A data processing method for improving the detection accuracy of track inspection instrument

Abstract

The present invention provides a data processing method for improving the detection accuracy of a track detector, comprising the following specific steps: Step S1: The track detector performs detection along the track to obtain detection data of the track and mileage data of the track detector's movement, wherein the detection data and the mileage data correspond one-to-one; Step S2: Identifying abnormal data segments in the detection data, and constructing a compensation function based on the data values ​​at the beginning and end of the abnormal data segments, and the mileage values ​​corresponding thereto; Step S3: Using the compensation function, interpolating and compensating between the data values ​​at the beginning and end of the abnormal data segments to obtain detection data without the abnormal data segments; Step S4: Filtering the detection data processed in Step S3. The method of the present invention does not affect the integrity of the entire detection data, while simultaneously eliminating abnormal data and eliminating data errors caused by data interference such as pits on the beam surface, gravel, beam seams, and burrs on the beam surface, thereby improving the detection accuracy of the track detector.

Description

Technical Field

[0001] The present invention relates to the technical field of track detection, and in particular to a data processing method for improving the detection accuracy of a track detector. Background Art

[0002] Defects on monorail tracks seriously affect the safety and comfort of trains and passengers. Currently, monorail inspections are primarily performed manually online, which is labor-intensive, has poor measurement accuracy, and is inefficient, posing potential safety risks. Existing research has proposed using a track inspection instrument equipped with a non-contact laser displacement sensor. The distance measured by the laser displacement sensor is then calculated using relevant formulas to derive track inspection indicators. However, existing laser displacement sensors for detecting monorail beams have the following problems:

[0003] 1) There are beam gaps of varying sizes at the joints of each monorail beam, which will cause the laser displacement sensor to fail to receive reflected light, resulting in the detected distance exceeding the limit and affecting the detection accuracy;

[0004] 2) Because the monorail track beam is a prefabricated cement beam, there may be pits and gravel on the beam surface, which may cause the distance measured by the laser displacement sensor to be greater or less than the actual track beam distance, affecting the detection accuracy;

[0005] 3) Since the track inspection instrument is a high-precision inspection instrument, the burr data caused by external interference during data acquisition and data transmission will also affect the accuracy of the inspection indicators.

[0006] As the latest development direction in the industry, monorail track inspection instruments can efficiently and quickly inspect monorail tracks, identify track defects, and provide data support for line maintenance. Therefore, the accuracy of the inspection data is crucial to the performance of such track inspection equipment and has a significant impact on subsequent track maintenance.

[0007] In summary, there is an urgent need for a data processing method that can improve the detection accuracy of a track inspection instrument to solve the problems existing in the prior art. Summary of the Invention

[0008] The present invention aims to provide a data processing method for improving the detection accuracy of track inspection instruments, aiming to solve the problem that beam gaps, pits, gravel and burrs affect the detection accuracy of track inspection instruments. The specific technical solution is as follows:

[0009] A data processing method for improving the detection accuracy of a track inspection instrument, the specific steps are as follows:

[0010] Step S1: The track inspection instrument inspects along the track to obtain inspection data of the track and mileage data of the track inspection instrument's movement, wherein the inspection data and mileage data correspond one to one;

[0011] Step S2: Identify abnormal data segments in the detection data, and construct a compensation function based on the data value at the beginning and end of the abnormal data segment and the corresponding mileage values;

[0012] Step S3: using a compensation function to perform interpolation compensation between the data value at the beginning and the data value at the end to obtain detection data with abnormal data segments removed;

[0013] Step S4: Filter the detection data processed in step S3.

[0014] Preferably, in the above technical solution, the types of the abnormal data segments include beam gap abnormal data, burr abnormal data, pit abnormal data and sand and gravel abnormal data.

[0015] In the above technical solution, preferably, the method for identifying abnormal beam joint data is:

[0016] Step A1: Set the over-limit threshold M, the mileage value L1 corresponding to the maximum beam gap width, and the mileage value L2 corresponding to the beam end chamfer;

[0017] Step A2: If the data value detected at the current moment is less than M and the data value detected at the next moment is greater than M, then record the mileage value X1 corresponding to the current moment;

[0018] Step A3: Search within the mileage range from X1 to X1+L1: If the data value detected at the previous moment is greater than M and the data value detected at the current moment is less than M, then record the mileage value X1' corresponding to the current moment;

[0019] Step A4: record the detection data value corresponding to X1-L2 as the starting data value of the beam gap abnormal data, and record the detection data value corresponding to X1'+L2 as the ending data value of the beam gap abnormal data.

[0020] In the above technical solution, the method for identifying abnormal burr data is preferably:

[0021] Set the burr preset value a, and record the data value a1 detected at the previous moment, the data value a2 detected at the current moment, and the data value a3 detected at the next moment;

[0022] If a2-a1>a and a2-a3>a, or a1-a2>a and a3-a2>a; then a2 is glitch data, a1 is used as the first end data value of the glitch abnormal data, and a3 is used as the last end data value of the glitch abnormal data.

[0023] In the above technical solution, the method for identifying abnormal pit data is preferably:

[0024] Step B1: Set the slope K1 and the mileage value L3 corresponding to the maximum width of the pit, and K1>0;

[0025] Step B2: If the slope of the straight line between the current mileage value X2 and its corresponding detection data value and the next mileage value and its corresponding detection data value is greater than K1, then the current mileage value X2 and its corresponding detection data value are recorded;

[0026] Step B3: Search within the mileage range from X2 to X2+L3: If the slope of the straight line between the previous mileage value and its corresponding detection data value and the current mileage value X2' and its corresponding detection data value is less than -K1, then record the current mileage value X2' and its corresponding detection data value;

[0027] Step B4: using the detection data value corresponding to X2 as the leading data value of the abnormal pit data, and using the detection data value corresponding to X2' as the trailing data value of the abnormal pit data.

[0028] In the above technical solution, the preferred method for identifying abnormal sand and gravel data is:

[0029] Step C1: Set the slope K2 and the mileage value L4 corresponding to the maximum width of gravel, and K2>0;

[0030] Step C2: If the slope of the straight line between the current mileage value X3 and its corresponding detection data value and the next mileage value and its corresponding detection data value is less than -K2, then the current mileage value X3 and its corresponding detection data value are recorded;

[0031] Step C3: Search within the mileage range from X3 to X3+L4: If the slope of the straight line between the previous mileage value and its corresponding detection data value and the current mileage value X3' and its corresponding detection data value is greater than K2, then record the current mileage value X3' and its corresponding detection data value;

[0032] Step C4: The detection data value corresponding to X3 is used as the head data value of the sand and gravel abnormal data, and the detection data value corresponding to X3' is used as the tail data value of the sand and gravel abnormal data.

[0033] In the above technical solution, preferably, in step S2, a linear function is constructed according to the data value at the beginning and end of the abnormal data segment and the mileage values ​​corresponding to the two; in step S3, the linear function is used to perform interpolation compensation between the data value at the beginning and the data value at the end.

[0034] Preferably, in the above technical solution, in step S4, a sliding average filter is used to process the detection data after the processing in step S3.

[0035] The preferred technical solution is to install a high-precision encoder on the running wheel of the track inspection instrument, and the track inspection instrument uses a laser displacement sensor to detect the track, and the laser displacement sensor is triggered by the high-precision encoder to perform detection.

[0036] In the above technical solutions, preferably, every time the track inspection instrument travels c miles, the high-precision encoder triggers the laser displacement sensor to perform a detection.

[0037] The application of the technical solution of the present invention has the following beneficial effects:

[0038] The data processing method of the present invention provides methods for identifying abnormal data such as beam gaps, pits, gravel, and burrs. After identifying the abnormal data, a linear function is used for interpolation compensation, which does not affect the integrity of the entire detection data. At the same time, the abnormal data is eliminated, eliminating data errors caused by data interference such as pits on the beam surface and gravel, beam gaps, and burrs on the beam surface, thereby improving the detection accuracy of the track inspection instrument. After actual testing, the method of identifying abnormal data such as beam gaps, pits, gravel, and burrs of the present invention has high accuracy. At the same time, it also allows for threshold adjustment based on actual conditions (or the experience of the inspection personnel), which can cope with the identification of abnormal data such as beam gaps, pits, gravel, and burrs in different situations.

[0039] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0041] Figure 1 is a flow chart of the data processing method of the present invention;

[0042] Figure 2a It is a schematic diagram of abnormal data of beam joints;

[0043] Figure 2b This is a schematic diagram after removing abnormal beam joint data and interpolation compensation;

[0044] Figure 3a It is a schematic diagram of glitch abnormal data;

[0045] Figure 3b This is a schematic diagram after removing burr data and interpolation compensation;

[0046] Figure 4a It is a schematic diagram of hole (i.e. pit) anomaly data;

[0047] Figure 4bThis is a schematic diagram after removing abnormal hole data and interpolation compensation;

[0048] Figure 5a It is a schematic diagram of sand and gravel anomaly data;

[0049] Figure 5b This is a schematic diagram after removing abnormal sand and gravel data and interpolation compensation;

[0050] Figure 6a This is a schematic diagram without sliding average filtering;

[0051] Figure 6b This is a schematic diagram after sliding average filtering. DETAILED DESCRIPTION

[0052] To facilitate understanding of the present invention, the present invention will be described more fully below, along with preferred embodiments thereof. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the disclosure of the present invention.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0054] Example 1:

[0055] A data processing method for improving the detection accuracy of a track detector, more specifically, a method for identifying and processing abnormal data segments in the detection data of a track detector. In this embodiment, a high-precision encoder is installed on the traveling wheel of the track detector, and the high-precision encoder is used to record the mileage of the track detector. The track detector uses a laser displacement sensor to detect the track, and the laser displacement sensor is triggered by the high-precision encoder for detection. Specifically, every time the track detector travels c miles, the high-precision encoder triggers the laser displacement sensor to perform a detection. The high-precision encoder is an existing product and can be purchased directly. At the same time, those skilled in the art can also understand that the smaller the value of c, the higher the detection frequency of the track detector, so the value of c can be set according to actual needs (c>0); in this embodiment, c=0.07mm is preferred, that is, the laser displacement sensor performs a detection every time the track detector travels 0.07mm.

[0056] See also Figure 1 , the specific steps of the data processing method are as follows:

[0057] Step S1: The track inspection instrument inspects along the track to obtain inspection data of the track and mileage data of the track inspection instrument's movement, wherein the inspection data and mileage data correspond one to one;

[0058] Step S2: Identify abnormal data segments in the detection data, and construct a compensation function based on the data value at the beginning and end of the abnormal data segment and the corresponding mileage values. Specifically, a linear function is constructed as the compensation function.

[0059] Step S3: using a compensation function, i.e., a linear function, to perform interpolation compensation between the data value at the head end and the data value at the tail end, thereby obtaining detection data with the abnormal data segment removed;

[0060] Step S4: filtering the detection data after the processing in step S3. Specifically, the detection data after the processing in step S3 is processed by using a sliding average filter.

[0061] Specifically, the types of abnormal data segments include beam gap abnormal data, burr abnormal data, pit abnormal data, and sand and gravel abnormal data. In this embodiment, different types of abnormal data identification methods are provided, as follows:

[0062] When the track detector is running, the laser displacement sensor continuously measures the track. When the laser displacement sensor passes through the beam, the measured distance is within the range. When the laser displacement sensor passes through the beam gap, the sensor may not receive the return light, and the measured distance will be an over-limit value (combined with the data before and after the beam gap, the over-limit data is displayed as a set of pulse data), which will affect the detection accuracy. Preferably, the method for identifying abnormal beam gap data is:

[0063] Step A1: Set the over-limit threshold M, the mileage value L1 corresponding to the maximum beam gap width, and the mileage value L2 corresponding to the beam end chamfer; M can be set according to actual needs, generally based on the experience of those skilled in the art;

[0064] Step A2: If the data value detected at the current moment is less than M and the data value detected at the next moment is greater than M, then record the mileage value X1 corresponding to the current moment;

[0065] Step A3: Search within the mileage range from X1 to X1+L1: If the data value detected at the previous moment is greater than M and the data value detected at the current moment is less than M, then record the mileage value X1' corresponding to the current moment;

[0066] Step A4: record the detection data value corresponding to X1-L2 as the starting data value of the beam gap abnormal data, and record the detection data value corresponding to X1'+L2 as the ending data value of the beam gap abnormal data.

[0067] Furthermore, the method for identifying the burr abnormal data is:

[0068] Set a glitch preset value a, and record the data value a1 detected at the previous moment, the data value a2 detected at the current moment, and the data value a3 detected at the next moment; the glitch preset value a can be flexibly selected by those skilled in the art, and is generally given based on the experience of those skilled in the art;

[0069] If a2-a1>a and a2-a3>a, or a1-a2>a and a3-a2>a; then a2 is glitch data, a1 is used as the first end data value of the glitch abnormal data, and a3 is used as the last end data value of the glitch abnormal data.

[0070] Furthermore, the method for identifying abnormal pit data (pits, i.e., holes) is as follows:

[0071] Step B1: Set the slope K1 and the mileage value L3 corresponding to the maximum width of the pit, with K1>0; K1 and L3 can be given based on the experience of those skilled in the art;

[0072] Step B2: If the slope of the straight line between the current mileage value X2 and its corresponding detection data value and the next mileage value and its corresponding detection data value is greater than K1, then the current mileage value X2 and its corresponding detection data value are recorded;

[0073] Step B3: Search within the mileage range from X2 to X2+L3: If the slope of the straight line between the previous mileage value and its corresponding detection data value and the current mileage value X2' and its corresponding detection data value is less than -K1, then record the current mileage value X2' and its corresponding detection data value;

[0074] Step B4: using the detection data value corresponding to X2 as the leading data value of the abnormal pit data, and using the detection data value corresponding to X2' as the trailing data value of the abnormal pit data.

[0075] Furthermore, the method for identifying abnormal sand and gravel data is:

[0076] Step C1: Set the slope K2 and the mileage value L4 corresponding to the maximum width of the gravel, and K2>0; K2 and L4 can be given based on the experience of those skilled in the art;

[0077] Step C2: If the slope of the straight line between the current mileage value X3 and its corresponding detection data value and the next mileage value and its corresponding detection data value is less than -K2, then the current mileage value X3 and its corresponding detection data value are recorded;

[0078] Step C3: Search within the mileage range from X3 to X3+L4: If the slope of the straight line between the previous mileage value and its corresponding detection data value and the current mileage value X3' and its corresponding detection data value is greater than K2, then record the current mileage value X3' and its corresponding detection data value;

[0079] Step C4: The detection data value corresponding to X3 is used as the head data value of the sand and gravel abnormal data, and the detection data value corresponding to X3' is used as the tail data value of the sand and gravel abnormal data.

[0080] See also Figure 2a and Figure 2b As shown, before the method of this embodiment is used for processing, the data at the beam joints are in an abnormal state, affecting the detection accuracy; after being processed by the method of this embodiment, it can be found that the abnormal data at the beam joints are accurately identified, and the abnormal data at the beam joints are effectively eliminated, thereby improving the detection accuracy.

[0081] See also Figure 3a and Figure 3b As shown, before the method of this embodiment is used for processing, the data contains many abnormal burrs, which affect the detection accuracy. After the method of this embodiment is used for processing, the abnormal burrs are accurately identified and effectively eliminated, thereby improving the detection accuracy.

[0082] See also Figure 4a and Figure 4b As shown, when the laser displacement sensor detects a pit (i.e., a hole), the data will jump abnormally, affecting the detection accuracy. After processing by the method in this embodiment, the pit abnormal data is accurately identified and effectively eliminated, thereby improving the detection accuracy.

[0083] See also Figure 5a and Figure 5b As shown, when the laser displacement sensor detects gravel on the track beam surface, the data will jump abnormally, affecting the detection accuracy. After processing by the method in this embodiment, the abnormal data of gravel is accurately identified and effectively eliminated, thereby improving the detection accuracy.

[0084] See also Figure 6a and Figure 6b As shown in the figure, before the sliding average filter, the data was very noisy. However, after the sliding average filter was applied, the interference caused by outliers during the detection process was effectively eliminated, improving the continuity and smoothness of the data indicators while ensuring detection accuracy. Therefore, the sliding average filter can significantly improve detection accuracy and make the detected indicator data more accurate.

[0085] Furthermore, in this embodiment, the sliding average filtering process treats the 359 consecutive detection data values ​​as a queue with a fixed length of 359. Each time a new data value is sampled, it is placed at the end of the queue, and the data at the head of the queue is discarded. The 359 data values ​​in the queue are averaged to obtain a new filtering result. After multiple data verifications, the queue length is fixed at 359 (corresponding to a mileage length of 25.13mm). This effectively eliminates the interference caused by outliers during the detection process, improves the continuity and smoothness of data indicators, and ensures detection accuracy.

[0086] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A data processing method for improving the detection accuracy of a track inspection instrument, characterized in that: The specific steps are as follows: Step S1: The track inspection instrument inspects along the track to obtain inspection data of the track and mileage data of the track inspection instrument's movement, wherein the inspection data and mileage data correspond one to one; Step S2: Identify abnormal data segments in the detection data, and construct a compensation function based on the data value at the beginning and end of the abnormal data segment and the corresponding mileage values; Step S3: using a compensation function to perform interpolation compensation between the data value at the beginning and the data value at the end to obtain detection data with abnormal data segments removed; Step S4: filtering the detection data after processing in step S3; The types of abnormal data segments include beam gap abnormal data, pit abnormal data and sand and gravel abnormal data; The identification method of the abnormal beam joint data is as follows: Step A1: Set the over-limit threshold M, the mileage value L1 corresponding to the maximum beam gap width, and the mileage value L2 corresponding to the beam end chamfer; Step A2: If the data value detected at the current moment is less than M and the data value detected at the next moment is greater than M, then record the mileage value X1 corresponding to the current moment; Step A3: Search within the mileage range from X1 to X1+L1: If the data value detected at the previous moment is greater than M and the data value detected at the current moment is less than M, then record the mileage value X1' corresponding to the current moment; Step A4: record the detection data value corresponding to X1-L2 as the starting data value of the beam joint abnormal data, and record the detection data value corresponding to X1'+L2 as the ending data value of the beam joint abnormal data; The method for identifying abnormal pit data is as follows: Step B1: Set the slope K1 and the mileage value L3 corresponding to the maximum width of the pit, and K1>0; Step B2: If the slope of the straight line between the current mileage value X2 and its corresponding detection data value and the next mileage value and its corresponding detection data value is greater than K1, then the current mileage value X2 and its corresponding detection data value are recorded; Step B3: Search within the mileage range from X2 to X2+L3: If the slope of the straight line between the previous mileage value and its corresponding detection data value and the current mileage value X2' and its corresponding detection data value is less than -K1, then record the current mileage value X2' and its corresponding detection data value; Step B4: using the detection data value corresponding to X2 as the leading data value of the abnormal pit data, and using the detection data value corresponding to X2' as the trailing data value of the abnormal pit data; The identification method of the sand and gravel abnormal data is: Step C1: Set the slope K2 and the mileage value L4 corresponding to the maximum width of the gravel, and K2>0; Step C2: If the slope of the straight line between the current mileage value X3 and its corresponding detection data value and the next mileage value and its corresponding detection data value is less than -K2, then the current mileage value X3 and its corresponding detection data value are recorded; Step C3: Search within the mileage range from X3 to X3+L4: If the slope of the straight line between the previous mileage value and its corresponding detection data value and the current mileage value X3' and its corresponding detection data value is greater than K2, then record the current mileage value X3' and its corresponding detection data value; Step C4: The detection data value corresponding to X3 is used as the head data value of the sand and gravel abnormal data, and the detection data value corresponding to X3' is used as the tail data value of the sand and gravel abnormal data.

2. The data processing method for improving the detection accuracy of a track inspection instrument according to claim 1, characterized in that: The types of abnormal data segments also include glitch abnormal data.

3. The data processing method for improving the detection accuracy of a track inspection instrument according to claim 2, characterized in that: The method for identifying the burr abnormal data is: Set the burr preset value a, and record the data value a1 detected at the previous moment, the data value a2 detected at the current moment, and the data value a3 detected at the next moment; If a2-a1>a and a2-a3>a, or a1-a2>a and a3-a2>a; then a2 is glitch data, a1 is used as the first end data value of the glitch abnormal data, and a3 is used as the last end data value of the glitch abnormal data.

4. The data processing method for improving the detection accuracy of a track inspection instrument according to any one of claims 1 to 3, characterized in that: In step S2, a linear function is constructed based on the data value at the beginning and end of the abnormal data segment and the corresponding mileage values; in step S3, the linear function is used to perform interpolation compensation between the data value at the beginning and the data value at the end.

5. The data processing method for improving the detection accuracy of a track inspection instrument according to any one of claims 1 to 3, characterized in that: In step S4, the detection data after the processing in step S3 is processed using a sliding average filter.

6. The data processing method for improving the detection accuracy of a track inspection instrument according to any one of claims 1 to 3, characterized in that: A high-precision encoder is installed on the running wheel of the track inspection instrument. The track inspection instrument uses a laser displacement sensor to detect the track, and the laser displacement sensor is triggered by the high-precision encoder for detection.

7. The data processing method for improving the detection accuracy of a track inspection instrument according to claim 6, characterized in that: Every time the track inspection instrument travels c miles, the high-precision encoder triggers the laser displacement sensor to perform a detection.

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