Method and apparatus for identifying periodic track irregularities

Abstract

This invention discloses a method and apparatus for identifying periodic track irregularities. The method includes: performing spectral analysis on a track time-domain signal to obtain power spectral density data; determining track irregularity wavelength characteristic data based on the power spectral density data; performing wavelet transform on the track time-domain signal based on the track irregularity wavelength characteristic data to obtain wavelet transform coefficients; calculating the corresponding periodic irregularity index using the Effective Value Method (RMS) algorithm based on the wavelet transform coefficients; and identifying track periodic irregularities based on the periodic irregularity index. This invention can identify track periodic irregularities, reducing manpower and material resources and improving identification accuracy.

Description

Technical Field

[0001] This invention relates to the field of track management technology, and in particular to a method and apparatus for identifying periodic track irregularities. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] As an excitation source in wheel-rail systems, track irregularities are the main cause of locomotive and rolling stock vibrations. They significantly impact the lifespan of vehicles and track components, the safety, smoothness, and comfort of train operation, as well as environmental noise. Good track smoothness is a prerequisite for safe train operation and passenger comfort. Current technologies generally employ dynamic testing vehicles to periodically and dynamically inspect the track geometry, using local amplitude and section mean (TQI) measurements for evaluation to ensure that track geometric smoothness meets requirements.

[0004] Based on the harmonic characteristics of track irregularities, they can be divided into two types: periodic irregularities and aperiodic irregularities. Periodic irregularities are characterized by multiple continuous waves, a common fundamental wavelength, and random amplitude. Aperiodic irregularities have varying wavelengths and no distinct fundamental wavelength. As a linear structure, railway tracks exhibit primarily random irregularities along the track direction. To meet high smoothness requirements, rails must achieve high straightness at the factory. Therefore, rail straightening is required during processing. However, insufficient precision in the straightening process can easily lead to rolling-induced periodic irregularities in the rails. These irregularities are geometric irregularities caused by rail processing. When rails with rolling irregularities are laid on the track, the periodic deformation will be exacerbated under train loads. For the commonly used CRTSⅠ, CRTSⅡ, and CRTSⅢ type slab track, the under-rail structure is made up of track slabs of equal length spliced ​​together. When the track slabs deform, the track geometry will also show a periodic change that is close to the length of the track slabs.

[0005] Numerous studies have been conducted using track irregularity amplitude and TQI (Traffic Quality Index) to evaluate random track irregularities and guide maintenance. However, reports on how to describe the characteristics of periodic track irregularities and promptly detect periodic track defects are still very limited. High-speed railways, after years of operation, have frequently exhibited continuous, multi-wave periodic irregularities, such as rail defects, track slab camber, roadbed frost heave, and bridge creep. Although the track geometric peak value may not exceed limits, periodic track irregularities are more likely to cause vehicle vibration than random track irregularities. Periodic irregularities of specific wavelengths can induce harmonic responses in the vehicle suspension, significantly increasing the risk of train derailment and negatively impacting operational safety and passenger comfort. Some countries and regions have formulated maintenance standards for single-wave irregularities in elevation and track direction and three consecutive non-overlapping irregularities for tracks of grade 5 and above. The "Rules for Maintenance of Conventional Railway Lines" clearly states that attention should be paid to the identification of periodic consecutive three-wave and multi-wave track irregularities. For consecutive multi-wave elevation irregularities with an amplitude of 14mm, timely handling is required. The "Rules for Maintenance of Ballastless Track Lines of High-Speed ​​Railways" also clearly states that consecutive multi-wave irregularities should be avoided.

[0006] Currently, track irregularities are mainly detected using integrated inspection trains, with an inspection cycle of 1-2 times per month. With the widespread application of integrated inspection trains and other track inspection equipment, a large amount of track irregularity detection data has been accumulated. In sections where track geometry changes periodically, the track geometry obtained by the integrated inspection train also exhibits obvious periodicity. Currently, periodic irregularities are mainly detected through manual screening, which is not only costly in terms of manpower and resources but also has a low accuracy rate.

[0007] Therefore, there is an urgent need for a scheme to identify orbital periodic irregularities that can overcome the above problems. Summary of the Invention

[0008] This invention provides a method for identifying periodic irregularities in orbits, which reduces manpower and material resources and improves identification accuracy. The method includes:

[0009] Spectral analysis was performed on the orbital time-domain signal to obtain power spectral density data;

[0010] Based on the power spectral density data, determine the wavelength characteristic data of orbital irregularities;

[0011] Based on the wavelength characteristic data of track irregularities, wavelet transform is performed on the time-domain signal of the track to obtain wavelet transform coefficients;

[0012] Based on the wavelet transform coefficients, the corresponding periodic irregularity index is calculated using the Effective Value (RMS) algorithm.

[0013] Based on the aforementioned periodic irregularity index, periodic irregularities in the track are identified.

[0014] This invention provides a method for identifying periodic irregularities in orbits, which reduces manpower and material resources and improves identification accuracy. The method includes:

[0015] The spectrum analysis module is used to perform spectrum analysis on the orbit time-domain signal to obtain power spectral density data.

[0016] The wavelength characteristic determination module is used to determine the orbital irregularity wavelength characteristic data based on the power spectral density data.

[0017] The wavelet transform module is used to perform wavelet transform on the time-domain signal of the track based on the wavelength characteristic data of track irregularities, and obtain wavelet transform coefficients.

[0018] The index calculation module is used to calculate the corresponding periodic irregularity index based on the wavelet transform coefficients using the effective value algorithm (RMS).

[0019] The irregularity identification module is used to identify periodic irregularities in the track based on the periodic irregularity index.

[0020] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for identifying periodic irregularities in orbits.

[0021] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for identifying periodic irregularities in orbits.

[0022] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method for identifying periodic irregularities in orbits.

[0023] This invention provides an embodiment of the method that obtains power spectral density data by performing spectral analysis on the track time-domain signal; determines track irregularity wavelength characteristic data based on the power spectral density data; performs wavelet transform on the track time-domain signal based on the track irregularity wavelength characteristic data to obtain wavelet transform coefficients; calculates the corresponding periodic irregularity index using the RMS algorithm based on the wavelet transform coefficients; and identifies periodic track irregularities based on the periodic irregularity index. This invention eliminates the need for manual screening. By using the power spectral density data obtained from spectral analysis to determine the wavelength characteristics of track periodic irregularities, and then combining the wavelet transform coefficients and the RMS algorithm to calculate the periodic irregularity index, it enables rapid detection and accurate identification of periodic track defects. This reduces manpower and material resources while improving identification accuracy, effectively guiding track maintenance and repair, significantly reducing the risk of vehicle swaying and derailment, and improving vehicle operation quality and track lifespan. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0025] Figure 1 This is a schematic diagram of the method for identifying periodic irregularities in the track in an embodiment of the present invention;

[0026] Figure 2 This is a waveform diagram of unevenness caused by rail rolling in an embodiment of the present invention;

[0027] Figure 3 This is a waveform diagram showing the unevenness caused by the deformation of the track slab in an embodiment of the present invention;

[0028] Figure 4 This is the power spectral density diagram of the unevenness caused by rail rolling in an embodiment of the present invention;

[0029] Figure 5 This is a power spectral density diagram of the unevenness caused by the deformation of the track slab in an embodiment of the present invention.

[0030] Figure 6 This is the distribution of wavelet transform coefficients in an embodiment of the present invention;

[0031] Figure 7 This is a cumulative distribution map of PII (2-4m) in an embodiment of the present invention;

[0032] Figure 8 This is a distribution diagram of PII (2-4m) in an embodiment of the present invention;

[0033] Figure 9 This is a waveform diagram of the K178+800~K180+200 section in an embodiment of the present invention;

[0034] Figure 10 This is a distribution diagram of PII (4-8m) in an embodiment of the present invention;

[0035] Figure 11 This is a waveform diagram of the K30+723~K31+340 section in an embodiment of the present invention;

[0036] Figure 12 This is a distribution diagram of PII (8-20m) in an embodiment of the present invention;

[0037] Figure 13 This is a waveform diagram of the K540~K540+400 section in an embodiment of the present invention;

[0038] Figure 14 This is a structural diagram of the track periodic irregularity identification device in an embodiment of the present invention;

[0039] Figure 15 This is a schematic diagram of the computer device structure according to an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0041] To identify periodic irregularities in orbits, reduce manpower and material resources, and improve identification accuracy, embodiments of the present invention provide a method for identifying periodic irregularities in orbits, such as... Figure 1 As shown, the method may include:

[0042] Step 101: Perform spectral analysis on the orbital time-domain signal to obtain power spectral density data;

[0043] Step 102: Determine the orbital irregularity wavelength characteristic data based on the power spectral density data;

[0044] Step 103: Perform wavelet transform on the time-domain signal of the track based on the wavelength characteristic data of track irregularities to obtain wavelet transform coefficients;

[0045] Step 104: Calculate the corresponding periodic irregularity index using the effective value algorithm (RMS) based on the wavelet transform coefficients.

[0046] Step 105: Identify track periodic irregularities based on the periodic irregularity index.

[0047] Depend on Figure 1 As shown, this embodiment of the invention obtains power spectral density data by performing spectral analysis on the track time-domain signal; determines track irregularity wavelength characteristic data based on the power spectral density data; performs wavelet transform on the track time-domain signal based on the track irregularity wavelength characteristic data to obtain wavelet transform coefficients; calculates the corresponding periodic irregularity index using the RMS algorithm based on the wavelet transform coefficients; and identifies track periodic irregularities based on the periodic irregularity index. This embodiment of the invention eliminates the need for manual screening. It uses the power spectral density data obtained from spectral analysis to grasp the wavelength characteristics of track periodic irregularities, and then combines the wavelet transform coefficients and the RMS algorithm to calculate the periodic irregularity index. This allows for the rapid detection and accurate identification of track periodic defects, reducing manpower and material resources while improving identification accuracy. It effectively guides line maintenance and repair, significantly reduces the risk of vehicle swaying and derailment, and improves vehicle operation quality and line lifespan.

[0048] The inventors discovered that the track structure is vertically layered, consisting of rails, sleepers, track slabs, base plates, and foundations from top to bottom. Different structures exhibit different deformation patterns and characteristic wavelengths. Therefore, it is necessary to understand the wavelength characteristics of each track structure and conduct spectral analysis of track irregularities in corresponding sections to grasp the wavelength characteristics of periodic irregularities. To accurately locate defective sections, time-frequency analysis is required to accurately calculate the wavelength components of different mileage sections. Combined with statistical methods, periodic irregularity evaluation indicators are proposed, reasonable thresholds are set, and periodic defective sections are accurately screened. After repeated on-site verification, the final indicators and management standards were determined.

[0049] The following is a detailed analysis of each step.

[0050] In steps 101 to 102, the orbit time-domain signal is subjected to spectral analysis to obtain power spectral density data. Based on the power spectral density data, the orbit irregularity wavelength characteristic data is determined.

[0051] In one embodiment, the characteristic lengths of the track structure are extracted based on the various structural types at the bottom of the track. Some typical characteristic lengths of track structures are shown in Table 1.

[0052] Table 1

[0053] Track structure rails Type I track slab Type II track slab Type III track slab base plate bridge Feature length / m 3.2 5 6.5 5.5 10 / 20 32

[0054] Select a typical section of the track with periodic irregularities caused by rail deformation and track slab deformation, the waveform of which is as follows: Figure 2 and Figure 3 As shown, the geometric wavelengths of the track in the periodic irregularity sections are basically the same, but the amplitudes differ to some extent. Spectral analysis of the track time-domain signal yields power spectral density data, thus confirming the frequency domain distribution characteristics of the track irregularities. Figure 4 and Figure 5 This represents the power spectral density of periodic irregularities caused by rail and track slab deformation. The peak positions of the power spectral density of track irregularities indicate the periodic components contained within the irregularities. Furthermore, based on the power spectral density data, the wavelength characteristics of the track irregularities can be determined. A peak exists at a frequency of 0.31 Hz, corresponding to a track irregularity wavelength of 3.2 m, caused by the rail rolling process. Another peak exists at 0.2 Hz, corresponding to a track irregularity wavelength of 5 m, caused by track slab deformation. This reflects the correlation between periodic track irregularities and the frequency domain.

[0055] In step 103, wavelet transform is performed on the time-domain signal of the track according to the wavelength characteristic data of track irregularity to obtain wavelet transform coefficients.

[0056] In one embodiment, performing wavelet transform on the orbital time-domain signal based on the orbital irregularity wavelength characteristic data to obtain wavelet transform coefficients includes: performing time-frequency analysis on the orbital time-domain signal using the wavelet transform method; and selecting the frequency band range of the wavelet transform coefficients based on the orbital irregularity wavelength characteristic data.

[0057] In one embodiment, the method for identifying orbital periodic irregularities further includes: after obtaining the wavelet transform coefficients, smoothing the wavelet transform coefficients.

[0058] In practical implementation, to accurately locate the position of the periodic wavelength, wavelet transform is used for time-frequency analysis to accurately locate the mileage and the corresponding periodic components. First, a suitable mother wavelet ψ(t) and wavelet basis functions are selected. The mother wavelet should satisfy formula (1) and have a frequency of ω. By scaling the mother wavelet ψ(t) by a and translating it by b, many copies ψ(t) with similar shapes to the mother wavelet but different "weight" and "position" can be obtained. a,τ (t), which is the wavelet basis function, see formula (2).

[0059]

[0060]

[0061] The wavelet transform coefficients are calculated using the wavelet transform method. CWT (Continuous Wavelet Transform) involves applying wavelet basis functions to a finite-energy signal f(t) in the real domain R, or in other words, projecting and decomposing f(t) under these wavelet basis functions to obtain the wavelet transform coefficients WT. f(a,τ), see formula (3) for details. The wavelengths of the principal components are the time and frequency domains where the wavelet transform coefficients are significant. The wavelet coefficients only reach their maximum values ​​when the wavelet center frequency is close to the natural frequency of the original signal. A series of different center frequencies can be obtained through scaling factors, and signals at different positions in the time domain can be detected through translation coefficients. The results are as follows: Figure 6 As shown.

[0062]

[0063] Furthermore, a reasonable frequency band for the wavelet transform coefficients is selected, with the band range determined based on the periodic irregularity of the wavelength. After obtaining the wavelet transform coefficients, they are smoothed using the following formula:

[0064]

[0065] A reasonable length is selected to evaluate the segment characteristics of wavelet transform coefficients. Since the wavelet transform coefficients are densely distributed along the mileage direction and suffer from time-domain resolution issues, it is advisable to evaluate them according to mileage segments. Because rails are typically 100-meter gauges, the distribution range of wavelet transform coefficients in continuous multi-wave periodic irregularities generally ranges from 100 meters to several hundred meters. Therefore, the unit segment length N is set to 100 meters.

[0066] In steps 104 to 105, the corresponding periodic irregularity index is calculated using the effective value algorithm (RMS) based on the wavelet transform coefficients, and the periodic irregularity of the track is identified based on the periodic irregularity index.

[0067] In one embodiment, identifying orbital periodic irregularities based on the periodic irregularity index includes:

[0068] The value of the periodic irregularity index is compared with a preset index threshold;

[0069] Based on the comparison results, periodic irregularities in the orbit are identified.

[0070] In practice, the Periodic Irregularity Index (PII) is calculated using the Effective Value (RMS) algorithm. The PII reflects the components within a given wavelength range that are present in the cell segment; a higher PII indicates more pronounced periodic irregularities in the corresponding wavelength range. The periodic irregularity index is calculated using the following formula:

[0071]

[0072] The periodic irregularity index (PII) caused by deformation of different orbital structures was statistically analyzed, and a 95% confidence interval was used as the PII threshold. Taking the PII at wavelengths of 2–4 m as an example, the statistical results are as follows: Figure 7 As shown, the threshold can be set to 0.3. Furthermore, PII thresholds for different wavelength bands can be set in the same manner as described above, and the results are shown in Table 2.

[0073] Table 2

[0074] wavelength range 2～4 4～8 8～20 PII Management Value 0.3 2 4

[0075] The following is a specific embodiment illustrating the application of track periodicity irregularity identification in this invention. In this specific embodiment, the measured track irregularities of a high-speed railway comprehensive inspection train are calculated, periodic components in different sections are identified, and waveform diagrams are used for verification.

[0076] (1) The distribution of PII (2-4m wavelength) along the line in the line section is as follows: Figure 8 As shown in the figure, it can be seen that PII can significantly distinguish the track geometry features of different sections. Using 0.3 as a threshold can effectively screen out sections with periodic track geometry irregularities. Some identification results are shown in Table 3, where the waveform of the K178+800~K180+200 section is as follows. Figure 9 As shown, Figure 9 The unevenness at different heights exhibits a clear periodicity of about 3 meters, which is a periodicity generated by the rail rolling process, demonstrating the feasibility of using PII (2-4m wavelength) to identify the periodic deformation of the rail itself.

[0077] Table 3

[0078]

[0079]

[0080] (2) The distribution of PII (4-8m wavelength) along the line section is as follows: Figure 10 As shown, the PII (Plate Indicator) can also significantly distinguish the track geometry characteristics of different sections. Using 2.0 as a threshold, it can effectively screen out sections with periodic track geometry irregularities caused by track slab deformation. Some identified sections are shown in Table 4, among which the waveform of the K30+723~K31+340 section is as follows: Figure 11 As shown.

[0081] Table 4

[0082] Starting mileage Termination Mileage PII 1 K21+912 K22+020 2.58 2 K22+803 K23+701 3.66 3 K24+349 K24+753 3.26 4 K25+303 K25+430 3.47 5 K26+696 K26+797 4.02 6 K28+486 K28+586 3.07 7 K28+905 K29+203 2.66 8 K30+723 K31+340 2.61 9 K35+409 K35+718 3.00 10 K41+358 K41+481 2.74 11 K44+138 K44+638 3.14 12 K47+121 K47+710 2.42 13 K49+859 K50+274 2.25 14 K54+427 K54+756 2.39 16 K91+755 K91+953 2.93

[0083] (3) The distribution of wavelength PII in the 8-20m unit section along the line is as follows: Figure 12As shown, the PII (Platelet-In-Picture) can also significantly distinguish the track geometry features of different sections. Using 4.0 as a threshold, it can effectively screen out sections with periodic track geometry irregularities caused by base plate deformation. Some identified sections are shown in Table 5, among which the waveform of the K30+723~K31+340 section is as follows: Figure 13 As shown.

[0084] Table 5

[0085] Starting mileage Termination Mileage PII 1 K508+800 K509+00 4.45 2 K540+00 K540+200 5.94

[0086] As can be seen from the analysis of this specific embodiment, the method for identifying periodic irregularities based on dynamic detection data of integrated inspection trains helps to quickly discover periodic geometric defects in the track using data analysis, thereby guiding track maintenance and repair, greatly reducing the risk of vehicle swaying and derailment, and improving vehicle operation quality and track service life. This embodiment of the invention utilizes time-frequency analysis to discover periodic track defects and uses the effective value method of wavelet transform coefficients to solve the time-frequency analysis resolution problem.

[0087] This invention utilizes the periodicity of track irregularities and combines spectral analysis, time-frequency analysis, and statistics to propose periodic evaluation indicators. These indicators are used to analyze track irregularities, identify sections exceeding limits, and conduct on-site verification, effectively identifying periodic defects. By utilizing dynamic detection data from integrated inspection trains, continuous multi-wave periodic irregularities in track geometry can be quickly identified, thereby diagnosing track structural problems and guiding on-site maintenance.

[0088] Based on the same inventive concept, embodiments of the present invention also provide a track periodicity irregularity identification device, as described in the following embodiments. Since the principles underlying these solutions are similar to those of the track periodicity irregularity identification method, the implementation of the track periodicity irregularity identification device can refer to the implementation of the method; repeated details will not be elaborated further.

[0089] Figure 14 This is a structural diagram of the track periodicity irregularity identification device in an embodiment of the present invention, as shown below. Figure 14 As shown, the track periodic irregularity identification device includes:

[0090] The spectrum analysis module 1401 is used to perform spectrum analysis on the orbit time-domain signal to obtain power spectral density data.

[0091] The wavelength characteristic determination module 1402 is used to determine the orbital irregularity wavelength characteristic data based on the power spectral density data.

[0092] Wavelet transform module 1403 is used to perform wavelet transform on the time domain signal of the track according to the wavelength characteristic data of track irregularity, and obtain wavelet transform coefficients.

[0093] The index calculation module 1404 is used to calculate the corresponding periodic irregularity index based on the wavelet transform coefficients using the effective value algorithm RMS.

[0094] The irregularity identification module 1405 is used to identify periodic track irregularities based on the periodic irregularity index.

[0095] In one embodiment, the wavelet transform module 1403 is further configured to:

[0096] Time-frequency analysis of orbital time-domain signals was performed using wavelet transform.

[0097] Based on the wavelength characteristic data of track irregularities, the frequency band range of wavelet transform coefficients is selected.

[0098] In one embodiment, the irregularity identification module 1405 is further configured to:

[0099] The value of the periodic irregularity index is compared with a preset index threshold;

[0100] Based on the comparison results, periodic irregularities in the orbit are identified.

[0101] Based on the aforementioned inventive concept, such as Figure 15 As shown, this embodiment of the invention also provides a computer device 1500, including a memory 1510, a processor 1520, and a computer program 1530 stored in the memory 1510 and executable on the processor 1520. When the processor 1520 executes the computer program 1530, it implements the above-mentioned method for identifying periodic irregularities in orbits.

[0102] Based on the foregoing inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for identifying periodic irregularities in orbits.

[0103] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method for identifying periodic irregularities in orbits.

[0104] This invention provides an embodiment of the method that obtains power spectral density data by performing spectral analysis on the track time-domain signal; determines track irregularity wavelength characteristic data based on the power spectral density data; performs wavelet transform on the track time-domain signal based on the track irregularity wavelength characteristic data to obtain wavelet transform coefficients; calculates the corresponding periodic irregularity index using the RMS algorithm based on the wavelet transform coefficients; and identifies periodic track irregularities based on the periodic irregularity index. This invention eliminates the need for manual screening. By using the power spectral density data obtained from spectral analysis to determine the wavelength characteristics of track periodic irregularities, and then combining the wavelet transform coefficients and the RMS algorithm to calculate the periodic irregularity index, it enables rapid detection and accurate identification of periodic track defects. This reduces manpower and material resources while improving identification accuracy, effectively guiding track maintenance and repair, significantly reducing the risk of vehicle swaying and derailment, and improving vehicle operation quality and track lifespan.

[0105] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0107] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0108] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0109] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying periodic irregularities in a track, characterized in that, include: Spectral analysis was performed on the orbital time-domain signal to obtain power spectral density data; Based on the power spectral density data, determine the wavelength characteristic data of orbital irregularities; Based on the wavelength characteristic data of track irregularities, wavelet transform is performed on the time-domain signal of the track to obtain wavelet transform coefficients; Based on the wavelet transform coefficients, the corresponding periodic irregularity index is calculated using the Effective Value (RMS) algorithm. Based on the aforementioned periodic irregularity index, track periodic irregularities are identified; Based on the power spectral density data, determine the wavelength characteristic data of orbital irregularities, including: directly determining the corresponding physical wavelength by identifying the characteristic peaks in the power spectral density data; Based on the periodic irregularity index, track periodic irregularity identification is performed, including: selecting a preset index threshold corresponding to the wavelength range to which the value of the periodic irregularity index belongs; comparing the value of the periodic irregularity index with the corresponding preset index threshold; and determining whether there is a periodic irregularity defect in the track structure based on the comparison result.

2. The method for identifying periodic irregularities in a track as described in claim 1, characterized in that, Also includes: After obtaining the wavelet transform coefficients, the wavelet transform coefficients are smoothed.

3. The method for identifying periodic irregularities in a track as described in claim 1, characterized in that, Based on the wavelength characteristic data of the track irregularities, a wavelet transform is performed on the time-domain signal of the track to obtain wavelet transform coefficients, including: Time-frequency analysis of orbital time-domain signals was performed using wavelet transform. Based on the wavelength characteristic data of track irregularities, the frequency band range of wavelet transform coefficients is selected.

4. A device for identifying periodic track irregularities, characterized in that, include: The spectrum analysis module is used to perform spectrum analysis on the orbit time-domain signal to obtain power spectral density data. The wavelength characteristic determination module is used to determine the orbital irregularity wavelength characteristic data based on the power spectral density data. The wavelet transform module is used to perform wavelet transform on the time-domain signal of the track based on the wavelength characteristic data of track irregularities, and obtain wavelet transform coefficients. The index calculation module is used to calculate the corresponding periodic irregularity index based on the wavelet transform coefficients using the effective value algorithm (RMS). The irregularity identification module is used to identify periodic track irregularities based on the periodic irregularity index. Based on the power spectral density data, determine the wavelength characteristic data of orbital irregularities, including: directly determining the corresponding physical wavelength by identifying the characteristic peaks in the power spectral density data; Based on the periodic irregularity index, track periodic irregularity identification is performed, including: selecting a preset index threshold corresponding to the wavelength range to which the value of the periodic irregularity index belongs; comparing the value of the periodic irregularity index with the corresponding preset index threshold; and determining whether there is a periodic irregularity defect in the track structure based on the comparison result.

5. The track periodic irregularity identification device as described in claim 4, characterized in that, The wavelet transform module is further used for: Time-frequency analysis of orbital time-domain signals was performed using wavelet transform. Based on the wavelength characteristic data of track irregularities, the frequency band range of wavelet transform coefficients is selected.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 3.

7. 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 3.

8. 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 3.

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