A method and device for diagnosing continuous oblique cracks of high-speed railway curve segments

Abstract

The present application relates to a method and device for diagnosing continuous oblique cracks in high-speed rail curve sections. First, the axle box acceleration signal of the high-speed rail line to be analyzed is obtained, and modal decomposition processing is performed to obtain the refined time-frequency representation of several impact components. The time-frequency marginal spectrum corresponding to each impact component is calculated. The line to be analyzed is divided into several evaluation sections, and the section track impact index corresponding to each evaluation section is calculated based on the time-frequency marginal spectrum of each impact component. According to the curve section positioning of the line account, the section track impact index is compared with the preset diagnosis threshold to determine whether there is a continuous oblique crack. The method of the present application can completely retain the full-band impact characteristics, eliminate background vibration interference, improve the early detection rate and diagnosis accuracy of oblique cracks, and adapt to the sectional evaluation needs of continuous diseases in curve sections.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of high-speed rail track defect detection technology, and in particular to a diagnostic method and device for continuous oblique cracks in high-speed rail curved sections. Background Technology

[0002] With the continuous increase in the operating mileage of high-speed railways and the increasing density of train operations, rails are prone to various types of damage under long-term, high-frequency wheel-rail dynamics. Among them, rails on curved sections of high-speed railways are subject to greater lateral wheel-rail forces, resulting in a high incidence of short-wave defects such as oblique cracks, which often exhibit continuous distribution characteristics; early oblique cracks are highly concealed and difficult to identify directly by appearance.

[0003] Currently, the track impact index detection method based on axle box acceleration signals does not require additional on-track detection equipment and can be used for routine track inspections with operating trains. It is the mainstream technology for large-scale screening of short-wave rail defects. The general implementation logic of this method is as follows: first, the collected axle box acceleration signals are subjected to fixed-frequency bandpass filtering; then, the track impact index is calculated based on the time-domain energy of the filtered signal; and the severity of rail damage is determined by the index value.

[0004] However, existing track impact index detection methods have significant technical drawbacks when applied to the diagnosis of oblique cracks in high-speed rail curves: First, the rate of missed detection of early hidden oblique cracks remains high; second, it is difficult to achieve accurate segmented damage assessment, and problems such as the fragmentation of damage characteristics at segment boundaries and large deviations in assessment results are easily encountered; third, it is impossible to effectively separate the impact components caused by rail damage from background vibration components such as vehicle vibration and random track irregularities, resulting in a low signal-to-noise ratio, and minor impact characteristics are easily drowned out by background noise, leading to insufficient diagnostic accuracy. Summary of the Invention

[0005] To address the problems existing in the prior art, this specification provides a method and apparatus for diagnosing continuous oblique cracks in high-speed rail curved sections.

[0006] The specific technical solutions of the embodiments in this specification are as follows: On the one hand, embodiments of this specification provide a method for diagnosing continuous oblique cracks in high-speed rail curved sections, the method comprising: Acquire the axle box acceleration signal of the high-speed railway line to be analyzed; The axle box acceleration signal is subjected to mode decomposition processing to obtain a refined time-frequency representation of multiple impact components; The time-frequency marginal spectrum corresponding to each impact component is calculated based on the time-frequency representation of each impact component; The line to be analyzed is divided into multiple line sections. Based on the time-frequency marginal spectrum of each impact component, the section track impact index corresponding to each line section is calculated. Based on the route ledger, the curved sections among the multiple route sections are located. The track impact index of the corresponding curved section is compared with a preset diagnostic threshold. If the track impact index of the curved section is greater than or equal to the preset diagnostic threshold, it is determined that the curved section has continuous oblique cracks.

[0007] Furthermore, the axle box acceleration signal is subjected to mode decomposition processing to obtain a refined time-frequency representation of multiple impact components, including: The preset time-frequency transformation window length, fixed bandwidth, mode decomposition termination energy threshold, and minimum kurtosis coefficient threshold for impact component determination are set. A third-order B-spline function is used as the window function. The window length of the window function is determined according to the time-frequency transformation window length. A short-time Fourier transform is performed on the axle box acceleration signal to obtain the initial time-frequency representation, which is used as the residual time-frequency representation of the first iteration. Iteratively perform the following shock component extraction operations: Locate the time-frequency ridge of the impact component with the highest energy from the current residual time-frequency representation; delineate the time-frequency energy range of the impact component with the time-frequency ridge as the center and according to the fixed bandwidth; calculate the time-domain modal signal corresponding to the impact component based on the time-frequency data within the time-frequency energy range; remove the time-domain modal signal from the current residual signal and update the residual time-frequency representation; The iteration stops when a preset termination condition is met. The preset termination condition includes: the proportion of the current residual signal energy to the total energy of the original axle box acceleration signal is less than the mode decomposition termination energy threshold, or the kurtosis coefficient of the analytical signal corresponding to the next impact component to be extracted is less than the minimum kurtosis coefficient threshold. The time-frequency energy of each extracted impact component is compressed to the corresponding time-frequency ridge to obtain a refined time-frequency representation of each impact component.

[0008] Furthermore, the formula for calculating the time-domain modal signal corresponding to the impact component based on the time-frequency data within this time-frequency energy range is as follows:

[0009] in, For the first m The impact component at time The time-domain modal signal, This indicates the operation of taking the real part. The normalized magnitude of the window function at time zero. For the first m The impact component at time , frequency is Time and frequency data at the location, For all discrete frequency points on the frequency axis, Frequency resolution; time The range of values ​​is ,in, For the first m The time-frequency ridge of the impact component at frequency The corresponding time point, For the first m The impact component at frequency The upper bandwidth in the fixed bandwidth mentioned above. For the first m The impact component at frequency The lower bandwidth in the fixed bandwidth mentioned above.

[0010] Furthermore, the formula for determining whether the proportion of the current residual signal energy to the total energy of the original axle box acceleration signal is less than the mode decomposition termination energy threshold is as follows:

[0011] The formula for calculating the analytic signal is:

[0012] The formula for determining whether the kurtosis coefficient of the analytical signal corresponding to the next impact component to be extracted is less than the minimum kurtosis coefficient threshold is as follows:

[0013] in, For a moment The m The residual signal after the next iteration, from time... The m -1 residual signal minus time The m The time-domain modal signals extracted in the next iteration are obtained. The square of the second norm of the signal represents the total energy of the signal. For a moment The axle box acceleration signal, The energy threshold for mode decomposition termination. For a moment The m The analytical signal of the residual signal corresponding to +1 iterations The imaginary unit, For Hilbert transform operations, For operations involving taking the modulus of complex numbers, To analyze the kurtosis coefficients of the signal, This represents the total number of discrete sampling points of the signal. For all discrete-time sampling points of the signal, For the firstm The mean of the analytic signal of the residual signal at each time step corresponding to +1 iteration. This is the minimum kurtosis coefficient threshold.

[0014] Furthermore, the formula for calculating the time-frequency marginal spectrum corresponding to each impact component based on its time-frequency representation is as follows:

[0015] in, For the first s The impact component in the first i The time-frequency marginal spectrum corresponding to each discrete-time sampling point For the first s The impact component in the first i The discrete-time sampling points, the first j The time-frequency energy value corresponding to each discrete frequency point N This represents the total number of discrete frequency points on the frequency axis. For the first i Discrete-time sampling points, It is the first j Discrete frequency points.

[0016] Furthermore, the formula for calculating the track impact index for each line segment is as follows:

[0017] in, For the first The section track impact index corresponding to each line segment For the first The square root of the total energy of the time-frequency marginal spectrum of all impact components within a line segment. This is the square root value of the average time-frequency marginal spectral energy of the impact component across the entire high-speed railway line to be analyzed. For the first s The impact component in the first i The time-frequency marginal spectrum value corresponding to each discrete-time sampling point This represents the total number of impact components obtained from the decomposition across the entire high-speed railway line to be analyzed. L This represents the total number of discrete sampling points for the axle box acceleration signal across the entire high-speed railway line to be analyzed.

[0018] Furthermore, the line to be analyzed is divided into multiple line segments, including: Along the mileage direction of the route to be analyzed, a sliding window is divided using a preset segment length as the window width and a mileage distance corresponding to a preset overlap rate as the sliding step size to obtain the multiple route segments.

[0019] On the other hand, embodiments of this specification also provide a diagnostic device for continuous oblique cracks in high-speed rail curved sections, the device comprising: The signal acquisition unit is used to acquire the axle box acceleration signal of the high-speed railway line to be analyzed. The mode decomposition unit is used to perform mode decomposition processing on the axle box acceleration signal to obtain a refined time-frequency representation of multiple impact components; The time-frequency marginal spectrum calculation unit is used to calculate the time-frequency marginal spectrum corresponding to each impact component based on the time-frequency representation of each impact component; The impact index calculation unit is used to divide the line to be analyzed into multiple line sections and calculate the section track impact index corresponding to each line section based on the time-frequency marginal spectrum of each impact component. The continuous oblique crack diagnosis unit is used to locate the curved section among the multiple line sections according to the line ledger, compare the section track impact index corresponding to the curved section with a preset diagnosis threshold, and if the section track impact index of the curved section is greater than or equal to the preset diagnosis threshold, it is determined that there is a continuous oblique crack in the curved section.

[0020] On the other hand, embodiments of this specification also provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the above-described method.

[0021] On the other hand, embodiments of this specification also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0022] On the other hand, embodiments of this specification also provide a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method.

[0023] The methods described in this specification have at least the following technical effects: A refined time-frequency representation of the impact component is directly extracted from the axle box acceleration signal using modal decomposition processing. This method does not rely on fixed-band bandpass filtering and can completely preserve the full-band impact characteristics of the oblique crack excitation. It effectively avoids the filtering out of early high-frequency impact components and improves the detection rate of early hidden oblique cracks in the curve segment.

[0024] The time-frequency marginal spectrum calculation of the impact index of the track section based on the impact component only counts the impact energy corresponding to the rail damage, eliminating the interference of background vibration components and improving the signal-to-noise ratio and diagnostic accuracy of the impact index.

[0025] By adopting a segmented evaluation method, the line is divided into several evaluation segments to calculate the corresponding impact index. This method is adapted to the characteristics of continuous distribution of oblique cracks in curved sections, enabling segmented quantitative assessment of the damage degree of continuous oblique cracks, which is more in line with the segmented maintenance needs of on-site operation and maintenance.

[0026] By combining the location of curved sections in the line ledger and conducting targeted threshold comparison diagnosis, focusing on the high-incidence scenario of oblique cracks in high-speed rail curved sections, the diagnosis is highly targeted and can effectively reduce the interference of normal vibrations in non-curved sections on the diagnosis results, further improving the reliability of the diagnosis results. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 The diagram shown is a flowchart illustrating a diagnostic method for continuous oblique cracks in a high-speed rail curve section according to an embodiment of this specification. Figure 2 The diagram shown is a flowchart illustrating the process of performing modal decomposition on the axle box acceleration signal in an embodiment of this specification to obtain a refined time-frequency representation of multiple impact components. Figure 3 The above is a schematic diagram of the FPMD time-frequency analysis process in the embodiments of this specification; Figure 4 To utilize the results of traditional track impact index diagnosis; Figure 5 The figure shows the results of FPMD mode decomposition obtained using the embodiments of this specification; Figure 6 The image shows the result obtained using the FPMD refined time-frequency representation of the embodiments in this specification; Figure 7 The diagram shown is a schematic representation of a diagnostic device for continuous oblique cracks in a high-speed rail curve section according to an embodiment of this specification. Figure 8 The diagram shown is a structural schematic of the computer device in an embodiment of this specification.

[0029] [Explanation of Figure Markers]: 701. Signal Acquisition Unit; 702. Modal decomposition element; 703. Time-frequency marginal spectrum calculation unit; 704. Impact Index Calculation Unit; 705. Continuous oblique crack diagnostic unit; 802. Computer equipment; 804. Processing equipment; 806. Storage resources; 808. Drive mechanism; 810. Input / Output Module; 812. Input devices; 814. Output devices; 816. Presentation equipment; 818. Graphical User Interface; 820. Network interface; 822. Communication link; 824. Communication bus. Detailed Implementation

[0030] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the embodiments of this specification.

[0031] It should be noted that the terms "first," "second," etc., in the description, claims, and accompanying drawings of the embodiments in this specification are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data can be used interchangeably where appropriate so that the embodiments described herein can be implemented in an order different from that illustrated or described in the specification. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0032] It should be noted that the acquisition, storage, use, and processing of data in the technical solutions of the embodiments of this specification all comply with the relevant provisions of national laws and regulations.

[0033] It should be noted that in the embodiments of this specification, certain software, components, models, and other existing solutions in the industry may be mentioned. These should be considered as exemplary, and their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but does not mean that the applicant has already used or necessarily used such a solution. Similarly, the descriptions of prediction algorithms, table organization forms, and hardware structures are also exemplary.

[0034] The inventors discovered in their research that current diagnostic and evaluation systems for track shortwave defects lack a complete management framework. While the formation mechanisms and diagnostic evaluation methods for individual track shortwave defects are relatively well understood, in-depth research has not yet been conducted on methods for sectional diagnostic evaluation of accumulated track shortwave defects. For these defect-prone sections of high-speed railway lines, such as curved sections, engineering applications require focused monitoring and early warning of defect accumulation. Therefore, research on sectional evaluation of continuous oblique cracks in high-speed railway curved sections has significant application value. However, no relevant literature reports or patent descriptions have been found regarding time-frequency feature extraction and sectional diagnostic evaluation methods for this type of defect.

[0035] This specification addresses the challenges of identifying continuous oblique cracks in high-speed rail curved sections and the high demand for diagnosing the condition of oblique cracks in these sections. Based on massive amounts of vehicle dynamic response signal data collected by high-speed integrated inspection trains, it utilizes Fast Polymorphic Mode Decomposition (FPMD) to refine the time-frequency characteristics of axle box acceleration vibration signals excited by short-wave track defects in sections. From the marginal spectrum of the time-frequency plane, it proposes the Sectional Track Impact Index (STII), characterizing the frequency of impact components within the track section, and its calculation method. This forms a refined time-frequency characteristic extraction and diagnosis method for continuous oblique cracks in high-speed rail curved sections. Field verification and stability validation of the continuous oblique cracks diagnosed by this method, along with comparison with existing diagnostic results using the Track Impact Index (TII), validate the effectiveness and advantages of the proposed diagnostic method.

[0036] The method described in this specification aims to solve the problem of difficulty in diagnosing and evaluating continuous oblique cracks in high-speed rail curved sections, specifically including: (1) How to describe and quantify the time-frequency characteristics of continuous oblique cracks in high-speed rail curves; (2) How to express the time-frequency characteristics of continuous oblique cracks in high-speed rail curves in a refined way on the time-frequency plane; (3) How to diagnose continuous oblique cracks in high-speed rail curves and provide a basis and technical support for on-site maintenance and repair, including key monitoring or oblique crack treatment.

[0037] To address the problems existing in the prior art, this specification provides a method for diagnosing continuous oblique cracks in high-speed rail curved sections. Figure 1 The diagram shown is a flowchart illustrating a method for diagnosing continuous oblique cracks in a high-speed rail curve section according to an embodiment of this specification. The order of steps listed in the embodiment is merely one possible execution order among many and does not represent the only possible execution order. In actual system or device products, the methods shown in the embodiment or accompanying drawings can be executed sequentially or in parallel. Specifically, as shown... Figure 1 As shown, the method may include: Step 101: Obtain the axle box acceleration signal of the high-speed railway line to be analyzed; Step 102: Perform mode decomposition processing on the axle box acceleration signal to obtain a refined time-frequency representation of multiple impact components; Step 103: Calculate the time-frequency marginal spectrum corresponding to each impact component based on the time-frequency representation of each impact component; Step 104: Divide the line to be analyzed into multiple line sections, and calculate the section track impact index corresponding to each line section based on the time-frequency marginal spectrum of each impact component. Step 105: Locate the curved section among the multiple line sections according to the line ledger, compare the section track impact index corresponding to the curved section with the preset diagnostic threshold, and if the section track impact index of the curved section is greater than or equal to the preset diagnostic threshold, it is determined that the curved section has continuous oblique cracks.

[0038] In the embodiments of this specification, a Fast Multimorphic Mode Decomposition (FPMD) method is proposed, which focuses on extracting the impact component from the signal to be analyzed. The analysis parameters are set according to the dynamic response characteristics of the continuous rail oblique cracks in the high-speed rail curve section. This eliminates the cumbersome iterative optimization steps in the Adaptive Polymorphic Mode Decomposition (APMD) method. By sacrificing the detail and smoothness that do not affect the engineering application effect, the calculation time is greatly reduced, and sufficiently accurate impact component signal modes are obtained, thereby improving the analysis efficiency of long signals of short-wave defects in section-type tracks in engineering applications.

[0039] Specifically, such as Figure 2 The diagram shown is a flowchart illustrating the process of performing modal decomposition on the axle box acceleration signal to obtain a refined time-frequency representation of multiple impact components in an embodiment of this specification, including the following steps: Step 201: Preset the time-frequency transformation window length, fixed bandwidth, mode decomposition termination energy threshold, and minimum kurtosis coefficient threshold for impact component determination; Step 202: Using a third-order B-spline function as the window function, the window length of the window function is determined according to the time-frequency transformation window length. The short-time Fourier transform is performed on the axle box acceleration signal to obtain the initial time-frequency representation, which is used as the residual time-frequency representation of the first iteration. Iteratively perform the following shock component extraction operations: Step 203: Locate the time-frequency ridge of the impact component with the highest energy from the current residual time-frequency representation; delineate the time-frequency energy range of the impact component with the time-frequency ridge as the center and according to the fixed bandwidth; calculate the time-domain modal signal corresponding to the impact component based on the time-frequency data within the time-frequency energy range; remove the time-domain modal signal from the current residual signal and update the residual time-frequency representation; Step 204: Stop the iteration when the preset termination condition is met. The preset termination condition includes: the proportion of the current residual signal energy to the total energy of the original axle box acceleration signal is less than the mode decomposition termination energy threshold, or the kurtosis coefficient of the analytical signal corresponding to the next impact component to be extracted is less than the minimum kurtosis coefficient threshold. Step 205: Compress the time-frequency energy of each extracted impact component to the corresponding time-frequency ridge line to obtain a refined time-frequency representation of each impact component.

[0040] In this embodiment, the time-frequency transformation window length is first preset. w L Fixed bandwidth D, mode decomposition termination energy threshold Minimum kurtosis coefficient threshold for impact component determination Then, a third-order B-spline function is used. The short-time Fourier transform (STFT) used as a window function yields the signal to be analyzed. Initial time-frequency representation This is used as the residual time-frequency representation of the first iteration, as shown in equation (1).

[0041] (1)

[0042] in, Represents the Discrete Fourier Transform. For discrete-time sampling points, This represents the length of the time axis (total number of sampling points). For discrete frequency points, The length of the frequency axis. for A matrix of dimensionality.

[0043] Next, from the initial time-frequency representation (i.e., the current residual time-frequency) The time-frequency ridges of the highest energy impact modes (frequency-varying modes) are calculated sequentially. (The calculation method is common knowledge in this field and will not be elaborated here) and signal time-domain modes (The calculation method is shown in equation (2)), set the upper and lower bandwidths. .in Indicates the signal mode number.

[0044]

[0045] in, For the first m The impact component at time The time-domain modal signal, This indicates the operation of taking the real part. The window function at time zero ( The normalized amplitude of ) For the first m The impact component at time , frequency is Time and frequency data at the location, For all discrete frequency points on the frequency axis, Frequency resolution; time The range of values ​​is ,in, For the first m The time-frequency ridge of the impact component at frequency The corresponding time point, For the first m The impact component at frequency The upper bandwidth in the fixed bandwidth mentioned above. For the first m The impact component at frequency The lower bandwidth in the fixed bandwidth mentioned above.

[0046] Then, the time-domain modal signal is removed from the current residual signal, and the residual time-frequency representation is updated.

[0047] Then, if the proportion of the current residual signal energy to the total energy of the original axle box acceleration signal is less than the mode decomposition termination energy threshold (Equation (3)), or the kurtosis coefficient of the analytical signal corresponding to the next impact component to be extracted is less than the minimum kurtosis coefficient threshold (Equation (4)), the mode decomposition ends.

[0048]

[0049] in, For a moment The mThe residual signal after the next iteration, from time... The m -1 residual signal minus time The m The time-domain modal signals extracted in the next iteration are obtained. The square of the second norm of the signal represents the total energy of the signal. For a moment The axle box acceleration signal, The energy threshold for mode decomposition termination. For a moment The m The analytical signal of the residual signal corresponding to +1 iterations The imaginary unit, For Hilbert transform operations, For operations involving taking the modulus of complex numbers, To analyze the kurtosis coefficients of the signal, This represents the total number of discrete sampling points of the signal. For all discrete-time sampling points of the signal, For the first m The mean of the analytic signal of the residual signal at each time step corresponding to +1 iteration. This is the minimum kurtosis coefficient threshold.

[0050] Finally, the energy of all signal modes except the residual terms within the bandwidth is compressed to near the time-frequency ridge (the calculation method is common knowledge in the field and will not be elaborated here), while the residual time-frequency representation at the time of stopping iteration is retained as the time-frequency representation of the residual terms, thus obtaining the final refined time-frequency representation. The FPMD time-frequency analysis process is as follows: Figure 3 As shown.

[0051] In this embodiment, the axle box acceleration signals of the entire line to be analyzed are... Utilize Figure 2 or Figure 3 The FPMD method shown yields a refined time-frequency representation. As shown in equation (6).

[0052] (6)

[0053] in, This is a method for FPMD modal decomposition and time-frequency analysis. ( s =1,2,..., N cj ) is the decomposed N cj Time-frequency representation of each impact component, This is the time-frequency representation of the residual term. In the FPMD method, this is the compressed, refined time-frequency representation. Each data point takes the square of the modulus of the corresponding complex time-frequency spectrum. Therefore, the total energy in the time-frequency representation is the linear superposition of the energies of each signal component, which yields:

[0054] Define the time-frequency marginal spectrum of FPMD As shown in equation (8).

[0055]

[0056] Similarly, we can conclude that:

[0057] That is, the FPMD time-frequency marginal spectrum of a signal can be expressed as the time-frequency marginal spectrum of all impulse components. Time-frequency marginal spectrum of the residual term The linear superposition of the impact components. The formula for the time-frequency marginal spectrum corresponding to each impact component is:

[0058] in, For the first s The impact component in the first i The time-frequency marginal spectrum corresponding to each discrete-time sampling point For the first s The impact component in the first i The discrete-time sampling points, the first j The time-frequency energy value corresponding to each discrete frequency point N This represents the total number of discrete frequency points on the frequency axis. For the first i Discrete-time sampling points, It is the first j Discrete frequency points.

[0059] The time-frequency marginal spectrum of the residual term is:

[0060] in, For the residual term in the th i The time-frequency marginal spectrum value corresponding to each discrete-time sampling point N This represents the total number of discrete frequency points on the frequency axis. For the first i Discrete-time sampling points, It is the first j Discrete frequency points, For the residual term in the th i The discrete-time sampling points, the first jThe time-frequency energy value corresponding to each discrete frequency point.

[0061] Then the entire line is divided into K Specifically, along the mileage direction of the route to be analyzed, a sliding window is used to divide the route into multiple segments, with a preset segment length as the window width and a preset overlap rate corresponding to the mileage distance as the sliding step size. For example, each segment is 1 km long, and a 66% overlap rate is used between segments to ensure data stability.

[0062] Based on the time-frequency marginal spectrum, the line's first... Each section ( k =1,2,..., K , K The segment track impact index (total number of segments) The definition is shown in equation (12).

[0063]

[0064] in, For the first The section track impact index corresponding to each line segment For the first The square root of the total energy of the time-frequency marginal spectrum of all impact components within a line segment. This is the square root value of the average time-frequency marginal spectral energy of the impact component across the entire high-speed railway line to be analyzed. For the first s The impact component in the first i The time-frequency marginal spectrum value corresponding to each discrete-time sampling point This represents the total number of impact components obtained from the decomposition across the entire high-speed railway line to be analyzed. L This represents the total number of discrete sampling points for the axle box acceleration signal across the entire high-speed railway line to be analyzed.

[0065] As shown in Equation (12), the section track impact index is defined as the ratio of the sum of the time-frequency marginal spectral energies of all impact components within a certain section to the average value of the time-frequency marginal spectral energies of all impact components along the entire line. Its physical meaning lies in measuring the magnitude of the impact energy within that section relative to the average impact level along the entire line. The more obvious the impact, the greater the impact energy, and the more impacts a section experiences, the larger the section track impact index will be. Compared with traditional methods of disease diagnosis and evaluation, the section track impact index considers the frequency of impact signal components excited by shortwave track defects within a 1km section. It uses the signal mode decomposition method to quantify the impact level of the section from an energy perspective. Furthermore, the refined time-frequency representation of FPMD can also well express the location of each impact component and the main spectral energy distribution of the impact.

[0066] Finally, based on the route log, the curved sections among the multiple route segments are located. The track impact index corresponding to the curved section is compared with a preset diagnostic threshold. If the track impact index of the curved section is greater than or equal to the preset diagnostic threshold, it is determined that the curved section has continuous oblique cracks. For example, the preset diagnostic threshold is set to 4.0. If the track impact index corresponding to the curved section is greater than or equal to 4.0, then the curved section has continuous oblique cracks; if the track impact index corresponding to the curved section is less than 4.0, then the curved section does not have continuous oblique cracks.

[0067] To verify the effectiveness of the analysis and diagnosis method in the embodiments of this specification, this embodiment applies the diagnostic method proposed in this invention to the time-frequency analysis and diagnosis of continuous oblique cracks in a curved section of a high-speed railway line. A small-radius left curve section (curve radius 2200m) in the bridge section of a ballastless high-speed railway line with a design speed of 350km / h is selected. The data consists of axle box acceleration signals collected by a high-speed comprehensive inspection train. The detection speed is approximately 188km / h, and the train is accelerating when passing through this section. The data is the vertical acceleration signal of the left axle box, corresponding to the lower rail (lower rail) of the curve. Sampling frequency... Fs =5000Hz, the original signal was filtered by an anti-aliasing filter with a cutoff frequency of 2kHz, and a 1km curve segment was extracted according to the ledger for analysis, and compared with the traditional method using the track impact index ( TII The diagnostic methods were compared.

[0068] Figure 4 To utilize the results of traditional track impact index diagnosis, the original signal was filtered between 20 and 500 Hz, reducing the energy by 98%. The resulting impact index waveform shows that the impact index in this section does not exceed 3.0, far below the warning threshold of 6.0. However, both the original waveform and the impact index indicate that the signal in this section... t =2s、 tThe presence of multiple distinct impact characteristics around time points such as =12s indicates that the main energy range of the impact in this section is not within 20~500Hz, resulting in a relatively small impact index. Therefore, the current management model for large values ​​of shortwave orbit states cannot diagnose the potential defects in this section.

[0069] Figure 5 The image shows the results obtained using the FPMD modal decomposition method described in the embodiments of this specification. Figure 6 The result shown is obtained by using the FPMD refined time-frequency representation of the embodiment in this specification. Figure 5 In the red section, the decomposed impact components are represented, and in the blue section, the residual terms are represented. After modal integration, a total of 23 impact modes were decomposed. The impact modes of this section were calculated using the segment-type shortwave fault diagnosis method. STII =5.6, exceeding the set threshold of 4.0. Compared with traditional methods, this method decomposes... t =2s、 t The system exhibits strong impact characteristics around time points such as 12s, and decomposes into other weaker energy components, such as... t =6s、 t Impact components around the 15.5s time point. From Figure 6 The refined time-frequency representation shows that the main energy of the impact component is concentrated around 600Hz. t =2s、 t =10.4s t =12.5s t Impacts around time points such as 12.9s still possess significant energy within the 1000–2000 Hz frequency band. The main energy frequencies in the residual terms characterizing random and periodic line vibrations are around 600 Hz and 1450 Hz, overlapping with the energy of the impact component. This demonstrates that FPMD can clearly extract and express the time-frequency characteristics of the impact component in the signal.

[0070] An on-site inspection of the section revealed multiple instances of rail surface damage, primarily consisting of continuous, irregular diagonal cracks. Most of these diagonal cracks were accompanied by minor rail surface abrasions and poor smoothness, while a few diagonal cracks contained minor rail surface spalling. The average flatness of the defects was measured to be 0.184 mm below the required level of 0.2 mm, which is below the standard for high-speed rail repair.

[0071] This embodiment demonstrates that traditional impact index diagnostics for impact-type shortwave track defects, due to their filter frequency setting of 20-500Hz, primarily target structural impacts such as poor joints and faulty turnouts. However, some rail surface damage, such as diagonal cracks and minor rail surface abrasions, can excite vibrations in the rail itself within the 600-800Hz frequency band, as well as rail pinned-pinned resonances in the 1150-1450Hz range. Traditional methods lose these signal characteristics after filtering and therefore cannot diagnose them. In this curved section, the train is accelerating, and the wheel-rail creep force leads to frequent diagonal cracks and abrasions. These types of damage are highly concealed and hazardous. The method for diagnosing continuous diagonal cracks, as described in this specification, can effectively identify diagonal cracks in their early stages, guiding timely on-site intervention and remediation.

[0072] Based on the same inventive concept, embodiments of this specification also provide a diagnostic device for continuous oblique cracks in high-speed rail curved sections, such as... Figure 7 As shown, the device includes: The signal acquisition unit 701 is used to acquire the axle box acceleration signal of the high-speed railway line to be analyzed; The mode decomposition unit 702 is used to perform mode decomposition processing on the axle box acceleration signal to obtain a refined time-frequency representation of multiple impact components; The time-frequency marginal spectrum calculation unit 703 is used to calculate the time-frequency marginal spectrum corresponding to each impact component based on the time-frequency representation of each impact component; The impact index calculation unit 704 is used to divide the line to be analyzed into multiple line sections and calculate the section track impact index corresponding to each line section based on the time-frequency marginal spectrum of each impact component. The continuous oblique crack diagnosis unit 705 is used to locate the curved section among the multiple line sections according to the line ledger, compare the section track impact index corresponding to the curved section with a preset diagnosis threshold, and if the section track impact index of the curved section is greater than or equal to the preset diagnosis threshold, it is determined that there is a continuous oblique crack in the curved section.

[0073] The beneficial effects obtained by the above-described device are the same as those obtained by the above-described method, and will not be described in detail in the embodiments of this specification.

[0074] like Figure 8 The diagram shown is a structural schematic of a computer device according to an embodiment of this specification. The methods described in this specification can be applied to the computer device of this embodiment.

[0075] Computer device 802 may include one or more processing devices 804, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads.

[0076] The computer device 802 may also include any storage resource 806 for storing any kind of information such as code, settings, data, etc.

[0077] Non-limiting, for example, storage resource 806 may include any one or more of the following: any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc.

[0078] More generally, any storage resource can use any technology to store information.

[0079] Furthermore, any storage resource can provide volatile or non-volatile retention of information.

[0080] Furthermore, any storage resource can represent a fixed or removable component of the computer device 802.

[0081] In one scenario, when processing device 804 executes associated instructions stored in any storage resource or combination of storage resources, computer device 802 can perform any operation of the associated instructions. Computer device 802 also includes one or more drive mechanisms 808 for interacting with any storage resource, such as hard disk drive mechanisms, optical disk drive mechanisms, etc.

[0082] Computer device 802 may also include an input / output module 810 (I / O) for receiving various inputs (via input device 812) and providing various outputs (via output device 814). A specific output mechanism may include a presentation device 816 and an associated graphical user interface (GUI) 818. In other embodiments, the input / output module 810 (I / O), input device 812, and output device 814 may be omitted, and the device may function solely as a computer device within a network. Computer device 802 may also include one or more network interfaces 820 for exchanging data with other devices via one or more communication links 822. One or more communication buses 824 couple the components described above together.

[0083] Communication link 822 can be implemented in any way, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication link 822 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0084] This specification also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0085] This specification also provides computer-readable instructions, wherein when a processor executes the instructions, the program therein causes the processor to perform the above-described method.

[0086] It should be understood that in the various embodiments of this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this specification.

[0087] It should also be understood that, in the embodiments of this specification, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the embodiments of this specification, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0088] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this specification can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the embodiments in this specification.

[0089] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0090] In the embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, or they may be electrical, mechanical, or other forms of connection.

[0091] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments described in this specification, depending on actual needs.

[0092] Furthermore, the functional units in the various embodiments of this specification can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0093] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this specification, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] This specification describes the principles and implementation methods of the embodiments using specific examples. The above descriptions of the embodiments are only for the purpose of helping to understand the methods and core ideas of the embodiments in this specification. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the embodiments in this specification. Therefore, the content of this specification should not be construed as a limitation on the embodiments in this specification.

Claims

1. A method for diagnosing continuous oblique cracks in a high-speed rail curved section, characterized in that, The method includes: Acquire the axle box acceleration signal of the high-speed railway line to be analyzed; The axle box acceleration signal is subjected to mode decomposition processing to obtain a refined time-frequency representation of multiple impact components; The time-frequency marginal spectrum corresponding to each impact component is calculated based on the time-frequency representation of each impact component; The line to be analyzed is divided into multiple line sections. Based on the time-frequency marginal spectrum of each impact component, the section track impact index corresponding to each line section is calculated. Based on the route ledger, the curved sections among the multiple route sections are located. The track impact index of the corresponding curved section is compared with a preset diagnostic threshold. If the track impact index of the curved section is greater than or equal to the preset diagnostic threshold, it is determined that the curved section has continuous oblique cracks.

2. The method according to claim 1, characterized in that, The axle box acceleration signal is subjected to mode decomposition processing to obtain a refined time-frequency representation of multiple impact components, including: The preset time-frequency transformation window length, fixed bandwidth, mode decomposition termination energy threshold, and minimum kurtosis coefficient threshold for impact component determination are set. A third-order B-spline function is used as the window function. The window length of the window function is determined according to the time-frequency transformation window length. A short-time Fourier transform is performed on the axle box acceleration signal to obtain the initial time-frequency representation, which is used as the residual time-frequency representation of the first iteration. Iteratively perform the following shock component extraction operations: Locate the time-frequency ridge of the impact component with the highest energy from the current residual time-frequency representation; delineate the time-frequency energy range of the impact component with the time-frequency ridge as the center and according to the fixed bandwidth; calculate the time-domain modal signal corresponding to the impact component based on the time-frequency data within the time-frequency energy range; remove the time-domain modal signal from the current residual signal and update the residual time-frequency representation; The iteration stops when a preset termination condition is met. The preset termination condition includes: the proportion of the current residual signal energy to the total energy of the original axle box acceleration signal is less than the mode decomposition termination energy threshold, or the kurtosis coefficient of the analytical signal corresponding to the next impact component to be extracted is less than the minimum kurtosis coefficient threshold. The time-frequency energy of each extracted impact component is compressed to the corresponding time-frequency ridge to obtain a refined time-frequency representation of each impact component.

3. The method according to claim 2, characterized in that, The formula for calculating the time-domain modal signal corresponding to the impact component based on the time-frequency data within this time-frequency energy range is as follows: in, For the first m The impact component at time The time-domain modal signal, This indicates the operation of taking the real part. The normalized magnitude of the window function at time zero. For the first m The impact component at time , frequency is Time and frequency data at the location, For all discrete frequency points on the frequency axis, Frequency resolution; time The range of values ​​is ,in, For the first m The time-frequency ridge of the impact component at frequency The corresponding time point, For the first m The impact component at frequency The upper bandwidth in the fixed bandwidth mentioned above. For the first m The impact component at frequency The lower bandwidth in the fixed bandwidth mentioned above.

4. The method according to claim 2, characterized in that, The formula for determining whether the proportion of the current residual signal energy to the total energy of the original axle box acceleration signal is less than the mode decomposition termination energy threshold is as follows: The formula for calculating the analytic signal is: The formula for determining whether the kurtosis coefficient of the analytical signal corresponding to the next impact component to be extracted is less than the minimum kurtosis coefficient threshold is as follows: in, For a moment The m The residual signal after the next iteration, from time... The m -1 residual signal minus time The m The time-domain modal signals extracted in the next iteration are obtained. The square of the second norm of the signal represents the total energy of the signal. For a moment The axle box acceleration signal, The energy threshold for mode decomposition termination. For a moment The m The analytical signal of the residual signal corresponding to +1 iterations The imaginary unit, For Hilbert transform operations, For operations involving taking the modulus of complex numbers, To analyze the kurtosis coefficients of the signal, This represents the total number of discrete sampling points of the signal. For all discrete-time sampling points of the signal, For the first m The mean of the analytic signal of the residual signal at each time step corresponding to +1 iteration. This is the minimum kurtosis coefficient threshold.

5. The method according to claim 1, characterized in that, The formula for calculating the time-frequency marginal spectrum corresponding to each impact component based on its time-frequency representation is as follows: in, For the first s The impact component in the first i The time-frequency marginal spectrum corresponding to each discrete-time sampling point For the first s The impact component in the first i The discrete-time sampling points, the first j The time-frequency energy value corresponding to each discrete frequency point N This represents the total number of discrete frequency points on the frequency axis. For the first i Discrete-time sampling points, It is the first j Discrete frequency points.

6. The method according to claim 1, characterized in that, The formula for calculating the track impact index for each line section is as follows: in, For the first The section track impact index corresponding to each line segment For the first The square root of the total energy of the time-frequency marginal spectrum of all impact components within a line segment. This is the square root value of the average time-frequency marginal spectral energy of the impact component across the entire high-speed railway line to be analyzed. For the first s The impact component in the first i The time-frequency marginal spectrum value corresponding to each discrete-time sampling point This represents the total number of impact components obtained from the decomposition across the entire high-speed railway line to be analyzed. L This represents the total number of discrete sampling points for the axle box acceleration signal across the entire high-speed railway line to be analyzed.

7. The method according to claim 1, characterized in that, The line to be analyzed is divided into multiple line segments, including: Along the mileage direction of the route to be analyzed, a sliding window is divided using a preset segment length as the window width and a mileage distance corresponding to a preset overlap rate as the sliding step size to obtain the multiple route segments.

8. A diagnostic device for continuous oblique cracks in a high-speed railway curved section, characterized in that, The device includes: The signal acquisition unit is used to acquire the axle box acceleration signal of the high-speed railway line to be analyzed. The mode decomposition unit is used to perform mode decomposition processing on the axle box acceleration signal to obtain a refined time-frequency representation of multiple impact components; The time-frequency marginal spectrum calculation unit is used to calculate the time-frequency marginal spectrum corresponding to each impact component based on the time-frequency representation of each impact component; The impact index calculation unit is used to divide the line to be analyzed into multiple line sections and calculate the section track impact index corresponding to each line section based on the time-frequency marginal spectrum of each impact component. The continuous oblique crack diagnosis unit is used to locate the curved section among the multiple line sections according to the line ledger, compare the section track impact index corresponding to the curved section with a preset diagnosis threshold, and if the section track impact index of the curved section is greater than or equal to the preset diagnosis threshold, it is determined that there is a continuous oblique crack in the curved section.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

11. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

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