A train wheel position monitoring method, device and system

Abstract

The application provides a train wheel position monitoring method, device and system, which can accurately measure the position of the train wheel by periodically using a stereo camera and a thermal imager, and automatically display the measurement image, thereby significantly improving the accuracy and robustness of the measurement. When an image selection instruction is received, the system automatically marks the image to be measured and the train wheel to be measured in the image, then processes the image through an edge detector to extract the vertical edge of the train wheel, and further uses an iterative algorithm to accurately extract the horizontal coordinate of the edge white point. The arc angle between the horizontal center of the image and the edge white point is calculated to determine the width of the train wheel, and the offset is compared with a preset template to determine whether the train wheel is displaced. This method not only reduces the dependence on camera settings, improves the operation efficiency, and reduces the implementation and operation cost, but also meets the strict requirements of the railway transportation system for high safety and high efficiency, and has great popularization value.

Description

Technical Field

[0001] This invention relates to the field of position monitoring and positioning technology, and in particular to a method, device and system for monitoring the position of train wheels. Background Technology

[0002] In railway transportation systems, accurate monitoring of train wheel positions is crucial for ensuring operational safety, improving efficiency, and preventing major accidents. First, abnormal wheel positions can lead to serious consequences such as derailments or collisions, making real-time monitoring an effective preventative measure. Second, the railway operating environment is complex, including curves, gradients, and bridges; accurate wheel position monitoring helps trains operate safely under various conditions. With continuous advancements in railway technology and increasingly stringent safety requirements, wheel position monitoring remains particularly important.

[0003] Currently, accelerometers are primarily used to monitor train wheel position. Their principle is based on oscillation, enabling them to detect the acceleration of an object in a specific direction, thereby measuring its motion. In this process, the accelerometer works by capturing vibration signals between the wheel and the rail. However, factors such as rail mass, rail gaps, curves, and track geometry can affect the characteristics of the vibration signal, potentially leading to decreased measurement accuracy. Furthermore, accelerometers require regular calibration, which increases both cost and operational complexity. Although they can monitor vibration signals, their measurement range is limited by factors such as wheel size and operating speed.

[0004] Therefore, improving the accuracy and reliability of wheel position monitoring is an urgent problem to be solved. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method, apparatus and system for monitoring the position of train wheels to solve the problem that the current train wheel position monitoring is not accurate enough.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0007] The first aspect of this invention discloses a method for monitoring the position of train wheels, the method comprising:

[0008] The positions of train wheels are periodically measured using a stereo camera and a thermal imager, and the measured images are displayed; wherein the thermal imager is selected in advance from multiple sensors;

[0009] When an image selection instruction is received, the image corresponding to the selection instruction is marked as the image to be measured, and the object selected by the selection instruction in the image to be measured is marked as the wheel to be measured.

[0010] The image to be measured is processed by an edge detector to obtain a binary image containing the vertical edge of the wheel to be measured;

[0011] The coordinates of the cursor in the image to be measured are obtained, and the horizontal coordinates of the left and right white points on the vertical edge of the wheel to be measured are extracted from the binary image using an iterative algorithm. The left white point is the edge point on the vertical left edge of the wheel to be measured that is closest to the coordinates; the right white point is the edge point on the vertical right edge of the wheel to be measured that is closest to the coordinates.

[0012] The width of the wheel under test is determined by calculating the radian angle between the horizontal center of the binary image, the white point on the left edge, and the white point on the right edge.

[0013] The width is compared with the template edge image of the wheel and guide rail to obtain the offset;

[0014] If the offset is not greater than the preset value, it is determined that the wheel under test has not shifted.

[0015] Preferably, the process of selecting a thermal imager from multiple sensors includes:

[0016] Obtain the weighted ranking results of each expert on each horizontal wheel position measurement factor to construct a ranking matrix, and calculate the Kendall consistency coefficient;

[0017] Calculate the chi-square value based on the Kendall consistency coefficient;

[0018] If the chi-square value is greater than the preset threshold, it is determined that the weight ranking results of each expert on each horizontal rotation measurement factor are consistent.

[0019] The ranking matrix is ​​analyzed using the analytic hierarchy process (AHP) to obtain the AHP level matrix.

[0020] Thermal imagers are selected from multiple sensors based on the hierarchical analysis hierarchy matrix.

[0021] Preferably, the step of calculating the radian angle between the horizontal center of the binary image and the white point on the left edge, and the white point on the right edge, respectively, to determine the width of the wheel to be measured includes:

[0022] Calculate the left distance between the white point at the left edge and the horizontal center of the image, and the right distance between the white point at the right edge and the horizontal center of the image;

[0023] Based on the left distance and the right distance, calculate the left radian angle between the horizontal center of the image and the white point on the left edge, and the right radian angle between the horizontal center of the image and the white point on the right edge, respectively.

[0024] The width of the wheel to be tested is calculated based on the left radian angle and the right radian angle.

[0025] Preferably, the step of calculating the left radian angle between the horizontal center of the image and the white point at the left edge, and the right radian angle between the horizontal center of the image and the white point at the right edge, based on the left distance and the right distance, includes:

[0026] Obtain the horizontal field of view angle of the stereo camera;

[0027] The angle of each pixel is calculated based on the horizontal field of view angle and the horizontal center of the image;

[0028] Calculate the product of the angle of each pixel and the left distance to obtain the left radian angle between the horizontal center of the image and the white point on the left edge;

[0029] Calculate the product of the angle of each pixel and the right distance to obtain the right radian angle between the horizontal center of the image and the white point on the right edge.

[0030] Preferably, the step of calculating the width of the wheel under test based on the left radian angle and the right radian angle includes:

[0031] The distance between the left end of the wheel under test and the horizontal center of the image is calculated based on the left radian angle and the right radian angle; the distance between the right end of the wheel under test and the horizontal center of the image is calculated based on the left radian angle and the right radian angle.

[0032] The width of the wheel under test is obtained by calculating the modulus of the distance difference between the left end distance and the right end distance.

[0033] A second aspect of the present invention discloses a train wheel position monitoring device, the device comprising:

[0034] A measurement unit is used to periodically measure the position of train wheels using a stereo camera and a thermal imager, and to display the measured images; wherein the thermal imager is pre-selected from multiple sensors;

[0035] The marking unit is used to mark the image corresponding to the image selection instruction as the image to be measured when an image selection instruction is received, and to mark the object selected by the selection instruction in the image to be measured as the wheel to be measured.

[0036] The processing unit is used to process the image to be measured by an edge detector to obtain a binary image containing the vertical edge of the wheel to be measured.

[0037] The extraction unit is used to obtain the coordinates of the cursor in the image to be measured, and to extract the horizontal coordinates of the left and right edge white points of the vertical edge of the wheel to be measured from the binary image through an iterative algorithm. The left edge white point is the edge point on the vertical left edge of the wheel to be measured that is closest to the coordinates; the right edge white point is the edge point on the vertical right edge of the wheel to be measured that is closest to the coordinates.

[0038] The first calculation unit is used to calculate the arc angle between the horizontal center of the binary image and the white point on the left edge, and the white point on the right edge, respectively, to determine the width of the wheel to be measured;

[0039] A comparison unit is used to compare the width with the template edge image of the wheel and guide rail to obtain the offset;

[0040] The first determining unit is used to determine that the wheel under test has not shifted if the offset is not greater than a preset value.

[0041] Preferably, the device further includes:

[0042] The acquisition unit is used to acquire the weight ranking results of each expert on each horizontal wheel position measurement factor to construct a ranking matrix and calculate the Kendall consistency coefficient.

[0043] The second calculation unit is used to calculate the chi-square value based on the Kendall consistency coefficient.

[0044] The second determining unit is used to determine that the weight ranking results of each expert on each horizontal wheel position measurement factor are consistent if the chi-square value is greater than a preset threshold.

[0045] The analysis unit is used to analyze the ranking matrix using the analytic hierarchy process (AHP) to obtain the AHP ranking matrix.

[0046] A screening unit is used to select thermal imagers from multiple sensors based on the hierarchical analysis level matrix.

[0047] Preferably, the first computing unit includes:

[0048] The first calculation module is used to calculate the left distance between the left edge white point and the horizontal center of the image, and the right distance between the right edge white point and the horizontal center of the image;

[0049] The second calculation module is used to calculate, based on the left distance and the right distance, the left radian angle between the horizontal center of the image and the white point on the left edge, and the right radian angle between the horizontal center of the image and the white point on the right edge, respectively.

[0050] The third calculation module is used to calculate the width of the wheel to be measured based on the left radian angle and the right radian angle.

[0051] Preferably, the second calculation module includes:

[0052] The acquisition submodule is used to acquire the horizontal field of view angle of the stereo camera;

[0053] The first calculation submodule is used to calculate the angle of each pixel based on the horizontal field of view angle and the horizontal center of the image;

[0054] The second calculation submodule is used to calculate the product of the angle of each pixel and the left distance to obtain the left radian angle between the horizontal center of the image and the white point on the left edge;

[0055] The third calculation submodule is used to calculate the product of the angle of each pixel and the right distance to obtain the right radian angle between the horizontal center of the image and the white point on the right edge.

[0056] A third aspect of this invention discloses a train wheel position monitoring system, the system comprising a controller and a measuring device;

[0057] The measuring device is set at a preset measuring position on the train; the outer surface of the measuring device is provided with a shell;

[0058] The measuring device includes a stereo camera and a thermal imager; the stereo camera and the thermal imager are respectively connected to the controller;

[0059] The stereo camera is used to capture images of the positions of the train's wheels and send them to the controller;

[0060] The thermal imager is used to measure the position of the train wheels and send the data to the controller;

[0061] The controller is used to execute the train wheel position monitoring method disclosed in the first aspect of the present invention.

[0062] Based on the above embodiments of the present invention, a method, apparatus, and system for monitoring train wheel positions are provided. By periodically using a stereo camera and a thermal imager to accurately measure the position of train wheels and automatically displaying the measurement images, the accuracy and robustness of the measurement are significantly improved. When an image selection command is received, the system automatically marks the image to be measured and the wheel to be measured within it. Then, the image is processed using an edge detector to extract the vertical edge of the wheel, and further, an iterative algorithm is used to accurately extract the horizontal coordinates of the white points on the edge. The wheel width is determined by calculating the radian angle between the horizontal center of the image and the white points on the edge, and compared with a preset template to obtain the offset, thereby determining whether the wheel has shifted. This method not only reduces dependence on camera settings and improves operational efficiency but also reduces implementation and operating costs, while meeting the stringent requirements of high safety and high efficiency in railway transportation systems, demonstrating significant potential for widespread application. Attached Figure Description

[0063] 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0064] Figure 1 A flowchart of a train wheel position monitoring method provided in an embodiment of the present invention;

[0065] Figure 2 A flowchart for obtaining a thermal imager through screening, provided in an embodiment of the present invention;

[0066] Figure 3 A schematic diagram illustrating the motion characteristics of a wheelset on a train track according to an embodiment of the present invention;

[0067] Figure 4 This is a schematic diagram of the motion of a wheelset on a tangential track provided in an embodiment of the present invention;

[0068] Figure 5 A schematic diagram illustrating the wavelength formed on the rail by the periodic lateral movement (oscillation) of a wheel, as provided in an embodiment of the present invention;

[0069] Figure 6 Example images provided for embodiments of the present invention;

[0070] Figure 7 An example image of a binary image containing the vertical edge of the wheel under test, provided for an embodiment of the present invention;

[0071] Figure 8 This is a schematic diagram illustrating the determination of the width of a wheel to be measured, provided in an embodiment of the present invention.

[0072] Figure 9 A flowchart for determining the width of a wheel to be tested, provided as an embodiment of the present invention;

[0073] Figure 10 A flowchart for calculating the left and right radian angles provided in an embodiment of the present invention;

[0074] Figure 11 This is a schematic diagram of a train wheel position monitoring system provided in an embodiment of the present invention;

[0075] Figure 12 An example diagram illustrating the setup of the measuring device provided in an embodiment of the present invention;

[0076] Figure 13 Example diagram of the housing of the measuring device provided in the embodiment of the present invention;

[0077] Figure 14 This is a structural block diagram of a train wheel position monitoring device provided in an embodiment of the present invention. Detailed Implementation

[0078] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0079] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0080] As the background technology indicates, current methods for monitoring train wheel position primarily rely on the oscillation principle of accelerometers. However, factors such as rail quality, rail gaps, curves, and track geometry can affect the characteristics of the vibration signal, leading to decreased measurement accuracy. Furthermore, accelerometer sensors require periodic calibration, increasing cost and operational complexity. The measurement range is also limited by factors such as wheel size and operating speed.

[0081] Therefore, embodiments of the present invention provide a method, apparatus, and system for monitoring train wheel positions. By periodically utilizing a stereo camera and a thermal imager to accurately measure the position of train wheels and automatically displaying the measured images, the accuracy and robustness of the measurement are significantly improved. When an image selection command is received, the system automatically marks the image to be measured and the wheel to be measured within it. Subsequently, the image is processed by an edge detector to extract the vertical edge of the wheel, and further, an iterative algorithm is used to accurately extract the horizontal coordinates of the white points on the edge. The wheel width is determined by calculating the radian angle between the horizontal center of the image and the white points on the edge, and compared with a preset template to obtain the offset, thereby determining whether the wheel has shifted. This method not only reduces dependence on camera settings and improves operational efficiency but also reduces implementation and operating costs, while meeting the stringent requirements of high safety and high efficiency in railway transportation systems, demonstrating significant potential for widespread application.

[0082] See Figure 1 The flowchart illustrates a train wheel position monitoring method provided by an embodiment of the present invention. The monitoring method includes:

[0083] Step S101: Periodically measure the position of the train wheels using a stereo camera and a thermal imager, and display the measured images.

[0084] It is understood that the thermal imager is selected in advance from multiple sensors; the specific selection process is detailed in the embodiments of this invention. Figures 2 to 5 The content in [the document / article].

[0085] In the specific implementation step S101, the lateral position of the train wheel and rail, i.e. the position of the train wheel, is periodically measured using a stereo camera (e.g., ZED Stereo Camera, or other stereo cameras) and a thermal imager (e.g., FLIR Infrared Thermal Imager, FIR) and the measured images are displayed on a computer monitor so that the user can select the wheel to be measured.

[0086] It should be noted that the measured image is a visual image containing stereo depth information. For example... Figure 6 The example image shown allows the user to select the object whose width they want to measure, i.e., the wheel. Figure 6 In the displayed content, the crosshair indicates the position of the mouse cursor.

[0087] In some specific embodiments, if the depth image measured by the stereo camera and thermal imager is available, the distance between the selected cursor position and the stereo camera lens is displayed next to the mouse cursor.

[0088] Step S102: When an image selection instruction is received, the image corresponding to the selection instruction is marked as the image to be measured, and the object selected by the selection instruction in the image to be measured is marked as the wheel to be measured.

[0089] In the specific implementation of step S102, after receiving the image selection instruction triggered by the user from the computer monitor, the image corresponding to the selection instruction is taken as the image to be measured; and the object selected by the user in the image to be measured is marked as the wheel to be measured; so that the width of the wheel to be measured in the image to be measured can be measured in the future to determine whether the wheel to be measured has been deviated.

[0090] Step S103: The image to be measured is processed by an edge detector to obtain a binary image containing the vertical edge of the wheel to be measured.

[0091] In the specific implementation step S103, the edge detector method (Canny edge detector) is used to process the user-selected image to be measured, resulting in a binary image containing the vertical edge of the wheel to be measured. The binary image is, for example... Figure 7 As shown.

[0092] It's important to note that the Canny edge detector is an edge detection algorithm that identifies points in an image where there are significant changes in intensity, i.e., edges. These edges typically correspond to the outlines of objects in the image or the boundaries between different regions. A binary image, also known as an edge image, contains only edge pixels (usually white) and non-edge pixels (usually black).

[0093] Step S104: Obtain the coordinates of the cursor in the image to be measured, and extract the horizontal coordinates of the left and right edge white points of the vertical edge of the wheel to be measured from the binary image using an iterative algorithm.

[0094] It should be noted that the white dot on the left edge is the edge point with the shortest distance coordinates on the vertical left edge of the wheel under test; the white dot on the right edge is the edge point with the shortest distance coordinates on the vertical right edge of the wheel under test.

[0095] In the specific implementation step S104, the points selected by the user in the image to be measured, that is, the coordinates of the mouse cursor, are obtained; then, through an iterative algorithm, the edges in the binary image are searched to both sides of the coordinates, thereby extracting the horizontal coordinates of the left edge white point and the right edge white point in the vertical edge of the wheel to be measured.

[0096] It is understandable that the horizontal coordinates of the left and right edge white points are obtained by iteratively checking pixels one by one on both sides of the cursor's coordinates on the binary image until an edge pixel (white pixel) is found.

[0097] Step S105: Calculate the radian angle between the horizontal center and the white point on the left edge of the binary image, and the white point on the right edge, respectively, to determine the width of the wheel to be measured.

[0098] In the specific implementation of step S105, combined with Figure 8 The diagram shown illustrates the determination of the width of the wheel to be measured. The left distance between the left edge white dot and the horizontal center of the image is calculated based on the horizontal coordinate of the left edge white dot; the right distance between the right edge white dot and the horizontal center of the image is calculated based on the horizontal coordinate of the right edge white dot; the left and right radian angles are calculated based on the left and right distances, and thus the width of the wheel to be measured is calculated based on the left and right radian angles.

[0099] It should be noted that the specific process for determining the width of the wheel to be measured is detailed in the embodiments of this invention. Figure 9 The content in Figure 9 include:

[0100] Step S901: Calculate the left distance between the white point on the left edge and the horizontal center of the image, and the right distance between the white point on the right edge and the horizontal center of the image.

[0101] In the specific implementation step S901, the left distance between the left edge white point and the horizontal center of the image is calculated based on the horizontal coordinate of the left edge white point, as shown in formula (1); the right distance between the right edge white point and the horizontal center of the image is calculated based on the horizontal coordinate of the right edge white point, as shown in formula (2).

[0102]

[0103] Where, d xL Indicates the left distance between the white dot at the left edge and the horizontal center of the image; x L The horizontal coordinates of the white dot on the left edge; w I This represents the width of the horizontal center of the image (specifically, the image width in units of dots).

[0104]

[0105] Where, d xR Indicates the right distance between the white dot at the right edge and the horizontal center of the image; x R The horizontal coordinates of the white dot on the right edge; w I This represents the width of the horizontal center of the image (specifically, the image width in units of dots).

[0106] Step S902: Based on the left distance and the right distance, calculate the left radian angle between the horizontal center of the image and the white point on the left edge, and the right radian angle between the horizontal center of the image and the white point on the right edge.

[0107] In the specific implementation step S902, the left radian angle between the horizontal center of the image and the left edge white point is calculated based on the left distance between the left edge white point and the horizontal center of the image; the right radian angle between the horizontal center of the image and the right edge white point is calculated based on the right distance between the right edge white point and the horizontal center of the image.

[0108] It should be noted that the specific process for calculating the left and right radian angles is detailed in the embodiments of this invention. Figure 10 The content in Figure 10 include:

[0109] Step S1001: Obtain the horizontal field of view angle of the stereo camera.

[0110] Step S1002: Calculate the angle of each pixel based on the horizontal field of view angle and the horizontal center of the image.

[0111] In the specific implementation of step S1002, the angle of each pixel is calculated based on the horizontal field of view of the stereo camera and the horizontal center of the image, as shown in formula (3).

[0112] aPIX = aHFOV / w I (3)

[0113] Where aPIX represents the angle of each pixel, and aHFOV represents the horizontal field of view of the stereo camera.

[0114] Step S1003: Calculate the product of the angle of each pixel and the left distance to obtain the left radian angle between the horizontal center of the image and the white point on the left edge.

[0115] It should be noted that the process of calculating the left radian angle is shown in formula (4).

[0116] ∠XLC=d xL ·aPIX (4)

[0117] Where ∠XLC represents the left radian angle between the horizontal center of the image and the white dot on the left edge; d xL aPIX represents the left distance between the white dot at the left edge and the horizontal center of the image; aPIX represents the angle of each pixel.

[0118] Step S1004: Calculate the product of the angle of each pixel and the right distance to obtain the right radian angle between the horizontal center of the image and the white point on the right edge.

[0119] It should be noted that the process of calculating the right radian angle is shown in formula (5).

[0120] ∠XRC=d xR ·aPIX (5)

[0121] Where ∠XRC represents the right radian angle between the horizontal center of the image and the white dot on the right edge; d xR aPIX represents the right distance between the white dot at the right edge and the horizontal center of the image; aPIX represents the angle of each pixel.

[0122] Step S903: Calculate the width of the wheel to be measured based on the left and right radian angles.

[0123] In the specific implementation step S903, the left end distance between the left end of the wheel under test and the horizontal center of the image is calculated based on the left radian angle and the right radian angle, as shown in formula (6), and the right end distance between the right end of the wheel under test and the horizontal center of the image, as shown in formula (7); then the modulus of the distance difference between the left end distance and the right end distance is calculated to obtain the width of the wheel under test, as shown in formula (8).

[0124] d xLmm =d C2OX ·sin(∠XLC) (6)

[0125] d xRmm =d C2OX ·sin(∠XRC) (7)

[0126] d xRxLmm =|d xRmm -d xLmm | (8)

[0127] Where, d xRmm Indicates the distance to the left end; d xRmm Indicates the distance to the right end; d C2OX d represents the distance from the stereo camera to the x-plane where the object is located; xRxLmm This indicates the width of the wheel being measured.

[0128] Step S106: Compare the width with the template edge images of the wheel and guide rail to obtain the offset.

[0129] Understandably, the template edge images of the wheel and guide rail are reference images. The offset of the wheel under test is obtained by comparing the width of the wheel under test with the reference image.

[0130] Step S107: If the offset is not greater than the preset value, it is determined that the wheel under test has not been displaced.

[0131] Understandably, if the offset of the wheel under test is greater than the preset value, it is determined that the wheel under test has shifted.

[0132] In this embodiment of the invention, the accuracy and robustness of the measurement are significantly improved by periodically using a stereo camera and a thermal imager to accurately measure the position of train wheels and automatically displaying the measurement images. When an image selection command is received, the system automatically marks the image to be measured and the wheel to be measured within it. The image is then processed by an edge detector to extract the vertical edge of the wheel, and an iterative algorithm is used to accurately extract the horizontal coordinates of the white points on the edge. The wheel width is determined by calculating the radian angle between the horizontal center of the image and the white points on the edge, and compared with a preset template to obtain the offset, thereby determining whether the wheel has shifted. This method not only reduces dependence on camera settings and improves operational efficiency but also reduces implementation and operating costs, while meeting the stringent requirements of high safety and high efficiency in railway transportation systems, demonstrating significant potential for widespread application.

[0133] The above embodiments of the present invention Figure 1 For the specific implementation method of pre-selecting thermal imagers from multiple sensors involved in the above, please refer to [link / reference]. Figure 2 The flowchart illustrating the selection of thermal imagers provided in an embodiment of the present invention is shown, including:

[0134] Understandably, a thorough study and evaluation of the wheelset's motion characteristics on the train track is necessary before determining a method for monitoring train wheel positions. See also Figure 3 As shown, when a train travels along a track, the wheelset not only moves linearly along the longitudinal axis of the track, but also undergoes a lateral oscillating motion along the transverse axis. This phenomenon is called Klingel motion, which manifests as a sinusoidal lateral oscillation.

[0135] During operation, the wheelsets will continuously move closer to the center of the track. Figure 4 The diagram illustrates the motion of a wheelset on a tangential track, showing that the wheel not only undergoes lateral displacement but also rotates around both the vertical (Z-axis) and horizontal (Y-axis) axes. During the oscillation of the wheelset, longitudinal and lateral creep forces are generated between the wheel and the track, along with a creep torque around the Z-axis.

[0136] Based on this motion characteristic Figure 5 This diagram illustrates the wavelengths formed on the rail by the periodic lateral movement (oscillation) of the wheel. These oscillations create ripple-like variations on the rail surface, significantly impacting the wear and stability of both the rail and the wheel.

[0137] It can be seen that wheel movement is related to multiple factors. Therefore, the embodiments of the present invention adopt multi-criteria decision-making technology to ultimately select the most suitable thermal imager as the sensor used in the embodiments of the present invention.

[0138] Step S201: Obtain the weight ranking results of each expert on each horizontal wheel position measurement factor to construct a ranking matrix, and calculate Kendall's consistency coefficient.

[0139] It is understood that, in this embodiment of the invention, a questionnaire survey method was used to collect, in advance, the weighted ranking results of multiple experts on the importance of the following cross-sectional wheel position measurement factors. These cross-sectional wheel position measurement factors include, but are not limited to:

[0140] Dimensions and weight: This includes the system's geometric parameters, weight, and installation method.

[0141] Energy consumption: This involves aspects such as system energy efficiency and input voltage.

[0142] Life Cycle Cost (LCC): This includes factors such as system price, maintenance costs, lifespan, durability, and reliability.

[0143] System robustness: including resistance to vibration, shock, high temperature, and dust, as well as resistance to interference with railway signaling and automation systems, and adaptability to various environmental conditions (such as rain, snow, mud, sleet, fog, etc.).

[0144] Measurement accuracy: This includes the sampling rate, and the system's accuracy depends on the train speed.

[0145] Technical compatibility (interoperability): This includes the possibility of installing the system under different operating conditions, as well as compatibility with different instruments, signal / automation systems and interfaces.

[0146] Output data: This includes the type, format, volume, and additional data (such as location, time, date, visualization data, etc.) of the output data, as well as the complexity of the final parameter estimates derived from it.

[0147] Measurement repeatability: refers to the reliability of the results, ensuring that the measurement results are consistent.

[0148] It should be noted that if certain lateral wheel position measurement factors are considered equally important by experts, they will be assigned the same weight, which is the arithmetic mean of the weights of these lateral wheel position measurement factors.

[0149] Understandably, the weighted ranking of these factors will provide an important basis for the determination of the sensor.

[0150] It should be noted that, in order to assess whether the evaluations of various experts on the various lateral wheel position measurement factors are consistent, a consistency coefficient is used for evaluation.

[0151] In the specific implementation step S201, the weight ranking results of each expert on each horizontal wheel position measurement factor are obtained. That is, each expert ranks each horizontal wheel position measurement factor. The most important factor has a level of 1, the second most important factor has a level of 2, and so on down. Then, a ranking matrix is ​​constructed based on these ranking results, and the Kendall consistency coefficient is calculated. The Kendall consistency coefficient is used to quantify the consistency level of expert evaluation.

[0152] It should be noted that Kendall's consistency coefficient is related to the sum of the rankings of all lateral wheel position measurement factors, and it can help determine whether the experts' estimates are still consistent.

[0153] Understandably, the ranking matrix constructed based on the ranking results is shown in Table 1. The ranking matrix contains the ranking information of each expert for each horizontal rotation measurement factor.

[0154] Table 1

[0155]

[0156] It should be noted that when the number of measurement factors for the lateral wheel position exceeds a preset value (e.g., 7), the chi-square distribution is used to determine the significance of the Kendall consistency coefficient.

[0157] Step S202: Calculate the chi-square value based on Kendall's consistency coefficient.

[0158] It should be noted that, to determine the significance of Kendall's consistency coefficient, a specific significance level (e.g., 0.05) is first set, and the boundary values ​​of the Kendall's consistency coefficient are determined based on this level. When the number of lateral rotation measurement factors exceeds a preset value (e.g., 7), the chi-square (x̄ ± 0.05) can be used. 2 The significance of Kendall's consistency coefficient was tested using the distribution.

[0159] In the specific implementation step S202, the chi-square value (x) is calculated based on Kendall's consistency coefficient. 2 As shown in formula (9).

[0160]

[0161] Where, x 2 denoted by chi-square value, W represents Kendall's consistency coefficient; m represents the total number of lateral wheel position measurement factors; r represents the number of experts; and S represents the sum of the ranks of lateral wheel position measurement factors.

[0162] Step S203: If the chi-square value is greater than the preset threshold, then it is determined that the weight ranking results of each expert on each horizontal rotation measurement factor are consistent.

[0163] Specifically, if the calculated chi-square value x 2 If the value is greater than the critical value at the corresponding degrees of freedom and significance level, then the expert's judgment can be considered to be consistent at a 95% confidence level.

[0164] Step S204: Analyze the ranking matrix using the analytic hierarchy process (AHP) to obtain the AHP level matrix.

[0165] Understandably, the Analytic Hierarchy Process (AHP) is a decision-making method based on a pairwise comparison matrix. In this matrix, each element pij (where i and j range from 1 to m) represents the relative importance between two lateral wheel position measurement factors Ri and Rj. Here, m is the total number of lateral wheel position measurement factors. Experts determine the relative importance between these lateral wheel position measurement factors by comparing all possible pairs of lateral wheel position measurement factors. Ideally, the elements of this pairwise comparison matrix represent the relationship between the weights of unknown criteria, as shown in Equation (10):

[0166]

[0167] Where ω represents the importance of different lateral wheel position measurement factors, p ij This indicates the importance of factor 'i' to factor 'j'.

[0168] It should be noted that this comparison is qualitative and easy to perform; it indicates whether one lateral wheel position measurement factor is more important than another, and what level of priority it belongs to. This technique allows qualitative estimates obtained from experts to be transformed into quantitative estimates.

[0169] It is understandable that the pairwise comparison matrix is ​​an inverse symmetric (reciprocal) matrix, for example:

[0170]

[0171] The main principle of filling the matrix is ​​simple: experts should indicate how much more important a particular lateral wheel position measurement factor is than another lateral wheel position measurement factor.

[0172] Understandably, the normalized subjective weights or relative importance are calculated as shown in formula (11):

[0173]

[0174] Among them, W i This indicates the supervisor's weight.

[0175] The eigenvalues ​​are calculated by multiplying the comparison matrix and the weight vector pairwise: Pw = λMCDMwi.

[0176] Where λMCDM is the comparison matrix.

[0177] In the method, the weights, and the vector ω, are the normalized components of the eigenvectors, corresponding to the largest eigenvalue that can be computed, as shown in formula (12):

[0178] det A=P-λMCDME=0 (12)

[0179] Where det A represents the largest eigenvalue; P represents the aforementioned inverse symmetric (reciprocal) matrix; and λMCDME represents the comparison matrix.

[0180] The AHP method uses a consistency index to assess the consistency of each expert's estimate (as shown in Equation (13)):

[0181]

[0182] Where CI represents the consistency result; λMCDM,max represents the largest eigenvalue of the matrix; and m represents the order of the matrix.

[0183] The relationship between the calculated consistency indices is as follows: I. A specific matrix and the random consistency index RI (Table 3) are called the consistency relationship. It determines the degree of consistency of the matrix (as shown in formula (14)):

[0184]

[0185] Here, CR represents the degree of consistency of the matrix.

[0186] A matrix consistency value R less than or equal to 0.1 is acceptable, meaning the matrix is ​​consistent.

[0187] The AHP method described above was used to rank and calculate the experts. The consistency coefficient was calculated, and the estimated value was W = 0.805. The value of parameter 2 was 67.61, which is greater than the critical value 2kr = 14.067. The degrees of freedom ν = 8 - 1 = 7, and the importance α = 0.05. Using the ranking of one expert as a reference, an analytic hierarchy process (AHP) level matrix was developed (as shown in Table 2).

[0188] Table 2

[0189] 1 2 3 4 5 6 7 8 1 1 05 0.14 0.11 0.13 0.17 2 0.2 2 2 1 0.2 0.11 0.14 0.33 3 0.5 3 7 5 1 0.2 0.33 2 5 3 4 9 9 5 1 2 3 7 5 5 8 7 3 0.5 1 3 7 5 6 6 3 0.5 0.33 0.33 1 5 2 7 0.5 0.33 0.2 0.14 0.14 0.2 1 0.2 8 5 2 0.33 0.2 0.2 0.5 5 1

[0190] Step S205: Select thermal imagers from multiple sensors based on the hierarchical analysis hierarchy matrix.

[0191] In the specific implementation step S205, the most suitable sensor is comprehensively considered from multiple perspectives based on the hierarchical analysis level matrix, thereby selecting the thermal imager from multiple sensors.

[0192] In this embodiment of the invention, by constructing a ranking matrix and calculating Kendall's consistency coefficient, the method ensures the consistency of experts' ranking results regarding the weights of lateral wheel position measurement factors, thereby improving the objectivity and reliability of the decision-making process. Furthermore, by utilizing the analytic hierarchy process (AHP) to analyze the ranking matrix, the thermal imager is scientifically selected as the optimal sensor for monitoring lateral wheel positions. This not only improves monitoring efficiency and accuracy but also reduces costs and enhances the system's adaptability and flexibility. In addition, the transparency and fairness of the method are enhanced because each step of the decision-making process is based on explicit calculations and evaluations, thus promoting the scientific and rational nature of the decision-making process.

[0193] See Figure 11 The present invention also provides a train wheel position monitoring system, which includes a controller 1101 and a measuring device 1102.

[0194] Specifically, the measuring device 1102 is set at a preset measuring position on the train, for example... Figure 12 The rectangular frame shown.

[0195] It should be noted that the specific location of the measuring device 1102 needs to be determined according to the actual situation. In this embodiment of the invention... Figure 12 The locations shown are for illustrative purposes only and do not restrict the specific settings.

[0196] Specifically, such as Figure 13 As shown, the outer surface of the measuring device 1102 is provided with a housing.

[0197] Understandably, the housing effectively protects the measuring device 1102 from environmental conditions and particulate emissions. If the measuring device 1102 is covered by dust, water, mud, or snow, the measurement may not be possible.

[0198] It should be noted that this outer shell structure requires the introduction of additional airflow to increase its internal pressure, thereby enhancing the overall system's durability. The initial model design is as follows: Figure 13 As shown in the diagram, a minimum size was chosen in the design to reduce the overall volume of the measuring device and minimize air consumption. This design considers both the protection requirements of the equipment and the optimization of energy efficiency and space.

[0199] However, this housing design may limit the camera's field of view. To address this issue, aluminum was chosen as the housing material because it is not only suitable for manufacturing the final product but also offers other advantages. Considering that surface reflections from the perforated plate could negatively impact system performance, experiments determined that the system functions correctly and its performance is not significantly affected as long as the distance between the rear wall of the housing and the camera surface remains within 17 mm. This design ensures both the camera's field of view and the system's stability and reliability.

[0200] Specifically, the measuring device 1102 includes a stereo camera and a thermal imager; wherein the stereo camera and the thermal imager are respectively connected to the controller 1101.

[0201] It is understood that a stereo camera is used to capture images of the train's wheel positions and send them to the controller 1101; a thermal imager is used to measure the train's wheel positions and send them to the controller 1101. The controller 1101 is used to execute embodiments of the present invention. Figure 1 The provided method for monitoring train wheel positions.

[0202] In this embodiment of the invention, a non-contact monitoring method is employed, which can meet the high requirements of railway transportation systems for wheel position monitoring. This design scheme boasts strong portability and modifiability, while being low-cost, easy to implement, and well-suited to meet actual field needs. By improving measurement accuracy and robustness, this invention reduces reliance on camera settings, thereby enhancing the safety and operational efficiency of railway transportation. Therefore, this invention not only closely aligns with real-world needs but also possesses significant potential for widespread application, playing a crucial role in ensuring railway transportation safety and improving operational efficiency.

[0203] Corresponding to the train wheel position monitoring method provided in the above embodiments of the present invention, see also... Figure 14 The diagram shows a structural block diagram of a train wheel position monitoring device provided in an embodiment of the present invention.

[0204] Specifically, the train wheel position monitoring device includes: a measurement unit 1401, a marking unit 1402, a processing unit 1403, an extraction unit 1404, a first calculation unit 1405, a comparison unit 1406, and a first determination unit 1407.

[0205] The measurement unit 1401 is used to periodically measure the position of train wheels using a stereo camera and a thermal imager, and to display the measured images; wherein the thermal imager is selected in advance from multiple sensors.

[0206] The marking unit 1402 is used to mark the image corresponding to the image selection instruction as the image to be measured when an image selection instruction is received, and to mark the object selected by the selection instruction in the image to be measured as the wheel to be measured.

[0207] The processing unit 1403 is used to process the image to be measured by an edge detector to obtain a binary image containing the vertical edge of the wheel to be measured.

[0208] Extraction unit 1404 is used to obtain the coordinates of the cursor in the image to be measured, and to extract the horizontal coordinates of the left and right edge white points of the vertical edge of the wheel to be measured from the binary image through an iterative algorithm. The left edge white point is the edge point with the shortest distance coordinate in the vertical left edge of the wheel to be measured, and the right edge white point is the edge point with the shortest distance coordinate in the vertical right edge of the wheel to be measured.

[0209] The first calculation unit 1405 is used to calculate the radian angle between the horizontal center of the binary image and the white point on the left edge, as well as the white point on the right edge, to determine the width of the wheel to be measured.

[0210] Comparison unit 1406 is used to compare the width with the template edge image of the wheel and guide rail to obtain the offset.

[0211] The first determining unit 1407 is used to determine that the wheel under test has not shifted if the offset is not greater than a preset value.

[0212] In this embodiment of the invention, the accuracy and robustness of the measurement are significantly improved by periodically using a stereo camera and a thermal imager to accurately measure the position of train wheels and automatically displaying the measurement images. When an image selection command is received, the system automatically marks the image to be measured and the wheel to be measured within it. The image is then processed by an edge detector to extract the vertical edge of the wheel, and an iterative algorithm is used to accurately extract the horizontal coordinates of the white points on the edge. The wheel width is determined by calculating the radian angle between the horizontal center of the image and the white points on the edge, and compared with a preset template to obtain the offset, thereby determining whether the wheel has shifted. This method not only reduces dependence on camera settings and improves operational efficiency but also reduces implementation and operating costs, while meeting the stringent requirements of high safety and high efficiency in railway transportation systems, demonstrating significant potential for widespread application.

[0213] Combination Figure 14 As shown, the device also includes: an acquisition unit, a second calculation unit, a second determination unit, an analysis unit, and a filtering unit.

[0214] The acquisition unit is used to obtain the weight ranking results of each expert on each horizontal wheel position measurement factor to construct a ranking matrix and calculate the Kendall consistency coefficient.

[0215] The second calculation unit is used to calculate the chi-square value based on the Kendall consistency coefficient.

[0216] The second determining unit is used to determine that if the chi-square value is greater than a preset threshold, the weight ranking results of each expert on each horizontal rotation measurement factor are consistent.

[0217] The analysis unit is used to analyze the ranking matrix using the analytic hierarchy process (AHP) to obtain the AHP level matrix.

[0218] The filtering unit is used to select thermal imagers from multiple sensors based on the hierarchical analysis hierarchy matrix.

[0219] Combination Figure 14 The contents shown include the first calculation unit 1405, which includes a first calculation module, a second calculation module, and a third calculation module.

[0220] The first calculation module is used to calculate the left distance between the white point on the left edge and the horizontal center of the image, and the right distance between the white point on the right edge and the horizontal center of the image.

[0221] The second calculation module is used to calculate the left radian angle between the horizontal center of the image and the white point on the left edge, and the right radian angle between the horizontal center of the image and the white point on the right edge, based on the left distance and the right distance, respectively.

[0222] The third calculation module is used to calculate the width of the wheel under test based on the left and right radian angles.

[0223] The third calculation module is specifically used to calculate the left-end distance between the left end of the wheel under test and the horizontal center of the image, and the right-end distance between the right end of the wheel under test and the horizontal center of the image, based on the left and right radian angles; and to calculate the modulus of the distance difference between the left and right ends to obtain the width of the wheel under test.

[0224] Combination Figure 14 The content shown is that the second calculation module includes: an acquisition submodule, a first calculation submodule, a second calculation submodule, and a third calculation submodule.

[0225] The acquisition submodule is used to acquire the horizontal field of view angle of the stereo camera.

[0226] The first calculation submodule is used to calculate the angle of each pixel based on the horizontal field of view and the horizontal center of the image.

[0227] The second calculation submodule is used to calculate the product of the angle of each pixel and the left distance to obtain the left radian angle between the horizontal center of the image and the white point on the left edge.

[0228] The third calculation submodule is used to calculate the product of the angle of each pixel and the right distance to obtain the right radian angle between the horizontal center of the image and the white point on the right edge.

[0229] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0230] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein 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 the various examples 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 this invention.

[0231] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the position of train wheels, characterized in that, The method includes: The positions of train wheels are periodically measured using a stereo camera and a thermal imager, and the measured images are displayed; wherein the thermal imager is selected in advance from multiple sensors; When an image selection instruction is received, the image corresponding to the selection instruction is marked as the image to be measured, and the object selected by the selection instruction in the image to be measured is marked as the wheel to be measured. The image to be measured is processed by an edge detector to obtain a binary image containing the vertical edge of the wheel to be measured; The coordinates of the cursor in the image to be measured are obtained, and the horizontal coordinates of the left and right white points on the vertical edge of the wheel to be measured are extracted from the binary image using the coordinates of the cursor as prior information through an iterative algorithm. The left white point is the edge point on the vertical left edge of the wheel to be measured that is closest to the coordinates; the right white point is the edge point on the vertical right edge of the wheel to be measured that is closest to the coordinates. Based on the horizontal field of view of the stereo camera, the radian angle between the horizontal center of the binary image and the white point on the left edge, and the white point on the right edge, is calculated to determine the width of the wheel to be tested. The width is compared with the template edge image of the wheel and guide rail to obtain the offset; If the offset is not greater than the preset value, it is determined that the wheel under test has not shifted. The process of selecting a thermal imager from multiple sensors includes: Obtain the weighted ranking results of each expert on each horizontal wheel position measurement factor to construct a ranking matrix, and calculate the Kendall consistency coefficient; Calculate the chi-square value based on the Kendall consistency coefficient; If the chi-square value is greater than the preset threshold, it is determined that the weight ranking results of each expert on each horizontal rotation measurement factor are consistent. The ranking matrix is ​​analyzed using the analytic hierarchy process (AHP) to obtain the AHP level matrix. Thermal imagers are selected from multiple sensors based on the hierarchical analysis hierarchy matrix.

2. The method according to claim 1, characterized in that, The step of calculating the radian angle between the horizontal center of the binary image and the white point on the left edge, and the white point on the right edge, respectively, to determine the width of the wheel to be measured, includes: Calculate the left distance between the white point at the left edge and the horizontal center of the image, and the right distance between the white point at the right edge and the horizontal center of the image; Based on the left distance and the right distance, calculate the left radian angle between the horizontal center of the image and the white point on the left edge, and the right radian angle between the horizontal center of the image and the white point on the right edge, respectively. The width of the wheel to be tested is calculated based on the left radian angle and the right radian angle.

3. The method according to claim 2, characterized in that, The step of calculating the left radian angle between the horizontal center of the image and the white point at the left edge, and the right radian angle between the horizontal center of the image and the white point at the right edge, based on the left distance and the right distance, includes: Obtain the horizontal field of view angle of the stereo camera; The angle of each pixel is calculated based on the horizontal field of view angle and the horizontal center of the image; Calculate the product of the angle of each pixel and the left distance to obtain the left radian angle between the horizontal center of the image and the white point on the left edge; Calculate the product of the angle of each pixel and the right distance to obtain the right radian angle between the horizontal center of the image and the white point on the right edge.

4. The method according to claim 2, characterized in that, The calculation of the width of the wheel under test based on the left radian angle and the right radian angle includes: The distance between the left end of the wheel under test and the horizontal center of the image is calculated based on the left radian angle and the right radian angle; the distance between the right end of the wheel under test and the horizontal center of the image is calculated based on the left radian angle and the right radian angle. The width of the wheel under test is obtained by calculating the modulus of the distance difference between the left end distance and the right end distance.

5. A train wheel position monitoring device, characterized in that, The device includes: A measurement unit is used to periodically measure the position of train wheels using a stereo camera and a thermal imager, and to display the measured images; wherein the thermal imager is pre-selected from multiple sensors; The marking unit is used to mark the image corresponding to the image selection instruction as the image to be measured when an image selection instruction is received, and to mark the object selected by the selection instruction in the image to be measured as the wheel to be measured. The processing unit is used to process the image to be measured by an edge detector to obtain a binary image containing the vertical edge of the wheel to be measured. The extraction unit is used to obtain the coordinates of the cursor in the image to be measured, and to extract the horizontal coordinates of the left and right edge white points of the vertical edge of the wheel to be measured from the binary image using an iterative algorithm, with the coordinates of the cursor as prior information. The left edge white point is the edge point on the vertical left edge of the wheel to be measured that is closest to the coordinates; the right edge white point is the edge point on the vertical right edge of the wheel to be measured that is closest to the coordinates. The first calculation unit is used to calculate the radian angle between the horizontal center of the binary image and the white point on the left edge, and the white point on the right edge, based on the horizontal field of view of the stereo camera, and to determine the width of the wheel to be measured. A comparison unit is used to compare the width with the template edge image of the wheel and guide rail to obtain the offset; The first determining unit is used to determine that the wheel under test has not shifted if the offset is not greater than a preset value. The device further includes: The acquisition unit is used to acquire the weight ranking results of each expert on each horizontal wheel position measurement factor to construct a ranking matrix and calculate the Kendall consistency coefficient. The second calculation unit is used to calculate the chi-square value based on the Kendall consistency coefficient. The second determining unit is used to determine that the weight ranking results of each expert on each horizontal wheel position measurement factor are consistent if the chi-square value is greater than a preset threshold. The analysis unit is used to analyze the ranking matrix using the analytic hierarchy process (AHP) to obtain the AHP ranking matrix. A screening unit is used to select thermal imagers from multiple sensors based on the hierarchical analysis level matrix.

6. The apparatus according to claim 5, characterized in that, The first computing unit includes: The first calculation module is used to calculate the left distance between the left edge white point and the horizontal center of the image, and the right distance between the right edge white point and the horizontal center of the image; The second calculation module is used to calculate, based on the left distance and the right distance, the left radian angle between the horizontal center of the image and the white point on the left edge, and the right radian angle between the horizontal center of the image and the white point on the right edge, respectively. The third calculation module is used to calculate the width of the wheel to be measured based on the left radian angle and the right radian angle.

7. The apparatus according to claim 6, characterized in that, The second computing module includes: The acquisition submodule is used to acquire the horizontal field of view angle of the stereo camera; The first calculation submodule is used to calculate the angle of each pixel based on the horizontal field of view angle and the horizontal center of the image; The second calculation submodule is used to calculate the product of the angle of each pixel and the left distance to obtain the left radian angle between the horizontal center of the image and the white point on the left edge; The third calculation submodule is used to calculate the product of the angle of each pixel and the right distance to obtain the right radian angle between the horizontal center of the image and the white point on the right edge.

8. A train wheel position monitoring system, characterized in that, The system includes a controller and measuring devices; The measuring device is set at a preset measuring position on the train; the outer surface of the measuring device is provided with a shell; The measuring device includes a stereo camera and a thermal imager; the stereo camera and the thermal imager are respectively connected to the controller; The stereo camera is used to capture images of the positions of the train's wheels and send them to the controller; The thermal imager is used to measure the position of the train wheels and send the data to the controller; the thermal imager is selected in advance from multiple sensors. The controller is used to perform the train wheel position monitoring method as described in any one of claims 1-4.

Images