click.

[0072] 6. Through the above simulation verification, the crack length, pocket depth and tilt angle were quantitatively evaluated for the first time using the By signal.

[0073] In a specific embodiment, a dedicated AC flux method (ACFM) finite element model was established for the quantitative description of complex rolling contact fatigue (RCF) cracks and cracks conforming to actual working conditions. During the simulation, two cuboid blocks with dimensions of 0.07m × 0.1m × 0.01m were used to represent the rail with crack defects and the air above the rail, respectively. Specific material property parameters of the railway model (such as relative permeability, conductivity, and relative permittivity) were set as shown in Appendix Table 1. A uniform current was generated by applying surface current boundary conditions to the front, upper, and rear surfaces of the air block, with the current direction at 90° to the crack surface length, to accurately measure the ACFM detection signal.

[0074] Table 1

[0075]

[0076] Based on the mechanism of By signal generation, the magnetic induction intensity response range in the By direction changes with changes in crack length and crack pocket depth. Using the controlled variable method, the variation patterns of crack length, pocket depth, and crack inclination angle were studied. First, six control groups were designed, with a pocket depth of 6 mm and crack surface length starting at 2.5 mm, gradually increasing to 20 mm at 2.5 mm intervals. Simultaneously, the crack inclination angle for each group was changed from 10° to 90° at 20° intervals. Second, five control groups were designed, with a crack length of 10 mm and crack pocket depth changed from 1 mm to 9 mm at 2 mm intervals. The crack inclination angle for each group was also changed from 10° to 170° at 90° intervals. Third, when the crack length is fixed at 7.5 mm and the crack pocket depth is 6 mm, the crack inclination angle is gradually increased from 10° to 170°, and 9 control groups are designed for experimental simulation.

[0077] Based on the above control experiment, the parameters of crack length, pocket depth, and tilt angle were further changed, a database was constructed, and experimental simulation data were obtained. The results obtained according to the specific implementation steps 3-5 are shown below.

[0078] Example 1

[0079] The horizontal spacing between By signal troughs is used to characterize crack length. When the crack inclination angle and pocket depth remain constant, the longer the crack length, the larger the horizontal spacing between By signal troughs. Based on this pattern, the study is extended with controlled variables. To further prove the uniqueness of the effect of crack length solely on the By signal trough spacing, the relationship between the horizontal spacing between troughs and the crack pocket depth and inclination angle is explored when the crack length remains constant. Experimental simulations show that the horizontal spacing between By signal troughs does not change with the crack pocket depth and inclination angle, meaning the horizontal spacing between troughs (crack length) is independent of the crack pocket depth and inclination angle. Under the condition of controlling the crack inclination angle and pocket depth consistently, the horizontal spacing between By signal troughs increases with increasing crack length. Therefore, based on this pattern, crack length can be characterized by the horizontal spacing between troughs. Simultaneously, the fitting curve function Y = 0.00111X + 0.00531, R0 = 0.00111X + 0.00531 can be used. 2 =0.99803 means that X is the crack length and Y is the horizontal spacing between the troughs.

[0080] Example 2

[0081] The depth of the crack pocket is characterized using the By signal. For example... Figure 11 As shown, when the crack obstructs the current flow, the first trough of the By signal is significantly affected by the crack inclination angle. Therefore, the first trough is used as a characteristic trough to characterize the crack depth. Furthermore, when the crack length and inclination angle are the same, the study found that the By characteristic trough value decreases with increasing crack pocket depth. Moreover, the crack pocket depth is not the actual vertical crack depth Dt, as shown in the attached figure. Figure 13 The actual vertical depth of the crack is determined using the function Dt = D*sinθ, where D is the crack pocket depth. A scatter plot of the actual vertical depth corresponding to the characteristic trough values ​​is then generated to obtain the fitting function, which can quantitatively characterize the crack depth.

[0082] Example 3:

[0083] The By signal is used to characterize the crack inclination angle. Simulation data with varying crack inclination angles reveals that when the angle θ between the detection line and the crack is acute, its response curve is similar to the response curve of its corresponding supplementary angle α = 180° - θ in the By direction. To verify this pattern, the minimum troughs are aligned, as shown... Figure 12 (a)-(b) and Figure 13As shown, the minimum trough value of the five complementary angle By signals is approximately equal to the value of another trough. Simulation data analysis concludes that when the crack inclination angles are complementary, the two trough values ​​formed by the crack response in the By direction are the same. Since the By trough values ​​are the same for complementary angles, only the order of the responses differs. Therefore, for characterization, both complementary angles can be considered as the same angle less than or equal to 90°, and the crack inclination direction can be determined based on the direction of the detection line. The characteristic trough ratio is defined. Normalization was performed to characterize the crack inclination angle. Simulated data analysis revealed that for cracks of different lengths with the same pocket depth, when the crack length was the same and the pocket depth was 6 mm, a larger crack inclination angle resulted in a larger characteristic trough ratio ΔBy, with the ratio approaching 1. Furthermore, regardless of crack length variations, the ratio tended to a constant value. A function relating the crack inclination angle to the characteristic trough ratio was plotted, and the fitted function curve is shown below. Figure 14 As shown, the crack inclination angle can be quantitatively characterized.

[0084] Based on the above simulation experiments, it can be concluded that the By signal exhibits higher sensitivity and accuracy than the traditional Bx / Bz signal in the geometric characterization of RCF cracks. The horizontal spacing of the By signal troughs is highly linearly correlated with the crack surface length; when the detection line direction of the By signal is opposite to the crack tilt direction, the amplitude of the first trough can accurately reflect the pocket depth change; the By signal trough ratio can effectively quantify the crack tilt angle.

[0085] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a method for characterizing the RCF crack size of high-speed railway based on ACFM technology By signal as described above.

[0086] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0087] Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. Furthermore, those skilled in the art will recognize that, based on the ideas of this invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for characterizing the RCF crack size in high-speed railway based on ACFM technology by signal, characterized in that, Includes the following steps: S1. Establish a finite element model of high-speed railway rails; S2. Using the controlled variable method, one of the crack length, crack pocket depth and crack inclination angle is used as a variable, and the other two are quantitative. ACFM detection simulation is carried out. Cracks are detected on the surface of high-speed railway rails along a fixed angle detection line to obtain By signal and extract the characteristic parameters of By signal. S3. Establish the correlation between crack length, crack pocket depth, crack inclination angle and By signal characteristic parameters respectively, and obtain the quantitative relationship between By signal characteristic parameters and crack geometric parameters; the crack geometric parameters include crack length, crack pocket depth and crack inclination angle. S4. Based on the above quantitative relationship, establish a By signal-crack size mapping model using a multinomial regression algorithm or a support vector regression algorithm, and form a simulation database. S5. Input the By signal to be measured into the By signal-crack size mapping model, and output three quantitative results: crack length, crack pocket depth, and crack tilt angle.

2. The method for characterizing the RCF crack size of high-speed railway based on ACFM technology By signal according to claim 1, characterized in that, In S1, the finite element model of the high-speed railway rail is established based on COMSOL Multiphysics finite element software.

3. The method for characterizing the RCF crack size of high-speed railway based on ACFM technology By signal according to claim 1, characterized in that, In step S2, cracks are detected along a fixed-angle detection line on the surface of the high-speed railway rail to obtain a By signal. The feature parameters of the By signal are extracted, specifically including: Cracks are detected on the surface of high-speed railway rails along a 45° detection line to obtain By signals. Feature parameters of the By signals are extracted, including characteristic valley values, horizontal valley spacing, and valley ratio.

4. The method for characterizing the RCF crack size of high-speed railway based on ACFM technology By signal according to claim 1, characterized in that, In step S3, the quantitative relationship between the obtained By signal characteristic parameters and crack geometric parameters includes: Quantitative relationship between horizontal spacing of troughs and crack length: Crack length is calculated by measuring the horizontal spacing of troughs; Quantitative relationship between trough ratio and crack inclination angle: The trough ratio is defined as follows: Normalization was performed to characterize the crack inclination angle, where By troughmax The trough value where the By signal value is the largest. troughmin The valley value with the smallest By signal value; quantitative relationship between characteristic valley value and crack pocket depth: the crack pocket depth is calculated by measuring the characteristic valley value.

5. The method for characterizing the RCF crack size of high-speed railway based on ACFM technology By signal according to claim 4, characterized in that, The quantitative relationship between the horizontal spacing of the troughs and the crack length is as follows: Y=0.00111X+0.00531,R 2 =0.99803 Where X is the crack length and Y is the horizontal spacing between the troughs.

6. The method for characterizing the RCF crack size of high-speed railway based on ACFM technology By signal according to claim 1, characterized in that, S4-S5 also includes: Develop a graphical software platform in the MATLAB environment to automatically import By signal time-domain data in CSV format, extract feature parameters in real time, call the embedded By signal-crack size mapping model, and output three quantitative results—crack length, crack pocket depth, and crack tilt angle—with one click.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a method for characterizing the RCF crack size of high-speed railway based on ACFM technology By signal as described in any one of claims 1 to 6.

Images