Rail on-line eddy current flaw detection method and signal data analysis system thereof

Abstract

The application discloses a track online eddy current flaw detection method and a signal data analysis system thereof, and is used for nondestructive detection of tracks such as in-service railways. The detection method is used for adjusting the moving speed and the sampling rate of the eddy current flaw detection device by calling corresponding information in a central historical big data module and combining actual detection data of a multi-channel detection method. The application is started from the actual situation of the track. The application is designed to meet the situation that the track jumps and moves due to external vibration factors in the operation of the in-service track. The application captures the real crack or discontinuity signal on the surface of the track. After comparison and analysis are performed on the central modular classification big data, the signal characteristic quantity of the dangerous crack is obtained. The detection speed and the depth are adjusted, so that the missed detection is avoided, and the processing speed of the data sampling is greatly increased.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of nondestructive testing, in particular to a rail in-service detection analysis system, and particularly to a rail online eddy current flaw detection method and a signal data analysis system thereof. BACKGROUND

[0002] Rail transportation is the main artery of modern society, and its development has been valued by many countries. With the progress of technology, it is developing towards high speed, high load and high reliability. At present, in order to ensure the safe operation of rail transportation, the detection of in-service rail is particularly important. Most of the traffic accidents in the world are caused by changes in road conditions. Looking at the online nondestructive testing of modern rail, ultrasonic method is mainly used. However, ultrasonic method is not sensitive to rail surface cracks. In order to ensure the safety of high-speed rail operation, eddy current detection method must be added. However, in-service rail is usually laid in the wild and is subjected to rolling and movement of train wheels, which can easily produce various fatigue defects (such as fish scale). These surface cracks have different depths and angles, and can be detected by eddy current sensor. If the eddy current detection process used in fields such as aviation, aerospace and nuclear power is used, most of the in-service rail will be judged as waste. In fact, for the rail itself, the existence of certain fatigue cracks on the surface does not affect its safe operation, and the fact has proved this point. However, if the fatigue cracks develop to a certain extent, sudden rupture will occur from the perspective of fracture mechanics, which will cause serious consequences. Therefore, how to evaluate the eddy current detection signal, "remove the false and retain the true", and screen out the crack signals with great harm, has become a difficult problem for nondestructive testing workers in the railway department.

[0003] In view of the above problems, the present application is further improved by using the following technical scheme. SUMMARY

[0004] The purpose of the present application is to provide a rail online eddy current flaw detection method and a signal data analysis system thereof. The technical scheme disclosed is as follows:

[0005] A rail online eddy current flaw detection method, which adjusts the moving speed and sampling rate of the eddy current detection flaw detection device by retrieving corresponding information in the center historical big data module and combining the actual detection data of the multi-channel detection method. The specific eddy current detection method steps are as follows:

[0006] a. Center data classification modularization: the detection center classifies and modularizes the historical detection data, such as classifying and analyzing the turnout, bending and tunnel detection data, and classifying different modules according to the detection speed and detection data sampling method;

[0007] b. Detection device data calculation: the detection device passes through the vehicle movement speed, calls the central detection data module, sets the detection mode, and sets the running speed and detection data collection mode of the detection device; the detection device pre-line speed can be set according to the defect crack data standard information according to the actual requirements, and the track data information provided by the railway center big data;

[0008] c. Actual detection data collection: the eddy current detection device scans and detects the track according to the predetermined speed and detection data collection mode, obtains detection data, and sends the data to the center for processing by the detection instrument for statistical analysis;

[0009] d. Central data analysis: through the actual detection analysis of the defect information and other information of the eddy current detection, the state information of the detected object is analyzed, the central historical data module is compared, the detection device moving speed and the detection data collection mode are adjusted for calculation and judgment, and the judgment information is sent to the detection device;

[0010] e. Detection device adjustment: according to the central data, the detection device moving speed and data collection mode are adjusted to pre-judge the detection device moving speed and detection parameter data collection mode, analyze the defect situation combined with the actual eddy current detection data, adjust the data information collected by the detection device, including different detection parameters, increase or decrease detection parameters, and continue detection; or return to step c for continuous detection; repeat steps b to e for data modular classification analysis and detection device actual scanning detection process.

[0011] Among them, the nondestructive testing of in-service railway and other tracks can be carried out by a multi-channel detection method according to the detection data to adjust the moving speed of the eddy current detection device, and according to the moving speed of the detection device, the sampling rate can be changed, and the detection device pre-line speed can be set according to the defect crack data standard information according to the actual requirements, and the track data information provided by the railway network big data.

[0012] Further, the detection device data calculation in step b also includes detection device scanning moving direction signal, according to the moving direction to determine the turning information of the detection object, and to determine and adjust the detection device moving speed and parameter information collection mode. Various parameter information detected by the multi-channel detection device is sent to the central module for classification calculation of different parameter detection information, and after calculation and analysis classification storage, the detection device determines the predetermined detection device data through the GPS signal.

[0013] Further, the moving direction determination is determined by the angle evaluation of the orthogonal coil of the vortex detection device. The orthogonal vortex detection device is sensitive to the direction, and the moving direction of the sensor is detected to determine the data information such as the turning of the detection device. The detection information analysis evaluation determination also includes detecting the scanning moving direction signal of the detection device, determining the turning information of the detection object according to the moving direction determination, and adjusting the detection device moving speed and parameter information collection mode. Generally, the track turning and the turnout are the parts that are easy to be damaged and need to be carefully detected.

[0014] Further, the data calculation of the detection device in the b step is the center historical classification data module, which is requested by the classification data module of the center data module after the edge calculation analysis of the detection device moving speed and the detection data collection mode data collected in the actual detection data collection in the c step. The center data edge calculation analysis can not only reduce the calculation pressure of the center data classification module, but also is more conducive to the classification comparison of different parameter data and the request of the detection device to call data.

[0015] Further, it also includes the corresponding historical data module called by the center data module, and the difference of the detection parameter data module is returned to the center data after the comparison analysis calculation.

[0016] Further, the data module in the actual detection data collection is a multi-channel or multi-array vortex detection sensor, which collects a plurality of detection data. The detection setting can be set as a plurality of sensors in sequence through the same detection position, and the detection data implemented by the detection device or a plurality of detection data that cannot be detected by the detection device.

[0017] Further, the plurality of detection data is set as the data for adjusting the detection device moving speed and the detection mode. For example, the detection probe in the detection device is set to collect different parameter data, and the different parameter detection data collected by different detection devices at the same position in the moving scanning detection process is subjected to modular data calculation analysis and classification.

[0018] The application also discloses a detection data processing system for track online eddy current flaw detection, which adjusts the detection data processing system of the moving speed and sampling rate of the eddy current detection flaw detection device by calling corresponding information in the central historical big data module and combining the actual detection data of the multi-channel detection method; the detection data processing system is characterized by comprising a central data classification module for classifying and analyzing the storage and calling of different detection data modules, a preset module for setting the moving speed and detection mode before the detection device scans and detects, a historical data comparative analysis module for comparing and analyzing the data collected and analyzed by the detection device terminal with the central classification data module, a defect road condition evaluation and analysis system for evaluating and analyzing the defect density, depth and turning turnout of the detected track, a detection speed detection parameter adjustment module for adjusting the detection speed detection parameter according to the evaluated and analyzed defect road condition information, and a detection device data storage module for receiving and saving the collected detection data.

[0019] Further, the multi-channel detection module is further included for sequentially opening the sensors of different detection channels to detect and collect detection signal data.

[0020] Further, the multi-channel detection module is further included for sequentially opening the sensors of different detection channels to detect and collect detection signal data.

[0021] The detection device used in the application is used for nondestructive testing of in-service railways and other tracks, and comprises a detection instrument, a detection sensor and a moving device, the multi-channel detection sensor is provided as a first detection sensor and a second detection sensor of different channels, the first detection sensor and the second detection sensor are arranged in front of and behind the detection device in the moving scanning detection direction, and the detection parameters and moving speed of the second detection sensor are adjusted after the data detected by the first detection sensor is evaluated and calculated.

[0022] According to the above technical scheme, the present application has the following beneficial effects: the track online eddy current flaw detection starts from the actual situation of the track, and through design, the real crack or discontinuity signal on the track surface is captured under the condition that the track jumps or moves due to external vibration factors in the operation of the track in use, and after comparison and analysis are performed on the historical big data of the center modularization, the signal characteristic quantity of the dangerous crack is obtained, and the detection speed and depth are adjusted, so that the missed detection is ensured, and the processing speed of the data sampling is greatly accelerated. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 A vortex flaw detection detection use state diagram of the best embodiment of the present application;

[0024] Figure 2 A detected track crack defect diagram of the best embodiment of the present application;

[0025] Figure 3 A detection method flow diagram of the best embodiment of the present application;

[0026] Figure 4 A detection device structure diagram of the best embodiment of the present application;

[0027] Figure 5 A detection device structure diagram of the best embodiment of the present application;

[0028] Figure 6 A detection system module diagram of the best embodiment of the present application. EMBODIMENT

[0029] The present application will be further described below in combination with the drawings and specific embodiments.

[0030] As shown in the drawings, Figures 1 to 6 A track online eddy current flaw detection method for nondestructive testing of in-service railway 1 track 11, by calling corresponding information in the center historical big data module, combining the actual detection data of the multi-channel detection method, adjusting the moving speed and sampling rate of the eddy current flaw detection device, and the specific eddy current detection method steps are as follows:

[0031] a. Center data classification modularization: the detection center classifies and modularizes the historical detection data, such as turnout, bending, and tunnel detection data, and classifies different modules according to the detection speed and detection data sampling method;

[0032] b. Detection device data calculation: the detection device passes through the vehicle movement speed, calls the central detection data module, sets the detection mode, and sets the running speed and detection data collection mode of the detection device; the detection device pre-line speed can be set according to the defect crack data standard information according to the actual requirements, and the track data information provided by the railway center big data;

[0033] c. Actual detection data collection: the eddy current detection device scans and detects the track according to the predetermined speed and detection data collection mode, obtains detection data, and sends the data to the center for processing by the detection instrument for statistical analysis;

[0034] d. Central data analysis: through the actual detection analysis of the defect information and other information of the eddy current detection, the state information of the detected object is analyzed, the central historical data module is compared, the detection device moving speed and the detection data collection mode are adjusted for calculation and judgment, and the judgment information is sent to the detection device;

[0035] e. Detection device adjustment: according to the central data, the detection device moving speed and data collection mode are adjusted to pre-judge the detection device moving speed and detection parameter data collection mode, analyze the defect situation combined with the actual eddy current detection data, adjust the data information collected by the detection device, including different detection parameters, increase or decrease detection parameters, and continue detection; or return to step c for continuous detection; repeat steps b to e for data modular classification analysis and detection device actual scanning detection process.

[0036] As shown in Figure 3 The nondestructive testing of in-service railway tracks and the like can be performed by a multi-channel detection method to adjust the moving speed of the eddy current detection device according to the detection data, change the sampling rate according to the moving speed of the detection device, and set the detection device pre-line speed according to the defect crack data standard information according to the actual requirements and the track data information provided by the railway network big data.

[0037] As shown in Figure 3 The detection device data calculation in step b further includes a detection device scanning moving direction signal, which determines the turning information of the detection object according to the moving direction, and adjusts the detection device moving speed and parameter information collection mode. Various parameter information detected by the multi-channel detection device is sent to the central module for classification calculation of different parameter detection information, and after calculation and analysis, the detection device determines the predetermined detection device data through the GPS signal.

[0038] As shown in Figure 1 and Figure 4The moving direction determination is determined by the angle evaluation determination of the orthogonal coil of the eddy current detection device. The orthogonal eddy current detection device is sensitive to the directionality, detects the moving direction of the sensor, and is used to determine the turning of the detection device and other data information. The detection information analysis evaluation determination also includes detecting the scanning moving direction signal of the detection device, determining the turning information of the detection object according to the moving direction determination, and adjusting the determination of the moving speed and parameter information collection mode of the detection device. Generally, the track turning and the turnout are the parts that are easy to be damaged and need to be carefully detected.

[0039] As Figure 3 As shown in the b step, the detection device data calculation is the center historical classification data module, which is requested by the classification data module of the center data module after the edge calculation analysis of the detection device moving speed and the detection data collection mode data collected in the actual detection data collection of the c step. The center data edge calculation analysis can not only reduce the calculation pressure of the center data classification module, but also is more conducive to the classification comparison of different parameter data and the request and retrieval of the detection device.

[0040] And also includes the corresponding historical data module retrieved by the center data module, and the difference of the detection parameter data module is returned to the center data after the comparison and calculation of the actual detection data collection.

[0041] As Figure 4 As shown in the b step, the detection device data calculation is the center historical classification data module, which is requested by the classification data module of the center data module after the edge calculation analysis of the detection device moving speed and the detection data collection mode data collected in the actual detection data collection of the c step. The center data edge calculation analysis can not only reduce the calculation pressure of the center data classification module, but also is more conducive to the classification comparison of different parameter data and the request and retrieval of the detection device.

[0042] And, the multiple sets of detection data are set to be data for adjusting the moving speed and detection mode of the detection device. For example, the detection probe in the detection device is set to collect different parameter data, and in the moving scanning detection process, different detection devices collect different parameter detection data at the same position, and the modular data calculation analysis classification is performed.

[0043] As Figure 6As shown, this invention also discloses a detection data processing system for online eddy current flaw detection of tracks. This system adjusts the moving speed and sampling rate of the eddy current flaw detection device by retrieving relevant information from a central historical big data module and combining it with actual detection data from a multi-channel detection method. Its features include: a central data classification module for classifying and analyzing the storage and retrieval of different detection data modules; a preset module for setting the moving speed and detection method of the detection device before scanning; a historical data comparison and analysis module for comparing and analyzing data collected and analyzed by the detection device terminal with the central classification data module; a defect condition assessment and analysis system for assessing and analyzing the density and depth of defects in the detected track and the condition of turning points on the detected track; a detection speed and detection parameter adjustment module for adjusting the detection speed and detection parameters based on the assessed defect condition information; and a detection device data storage module for receiving and storing the collected detection data.

[0044] Furthermore, it includes a multi-channel detection module for sequentially activating sensors in different detection channels to collect detection signal data. This module allows sensors in different detection channels to collect different detection signal data. Different detection devices detect track conditions such as curves and turnouts, and after analysis, adjust the movement speed of the detection device and the parameters extracted. When the defect density is high, or when both the depth and length are large, the detection device is slowed down, and the electrical parameters are increased to improve detection accuracy. Moreover, when the detected defect crack is deep, the alternating frequency of the eddy current detection drive power supply is reduced to enhance the depth of eddy current detection.

[0045] like Figure 4 and Figure 5 As shown, the detection device used in this invention is for non-destructive testing of tracks 11 of in-service railways, including a detection instrument 3, a detection sensor 2, and a moving device 4. The detection sensor 2 is configured as a first detection sensor 21 and a second detection sensor 22 with different channels. The first detection sensor 21 and the second detection sensor 22 are arranged one behind the other in the X-direction of the detection device's moving scan detection. After evaluating and calculating the data detected by the first detection sensor 21, the detection parameters and moving speed of the second detection sensor are adjusted. Figure 5 As shown, the mobile device can be a detection vehicle, an intelligent control robot vehicle, or a motorized bicycle that rides on a track.

[0046] The above is one embodiment of the present invention. Furthermore, it should be noted that any equivalent or simple variations made to the structure, features, and principles described in this patent concept are included within the scope of protection of this patent.

Claims

1. A track online eddy current flaw detection method, by calling the corresponding information in the center historical big data module, combining the actual detection data of the multi-channel detection method to adjust the detection method of the moving speed and sampling rate of the eddy current detection flaw detection device; The specific eddy current detection method steps are as follows: a. Center data classification modularization: The detection center classifies the historical detection data, such as turnout, bending, and tunnel detection data, and analyzes them, and classifies them into different modules according to the detection speed and detection data sampling method; b. Detection device data calculation: The detection device adjusts the center detection data module through the vehicle moving speed, sets the detection method, and determines the running speed of the detection device and the detection data acquisition method; It can be set according to the actual requirement of the defect crack data standard information, the track data information provided by the railway center big data, and the pre-determined detection device pre-speed; c. Actual detection data acquisition: The eddy current detection device scans and detects the track according to the predetermined speed and detection data acquisition method, obtains the detection data, and sends the data to the center for processing by the detection instrument for statistical analysis; d. Center data analysis: Through the actual detection and analysis of the defect information and other information of the eddy current detection, the state information of the detected object is analyzed, the center historical data module is compared, the detection device moving speed and detection data acquisition method are adjusted for calculation and judgment, and the judgment information is sent to the detection device; e. Detection device adjustment: According to the center data, the detection device moving speed and data acquisition method are adjusted to pre-judge the detection device moving speed and detection parameter data acquisition method, analyze the defect situation combined with the actual eddy current detection data, adjust the data information collected by the detection device, including different detection parameters, increase or decrease detection parameters, and continue detection; Or no adjustment, return to step c for continuous detection; Repeat steps b to e for data modular classification analysis and detection device actual scanning detection process.

2. The method of claim 1, wherein The detection device data calculation in step b also includes detection device scanning moving direction signal, and the turning information of the detection object is determined according to the moving direction to determine and adjust the detection device moving speed and parameter information acquisition method.

3. The method of claim 2, wherein The moving direction determination is determined by the orthogonal coil detection angle evaluation of the eddy current detection device.

4. The method of claim 1, wherein The detection device data calculation in step b is a center historical classification data module, which is requested by the center data module after edge calculation analysis of the detection device moving speed and detection data acquisition method data collected in step c.

5. The method of claim 4, wherein It also includes the corresponding historical data module called by the center data module, and the difference of the detection parameter data module is calculated and returned to the center data after comparison and analysis.

6. The method of claim 5, wherein The data module in the actual detection data acquisition is a multi-channel or multi-array eddy current detection sensor that collects multiple detection data.

7. The method of claim 6, wherein the method is characterized by The multiple detection data is set as data for classifying and calculating the detection device moving speed and detection method.

8. A track online eddy current detection data processing system, which adjusts the moving speed and sampling rate of the eddy current detection device by calling the corresponding information in the central historical big data module and combining the actual detection data of the multi-channel detection method; characterized in that Including the center data classification module, for the analysis of different detection data module storage and call; Preset module, for setting the detection device before scanning detection speed and detection mode; Historical data comparative analysis module, for the detection device terminal acquisition analysis data and the comparison and analysis of the center classification data module; Defect road condition evaluation analysis system, for evaluating and analyzing the defect density, depth state and turning turnout of the detected track; Detection speed detection parameter adjustment module, through the evaluation and analysis of the defect road condition information, operation whether to adjust the detection speed detection parameter; Detection device data storage module, for receiving the collected detection data saving.

9. The detection data processing system of an on-line rail eddy current inspection according to claim 8, characterized in that Also includes a multi-channel detection module, for sequentially opening different detection channel sensors to detect and collect detection signal data.

10. The detection data processing system for on-line eddy current inspection of rails according to claim 9, characterized in that Also includes a multi-channel detection module, different detection channel sensors detect and collect different detection signal data.

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