Weak magnetic imaging positioning method for corrosion damage of bridge pull sling
By employing an adaptive threshold and gradient standard deviation method, the problem of locating internal corrosion damage in bridge suspension cables was solved, enabling high-precision imaging and quantitative assessment of the corrosion area, and providing a low-cost non-destructive testing technology.
Patent Information
- Application Number
- CN202511446265.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-10
- Publication Date
- 2025-11-14
AI Technical Summary
Existing technologies make it difficult to accurately quantify the corrosion morphology and damage extent inside the parallel steel wires of bridge suspension cables, leading to difficulties in determining corrosion areas.
An adaptive threshold is used to divide the corrosion area, and a pseudo-color image is reconstructed by gradient standard deviation and linear interpolation to achieve the visualization and localization of corrosion damage.
It achieves high-precision imaging localization and quantitative assessment of corrosion areas in bridge suspension cables, providing a low-cost and efficient non-destructive testing method.
Smart Images
Figure CN120948601A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of bridge cable structure damage detection, and more specifically to a weak magnetic imaging method for locating corrosion damage in bridge cables. Background Technology
[0002] Cable-stayed bridges, as a highly representative bridge structure, are widely used worldwide due to their high efficiency, aesthetics, and economy. Among them, the suspenders, as the core load-bearing components that transfer the bridge's weight and traffic loads, are exposed to complex environments for extended periods, making them susceptible to corrosion, fatigue, and other damage, seriously threatening bridge safety. Furthermore, due to the covering of the outer sheath, the internal corrosion damage of the parallel steel cables is difficult to detect, making corrosion detection of suspenders a persistent challenge in the industry.
[0003] Compared to traditional magnetic flux leakage (MF) technology, spontaneous MF leakage detection technology, with its advantage of not requiring an external magnetization device, has become an important means of detecting early corrosion damage in ferromagnetic materials. Under the combined influence of the Earth's magnetic field and service loads, the corrosion defect areas of suspension cables undergo irreversible reorientation of magnetic domains, forming a weak, stably retained spontaneous MF leakage field. Damage localization can be achieved by detecting the distortion of this weak magnetic field. In recent years, scholars have conducted extensive research on the damage of major ferromagnetic components (reinforcing bars, wires, and strands) in various bridge engineering projects, and this research has been gradually applied to practical engineering. However, while existing technologies can locate corrosion damage and predict the corrosion width range through multiple characteristic parameters, the mutual interference of magnetic signals from multiple wires within parallel cables prevents accurate quantification of corrosion morphology and damage degree, affecting the determination of corrosion areas in actual bridge inspection work.
[0004] Therefore, how to solve the above-mentioned technical problems still needs to be further studied by those skilled in the art. Summary of the Invention
[0005] In view of this, the present invention provides a weak magnetic imaging method for locating corrosion damage in bridge suspension cables, which constructs an adaptive threshold. This effectively delineates corrosion zones and enables visualization of cable damage.
[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for locating corrosion damage in bridge suspension cables using weak magnetic imaging includes the following steps: An N-channel Hall array is arranged at equal intervals along the circumferential direction of the suspension cable to synchronously acquire normal magnetic signals at fixed step intervals. and corresponding spatial coordinates; Obtain the maximum normal magnetic signal value of the acquisition channel at the corrosion center. and minimum value and the corresponding location information; Obtain the initial normal magnetic signal with the extreme value of the normal magnetic signal at the time when the sling installation is completed. and and environmental magnetic field ; Extracting from axial images crest-trough spacing Utilizing corrosion length A significant linear relationship between the magnetic signal characteristic parameters and the axial corrosion region is used to divide the region. Obtain the minimum value of the normal magnetic signal acquired from N channels. and maximum value ; Gradient calculations are performed on the extreme values of the normal magnetic signals in each channel to obtain the normal components. gradient standard deviation And set an adaptive threshold. ; Will ≥ The area was identified as a suspected corrosion area. Linear interpolation was used to fill the suspected area at the pixel level, and a pseudo-color image of the axial and circumferential corrosion range of the cable was reconstructed to achieve the division of the circumferential corrosion area. Magnetic signal data imaging was performed on the axial and circumferential corrosion areas to complete the localization.
[0007] Optionally, it also includes establishing the crest-trough spacing. With corrosion length The correspondence between them: ,in Indicates the goodness of fit, based on actual measurements. Determine corrosion length This allows for the evaluation of the degree of axial corrosion damage to the cable-stayed structure.
[0008] Optional, establish all channels mean and its standard deviation With adaptive threshold The relationship is as follows: ; Based on the calculated adaptive threshold The visible range of the circumferential corrosion zone is divided, thereby clearly reconstructing the corrosion range.
[0009] Optionally, gradient calculations are performed on the extreme values of the normal magnetic signals of each channel to obtain the normal components. gradient standard deviation The calculation formula is as follows: .
[0010] Optionally, PyCharm software can be used to image the magnetic signal data of the axial and circumferential corrosion areas. The specific process is as follows: Ensure the necessary environment is installed: Import the pandas library, matplotlib's pyplot module, seaborn library, numpy library, and scipy's RegularGridInterpolator and argrelextrema; Define sensor angles: Create a NumPy array containing angles from -180 to 180 degrees in 30-degree increments, and convert the 12 sensors into a 360-degree circumferential arrangement; Marking of axial local extrema and corrosion regions: Find the maximum and minimum values on each sensor path, filter out local maxima greater than 0.8 times the maximum value and local minima less than 0.8 times the minimum value, arrange the x values corresponding to the peak-valley values in order, and consider that when the first is a peak or valley and the next is a valley or peak, the two constitute a corrosion region. Circumferential gradient analysis and marking of corrosion zones: based on the already determined location of corrosion zones along the axial direction. The interval is calculated, and the gradient value on each channel within the interval is obtained by taking the absolute value of the gradient value to obtain the gradient change in the circumferential direction. The standard deviation of the circumferential gradient is calculated, and the threshold is calculated based on the standard deviation. The threshold is the mean of the gradient standard deviation plus the standard deviation of the gradient standard deviation. The gradient standard deviation at each angle position is compared to see if it exceeds the threshold. If the gradient standard deviation at a certain angle exceeds the threshold, it is considered that there may be corrosion defects. Data interpolation: The data on each channel is interpolated using linear interpolation to adjust the interpolated result to a 2000×2000 shape, generating a more continuous and detailed image; Corrected coordinate scale and arrangement: The data of channel L1 was placed in the middle, and counterclockwise L12, L11, L10, L9, L8, L7 corresponded to -30 degrees, -60 degrees, -90 degrees, -120 degrees, -150 degrees, and -180 degrees respectively. Clockwise L2, L3, L4, L5, L6, L7 corresponded to 30 degrees, 60 degrees, 90 degrees, 120 degrees, 150 degrees, and 180 degrees respectively. Create a heatmap: Generate horizontal and vertical axes, where the horizontal axis represents the length of the sensor scanning path and the vertical axis represents the corresponding angle of the sensor. Set the color bar attributes and set the color bar label to the degree of erosion to help interpret the correspondence between colors and values in the graph.
[0011] Optionally, the location information includes length location information and distance information, wherein the direction of magnetic signal acquisition is based on the straight line where the suspension cable is located as the horizontal axis. In the positive direction of the axis, the length position corresponding to the normal magnetic signal is recorded; the distance information is the distance between the magnetic sensor and the surface of the suspension cable when the normal magnetic signal is collected.
[0012] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a weak magnetic imaging localization method for corrosion damage of bridge suspension cables. By using gradient standard deviation adaptive threshold segmentation and linear interpolation fusion, it solves the problem of blurred corrosion boundaries caused by multi-wire magnetic signal interference inside parallel steel wire cables, realizes high-precision imaging localization and quantitative evaluation of corrosion areas, and provides a low-cost and high-efficiency non-destructive testing technology for the safe operation and intelligent maintenance of bridge suspension cables. Attached Figure Description
[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0014] Figure 1 This is a flowchart of a method for imaging and locating damage in a wire bundle structure based on weak magnetic signals, as disclosed in this invention. Figure 2 This is a schematic diagram of a sensor array device disclosed in this invention; Figure 3 The axial corrosion location-normal magnetic signal curve disclosed in this invention ; Figure 4 The normal leakage magnetic field distribution curves for different degrees of damage to the sling; Figure 5 For adaptive threshold A schematic diagram showing the division of corrosion zones; Figure 6 This is a diagram showing the effect of weak magnetic field imaging using the method disclosed in this invention. Detailed Implementation
[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0016] The embodiments of the present invention disclose as follows Figure 1 As shown, this invention discloses a method for imaging and locating damage in a wire bundle structure based on weak magnetic signals, comprising the following steps: S1. A 12-channel Hall sensor array is arranged at equal intervals along the circumferential direction of the suspension cable, such as... Figure 2 As shown, the normal magnetic signal is synchronously acquired with a fixed step size. and their corresponding spatial coordinates; S2. Obtain the maximum value of the normal magnetic signal of the acquisition channel corresponding to the corrosion center. and minimum value and its location information; S3. Obtain the initial normal magnetic signal corresponding to the extreme value of the above-mentioned normal magnetic signal when the installation of the sling is completed. and and environmental magnetic field ; After the installation of the suspension cable is completed, a suspension cable inspection robot and an array of magnetic sensors can be used to collect and record the initial normal magnetic signal of the suspension cable and the ambient magnetic field.
[0017] S4. Extract from the axial image crest-trough spacing Utilizing corrosion length A significant linear relationship between the magnetic signal characteristic parameters and the axial corrosion region is used to divide the region. S5. Obtain the minimum value of the normal magnetic signal acquired from 12 channels. and maximum value ( (12 data acquisition channels) S6. Perform gradient calculation on the extreme values of the normal magnetic signal of each channel to obtain the normal component. gradient standard deviation and with all channels mean Its standard deviation The sum as an adaptive threshold ; S7, will ≥ The area was identified as a suspected corrosion area. Linear interpolation was used to fill the suspected area at the pixel level, and a pseudo-color image of the axial and circumferential corrosion range of the cable was reconstructed to achieve the division of the circumferential corrosion area. S8. Based on PyCharm software, magnetic signal data imaging of axial and circumferential corrosion areas is performed, which intuitively reflects the corrosion area.
[0018] This invention constructs an adaptive threshold for damage to cable-stayed structures. The threshold is used to divide the corrosion damage area and locate the damage to the suspension cable.
[0019] In this embodiment, the location information includes length location information and distance information, wherein the direction of the magnetic signal acquisition is based on the straight line where the suspension cable is located as the horizontal axis. In the positive direction of the axis, the length position corresponding to the normal magnetic signal is recorded; the distance information is the distance between the magnetic sensor and the surface of the suspension cable when the normal magnetic signal is collected.
[0020] like Figure 3 As shown, the background magnetic field of the cable-stayed structure is composed of the remanent magnetization of the cable, the geomagnetic field, and the vector superposition of the environmental stray field. This invention uses field-acquired magnetic field signals to subtract the initial background magnetic field to obtain the change in magnetic signal, and then performs data analysis. The minimum value of the normal magnetic signal... The coordinates of the corresponding point are maximum value The coordinates of the corresponding point are The distance from the surface of the suspending cable is .
[0021] Furthermore, in this embodiment, the corrosion length is related to the distance between the peaks and troughs of the magnetic signal and the corrosion length. The fitting relationship is as follows: ; This parameter has a high goodness of fit ( Its slope is also closer to the ideal value of 1, indicating that the normal component can be used for quantitative characterization of axial corrosion areas. Therefore, the normal component is used to locate corrosion areas in subsequent imaging analysis.
[0022] Furthermore, in this embodiment, the absolute value of the magnetic signal at the peak-trough of the corrosion region of the suspension cable increases significantly with the increase of the circumferential corrosion width, and the signal change gradient is non-linearly positively correlated with the corrosion width. Figure 4 As shown, the signals from the 12 acquisition channels exhibit consistent changes in the corrosion area, with the peak-to-trough spacing having an error of less than 5% compared to the actual corrosion length. Furthermore, the expansion direction of the absolute value of the magnetic signal at the peak (trough) highly coincides with the location of the circumferential corrosion. Based on this pattern, a basis for subsequent determination of the circumferential corrosion width can be established.
[0023] Furthermore, in this embodiment, in step S6, the gradient value within the corrosion length range of each acquisition channel is calculated. Standard deviation Select each channel average value As a preliminary threshold, plus all 12 Standard deviation As a threshold variable. Threshold As an indicator for classifying the severity of spontaneous leakage magnetic field changes, its calculation formula is as follows: ; ; ; ; ; ; Therefore, based on the adaptive threshold This enables the localization of the corroded area, thereby achieving high-precision imaging localization and quantitative assessment of the corroded area.
[0024] Furthermore, in this embodiment, in step S7, as follows: Figure 5 As shown, ≥ The area was identified as a suspected corrosion zone. Linear interpolation was used to fill the suspected zone at the pixel level, and a pseudo-color image of the axial and circumferential corrosion range of the suspension cable was reconstructed to achieve the division of the circumferential corrosion region.
[0025] Furthermore, in this embodiment, in step S8, due to the large amount of magnetic signal data and the cumbersome imaging process, PyCharm software is used for magnetic signal data imaging. The specific process is as follows: Install the necessary environment. Import the pandas library (data processing), matplotlib's pyplot module (plotting), seaborn library (statistical graphics), numpy library (numerical computation), and scipy's RegularGridInterpolator (interpolation) and argrelextrema (finding local extrema).
[0026] Define the sensor angles. Create a NumPy array containing angles from -180 to 180 degrees in 30-degree increments, and convert the 12 sensors into a 360-degree circumferential arrangement.
[0027] Axial local extrema and corrosion zone marking. Find the maximum and minimum values on each sensor path, filter out the local maxima (peaks) greater than 0.8 times the maximum value and the local minima (valleys) less than 0.8 times the minimum value, arrange the x-values corresponding to these peaks and valleys in order, and consider the area between the first peak (valley) and the next valley (peak) to constitute a corrosion zone.
[0028] Circumferential gradient analysis and marking of corrosion zones. Based on the already determined locations of corrosion zones along the axial direction. The gradient value is calculated for each channel within a given interval, and the absolute value of the gradient value is taken to obtain the gradient change in the circumferential direction. The standard deviation of the circumferential gradient is calculated, and a threshold is calculated based on the standard deviation. The threshold is the sum of the mean and standard deviation of the gradient standard deviations. The gradient standard deviation at each angle is compared to see if it exceeds the threshold. If the gradient standard deviation at a certain angle exceeds the threshold, it is considered that there may be a corrosion defect.
[0029] Data interpolation. The data in each channel is interpolated using linear interpolation to adjust the interpolated result to a 2000×2000 shape, generating a more continuous and detailed image.
[0030] Correct the coordinate scale and arrangement. To make the erosion image more obvious, the data of the L1 channel was placed in the middle (0 degrees). Counterclockwise, L12, L11, L10, L9, L8, and L7 correspond to -30 degrees, -60 degrees, -90 degrees, -120 degrees, -150 degrees, and -180 degrees, respectively. Clockwise, L2, L3, L4, L5, L6, and L7 correspond to 30 degrees, 60 degrees, 90 degrees, 120 degrees, 150 degrees, and 180 degrees (at -180 degrees).
[0031] Draw a heatmap. For example... Figure 6 As shown, a horizontal and vertical axis are generated. The horizontal axis represents the length of the sensor scanning path, and the vertical axis represents the corresponding angle of the sensor. Color bar attributes are set, and the color bar label is set to the degree of erosion to help interpret the correspondence between colors and values in the figure.
[0032] The principle of this invention is as follows: Utilizing the spontaneous weak magnetic leakage field (i.e., "stress-magnetic coupling effect") generated by corrosion in ferromagnetic suspension cables, a high-density, integrated circumferential-axial scan is performed on the cable surface using an array of Hall sensors to acquire the normal magnetic signal and its spatial coordinates. Subsequently, based on the "statistical discreteness" characteristic caused by the sharp drop in magnetic signal peak at the corrosion zone boundary, macroscopic segmentation of the axial corrosion zone is first completed using a standard deviation adaptive threshold. Then, based on whether the standard deviation of each channel gradient exceeds the limit, the suspicious circumferential corrosion zone is precisely located. Finally, linear interpolation pixel-level filling is performed on the suspicious zone, and a pseudo-color weak magnetic image of the axial and circumferential damage distribution of the suspension cable is reconstructed on the PyCharm platform, achieving visualization, high precision, low-cost non-destructive testing, and quantitative evaluation of corrosion damage.
[0033] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.
[0034] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for locating corrosion damage in bridge suspension cables using weak magnetic imaging, characterized in that, Includes the following steps: An N-channel Hall array is arranged at equal intervals along the circumferential direction of the suspension cable to synchronously acquire normal magnetic signals at fixed step intervals. and corresponding spatial coordinates; Obtain the maximum normal magnetic signal value of the acquisition channel at the corrosion center. and minimum value and the corresponding location information; Obtain the initial normal magnetic signal with the extreme value of the normal magnetic signal at the time when the sling installation is completed. and and environmental magnetic field ; Extracting from axial image crest-trough spacing Utilizing corrosion length A significant linear relationship between the magnetic signal characteristic parameters and the axial corrosion region is used to divide the region. Obtain the minimum value of the normal magnetic signal acquired from N channels. and maximum value ; Gradient calculations are performed on the extreme values of the normal magnetic signals in each channel to obtain the normal components. gradient standard deviation And set an adaptive threshold. ; Will ≥ The area was identified as a suspected corrosion area. Linear interpolation was used to fill the suspected area at the pixel level, and a pseudo-color image of the axial and circumferential corrosion range of the cable was reconstructed to achieve the division of the circumferential corrosion area. Magnetic signal data imaging was performed on the axial and circumferential corrosion areas to complete the localization.
2. The method for weak magnetic imaging localization of corrosion damage in bridge suspension cables according to claim 1, characterized in that, This also includes establishing the crest-trough spacing. With corrosion length The correspondence between them: According to actual measurements Determine corrosion length This allows for the evaluation of the degree of axial corrosion damage to the cable-stayed structure.
3. The method for weak magnetic imaging localization of corrosion damage in bridge suspension cables according to claim 1, characterized in that, Establish all channels mean and its standard deviation With adaptive threshold The relationship is as follows: ； Based on the calculated adaptive threshold The visible range of the circumferential corrosion zone is divided, thereby clearly reconstructing the corrosion range.
4. The method for weak magnetic imaging localization of corrosion damage in bridge suspension cables according to claim 1, characterized in that, Gradient calculations are performed on the extreme values of the normal magnetic signals in each channel to obtain the normal components. gradient standard deviation The calculation formula is as follows: 。 5. The method for weak magnetic imaging localization of corrosion damage in bridge suspension cables according to claim 1, characterized in that, Magnetic signal data imaging of axial and circumferential corrosion areas was performed using PyCharm software. The specific process is as follows: Ensure the necessary environment is installed: Import the pandas library, matplotlib's pyplot module, seaborn library, numpy library, and scipy's RegularGridInterpolator and argrelextrema; Define sensor angles: Create a NumPy array containing angles from -180 to 180 degrees in 30-degree increments, and convert the 12 sensors into a 360-degree circumferential arrangement; Marking of axial local extrema and corrosion regions: Find the maximum and minimum values on each sensor path, filter out local maxima greater than 0.8 times the maximum value and local minima less than 0.8 times the minimum value, arrange the x values corresponding to the peak-valley values in order, and consider that when the first is a peak or valley and the next is a valley or peak, the two constitute a corrosion region. Circumferential gradient analysis and marking of corrosion zones: based on the already determined location of corrosion zones along the axial direction. The interval is calculated, and the gradient value on each channel within the interval is obtained by taking the absolute value of the gradient value to obtain the gradient change in the circumferential direction. The standard deviation of the circumferential gradient is calculated, and the threshold is calculated based on the standard deviation. The threshold is the mean of the gradient standard deviation plus the standard deviation of the gradient standard deviation. The gradient standard deviation at each angle position is compared to see if it exceeds the threshold. If the gradient standard deviation at a certain angle exceeds the threshold, it is considered that there may be corrosion defects. Data interpolation: The data on each channel is interpolated using linear interpolation to adjust the interpolated result to a 2000×2000 shape, generating a more continuous and detailed image; Corrected coordinate scale and arrangement: The data of channel L1 was placed in the middle, and counterclockwise L12, L11, L10, L9, L8, L7 corresponded to -30 degrees, -60 degrees, -90 degrees, -120 degrees, -150 degrees, and -180 degrees respectively. Clockwise L2, L3, L4, L5, L6, L7 corresponded to 30 degrees, 60 degrees, 90 degrees, 120 degrees, 150 degrees, and 180 degrees respectively. Create a heatmap: Generate horizontal and vertical axes, where the horizontal axis represents the length of the sensor scanning path and the vertical axis represents the corresponding angle of the sensor. Set the color bar attributes and set the color bar label to the degree of erosion to help interpret the correspondence between colors and values in the graph.
6. The method for weak magnetic imaging localization of corrosion damage in bridge suspension cables according to claim 1, characterized in that, Location information includes length and distance information. The horizontal axis is the line along which the suspension cable lies, and the direction of the acquired magnetic signal is... In the positive direction of the axis, the length position corresponding to the normal magnetic signal is recorded; the distance information is the distance between the magnetic sensor and the surface of the suspension cable when the normal magnetic signal is collected.