Track irregularity measurement and line shape extraction method for long bridges

Abstract

A method for measuring the irregularity of a long bridge track and extracting its linear shape includes: synchronously collecting the track geometry state parameters based on the inertial reference method and the midpoint chord measurement method, and recording the travel speed of the track inspection instrument at the sampling point to obtain the speed parameters; fusing the track space curve in the track geometry state parameters based on the inertial reference method with the track midpoint vector distance in the track geometry state parameters based on the midpoint chord measurement method, and timely updating the information fusion strategy between the inertial reference method and the midpoint chord measurement method according to the speed parameters to obtain the track geometry state parameter estimation value; using a high-pass filter to extract the track irregularity parameters within the cutoff wavelength, and using a low-pass filter to extract the track linear shape parameters. The present invention overcomes the zero-point drift of the inertial reference method and the integral saturation of the midpoint chord measurement method, and can effectively improve the accuracy of track inspection data measurement under non-uniform speed conditions, which helps to ensure the high smoothness and comfort of the long bridge track.

Description

Technical Field

[0001] The invention relates to the technical field of track data processing, and in particular to a method for measuring irregularity and extracting linearity of a long bridge track. Background Art

[0002] A notable feature of high-speed railways is that bridges replace roads, and a large number of long-span railway bridges are built. Under the effects of material creep, temperature wind load, secondary dead load, and train live load, large and complex deformations may occur in long railway bridges. When the main span of the bridge exceeds 300 m, the vertical flexural deformation at the mid-span often exceeds 200 mm. To ensure the high smoothness of the high-speed track structure, it is urgent to develop a track geometry parameter detection method for long bridge deformation, so as to accurately and efficiently detect and monitor the deformation of long bridge tracks, such as track alignment and unevenness.

[0003] At present, the measurement principles for track irregularity detection mainly include the inertial reference method and the midpoint chord measurement method. Among them, the inertial reference method uses inertial devices (such as gyroscopes and accelerometers) to form an inertial reference, and obtains track irregularity by integrating angular acceleration and linear acceleration, such as track inspection vehicles, high-speed comprehensive inspection trains, and most track inspection instruments. Subject to the inevitable zero drift of inertial devices, the result of the inertial device integration operation will reflect the influence of the zero drift and affect the measurement accuracy of the inertial reference method. Since the track inspection instrument adopts an equidistant sampling method, when the track inspection instrument is running at a low speed, the inertial integration time between adjacent sampling points becomes very long, and the influence of the zero drift will be more serious, which is an unfavorable working state of the inertial reference method. On the other hand, when the running speed of the track inspection instrument is uneven, the time interval of the inertial trajectory sampling will be uneven, which will lead to uneven influence of the zero drift of the inertial device between different sampling points, and will also reduce the measurement accuracy of the inertial reference method. For the deformation measurement of long bridges with millimeter-level accuracy requirements at a scale of hundreds of meters, the inertial reference method is not very reliable. Correspondingly, the midpoint chord measurement method collects the vertical or lateral vector distance from the midpoint of the two axes of the trolley or vehicle to the rail through a displacement meter or depth sensor, and describes the track unevenness with the midpoint vector distance. At present, this method is mainly used for manual string pulling operations and has also been widely used in early track inspection instruments. However, since the midpoint chord measurement method adopts the contact measurement principle and the "small to large" measurement model, it is easily affected by the inevitable mechanical vibration and impact of contact measurement. At the same time, it is subject to the integral saturation effect of the sum of polynomials. Its measurement accuracy is significantly lower than that of the inertial reference method and has been abandoned by high-precision track inspection instruments.

[0004] At present, the deformation of long bridges on high-speed railways is mainly measured by absolute measurement based on CPⅢ, that is, a number of CPⅢ pile points with known north-east coordinates are set on the bridge deck, and then the coordinates of the track surface are estimated by the total station moving on the track surface and intersecting with several CPⅢ points in the rear to make a strict adjustment. However, the reality is that the CPⅢ pile points will produce unpredictable deformation with the bridge deck, and the total station also lacks a stable benchmark, so it is difficult to guarantee the measurement accuracy even after adjustment. Under the combined effect of track deformation and measurement errors, track maintenance work on long bridges will be extremely difficult. Summary of the invention

[0005] The purpose of the present invention is to provide a method for measuring the unevenness of a long bridge track and extracting its linear shape. The present invention divides the geometric state of the long bridge track into track unevenness and track linear shape. Under the condition of ensuring that the linear shape is controllable, the high smoothness of the track surface is given priority. Considering that both the inertial reference method and the midpoint chord measurement method are homogeneous measurements of the geometric dimensions of the track surface, and the statistical characteristics of the sensor can be obtained by calibration, if the inertial reference method has good overall measurement accuracy, and the complementary characteristics of the inertial reference method being in an unfavorable working state during low-speed measurement but being in a favorable working state for the midpoint chord measurement method can be utilized, the data layer fusion of the inertial reference method and the midpoint chord measurement method can be realized, and the fusion strategy between the inertial reference method and the midpoint chord measurement method can be updated in time according to the speed parameter, the accuracy, integrity and reliability of the track inspection data under non-uniform speed conditions and low-speed conditions can be effectively improved, and effective detection and monitoring of the deformation of the long bridge track can be realized.

[0006] Step S11, synchronously collect the track geometry parameters based on the inertial reference method and the midpoint chord measurement method, and record the running speed of the track inspection instrument at the sampling point to obtain the speed parameter ;

[0007] Step S12: The orbit space curve in the orbit geometry state parameters based on the inertial reference method The orbital midpoint vector distance in the orbital geometric state parameters based on the midpoint chord measurement method Perform information fusion and calculate the speed parameters Update the information fusion strategy between the inertial reference method and the midpoint chord measurement method in a timely manner to obtain the estimated values ​​of the orbital geometric state parameters ;

[0008] Step S13, based on the estimated value of the track geometry state parameter , using a high-pass filter to extract the track irregularity parameters within the cutoff wavelength , using low-pass filter to extract track linear parameters ;

[0009] Wherein, step S12 specifically includes:

[0010] Step S121: convert the track space curve String length L Convert to midpoint distance ;

[0011] Step S122, by and Get the covariance matrix of process noise, measurement noise and prior error;

[0012] Step S123, based on the covariance matrix of process noise, measurement noise and prior error, introduce the speed parameter As the observation value, Kalman filter is used to obtain the estimated value of orbit geometry state parameters .

[0013] According to the method for measuring irregularity and extracting linear shape of long bridge track provided by the present invention, track geometric state parameters based on the inertial reference method and the midpoint chord measurement method are synchronously collected and information is fused, and the fusion strategy between the inertial reference method and the midpoint chord measurement method is timely updated according to the speed parameter, and then the track irregularity and track linear shape parameters are respectively extracted through a high-pass filter and a low-pass filter, thereby overcoming the zero-point drift of the inertial reference method and the integral saturation of the midpoint chord measurement method, and the accuracy of track inspection data measurement under non-uniform speed conditions can be effectively improved. The cost is low and the accuracy is high, which helps to ensure the high smoothness and high comfort of the long bridge track. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic flow chart of a method for measuring irregularity of a long bridge track and extracting linear shape in the first embodiment of the present invention;

[0015] Figure 2 is a schematic diagram of a track midpoint vector distance sample in the first embodiment of the present invention;

[0016] Figure 3 It is a flow chart of Kalman filter algorithm;

[0017] Figure 4 FIG. 4 is a schematic diagram of orbital midpoint vector distance samples in the second embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1The long bridge track irregularity measurement and linearity extraction method in the first embodiment of the present invention is applied to detect the deformation of the long bridge track to determine the track irregularity and linearity. The long bridge track irregularity measurement and linearity extraction method includes steps S11 to S13.

[0020] Step S11, synchronously collect the track geometry state parameters based on the inertial reference method and the midpoint chord measurement method, and record the running speed of the track inspection instrument at the sampling point.

[0021] In this embodiment, the track geometry parameters include the track space curve , the height and direction of the track under different chord lengths, the midpoint vector distance of the track The track geometry state parameters may be specifically collected by a level 0 track inspection instrument or by a track inspection vehicle.

[0022] Specifically, the inertial reference method uses inertial devices (such as gyroscopes and accelerometers) to collect angular acceleration or linear acceleration, and integrates it twice to obtain the orbital space curve. The midpoint chord measurement method uses a displacement sensor or a depth sensor to measure the chord length at a certain L Measuring the vertical or horizontal distance from the midpoint of the two axles of the track inspection trolley or vehicle to the rail . and Synchronization is achieved through clock pulses, and the speed parameters of the track inspection instrument are recorded synchronously In this embodiment, the data sample is shown in Figure 2 , the data sample is obtained by uniform sampling.

[0023] Step S12: The orbit space curve in the orbit geometry state parameters based on the inertial reference method The orbital midpoint vector distance in the orbital geometric state parameters based on the midpoint chord measurement method Perform information fusion and according to the speed parameter Update the information fusion strategy between the inertial reference method and the midpoint chord measurement method in a timely manner to obtain the estimated values ​​of the orbital geometric state parameters .

[0024] In this embodiment, the Kalman filter algorithm is used for information fusion. The flow chart of the Kalman filter algorithm is as follows: Figure 3 As shown, the initial state of the algorithm first obtains the covariance matrix of process / measurement noise and prior error, and then includes the following steps: 1. Obtain a priori estimate based on the previous state; 2. Calculate the Kalman gain; 3. Correct the prior estimate based on the observed value and the Kalman gain; 4. Calculate the posterior error covariance matrix; 5. Calculate the prior error covariance matrix used next time and return to step 1. Among them, observation is performed in step 3, and the speed parameter is introduced As the observed value, the best estimate is obtained.

[0025] Based on the above Kalman filter algorithm, step S12 includes:

[0026] Step S121: convert the track space curve String length L Convert to midpoint distance , as follows:

[0027]

[0028] In the formula, Represented by the orbital space curve The converted midpoint vector distance at mileage i, represents the track space curve data at mileage i, Indicated in mileage The orbital space curve data at Indicated in mileage The orbital space curve data at , .

[0029] Step S122, by and Get the covariance matrix of process noise, measurement noise and prior error;

[0030] Step S123, based on the covariance matrix of process noise, measurement noise and prior error, introduce the speed parameter As the observation value, Kalman filter is used to obtain the estimated value of orbit geometry state parameters .

[0031] In the above steps, in order to improve the calculation efficiency, a sliding window may be added to the sample data according to actual needs, for example, the sliding window length is 50m, 100m or 200m.

[0032] Step S13, based on the estimated value of the track geometry state parameter , using a high-pass filter to extract the track irregularity parameters within the cutoff wavelength , using low-pass filter to extract track linear parameters .

[0033] Wherein, step S13 specifically includes:

[0034] Step S131, determining the cutoff wavelength of the track geometry state.

[0035] The sensitive wavelength of the track geometry is closely related to the running speed of the locomotive. According to the acceleration spectrum analysis of the dynamic inspection vehicle, it is believed that the sensitive wavelength of the irregularity that affects the vehicle body response can reach 120 m. Based on the engineering experience, the filter cutoff wavelength is appropriately set, such as 200 m.

[0036] Step S132, designing a high-pass filter using the cut-off wavelength as the cut-off frequency With low pass filter ;

[0037] Specifically, the filter can be designed using an FIR structure or an IIR structure. Preferably, a 4th-order Butterworth IIR filter can be used to design a high-pass filter and a low-pass filter. Furthermore, in order to ensure the strict linear phase of the filter, a zero-phase digital filtering technique can be used to design a high-pass filter. With low pass filter .

[0038] Step S133: Input to Get track irregularity parameters ,Will Input to Get track linear parameters .

[0039] Specifically, the estimated values ​​of the orbital geometric state parameters are Input to high pass filter ,get The component below the cut-off wavelength, i.e., the orbital irregularity parameter ; The estimated values ​​of orbital geometric state parameters Input to low pass filter ,get The components above the cut-off wavelength, i.e., the orbital linearity parameters .

[0040] Furthermore, the track design linear parameters can be input into the low-pass filter , and with By comparison, the deviation of track linear parameters is obtained.

[0041] The embodiment of the present invention overcomes the zero drift of the inertial reference method and the integral saturation of the midpoint chord measurement method by synchronously collecting the track geometry state parameters based on the inertial reference method and the midpoint chord measurement method and fusing the information, and then extracts the track irregularity and track linear parameters through a high-pass filter and a low-pass filter respectively, and can effectively improve the accuracy of track inspection data measurement under non-uniform speed conditions, with low cost and high accuracy, which helps to ensure the high smoothness and high comfort of the long bridge track.

[0042] The second embodiment of the present invention provides a method for measuring the irregularity of a long bridge track and extracting its linear shape. The method is applied to detecting deformation of a long bridge track to determine the irregularity and linear shape of the track. The method for measuring the irregularity of a long bridge track and extracting its linear shape includes steps S21 to S23.

[0043] Step S21, synchronously collect the track geometry parameters based on the inertial reference method and the midpoint chord measurement method, and record the running speed of the track inspection instrument at the sampling point to obtain the speed parameter .

[0044] In this embodiment, the track geometry parameters include the track space curve , the midpoint vector distance of the orbit under different chord lengths The track geometry state parameters may be collected by a level 0 track inspection instrument or by a track inspection vehicle.

[0045] The inertial reference method uses inertial devices (such as gyroscopes and accelerometers) to obtain the orbit space curve The midpoint chord measurement method uses a displacement sensor or depth sensor to obtain the vertical or horizontal vector distance of the rail. . and Synchronization is achieved through clock pulses, and the speed parameters of the track inspection instrument are recorded synchronously .

[0046] In this embodiment, due to the random walk, temperature drift, zero drift and quantization noise of inertial devices (such as gyroscopes and accelerometers), the data under intermittent acquisition conditions will introduce zero-point errors, causing data distortion. The displacement measurement of displacement sensors or depth sensors is essentially a static measurement, which is not sensitive to intermittent or continuous acquisition methods. Therefore, in this embodiment, the data of the inertial reference method and the midpoint chord measurement method are fused to correct the data distortion caused by uneven travel speed. The data samples in this embodiment are shown in Figure 4 ,Depend on Figure 4 It can be seen from the dotted circle that there is a break at 0.088km-0.096km. The obtained midpoint distance Introduced errors.

[0047] Step S22: The orbit space curve in the orbit geometry state parameters based on the inertial reference method The orbital midpoint vector distance in the orbital geometric state parameters based on the midpoint chord measurement method Perform information fusion and according to the speed parameter Update the information fusion strategy between the inertial reference method and the midpoint chord measurement method in a timely manner to obtain the estimated values ​​of the orbital geometric state parameters .

[0048] Among them, in this embodiment, the Kalman filter algorithm is used for information fusion.

[0049] Specifically, step S22 includes:

[0050] Step S221: convert the track space curve String length L Convert to midpoint distance , as follows:

[0051]

[0052] In the formula, Represented by the orbital space curve The converted midpoint vector distance at mileage i, represents the track space curve data at mileage i, Indicated in mileage The orbital space curve data at Indicated in mileage The orbital space curve data at , .

[0053] Step S222, by and Get the covariance matrix of process noise, measurement noise and prior error;

[0054] Step S223, based on the covariance matrix of process noise, measurement noise and prior error, introduce the speed parameter As the observation value, Kalman filter is used to obtain the estimated value of orbit geometry state parameters .

[0055] In this embodiment, the introduction As an observation, the interruption of measurement will change the Kalman gain so that it can still be based on and Get the estimated values ​​of orbital geometry parameters .

[0056] Step S23, based on the estimated value of the track geometry state parameter , using a high-pass filter to extract the track irregularity parameters within the cutoff wavelength , using low-pass filter to extract track linear parameters .

[0057] Specifically, step S23 includes:

[0058] S231, determining the cutoff wavelength of the orbital geometry state;

[0059] S232, Design of high-pass filter using cut-off wavelength as cut-off frequency With low pass filter ;

[0060] S233, will Input to Get track irregularity parameters ,Will Input to Get track linear parameters .

[0061] Among them, Input to Get track irregularity parameters ,Will Input to Get track linear parameters , specifically including:

[0062] Will Input to high pass filter ,get The component below the cut-off wavelength, i.e., the orbital irregularity parameter ;Will Input to low pass filter ,get The components above the cut-off wavelength, i.e., the orbital linearity parameters .

[0063] This embodiment synchronously collects track geometry state parameters based on the inertial reference method and the midpoint chord measurement method and fuses the information, and then respectively extracts track irregularity and track linear parameters through high-pass filters and low-pass filters. It is insensitive to intermittent or continuous collection methods, thereby ensuring the data accuracy of the track inspection equipment under different working conditions.

[0064] In summary, the method for measuring the irregularity of the long bridge track and extracting the linear shape provided by the present invention synchronously collects the track geometric state parameters based on the inertial reference method and the midpoint chord measurement method and fuses the information, and timely updates the fusion strategy between the inertial reference method and the midpoint chord measurement method according to the speed parameter, and then extracts the track irregularity and track linear shape parameters respectively through a high-pass filter and a low-pass filter, overcomes the zero-point drift of the inertial reference method and the integral saturation of the midpoint chord measurement method, can effectively improve the accuracy of track inspection data measurement under non-uniform speed conditions, has low cost and high accuracy, and helps to ensure the high smoothness and high comfort of the long bridge track.

[0065] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0066] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for measuring track irregularity and extracting linearity of a long bridge, characterized in that: include: Step S11, synchronously collect the track geometry parameters based on the inertial reference method and the midpoint chord measurement method, and record the running speed of the track inspection instrument at the sampling point to obtain the speed parameter ; Step S12: The orbit space curve in the orbit geometry state parameters based on the inertial reference method The orbital midpoint vector distance in the orbital geometric state parameters based on the midpoint chord measurement method Perform information fusion and according to the speed parameter Update the information fusion strategy between the inertial reference method and the midpoint chord measurement method in a timely manner to obtain the estimated values ​​of the orbital geometric state parameters ; Step S13, based on the estimated value of the track geometry state parameter , using a high-pass filter to extract the track irregularity parameters within the cutoff wavelength , using low-pass filter to extract track linear parameters ; Wherein, step S12 specifically includes: Step S121: convert the track space curve String length L Convert to midpoint distance ; Step S122, by and Get the covariance matrix of process noise, measurement noise and prior error; Step S123, based on the covariance matrix of process noise, measurement noise and prior error, introduce the speed parameter As the observation value, Kalman filter is used to obtain the estimated value of orbit geometry state parameters .

2. The method for measuring irregularity of long bridge track and extracting linear shape according to claim 1 is characterized in that: The orbital geometric state parameters based on the inertial reference method are measured by a gyroscope and an accelerometer, and the orbital geometric state parameters based on the midpoint chord measurement method are measured by a displacement sensor or a depth sensor; the orbital geometric state parameters include the orbital space curve , the midpoint vector distance of the orbit under different chord lengths .

3. The method for measuring irregularity of long bridge track and extracting linear shape according to claim 1 is characterized in that: In step S121, the track space curve is converted into String length L Convert to midpoint distance : In the formula, Represented by the orbital space curve The converted midpoint vector distance at mileage i, represents the track space curve data at mileage i, Indicated in mileage The orbital space curve data at Indicated in mileage The orbital space curve data at , .

4. The method for measuring track irregularity and extracting linearity of a long bridge according to claim 1 is characterized in that: Based on the estimated values ​​of track geometry parameters , using a high-pass filter to extract the track irregularity parameters within the cutoff wavelength , using low-pass filter to extract track linear parameters , specifically including: Determine the cutoff wavelength of the orbital geometry; Designing a high-pass filter using the cutoff wavelength as the cutoff frequency With low pass filter ; Will Input to Get track irregularity parameters ,Will Input to Get track linear parameters .

5. The method for measuring irregularity of long bridge track and extracting linear shape according to claim 4 is characterized in that: Will Input to Get track irregularity parameters ,Will Input to Get track linear parameters , specifically including: Will Input to high pass filter ,get The component below the cut-off wavelength, i.e., the orbital irregularity parameter ;Will Input to low pass filter ,get The components above the cut-off wavelength, i.e., the orbital linearity parameters .

6. The method for measuring irregularity of long bridge track and extracting linear shape according to claim 1 is characterized in that: The track inspection instrument is a level 0 track inspection instrument or a track inspection vehicle.

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