Rail train wheel polygon detection system and control method thereof

Abstract

The invention relates to a rail train wheel polygon detection system and a control method thereof, the system comprises a graphene optical fiber sensor array module, a data acquisition module, a data processing module and a wheel polygon order display module, the graphene optical fiber sensor array module is arranged at the bottom of a steel rail; the sensor is used for sensing rail pressure change of a steel rail and generating optical signals; the data acquisition module is used for acquiring an optical signal output by the graphene optical fiber sensor array module and converting the optical signal into an electric signal; the data processing module is used for analyzing the electric signal through a signal processing algorithm and deducing to obtain a wheel polygon order; and the wheel polygon order display module is used for receiving the wheel polygon order from the data processing module and carrying out visualization processing on the wheel polygon order. Compared with the prior art, the method can achieve the high-precision measurement of the polygon order of the wheel, improves the precision of a measurement system, gives an alarm when the order is abnormal, and guarantees the operation safety of a train.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transit detection, and in particular to a rail train wheel polygon detection system and a control method thereof. Background Art

[0002] With the continuous increase in train speed and mileage, the wear problem faced by rail transit wheel-rail systems is becoming increasingly serious. Wheel polygonization, a common abnormal wear problem in rail transit, is caused by uneven geometric deformation of the wheel circumferential surface due to wear, material fatigue, or external impact during long-term operation, forming a polygon-like shape. This polygonal phenomenon can cause periodic vibration and noise during train operation, which not only affects passenger comfort, but also increases wear on track and vehicle components, and may even cause serious safety accidents such as derailment. Therefore, timely detection of wheel polygon status is of great significance for ensuring train operation safety, extending wheel service life, and reducing maintenance costs.

[0003] Currently, traditional wheel polygon detection methods mainly include vibration acceleration detection, noise detection, and wheel-rail force detection. The vibration acceleration detection method uses accelerometers installed on the train bogie or car body to collect vibration signals from the wheels during operation and identify the characteristic frequencies of the wheel polygons through spectrum analysis. The noise detection method uses acoustic sensors installed next to the track or on the train to collect noise signals generated when the wheels contact the track and use acoustic characteristic analysis to determine whether the wheel polygon phenomenon exists. The wheel-rail force detection method uses force sensors installed on the track to directly measure the contact force between the wheel and the track, thereby determining the wheel polygon state. Although the traditional vibration acceleration detection method, noise detection method, and wheel-rail force detection method meet the needs of wheel polygon detection to a certain extent, they each have certain shortcomings. The vibration acceleration detection method suffers from reduced detection accuracy because the vibration signal is easily affected by track irregularities, train speed, and external environmental noise. The noise detection method is difficult to detect because the noise signal is easily affected by environmental noise (such as wind noise and background noise), and the placement and number of acoustic sensors significantly influence the detection results. The wheel-rail force detection method is difficult to implement on a large scale because the installation and maintenance costs of the force sensor are high and the track needs to be modified. However, wheel polygon detection methods based on optical fiber sensors have gradually gained application. For example, patent application CN108734060A discloses a method for identifying wheel polygonization on high-speed EMUs. This method uses a longitudinal strain sensor at the rail bottom to monitor the response of the wheel excitation to the rail passing through the cross-section, obtaining the rail response signal under wheel excitation. The rail response signal under wheel excitation is then preprocessed. Based on the preprocessed rail response signal, signal features are extracted to construct a wheel polygonization index. The constructed wheel polygonization index is used to identify wheel polygonization faults. The strain sensor in this method is a fiber optic sensor, but traditional optical fibers are usually made of glass or plastic materials, and their mechanical strength is low. They cannot withstand the impact, vibration and pressure caused by the passing of trains. Long-term exposure to harsh environments can easily cause the optical fiber to break or be damaged, affecting its performance and lifespan. At the same time, the strong electromagnetic field of electrified railways will cause the optical fiber sensor to generate optical fiber signal noise. The detection accuracy of traditional optical fiber sensors cannot meet the high-precision measurement requirements. Summary of the Invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a railway train wheel polygon detection system and its control method, which can achieve high-precision measurement of the wheel polygon order, improve the accuracy of the measurement system, and alarm in the event of abnormal order to ensure the safety of train operation.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A railway train wheel polygon detection system includes a graphene fiber optic sensor array module, a data acquisition module, a data processing module, and a wheel polygon order display module. The graphene fiber optic sensor array module is arranged at the bottom of the rail and is used to sense the track pressure changes of the rail and obtain an optical signal with wavelength drift caused by periodic strain excitation; the data acquisition module is used to collect the optical signal with wavelength drift output by the graphene fiber optic sensor array module and convert the wavelength drift in the optical signal into an electrical signal; the data processing module is used to analyze the electrical signal through a signal processing algorithm to derive the wheel polygon order; and the wheel polygon order display module is used to receive the wheel polygon order from the data processing module and visualize the wheel polygon order.

[0007] Furthermore, the graphene fiber sensor array module includes multiple graphene fiber sensors, and the specific structure of the graphene fiber sensor is: a layer of graphene synthesized by CVD method is coated on the outside of the optical fiber, and a layer of graphene fiber sensor outer cladding is coated on the outside of the graphene.

[0008] Furthermore, the graphene optical fiber sensor is laterally arranged at the bottom of the rail between two adjacent sleepers through an adhesive, and the graphene optical fiber sensor is located in the mid-span area of ​​the rail.

[0009] Furthermore, the installation positions of each of the graphene optical fiber sensors are evenly spaced and avoid the joints and welds of the rails, and the adhesive is a polyurethane structural adhesive.

[0010] Furthermore, the data acquisition module includes a multi-channel photoelectric converter and a low-noise amplifier. The multi-channel photoelectric converter connects the optical signal output by each graphene fiber sensor to an independent channel, obtains the wavelength drift caused by periodic strain excitation, and converts it into an electrical signal; the low-noise amplifier is used to amplify the electrical signal to ensure that the signal dynamic range meets the input requirements of the analog-to-digital converter.

[0011] Furthermore, the output characteristics of the multi-channel photoelectric converter, low noise amplifier and graphene optical fiber sensor match each other.

[0012] Furthermore, the specific derivation steps of the wheel polygon order are as follows: when the polygonal wheel contacts the rail during train operation, the graphene fiber optic sensor is used to detect the optical signal of wavelength drift caused by periodic strain excitation on the rail; the optical signal with wavelength drift output by the graphene fiber optic sensor array module is collected by the data acquisition module, and the wavelength drift in the optical signal is converted into an electrical signal through a photoelectric converter; the electrical signal is decoded and converted by the data processing module to obtain the stress and strain signal generated by the rail under the wheel-rail interaction, and the stress and strain signal is subjected to time-frequency analysis to extract the main frequency components. The wheel polygon order corresponding to the main frequency components is derived by combining the train running speed and wheel parameters.

[0013] Furthermore, the wheel polygon order is:

[0014]

[0015] Where n is the wheel polygon order, R is the wheel radius, and f s is the main frequency component of the strain signal collected by the graphene fiber optic sensor, V(f) is the stress-strain signal, k is the conversion coefficient, Δλ(t) is the wavelength drift, t is time, f is frequency, v is the train speed, ω is the train angular velocity, d is the distance between adjacent sensors, and Δt is the time difference between the strain peak arrival times of adjacent sensors.

[0016] Furthermore, it also includes an alarm module, which is connected to the data processing module and is used for real-time monitoring and over-limit alarm of the wheel polygon order obtained by the data processing module. When the system detects that the wheel polygon order exceeds the set warning threshold, the alarm module automatically triggers an alarm.

[0017] According to another aspect of the present invention, a method for controlling a railway train wheel polygon detection system is provided, wherein polygon detection of railway train wheels is performed using the railway train wheel polygon detection system as described above, comprising the following steps:

[0018] When a train with polygonal wheels passes through the detection area, the graphene fiber optic sensors in the graphene fiber optic sensor array module sense the rail pressure changes in real time and obtain optical signals with wavelength drift caused by periodic strain excitation.

[0019] The data acquisition module collects the optical signal with wavelength drift output by the graphene optical fiber sensor array module, and converts the wavelength drift in the optical signal into an electrical signal through a photoelectric converter;

[0020] The electrical signal is decoded and converted by a data processing module to obtain a stress and strain signal generated by the rail under the interaction between the wheel and the rail, and a time-frequency analysis is performed on the stress and strain signal to derive the wheel polygon order;

[0021] The wheel polygon order is visualized through the wheel polygon order display module, and the wheel polygon order is monitored in real time and an over-limit alarm is issued through the alarm module.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. The present invention utilizes the high sensitivity and good stability of graphene fiber sensors to convert track strain into a significant drift in the central wavelength of the fiber Bragg grating through the graphene coating. Graphene is also used to enhance the local strain response in the wheel-track contact area, achieving high-precision measurement of the wheel polygon order. At the same time, the high conductivity of graphene shields the strong electromagnetic field of electrified railways, avoiding fiber signal noise and improving the accuracy of the measurement system.

[0024] 2. The graphene fiber optic sensor array module in the present invention adopts an integrated design, and the graphene fiber optic sensor is horizontally set in the mid-span area at the bottom of the rail between adjacent sleepers. It can be more conveniently integrated with the existing track monitoring system with a smaller volume. The graphene fiber optic sensor has excellent wear resistance and high temperature resistance, and is suitable for long-term use under high load, high temperature and harsh environmental conditions, reducing the installation and maintenance costs of the equipment.

[0025] 3. The present invention visualizes the wheel polygon order through the wheel polygon order display module, and monitors the wheel polygon order in real time and issues an over-limit alarm through the alarm module. It can obtain the wheel polygon order in a timely manner and issue an alarm in the event of an abnormal order, thereby ensuring the safety of train operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a schematic structural diagram of a railway train wheel polygon detection system proposed by the present invention;

[0027] Figure 2 This is a schematic diagram of the graphene fiber sensor array structure layout;

[0028] Figure 3 Schematic diagram of the layout of graphene optical fiber sensor;

[0029] Figure 4 Schematic diagram of the force on the graphene optical fiber sensor structure;

[0030] Figure 5Schematic diagram of wheel-rail stress and strain signals output by graphene fiber optic sensors in a graphene fiber optic sensor array when a train wheel passes by;

[0031] Figure 6 is a schematic diagram of the main frequency components;

[0032] Figure 7 The present invention provides a flow chart of a control method for a railway train wheel polygon detection system.

[0033] Legend: 1. Rail; 2. Graphene fiber optic sensor; 3. Track pressure; 4. Graphene fiber optic sensor outer cladding; 5. Graphene; 6. Optical fiber; 7. Rail sleeper. DETAILED DESCRIPTION

[0034] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0035] Abbreviations involved:

[0036] Chemical Vapor Deposition, CVD

[0037] Low Noise Amplifier: Low Noise Amplifier, LNA

[0038] Analog to Digital Converter (ADC)

[0039] Example 1

[0040] This embodiment provides a railway train wheel polygon detection system, such as Figure 1 As shown, it includes a graphene fiber optic sensor array module, a data acquisition module, a data processing module and a wheel polygon order display module. The graphene fiber optic sensor array module is arranged at the bottom of the rail 1, and is used to sense the change of the track pressure 3 of the rail 1 and obtain the optical signal with wavelength drift caused by periodic strain excitation; the data acquisition module is used to collect the optical signal with wavelength drift output by the graphene fiber optic sensor array module, and convert the wavelength drift in the optical signal into an electrical signal; the data processing module is used to analyze the electrical signal through a signal processing algorithm to derive the wheel polygon order; the wheel polygon order display module is used to receive the wheel polygon order from the data processing module and visualize the wheel polygon order.

[0041] like Figure 2 and Figure 3As shown, the graphene fiber optic sensor array module includes multiple graphene fiber optic sensors 2, which are horizontally attached to the bottom of the rail 1 between two adjacent sleepers 7 using adhesive. The graphene fiber optic sensors 2 are located in the mid-span area of ​​the rail 1. The installation steps for the graphene fiber optic sensor array module are as follows: First, at different locations on the bottom of the rigid rail 1, evenly space the sensors at the rail bottom according to measurement requirements. A tape measure is used to determine the installation position of each graphene fiber optic sensor 2, ensuring uniform spacing and avoiding rail joints and welds to avoid stress concentration. Then, adhesive is evenly applied to the graphene fiber optic sensors 2 and tightly adhered to the bottom of the rail. Appropriate pressure is applied to ensure a secure bond. After the bond is fully cured, the bond strength is checked to ensure seamless adhesion between the graphene fiber optic sensors 2 and the surface of the rail 1, thereby ensuring that the graphene fiber optic sensors 2 can directly contact or sense pressure changes on the rail. This is done to ensure durability under the harsh conditions of the rail operating environment, such as high-intensity loads, temperature fluctuations, vibration and shock, and long-term exposure to humidity and heat. The preferred adhesive material is a polyurethane structural adhesive with high shear strength (≥8MPa), good flexibility (elongation at break ≥10%), and outstanding weather and fatigue resistance. This adhesive can adapt to the complex stress state and micro-deformation of the rail bottom while maintaining sufficient bonding strength. Furthermore, this adhesive has low volatility and shrinkage to avoid optical fiber displacement and stress concentration during the curing process. Its moderate operation time (construction time ≥10 minutes) facilitates simultaneous multi-point construction on-site. Furthermore, this adhesive has low thermal conductivity and insulation properties to prevent sensor signal stability from being affected by rail temperature fluctuations or electromagnetic interference.

[0042] According to classical beam theory, under the action of wheel loads, rail 1 can be simplified to a simply supported beam, with its ends supported on sleepers 7. The wheel load can be considered a moving concentrated load on the simply supported beam. When the wheel load is located between the two sleepers 7, that is, at the mid-span of the simply supported beam, the bending moment on rail 1 reaches its maximum. According to the bending strain formula:

[0043]

[0044] Where ε is the axial strain at the fiber, M is the bending moment at that section, y is the distance from the neutral axis (maximum for the bottom fiber), E is the Young's modulus of the rail material, and I is the section moment of inertia.

[0045] It can be seen that the bottom of rail 1 at mid-span (furthest from the neutral axis) experiences the greatest tensile strain. Placing the graphene fiber sensor 2 there allows for sensitive capture of the rail's strain response under wheel load, achieving high measurement sensitivity and response accuracy.

[0046] The specific structure of the graphene optical fiber sensor 2 is as follows: Figure 4As shown, a layer of graphene 5 synthesized by CVD is coated on the outer surface of the optical fiber 6, and a graphene fiber sensor outer cladding 4 is coated on the outer surface of the graphene 5. Graphene fiber has excellent mechanical properties, including extremely high tensile strength and good flexibility. It can adapt to the complex stress environment at the bottom of the rail and is not prone to breakage or detachment, ensuring long-term stable operation. Therefore, installing the graphene fiber sensor in this area of ​​maximum tensile strain not only conforms to mechanical principles but also provides good reliability and durability under actual operating conditions. The coating thickness of the graphene 5 ranges from 5-50nm, with an optimal value of approximately 10nm, forming a 1-3 layer graphene structure. The degree of π electron delocalization in multilayer graphene is closely related to the strength of the interlayer van der Waals coupling, which affects its strain response sensitivity and mechanical stability. Thinner coatings improve strain response sensitivity but reduce mechanical strength. Thicker coatings enhance load-bearing capacity but may reduce response sensitivity. Therefore, the thickness needs to be comprehensively optimized. From a microscopic perspective, when the train wheel load acts on the track, the optical fiber sensor at the bottom of the rail is subjected to longitudinal tensile stress, which is transmitted to the graphene coating. Under the action of the load, the graphene lattice stretches, and the slight change in the C-C bond length leads to a change in the band structure, which is specifically manifested in the following three situations:

[0047] 1. The electron cloud is redistributed, reducing the local uniformity of electron density;

[0048] 2. The band gap is fine-tuned and the probability of electron transition changes;

[0049] 3. Refractive index changes can be related to electronic structure changes through the Kramers-Crony relationship.

[0050] These microstructural changes cause the effective refractive index of light propagating in the optical fiber to change, which in turn causes the reflection or interference wavelength of the optical fiber to drift, and is converted into electrical signals through photoelectric conversion for real-time reading.

[0051] The data acquisition module includes a multi-channel photoelectric converter and a low-noise amplifier, which are used to perform high-sensitivity parallel processing of the output signals of multiple graphene fiber sensors. The multi-channel photoelectric converter connects the optical signal output by each graphene fiber sensor 2 to an independent channel and introduces an InGaAs photodetector through a coupling structure. The typical response band of this photoelectric converter is 1,100-1,700nm, with a quantum efficiency of over 85%, a response speed of less than 10ns, and the characteristics of low dark current (<5nA) and low noise figure. It can accurately capture the weak wavelength drift caused by periodic strain excitation and convert it into a weak electrical signal. The converted weak electrical signal is amplified by a programmable low-noise amplifier (LNA) with a bandwidth of DC-10MHz and a gain range of 20-40dB to ensure that the signal dynamic range meets the input requirements of the analog-to-digital converter (ADC).

[0052] The ADC is a high-speed sampling module with 16-bit resolution and a sampling rate of up to 1MSa / s. It features multi-channel synchronous acquisition capabilities, ensuring synchronization of signals from different sensors. The system incorporates an automatic gain control and calibration module, which adaptively matches channels based on initial static calibration data and real-time signal strength. The output characteristics of the multi-channel photoelectric converter, low-noise amplifier, and graphene fiber sensor 2 are matched. To achieve this matching, the system design includes the following steps:

[0053] 1. Static initial wavelength calibration: record the central reflection wavelength of each optical fiber in the unloaded state;

[0054] 2. Gain adjustment matching: automatically adjust LNA gain according to the output voltage signal amplitude;

[0055] 3. Temperature drift compensation algorithm: using dual-channel comparison or redundant fiber optic temperature reference to eliminate interference caused by non-load;

[0056] 4. Sensitivity factor correction: Adjust the strain-wavelength drift coefficient according to the sensor batch parameters.

[0057] The data processing module is connected to the data acquisition module. The data processing module uses the analysis of the wavelength change of the optical signal to deduce the wheel polygon order based on the characteristic that the refractive index of graphene changes with pressure. The specific derivation steps of the wheel polygon order are as follows: when the polygonal wheel contacts the rail 1 during the train operation, the graphene fiber optic sensor 2 detects the optical signal with wavelength drift caused by periodic strain excitation on the rail 1; the data acquisition module collects the optical signal with wavelength drift output by the graphene fiber optic sensor array module, and converts the wavelength drift in the optical signal into an electrical signal through a photoelectric converter; the data processing module decodes and converts the electrical signal to obtain the stress and strain signal generated by the rail 1 under the wheel-rail interaction, such as Figure 5 As shown, the stress and strain signals are subjected to time-frequency analysis to extract the main frequency components, as shown in Figure 6 As shown in Figure 1, the wheel polygon order corresponding to the main frequency component is derived by combining the train running speed and wheel parameters.

[0058] The wheel polygon order is:

[0059]

[0060] Where n is the wheel polygon order, R is the wheel radius, and f s is the main frequency component of the strain signal collected by the graphene fiber optic sensor, and v is the train speed.

[0061] The main frequency components are:

[0062]

[0063] V(t)=k·Δλ(t)

[0064] Where, f s is the main frequency component of the strain signal collected by the graphene optical fiber sensor, V(f) is the stress-strain signal, V(t) is the electrical signal, k is the conversion coefficient, Δλ(t) is the wavelength drift, t is time, and f is frequency.

[0065] The train wheel radius measurement method is that when the wheel has polygonal or ovality, the features on the rim profile will appear in a periodic form in the sensor acquisition signal. Combined with the position and distance of the sensor layout, the corresponding angle change can be calculated to estimate the wheel radius. The wheel radius calculation expression is as follows:

[0066]

[0067] Where R is the wheel radius and ω is the angular velocity of the train.

[0068] The train speed measurement method uses two pre-placed sensors with a known spacing. When the train wheels reach each sensor position, the mechanical strain generated by the contact between the wheel and the rail excites the sensor wavelength drift. The strain peak arrival times t1 and t2 of each adjacent sensor are recorded. The time difference Δt between adjacent sensors is calculated, combined with the known spacing d between the two sensors, and the train speed is calculated using the speed formula. The train speed calculation expression is as follows:

[0069]

[0070] Δt=t1-t2

[0071] Where v is the train speed, ω is the train angular velocity, d is the distance between adjacent sensors, and Δt is the time difference between the strain peak arrival times of adjacent sensors.

[0072] The wheel polygon order display module is connected to the data processing module. It is responsible for receiving the wheel polygon order calculated by the data processing module and dynamically displaying the polygon order of each wheel in digital form on the interface, helping operators to monitor the system's operating status and test results in a timely manner.

[0073] The rail train wheel polygon detection system also includes an alarm module, connected to the data processing module, for real-time monitoring of the wheel polygon order detected by the data processing module and providing over-limit alarms. Upon receiving the calculation results from the data processing module, the alarm module compares the current wheel polygon order with a set warning threshold. If the polygon order of one or more wheelsets exceeds the threshold, an audible and visual alarm is automatically triggered or a signal is sent to the operation and maintenance system to prompt maintenance. The alarm information includes key data such as the detection time, axle number, order value, and the extent of the over-limit.

[0074] To ensure the accuracy and adaptability of the alarm module, the warning threshold is adjustable. Users can customize the configuration according to their actual needs in the system settings. The system provides a default initial threshold setting scheme when it leaves the factory. The threshold is determined based on the following four points:

[0075] 1. Wheel-rail system dynamics analysis results: Based on the relationship curve between polygon order and wheel-rail impact force obtained by simulation, the critical order value at which the impact force increases significantly is selected as the basic threshold;

[0076] 2. Industry standards and safety limits: Industry standards for limits on wheel roundness deviation and periodic out-of-roundness;

[0077] 3. Statistical analysis of historical fault data: Based on the analysis of fault cases during typical line operations, high-risk order concentrated distribution areas are extracted as warning values;

[0078] 4. Correlation with train speed: High-order polygons have a greater impact on high-speed trains, and the initial threshold is set according to the designed maximum operating speed.

[0079] To ensure that the alarm module adapts to different track scenarios and train types, the system will provide the following threshold adjustment guidelines:

[0080] A. Classification by train type:

[0081] A1. High-speed trains: It is recommended to set a low-order threshold (e.g., an alarm is triggered when the order is > 3), because high-speed operation is more sensitive to periodic geometric errors.

[0082] A2. Urban rail / subway: The threshold can be appropriately relaxed (e.g., order > 5) to account for lower vehicle speeds.

[0083] A3. Heavy-load freight trains: Set an appropriate threshold based on the actual wheel-rail impact tolerance and load level.

[0084] B. Adjustment by track type and structure:

[0085] B1. Welded rail line: The allowable order threshold is slightly higher because the track structure is strong and the waveform transmission is obvious;

[0086] B2. Turnout area or bridge section: A lower threshold can be set to increase sensitivity;

[0087] B3. Elastic roadbed and floating track: moderately relax and consider the vibration reduction capability of the structure itself.

[0088] C. Dynamic adjustment based on working conditions and operation and maintenance strategies:

[0089] C1. If the system identifies a certain order multiple times in a row without causing an actual fault, the order can be temporarily relaxed after evaluation by the operation and maintenance personnel;

[0090] C2. Through cloud platform backend analysis, the optimal threshold can be automatically adjusted based on big data retrospective analysis;

[0091] C3. Under special operating conditions such as the Spring Festival travel rush and high temperatures, it is recommended to set conservative thresholds to improve safety redundancy.

[0092] Example 2

[0093] This embodiment provides a control method for a railway train wheel polygon detection system. Figure 7 As shown, polygon detection of railway train wheels is performed by using the railway train wheel polygon detection system proposed in Example 1, including the following steps:

[0094] S1. When a train with polygonal wheels passes through the detection area, the graphene fiber sensor 2 in the graphene fiber sensor array module senses the change in track pressure 3 of the rail 1 in real time, and obtains an optical signal with wavelength drift caused by periodic strain excitation.

[0095] S2. The data acquisition module collects the optical signal with wavelength drift output by the graphene optical fiber sensor array module, and converts the wavelength drift in the optical signal into an electrical signal through a photoelectric converter.

[0096] The data acquisition module includes a multi-channel photoelectric converter and a low-noise amplifier. The multi-channel photoelectric converter connects the optical signal output by each graphene fiber sensor to an independent channel, obtains the wavelength drift caused by periodic strain excitation, and converts it into an electrical signal.

[0097] S3. Decode and convert the electrical signal through the data processing module to obtain the stress and strain signal generated by the rail 1 under the wheel-rail interaction, and perform time-frequency analysis on the stress and strain signal to derive the wheel polygon order.

[0098] The data processing module performs time-frequency analysis on the periodic strain signal, extracts the main frequency components, and combines the train running speed and wheel parameters to derive the wheel polygon order corresponding to the main frequency components.

[0099] S4. Visualize the wheel polygon order through the wheel polygon order display module, and monitor the wheel polygon order in real time and issue an over-limit alarm through the alarm module.

[0100] The rest is the same as in Example 1.

[0101] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A railway train wheel polygon detection system, characterized in that: The invention comprises a graphene optical fiber sensor array module, a data acquisition module, a data processing module and a wheel polygon order display module, wherein the graphene optical fiber sensor array module is arranged at the bottom of a rail (1) and is used to sense changes in the track pressure (3) of the rail (1) and obtain an optical signal with wavelength drift caused by periodic strain excitation; the data acquisition module is used to collect the optical signal with wavelength drift output by the graphene optical fiber sensor array module and convert the wavelength drift in the optical signal into an electrical signal; the data processing module is used to analyze the electrical signal through a signal processing algorithm and derive the wheel polygon order; and the wheel polygon order display module is used to receive the wheel polygon order from the data processing module and perform visual processing on the wheel polygon order.

2. The railway train wheel polygon detection system according to claim 1, characterized in that: The graphene optical fiber sensor array module comprises a plurality of graphene optical fiber sensors (2). The specific structure of the graphene optical fiber sensor (2) is as follows: a layer of graphene (5) synthesized by CVD is coated on the outside of an optical fiber (6), and a layer of graphene optical fiber sensor outer cladding (4) is coated on the outside of the graphene (5).

3. The railway train wheel polygon detection system according to claim 2, characterized in that: The graphene optical fiber sensor (2) is laterally arranged at the bottom of the rail (1) between two adjacent sleepers (7) by means of an adhesive, and the graphene optical fiber sensor (2) is located in the mid-span area of ​​the rail (1).

4. The railway train wheel polygon detection system according to claim 3, characterized in that: The installation positions of each graphene optical fiber sensor (2) are evenly spaced and avoid the connection points and welds of the rail (1), and the adhesive is a polyurethane structural adhesive.

5. The railway train wheel polygon detection system according to claim 1, characterized in that: The data acquisition module comprises a multi-channel photoelectric converter and a low-noise amplifier. The multi-channel photoelectric converter connects the optical signal output by each graphene optical fiber sensor (2) to an independent channel, obtains the wavelength drift caused by periodic strain excitation, and converts it into an electrical signal. The low-noise amplifier is used to amplify the electrical signal to ensure that the signal dynamic range meets the input requirements of the analog-to-digital converter.

6. The railway train wheel polygon detection system according to claim 5, characterized in that: The output characteristics of the multi-channel photoelectric converter, the low-noise amplifier and the graphene optical fiber sensor (2) match each other.

7. The railway train wheel polygon detection system according to claim 1, characterized in that: The specific derivation steps of the wheel polygon order are as follows: when a polygonal wheel contacts a rail (1) during train operation, a graphene optical fiber sensor (2) is used to detect a wavelength drift optical signal on the rail (1) caused by periodic strain excitation; a data acquisition module is used to acquire a wavelength drift optical signal output by a graphene optical fiber sensor array module, and the wavelength drift in the optical signal is converted into an electrical signal through a photoelectric converter; a data processing module is used to decode and convert the electrical signal to obtain a stress and strain signal generated by the rail (1) under wheel-rail interaction, and a time-frequency analysis is performed on the stress and strain signal to extract a main frequency component. The wheel polygon order corresponding to the main frequency component is derived by combining the train running speed and wheel parameters.

8. The railway train wheel polygon detection system according to claim 7, characterized in that: The wheel polygon order is: Where n is the wheel polygon order, R is the wheel radius, and f s is the main frequency component of the strain signal collected by the graphene fiber optic sensor, V(f) is the stress-strain signal, k is the conversion coefficient, Δλ(t) is the wavelength drift, t is time, f is frequency, v is the train speed, ω is the train angular velocity, d is the distance between adjacent sensors, and Δt is the time difference between the strain peak arrival times of adjacent sensors.

9. The railway train wheel polygon detection system according to claim 1, characterized in that: It also includes an alarm module, which is connected to the data processing module and is used to monitor the wheel polygon order obtained by the data processing module in real time and issue an over-limit alarm. When the system detects that the wheel polygon order exceeds the set warning threshold, the alarm module automatically triggers an alarm.

10. A control method for a railway train wheel polygon detection system, characterized in that: The method of performing polygon detection on a railway train wheel by using the railway train wheel polygon detection system according to any one of claims 1 to 9 comprises the following steps: When a train with polygonal wheels passes through a detection area, a graphene optical fiber sensor (2) in a graphene optical fiber sensor array module senses a change in track pressure (3) of a rail (1) in real time, and obtains an optical signal having a wavelength drift caused by periodic strain excitation; The data acquisition module collects the optical signal with wavelength drift output by the graphene optical fiber sensor array module, and converts the wavelength drift in the optical signal into an electrical signal through a photoelectric converter; Decoding and converting the electrical signal through a data processing module to obtain a stress and strain signal generated by the rail (1) under the interaction of the wheel and rail, and performing time-frequency analysis on the stress and strain signal to derive the wheel polygon order; The wheel polygon order is visualized through the wheel polygon order display module, and the wheel polygon order is monitored in real time and an over-limit alarm is issued through the alarm module.

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