Detection and collection device for leakage magnetic flux of staggered pipes in limited space and data alignment method

Abstract

The application provides a detection and collection device for staggered pipeline magnetic flux leakage in a limited space and a data alignment method, relates to the technical field of magnetic flux leakage detection, and maximizes sensor density by arranging double-row magnetic flux leakage sensors, so that axial and circumferential magnetic flux leakage signals can be captured, complex defects can be more accurately collected, the number of sensors is increased without sacrificing signal quality, the overall detection capability of the system is directly improved, clearer signal collection is ensured, spatial resolution is improved, and the detection capability for tiny cracks or corrosion is enhanced. The data alignment method for staggered pipeline magnetic flux leakage in a limited space provided by the application aligns the time of the second row of magnetic flux leakage sensors by constructing three pairs of diagonal linear equations and interpolation functions, determining the time of the wave peak, eliminating the error caused by the double-row magnetic flux leakage sensors, and improving data consistency.

Description

Technical Field

[0001] This invention relates to the field of magnetic flux leakage detection technology, specifically to a detection and acquisition device and data alignment method for magnetic flux leakage in intersecting pipelines in a confined space. Background Technology

[0002] Pipelines play a vital role in transporting energy, offering low-cost and reliable transportation. However, these pipelines are susceptible to defects such as cracks and corrosion due to natural factors (e.g., corrosion) and human factors (e.g., improper installation), potentially threatening safety and economic interests. To ensure the long-term operation of pipelines, non-destructive testing (NDT) technology is widely used, with magnetic flux leakage (MFD) being the most widely applied and effective method. MFD is a non-destructive testing technique based on electromagnetic principles. Its working principle involves saturating the pipe wall with a permanent magnet along the pipe's axial direction, forming a closed magnetic circuit (including the permanent magnet, steel brush, pipe wall, yoke, and air gap). When defects (such as cracks or corrosion) exist in the pipe wall, magnetic lines of force escape, forming a leakage magnetic field. This leakage magnetic field is detected by sensors, allowing for the identification of the defect location and characteristics. New oil and gas transmission pipelines are increasingly being built with higher steel grades and higher pressures, making circumferential weld cracking a major failure mode. Therefore, research on the detection of micro-cracks at circumferential welds has gradually become a focus of testing technology. Pipe cracks pose a huge safety hazard and can cause extremely serious accidents, making it imperative to improve detection accuracy.

[0003] Currently, ultrasonic testing (UT) can be used for inspection. UT emits high-frequency sound waves into the pipe through a transducer. The sound waves are reflected when they encounter defects or inner surfaces. Analyzing these reflected signals can detect defects, thickness measurements, or anomalies. It is suitable for detecting both surface and internal defects, but requires a binder (such as water or conductive material) to transmit the sound waves. However, UT has disadvantages such as requiring skilled operators to interpret complex signals; the need for a binder, limiting it to materials that conduct sound waves; and potentially significant setup time and higher costs. Eddy current testing (ECT) can also be used. ECT uses an AC-charged coil to sense eddy currents in the pipe material. Defects interfere with these eddy currents, and changes in the coil's electrical properties are detected. It is mainly used to detect surface and near-surface defects in conductive materials. However, ECT has disadvantages such as being only applicable to conductive materials; limited depth detection, primarily targeting surface and near-surface defects; complex signal interpretation; and susceptibility to lift-off effects. Infrared thermography (IR) can also be used. Specifically, infrared sensors capture temperature distribution differences on the pipe surface to identify thermal anomalies caused by corrosion, blockages, or leaks. However, IR (Infrared Rectification) has drawbacks: it is highly susceptible to interference from ambient temperature; it can only detect temperature-related anomalies, and its resolution is relatively low. Currently, visual inspection is also an option. This method uses cameras, robots, or direct visual observation of the pipes to detect visible problems such as blockages, deformation, or surface damage, but it cannot detect hidden internal defects and relies on operator experience and equipment accessibility. However, its disadvantages include: it is limited to visible defects and cannot detect hidden internal problems; it depends on operator skill and experience; it requires a straight line of sight, and some pipe sections are difficult to access.

[0004] The existing technology also suffers from limitations in space, resulting in fixed sensor spacing and making it difficult to increase the number of sensors. This reduces spatial resolution and detection capability. Furthermore, single-row sensor arrangements lead to insufficient signal accuracy, making it difficult to capture subtle changes in minute defects. This is because the limited number of sensors results in low system sensitivity, and weak signals are often masked by background noise. However, when sensors are closely arranged, electromagnetic interference between adjacent sensors, stemming from their close proximity, easily generates interference, making it difficult to distinguish defect signals from noise, leading to signal quality degradation, limiting the number of effective sensors, and impacting overall detection capability. Due to low sensor accuracy and mutual interference, traditional methods struggle to accurately assess the degree and location of pipeline damage and perform error correction, increasing the risk of missed detections. The large size of traditional sensors and patch components results in low space utilization for the sensors, limiting detection resolution. Additionally, existing single-axis sensors can only measure axial data, failing to capture radial and circumferential components, limiting the accurate identification of complex defects (such as dents, wrinkles, and cracks). Single-dimensional data makes it difficult to distinguish defect types (e.g., unable to differentiate between circumferential cracks and axial corrosion), reducing detection accuracy. This is particularly problematic in scenarios requiring multi-directional data analysis. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to propose a detection and acquisition device for magnetic flux leakage in intersecting pipes within a confined space, comprising eight magnetic flux leakage sensors, a main control chip, a crystal oscillator, a multiplexing chip, a reset button, a power indicator light, and an LDO.

[0006] The magnetic flux leakage sensor is used to collect magnetic flux leakage data inside the pipeline. The multiplexing chip is used to expand a single I²C bus into 8 independent multiplexed channels. The main control chip is used to control the multiplexed channels, collect and process magnetic flux leakage data. The crystal oscillator is used to provide a clock signal. The reset button is used to force the detection and acquisition device for magnetic flux leakage in interleaved pipelines in a confined space to return to its initial state. The power indicator light is used to indicate whether the 5V main power input is normal. The LDO is used to convert the 5V power input to 3.3V output and also to power the magnetic flux leakage sensor, main control chip, crystal oscillator, multiplexing chip, reset button, and power indicator light.

[0007] The eight magnetic flux leakage sensors are arranged in two rows. The four magnetic flux leakage sensors in each row have the same horizontal coordinate in the detection and acquisition device for magnetic flux leakage in the interlaced pipeline in a limited space. The distance between two adjacent magnetic flux leakage sensors in each row is d1=8mm, and the distance between the two rows of magnetic flux leakage sensors is d2=13mm.

[0008] A data alignment method for magnetic flux leakage of intersecting pipes in a confined space, implemented based on the aforementioned detection and acquisition device for magnetic flux leakage of intersecting pipes in a confined space, includes:

[0009] Step 1: The robot moves inside the pipe with a detection and acquisition device for magnetic leakage in intersecting pipes in a confined space. The robot collects magnetic leakage data inside the pipe through the detection and acquisition device to obtain the first data matrix and the second data matrix.

[0010] In this data matrix, the columns of the first data matrix and the second data matrix are magnetic flux leakage data of the pipe position at different times, and the rows of the first data matrix and the second data matrix are magnetic flux leakage data of each row of magnetic flux leakage sensors at different angles on the inner wall of the pipe.

[0011] Step 2: Based on the first data matrix, construct an interpolation function, and use the interpolation function to correct the second data matrix to obtain the corrected data matrix;

[0012] Step 3: Calculate the peaks of the first data matrix and the peaks of the correction data matrix. Based on the peaks of the first data matrix and the correction data matrix, align the peaks of the correction data matrix with the peaks of the first data matrix to obtain the aligned data matrix.

[0013] Optionally, step 1 specifically includes:

[0014] Step 1.1: The robot moves at a constant speed along the length of the pipe. At the time t at the j-th measurement point... j At that time, the leakage magnetic field sensor Nm collects circumferential leakage magnetic field data around the inner wall of the pipe. The circumferential leakage magnetic data Represented as:

[0015] ;

[0016] in, This represents the magnetic flux leakage data at the first angle on the inner wall of the pipe, collected by the magnetic flux leakage sensor Nm. This represents the magnetic flux leakage data at the second angle on the inner wall of the pipe, collected by the magnetic flux leakage sensor Nm. This represents the magnetic flux leakage data at the u-th angle on the inner wall of the pipe, collected by the magnetic flux leakage sensor Nm.

[0017] Step 1.2: Segment the circumferential magnetic leakage data collected by all the magnetic leakage sensors in each row to obtain the first magnetic leakage data and the second magnetic leakage data;

[0018] The first leakage magnetic field data is represented as follows:

[0019] ;

[0020] Among them, t j This represents the time at the j-th measurement point. This represents the magnetic flux leakage data of the first row of magnetic flux leakage sensors at the pipe location of measurement point j, i.e., the first magnetic flux leakage data. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N1. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N2. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N3. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N4;

[0021] The second leakage magnetic field data is represented as follows:

[0022] ;

[0023] in, This represents the magnetic flux leakage data of the second row of magnetic flux leakage sensors at the pipe location of measurement point j, i.e., the second magnetic flux leakage data. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N5. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N6. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N7. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N8;

[0024] Step 1.3: Concatenate the first magnetic flux leakage data from all time periods to obtain a first data matrix; concatenate the second magnetic flux leakage data from all time periods to obtain a second data matrix. The first data matrix is ​​represented as follows:

[0025] ;

[0026] in, Represents the first data matrix. This represents the first leakage magnetic field data at time t1. This represents the first leakage magnetic field data at time t2. Representing time t J The first leakage magnetic field data, where J represents the total number of measurement points;

[0027] The second data matrix is ​​represented as follows:

[0028] ;

[0029] in, This represents the second data matrix. The second leakage magnetic field data represents time t1. The second leakage magnetic field data represents time t2. Representing time t J The second leakage magnetic field data.

[0030] Optionally, step 2 specifically includes:

[0031] Step 2.1: Using the time axis of the first row of magnetic leakage sensors as the reference time axis, construct a tridiagonal linear equation system based on the first data matrix. The tridiagonal linear equation system is expressed as follows:

[0032] ;

[0033] Among them, h0, h1, h2, h n -1, h n-2 Represented as h j h j =t j+1 -t j , t j t represents the time of the j-th measurement point on the reference time axis. j+1 The time of the (j+1)th measurement point on the reference time axis, h j Indicates t j+1 and t j The time difference between y0, y1, y2, y3, y n y n-1 y n-2 Represented as y j y jThe values ​​M0, M1, M2, M3, and M represent the leakage magnetic field data at the j-th measurement point on the reference time axis. n M n-1 M n-2 Represented as M j M j This represents the second derivative at the j-th measurement point on the reference time axis;

[0034] Step 2.2: Solve the tridiagonal linear equation system using the chasing method to obtain the second derivative at each measurement point;

[0035] Step 2.3: In each time interval [t] j ,t j+1 On top of that, construct the interpolation function. ;

[0036] Step 2.4: For each measurement point of the second row of magnetic flux leakage sensors, determine the time interval of the first row of magnetic flux leakage sensors to which the measurement point belongs. Substitute the time of the measurement point into the interpolation function of its time interval to obtain the correction data. Replace the magnetic flux leakage data of the measurement point in the second data matrix with the correction data to obtain the correction data matrix.

[0037] Optionally, the interpolation function described in step 2.3 Represented as:

[0038] ;

[0039] in, The coefficient is calculated using the following formula:

[0040] .

[0041] Optionally, step 3 specifically includes:

[0042] Step 3.1: Divide the first data matrix into windows to obtain leakage magnetic data for multiple windows;

[0043] Step 3.2: Calculate the peak threshold of the window for the magnetic flux leakage data of each window;

[0044] Step 3.3: Obtain values ​​greater than the peak threshold within the window. The leakage magnetic field data is used as the peak of the window, and the time of the measurement point corresponding to the peak is obtained. The time of the measurement point corresponding to the peak is taken as the first time. Thus, the first time corresponding to the peak of each window is obtained.

[0045] Step 3.4: Divide the calibration data matrix into windows, calculate the peak threshold of each window for the calibration data, and then determine the second time corresponding to the peak of each window.

[0046] Step 3.5: For each first time point, calculate the difference between the first time point and each second time point. Find the smallest difference among all the differences and replace the second time point corresponding to the smallest difference with the first time point. After all the second time points have been replaced, the aligned data matrix is ​​obtained.

[0047] Optionally, step 3.2 specifically includes:

[0048] Step 3.2.1: For each measurement point i at the center of the window, calculate the local mean. Specifically, this is achieved through the following formula:

[0049] ;

[0050] in, This represents the magnetic flux leakage data of the l-th measurement point in the window, and W represents the window width.

[0051] Step 3.2.2: Based on local mean Calculate local standard deviation Specifically, this is achieved through the following formula:

[0052] ;

[0053] Step 3.2.3: Based on local mean and local standard deviation Calculate the peak threshold of the window. Specifically, this is achieved through the following formula:

[0054] ;

[0055] Where α is the adjustment parameter.

[0056] The beneficial effects of adopting the above technical solution are as follows:

[0057] The present invention provides a detection and acquisition device for magnetic flux leakage in interlaced pipes within a confined space. This device utilizes a dual-row magnetic flux leakage sensor to maximize sensor density, enabling the capture of axial and circumferential magnetic flux leakage signals. This allows for more precise acquisition of complex defects and solves the problem of sensor quantity limitations, allowing for an increase in the number of sensors without sacrificing signal quality. This directly improves the overall detection capability of the system and ensures clearer signal acquisition. It also improves spatial resolution and enhances the detection capability for minute cracks or corrosion. Furthermore, the data alignment method for magnetic flux leakage in interlaced pipes within a confined space provided by this invention aligns the timing of the peaks with that of the second row of magnetic flux leakage sensors by constructing a tridiagonal linear equation system and interpolation function. This eliminates the errors introduced by the dual-row magnetic flux leakage sensors and improves data consistency. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of a detection and acquisition device for magnetic flux leakage of intersecting pipes in a confined space according to an embodiment of the present invention, wherein (a) is a schematic diagram of the sensor surface and (b) is a schematic diagram of the main control surface;

[0059] Figure 2 This is a flowchart illustrating the data alignment method for interlaced pipeline leakage magnetic flux in a limited space according to an embodiment of the present invention. Detailed Implementation

[0060] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0061] To address the problems existing in the prior art, this invention provides a detection and acquisition device for magnetic flux leakage in intersecting pipes within a confined space, such as... Figure 1 The diagram below shows a circuit diagram of the detection and acquisition device for magnetic flux leakage of intersecting pipes in a confined space according to an embodiment of the present invention. (a) is a schematic diagram of the sensor surface, and (b) is a schematic diagram of the main control surface. The detection and acquisition device for magnetic flux leakage of intersecting pipes in a confined space includes 8 magnetic flux leakage sensors (N1-N8), a main control chip U1, a crystal oscillator U3, a multiplexing chip U4, a reset button RESET, a power indicator PWR, and an LDO. The LDOs include LDO_0, LDO_1, and LDO_2. BOOT represents the BOOT circuit.

[0062] In specific implementation, the circuit board size of the detection and acquisition device for magnetic leakage of intersecting pipes in a limited space provided by the present invention is 60mm×33mm, and the magnetic leakage sensor in the present invention can be a TLE493DW2B6 sensor.

[0063] The magnetic flux leakage sensor is used to collect magnetic flux leakage data inside the pipeline. The multiplexing chip is used to expand a single I²C bus into 8 independent multiplexed channels to resolve conflicts between sensors with the same address. The main control chip is used to control the multiplexed channels, collect and process magnetic flux leakage data. The crystal oscillator is used to provide a clock signal. The reset button is used to force the detection and acquisition device for magnetic flux leakage in interleaved pipelines under limited space to return to its initial state. The reset button is manually triggered and is used to trigger a hardware reset signal. The power indicator light is used to indicate whether the 5V main power input is normal, realizing passive voltage monitoring. The LDO is used to convert the 5V power input to 3.3V output and also to power the magnetic flux leakage sensor, main control chip, crystal oscillator, multiplexing chip, reset button, and power indicator light.

[0064] The eight magnetic flux leakage sensors are arranged in two rows, combined with Figure 1In (a), the first row of magnetic flux leakage sensors includes N1-N4, and the second row of magnetic flux leakage sensors includes N5-N8. The abscissa of the four magnetic flux leakage sensors in each row is consistent in the detection and acquisition device for the magnetic flux leakage of the interlaced pipeline in a limited space. The distance between two adjacent magnetic flux leakage sensors in each row is d1=8mm, and the distance between two rows of magnetic flux leakage sensors is d2=13mm.

[0065] It should be noted that the magnetic flux leakage sensor in this invention is in a double row. Furthermore, the number of rows of magnetic flux leakage sensors can be increased on this basis, with the same principle and compatible algorithms.

[0066] Based on the aforementioned detection and acquisition device for magnetic flux leakage of intersecting pipes in a confined space, this invention provides a data alignment method for magnetic flux leakage of intersecting pipes in a confined space, combined with... Figure 2 Specifically, it includes the following steps:

[0067] Step 1: The robot moves inside the pipe with a detection and acquisition device for magnetic leakage in intersecting pipes in a confined space. The robot collects magnetic leakage data inside the pipe through the detection and acquisition device to obtain the first data matrix and the second data matrix.

[0068] In this system, the columns of the first and second data matrices are all leakage magnetic field data corresponding to the pipe positions at different times. That is, each column corresponds to a time and includes the leakage magnetic field data of the pipe position at that time. The rows of the first and second data matrices are all leakage magnetic field data of each row of leakage magnetic field sensors at different angles on the inner wall of the pipe. That is, each row corresponds to an angle and includes the leakage magnetic field data of each leakage magnetic field sensor at that angle. When the first and second data matrices are represented in the form of a graph, the horizontal axis is the leakage magnetic field data of the pipe positions at different times, and the vertical axis is the leakage magnetic field data of each row of leakage magnetic field sensors at different angles on the inner wall of the pipe.

[0069] Step 1.1: The robot moves at a constant speed along the length of the pipe. At the time t at the j-th measurement point... j At that time, the leakage magnetic field sensor Nm collects circumferential leakage magnetic field data around the inner wall of the pipe. The circumferential leakage magnetic data Represented as:

[0070] ;

[0071] in, This represents the magnetic flux leakage data at the first angle on the inner wall of the pipe, collected by the magnetic flux leakage sensor Nm. This represents the magnetic flux leakage data at the second angle on the inner wall of the pipe, collected by the magnetic flux leakage sensor Nm. This represents the magnetic flux leakage data at the u-th angle on the inner wall of the pipe, collected by the magnetic flux leakage sensor Nm.

[0072] In this invention, the robot moves at a constant speed in the pipe. In other implementations, it can also be uniformly accelerated or decelerated, and the specific movement can be adjusted according to the actual situation. Similarly, the speed of uniform motion in this invention can also be adjusted according to actual needs. In this invention, the magnetic flux leakage sensor collects data at a fixed frequency f.

[0073] Step 1.2: Segment the circumferential magnetic leakage data collected by all the magnetic leakage sensors in each row to obtain the first magnetic leakage data and the second magnetic leakage data;

[0074] The first leakage magnetic field data is represented as follows:

[0075] ;

[0076] Among them, t j This represents the time at the j-th measurement point. This represents the magnetic flux leakage data of the first row of magnetic flux leakage sensors at the pipe location of measurement point j, i.e., the first magnetic flux leakage data. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N1. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N2. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N3. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N4;

[0077] The second leakage magnetic field data is represented as follows:

[0078] ;

[0079] in, This represents the magnetic flux leakage data of the second row of magnetic flux leakage sensors at the pipe location of measurement point j, i.e., the second magnetic flux leakage data. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N5. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N6. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N7. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N8;

[0080] Step 1.3: Concatenate the first magnetic flux leakage data from all time periods to obtain a first data matrix; concatenate the second magnetic flux leakage data from all time periods to obtain a second data matrix. The first data matrix is ​​represented as follows:

[0081] ;

[0082] in, Represents the first data matrix. This represents the first leakage magnetic field data at time t1. This represents the first leakage magnetic field data at time t2. Representing time t J The first leakage magnetic field data, where J represents the total number of measurement points;

[0083] The second data matrix is ​​represented as follows:

[0084] ;

[0085] in, This represents the second data matrix. The second leakage magnetic field data represents time t1. The second leakage magnetic field data represents time t2. Representing time t J The second leakage magnetic field data.

[0086] After obtaining the first data matrix and the second data matrix, this invention verifies whether the time of the first data matrix of the first row of leakage magnetic sensors and the second data matrix of the second row of leakage magnetic sensors are aligned. Specifically, this includes the following steps:

[0087] Step A1: Obtain K first maxima points from the first data matrix, and obtain the times corresponding to the K first maxima points to obtain a first time array E, wherein the time array E is represented as:

[0088] ;

[0089] Where e1 represents the time corresponding to the first maximum point 1, e2 represents the time corresponding to the first maximum point 2, and e K This represents the time corresponding to the first maximum point K;

[0090] Step A2: Obtain K second maxima points from the second data matrix and the corresponding times for each of the K second maxima points. Then, for each first maxima point in the first data matrix, among the K second maxima points in the second data matrix, determine the second maxima point whose time is closest to the first maxima point according to the first time array E. Take this second maxima point as the target maxima point and obtain the times corresponding to all target maxima points to obtain the second time array G. The second time array is represented as follows:

[0091] ;

[0092] Where g1 represents the time corresponding to target maximum point 1, g2 represents the time corresponding to target maximum point 2, and g K This represents the time corresponding to the target maximum point K;

[0093] Step A3: Subtract the corresponding times from the first time array E and the second time array G to obtain the time difference array P, which is represented as:

[0094] P=[p1p2... p K ];

[0095] Where p1 represents the time difference between e1 and g1, p2 represents the time difference between e2 and g2, and p K e K and g K Time difference;

[0096] Step A4: Calculate the standard error value p z Specifically, this is achieved through the following formula:

[0097] ;

[0098] Where v represents the robot's movement speed;

[0099] Determine whether each time difference is equal to the error standard value, and obtain the number of time differences that are equal to the error standard value. Use this number as the first value, and calculate the ratio of the first value to K. Determine whether the ratio exceeds a threshold, where the threshold can be determined empirically. If the ratio exceeds the threshold, it is considered that the time axis of the first data matrix and the second data matrix is ​​out of sync due to the staggered arrangement of multiple rows of sensors, and alignment processing is required. In the specific experimental process of this invention, the conclusion is that the ratio exceeds the threshold, so this invention needs further processing for time alignment.

[0100] Step 2: Based on the first data matrix, construct an interpolation function, and use the interpolation function to correct the second data matrix to obtain the corrected data matrix;

[0101] In order to ensure the smoothness of the interpolation function, it is necessary to calculate the second derivative at each measurement point.

[0102] Step 2.1: Using the time axis of the first row of magnetic leakage sensors as the reference time axis, construct a tridiagonal linear equation system based on the first data matrix. The tridiagonal linear equation system is expressed as follows:

[0103] ;

[0104] Among them, h0, h1, h2, h n -1, h n-2 Represented as h j h j =t j+1 -t j , t j t represents the time of the j-th measurement point on the reference time axis. j+1The time of the (j+1)th measurement point on the reference time axis, h j Indicates t j+1 and t j The time difference between y0, y1, y2, y3, y n y n-1 y n-2 Represented as y j y j The values ​​M0, M1, M2, M3, and M represent the leakage magnetic field data at the j-th measurement point on the reference time axis. n M n-1 M n-2 Represented as M j M j This represents the second derivative at the j-th measurement point on the reference time axis;

[0105] Step 2.2: Solve the tridiagonal linear equation system using the Thomas algorithm to obtain the second derivative at each measurement point;

[0106] Step 2.3: In each time interval [t] j ,t j+1 On top of that, construct the interpolation function. ;

[0107] Wherein, the interpolation function Represented as:

[0108] ;

[0109] in, The coefficient is calculated using the following formula:

[0110] .

[0111] Step 2.4: For each measurement point of the second row of magnetic flux leakage sensors, determine the time interval of the first row of magnetic flux leakage sensors to which the measurement point belongs. Substitute the time of the measurement point into the interpolation function of its time interval to obtain the correction data. Replace the magnetic flux leakage data of the measurement point in the second data matrix with the correction data to obtain the correction data matrix.

[0112] In this invention, a robot equipped with a magnetic flux leakage detection and acquisition device for interlaced pipes in a confined space moves inside the pipe. The first row of magnetic flux leakage sensors moves to a position first, and the second row of magnetic flux leakage sensors moves to that position afterward. For example, when the robot moves at a constant speed inside the pipe, the first row of magnetic flux leakage sensors moves to a position and collects data in the first second, the second row of magnetic flux leakage sensors moves to the same position as the first row of magnetic flux leakage sensors in the second second and collects data, and the first row of magnetic flux leakage sensors moves to the next position and collects data in the third second. Then, this invention constructs an interpolation function in the time interval [1,3]. For the measurement point in the second second, it can be determined that it belongs to the time interval [1,3] of the first row of magnetic flux leakage sensors. Then, let t=2, and substitute it into the interpolation function in the time interval [1,3] to obtain the correction data.

[0113] Furthermore, the present invention verifies whether the first data matrix and the correction data matrix are aligned, specifically including the following steps:

[0114] Step B1: Calculate the cross-correlation function values ​​of the leakage magnetic field data in the first data matrix and the correction data in the correction data matrix based on the cross-correlation function;

[0115] Wherein, the cross-correlation function Represented as:

[0116] ;

[0117] Where k is the discrete lag. This represents the leakage magnetic field data at the j-th measurement point in the first data matrix. This represents the correction data of the (j+k)th measurement point in the correction data matrix, where J represents the total number of measurement points;

[0118] Step B2: Obtain the maximum value of the cross-correlation function, and then obtain the value corresponding to the maximum value. ,exist A value close to 0 indicates that the first data matrix is ​​aligned with the correction data matrix.

[0119] This invention has been proven in specific implementation processes. The data is not close to zero. Due to the unavoidable error in the spacing of the sensors in the hardware, the data processed by cubic spline interpolation is still not perfectly aligned and there is still a slight difference. This part of the error needs to be further processed.

[0120] Step 3: Calculate the peaks of the first data matrix and the peaks of the correction data matrix. Based on the peaks of the first data matrix and the correction data matrix, align the peaks of the correction data matrix with the peaks of the first data matrix to obtain the aligned data matrix.

[0121] Step 3.1: Divide the first data matrix into windows to obtain leakage magnetic data for multiple windows;

[0122] Specifically, this can be achieved by sliding a window over the first data matrix.

[0123] Step 3.2: Calculate the peak threshold of the window for the magnetic flux leakage data of each window;

[0124] Step 3.2.1: For each measurement point i at the center of the window, calculate the local mean. Specifically, this is achieved through the following formula:

[0125] ;

[0126] in, The value represents the magnetic flux leakage data at the l-th measurement point in the window, and W represents the window width, which is selected based on the characteristics of the signal. The selection of the window width requires a trade-off between detection accuracy and computational complexity: a window width of 10% to 20% of the length of the first data matrix is ​​typically chosen.

[0127] Step 3.2.2: Based on local mean Calculate local standard deviation Specifically, this is achieved through the following formula:

[0128] ;

[0129] Step 3.2.3: Based on local mean and local standard deviation Calculate the peak threshold of the window. Specifically, this is achieved through the following formula:

[0130] ;

[0131] Where α is the adjustment parameter.

[0132] Step 3.3: Obtain values ​​greater than the peak threshold within the window. The leakage magnetic field data is used as the peak of the window, and the time of the measurement point corresponding to the peak is obtained. The time of the measurement point corresponding to the peak is taken as the first time. Thus, the first time corresponding to the peak of each window is obtained.

[0133] Step 3.4: Divide the calibration data matrix into windows, calculate the peak threshold of each window for the calibration data, and then determine the second time corresponding to the peak of each window.

[0134] The specific step 3.2 for calculating the peak threshold of the window differs in that the data substituted into the formula is the correction data for each window. Then, leakage magnetic data greater than the peak threshold is obtained within the window and taken as the peak of the window. The time of the measurement point corresponding to the peak is obtained and taken as the second time. Thus, the second time corresponding to the peak of each window is obtained.

[0135] Step 3.5: For each first time point, calculate the difference between the first time point and each second time point. Find the smallest difference among all the differences and replace the second time point corresponding to the smallest difference with the first time point. After all the second time points have been replaced, the aligned data matrix is ​​obtained.

[0136] Among them, the peak corresponding to the second time corresponding to the smallest difference is the peak closest to the peak corresponding to the first time. These two peaks are the peaks measured at the same defect point, but they do not appear at the same point due to the error in the arrangement of the sensors.

[0137] Furthermore, the present invention can also import the aligned data matrix and the first data matrix into the MATLAB plotting system to draw intuitive images of the acquired signals and determine the location, shape and size of defects.

[0138] The technical solution provided by this invention has the following advantages:

[0139] 1. Solves the problem that traditional magnetic flux leakage detection magnetic signal acquisition modules are not accurate enough and cannot detect minute defects.

[0140] 2. By innovatively proposing a staggered arrangement of sensors, the problems of limited sensor quantity and signal interference are solved.

[0141] 3. By setting a signal consistency compensation algorithm, the signal error caused by the staggered arrangement of sensors was resolved.

[0142] 4. By changing the selection of basic components and re-laying out the components, the problems of low board space utilization and low board integration was solved.

[0143] 5. By changing the sensor type, the problem that traditional single-axis sensors can only measure axial defects was solved.

[0144] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A data alignment method for leakage magnetic flux of interlaced pipes in a confined space, characterized in that, include: Step 1: The robot moves inside the pipe with a detection and acquisition device for magnetic leakage in intersecting pipes in a confined space. The robot collects magnetic leakage data inside the pipe through the detection and acquisition device to obtain the first data matrix and the second data matrix. In this data matrix, the columns of the first data matrix and the second data matrix are magnetic flux leakage data of the pipe position at different times, and the rows of the first data matrix and the second data matrix are magnetic flux leakage data of each row of magnetic flux leakage sensors at different angles on the inner wall of the pipe. The detection and acquisition device for magnetic flux leakage of intersecting pipes in a confined space includes 8 magnetic flux leakage sensors, a main control chip, a crystal oscillator, a multiplexer chip, a reset button, a power indicator light, and an LDO. The magnetic flux leakage sensor is used to collect magnetic flux leakage data inside the pipeline. The multiplexing chip is used to expand a single I²C bus into 8 independent multiplexed channels. The main control chip is used to control the multiplexed channels, collect and process magnetic flux leakage data. The crystal oscillator is used to provide a clock signal. The reset button is used to force the detection and acquisition device for magnetic flux leakage in interleaved pipelines in a confined space to return to its initial state. The power indicator light is used to indicate whether the 5V main power input is normal. The LDO is used to convert the 5V power input to 3.3V output and also to power the magnetic flux leakage sensor, main control chip, crystal oscillator, multiplexing chip, reset button, and power indicator light. Among them, the eight magnetic flux leakage sensors are divided into two rows. The four magnetic flux leakage sensors in each row have the same horizontal coordinate in the detection and acquisition device for magnetic flux leakage in the interlaced pipeline in a limited space. The distance between two adjacent magnetic flux leakage sensors in each row is d1=8mm, and the distance between the two rows of magnetic flux leakage sensors is d2=13mm. Step 2: Based on the first data matrix, construct an interpolation function, and use the interpolation function to correct the second data matrix to obtain the corrected data matrix; Step 3: Calculate the peaks of the first data matrix and the peaks of the correction data matrix. Based on the peaks of the first data matrix and the correction data matrix, align the peaks of the correction data matrix with the peaks of the first data matrix to obtain the aligned data matrix.

2. The data alignment method for interlaced pipe leakage magnetic flux in a confined space according to claim 1, characterized in that, Step 1 specifically includes: Step 1.1: The robot moves at a constant speed along the length of the pipe. At the time t at the j-th measurement point... j At that time, the leakage magnetic field sensor Nm collects circumferential leakage magnetic field data around the inner wall of the pipe. The circumferential leakage magnetic data Represented as: ； in, This represents the magnetic flux leakage data at the first angle on the inner wall of the pipe, collected by the magnetic flux leakage sensor Nm. This represents the magnetic flux leakage data at the second angle on the inner wall of the pipe, collected by the magnetic flux leakage sensor Nm. This represents the magnetic flux leakage data at the u-th angle on the inner wall of the pipe, collected by the magnetic flux leakage sensor Nm. Step 1.2: Segment the circumferential magnetic leakage data collected by all the magnetic leakage sensors in each row to obtain the first magnetic leakage data and the second magnetic leakage data; The first leakage magnetic field data is represented as follows: ； Among them, t j This represents the time at the j-th measurement point. This represents the magnetic flux leakage data of the first row of magnetic flux leakage sensors at the pipe location of measurement point j, i.e., the first magnetic flux leakage data. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N1. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N2. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N3. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N4; The second leakage magnetic field data is represented as follows: ； in, This represents the magnetic flux leakage data of the second row of magnetic flux leakage sensors at the pipe location of measurement point j, i.e., the second magnetic flux leakage data. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N5. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N6. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N7. This represents the circumferential leakage magnetic field data of the leakage magnetic field sensor N8; Step 1.3: Concatenate the first magnetic flux leakage data from all time periods to obtain a first data matrix; concatenate the second magnetic flux leakage data from all time periods to obtain a second data matrix. The first data matrix is ​​represented as follows: ； in, Represents the first data matrix. This represents the first leakage magnetic field data at time t1. This represents the first leakage magnetic field data at time t2. Representing time t J The first leakage magnetic field data, where J represents the total number of measurement points; The second data matrix is ​​represented as follows: ； in, This represents the second data matrix. The second leakage magnetic field data represents time t1. The second leakage magnetic field data represents time t2. Representing time t J The second leakage magnetic field data.

3. The data alignment method for interlaced pipe leakage magnetic flux in a confined space according to claim 1, characterized in that, Step 2 specifically includes: Step 2.1: Using the time axis of the first row of magnetic leakage sensors as the reference time axis, construct a tridiagonal linear equation system based on the first data matrix. The tridiagonal linear equation system is expressed as follows: ； Among them, h0, h1, h2, h n -1, h n-2 Represented as h j h j =t j+1 -t j , t j t represents the time of the j-th measurement point on the reference time axis. j+1 The time of the (j+1)th measurement point on the reference time axis, h j Indicates t j+1 and t j The time difference between y0, y1, y2, y3, y n y n-1 y n-2 Represented as y j y j The values ​​M0, M1, M2, M3, and M represent the leakage magnetic field data at the j-th measurement point on the reference time axis. n M n-1 M n-2 Represented as M j M j This represents the second derivative at the j-th measurement point on the reference time axis; Step 2.2: Solve the tridiagonal linear equation system using the chasing method to obtain the second derivative at each measurement point; Step 2.3: In each time interval [t] j ,t j+1 On top of that, construct the interpolation function. ; Step 2.4: For each measurement point of the second row of magnetic flux leakage sensors, determine the time interval of the first row of magnetic flux leakage sensors to which the measurement point belongs. Substitute the time of the measurement point into the interpolation function of its time interval to obtain the correction data. Replace the magnetic flux leakage data of the measurement point in the second data matrix with the correction data to obtain the correction data matrix.

4. The data alignment method for interlaced pipe leakage magnetic flux in a confined space according to claim 3, characterized in that, The interpolation function described in step 2.3 Represented as: ； in, The coefficient is calculated using the following formula: 。 5. The data alignment method for interlaced pipe leakage magnetic flux in a confined space according to claim 1, characterized in that, Step 3 specifically includes: Step 3.1: Divide the first data matrix into windows to obtain leakage magnetic data for multiple windows; Step 3.2: Calculate the peak threshold of the window for the magnetic flux leakage data of each window; Step 3.3: Obtain values ​​greater than the peak threshold within the window. The leakage magnetic field data is used as the peak of the window, and the time of the measurement point corresponding to the peak is obtained. The time of the measurement point corresponding to the peak is taken as the first time. Thus, the first time corresponding to the peak of each window is obtained. Step 3.4: Divide the calibration data matrix into windows, calculate the peak threshold of each window for the calibration data, and then determine the second time corresponding to the peak of each window. Step 3.5: For each first time point, calculate the difference between the first time point and each second time point. Find the smallest difference among all the differences and replace the second time point corresponding to the smallest difference with the first time point. After all the second time points have been replaced, the aligned data matrix is ​​obtained.

6. The data alignment method for interlaced pipe leakage magnetic flux in a confined space according to claim 5, characterized in that, Step 3.2 specifically includes: Step 3.2.1: For each measurement point i at the center of the window, calculate the local mean. Specifically, this is achieved through the following formula: ； in, This represents the magnetic flux leakage data of the l-th measurement point in the window, and W represents the window width. Step 3.2.2: Based on local mean Calculate local standard deviation Specifically, this is achieved through the following formula: ； Step 3.2.3: Based on local mean and local standard deviation Calculate the peak threshold of the window. Specifically, this is achieved through the following formula: ； Where α is the adjustment parameter.

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