Method for obtaining speed and displacement of moving steel wire rope based on magnetic flux leakage image texture

Abstract

The application discloses a method for obtaining the speed and displacement of a moving steel wire rope based on a magnetic leakage image texture, which comprises the following steps: collecting a magnetic leakage signal of the steel wire rope, performing a trend-removing treatment, and then forming a two-dimensional signal image with obvious strand wave corrugation through an interpolation method and a filtering treatment; marking a strand wave area of each image and performing a sliding window treatment, then performing image equalization treatment and edge detection based on a Canny algorithm on each window area; using an improved Hough transform method to obtain a strand wave slope in the image and determine the running direction of the steel wire rope; and finally determining the real-time speed and moving distance of the steel wire rope through the slope.

Description

Technical Field

[0001] This invention belongs to the field of non-destructive testing technology for steel wire ropes, and more specifically, it relates to a method for non-contactly obtaining the instantaneous velocity and displacement of a moving steel wire rope through leakage magnetic signals. Background Technology

[0002] Wire ropes are widely used in lifting or traction of large equipment, therefore, wire rope failure can lead to significant safety hazards and property losses. During use, due to the complexity of the wire rope structure and the diversity of application environments, wire ropes can develop various forms of damage, making non-destructive testing (NDT) extremely challenging. Based on the nature and characteristics of wire rope damage, it can be classified into two categories: Local Fault (LF) and Loss of Metallic Area (LMA). LF mainly includes broken wires, pitting, and localized shape deformation, while LMA mainly includes wear, corrosion, and reduction in wire rope diameter.

[0003] In recent years, various non-destructive testing technologies for steel wire ropes have been gradually improved. Based on different testing principles, the most widely used methods currently include image analysis, acoustic emission, and electromagnetic testing. Due to the good magnetic permeability of steel wire rope materials and the high penetration of electromagnetic signals, electromagnetic testing methods based on leakage magnetic signals have achieved good testing results. my country has also formulated relevant standards for electromagnetic non-destructive testing of ferromagnetic steel wire ropes, marking the gradual standardization and widespread application of this type of testing method in practical industry.

[0004] In the non-destructive testing of wire ropes, different types of speed encoders are often embedded inside sensors for equidistant sampling to estimate the wire rope's speed and locate defects. Taking a rotary incremental encoder as an example, the speed of the wire rope can be obtained based on the number of rotations of the pulley as the wire rope passes through it, thus enabling defect location. However, because the pulley and wire rope operate in contact, situations such as pulley free-spinning or jamming can easily occur. Furthermore, when the wire rope operates at high speeds or undergoes cyclical motion for extended periods, the pulley surface is prone to wear, leading to positioning accuracy and affecting defect location judgment. Therefore, finding a non-contact speed and displacement measurement method is of significant engineering importance for the non-destructive testing of wire ropes. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for obtaining the speed and displacement of a moving steel wire rope based on magnetic flux leakage image texture. By using an improved Hough law to calculate the strand tilt angle, the real-time speed and running direction of the steel wire rope can be calculated, thereby replacing the speed detection equipment in the non-destructive testing system of the steel wire rope and optimizing the detection structure.

[0006] To achieve the above-mentioned objective, the present invention provides a method for obtaining the velocity and displacement of a moving steel wire rope based on magnetic flux leakage image texture, characterized by comprising the following steps:

[0007] (1) Collect the leakage magnetic signal of the steel wire rope;

[0008] The leakage magnetic signal of a saturated magnetized moving steel wire rope is detected by an X-channel Hall sensor. After sampling over time, the X-channel leakage magnetic signal S(x) is obtained, where x = 1, 2, ..., X.

[0009] (2) Preprocessing of leakage magnetic signal;

[0010] (2.1) The X-channel leakage magnetic signal S(x) is interpolated using spline interpolation to form the Y-channel leakage magnetic signal S(y), where y = 1, 2, ..., Y;

[0011] (2.2) Noise reduction processing is performed on each leakage magnetic signal S(y) to form an image signal with obvious wave ripples.

[0012] (3) Mark each image signal The stock wave area;

[0013] (3.1) The signal image Convert to grayscale image, denoted as

[0014] (3.2) Determine the region containing stock waves;

[0015] Set a grayscale range threshold K; for each grayscale image In the process, if the grayscale difference between any two pixels in each column is greater than K, then the column is determined to be a wave region.

[0016] (4) Grayscale images Edge detection is performed on the wave region within the data;

[0017] (4.1) In grayscale images In the middle, a sliding window with a size of M and a step size of 1 is used to slide across the wave region and capture multiple window regions S. y (n), where each window region contains M pixels, n = 1, 2, ..., where n represents the number of the window region;

[0018] (4.2) For each window region S y (n) First, perform image equalization, then use the Canny algorithm to extract edge points to obtain the edge detection map.

[0019] (5) Solve for the inclination angle α of the strand wave using the improved Hough transform;

[0020] (5.1) In each edge detection map In the process, all connected lines are found using the 8-neighborhood condition and marked as... Where x = 1, 2, ..., x is the number of the connecting line;

[0021] (5.2) For each connecting line Construct a blank image that matches the size of the sliding window. Then connect the lines Corresponding mapping to In the image Middle of the connecting line Perform the Hough transform to obtain the extrema and connect the lines. The coordinates of each pixel are then used to perform edge detection on the image. The middle part is displayed as a line segment;

[0022] (5.3) Traversing the edge detection map Of all the displayed line segments, the q line segments with the largest corresponding extreme values ​​are taken as the effective lines of the stock wave at that point.

[0023] (5.4) Calculate the angle values ​​of all valid lines in the rectangular coordinate system, and then take the median of all angle values ​​as the wave tilt angle α of the window area;

[0024] (6) Use the relationship between the slope and the wire rope lay to solve the speed change and the distance traveled by the wire rope;

[0025] Each window region is taken as a sampling moment, and the running speed v of the wire rope is calculated by using the mathematical relationship between the physical shape of the wire rope and the sensor sampling.

[0026]

[0027] Where L is the wire rope lay length, Y is the number of rows of the signal after spline interpolation, α is the strand tilt angle, and f is the sensor sampling frequency;

[0028] Integrating over the velocity v, we can solve for the distance the wire rope travels, s = ∫vdt.

[0029] The objective of this invention is achieved as follows:

[0030] This invention discloses a method for obtaining the velocity and displacement of a moving steel wire rope based on magnetic flux leakage image texture. First, the magnetic flux leakage signal of the steel wire rope is acquired. After detrending processing, interpolation and filtering are used to form a two-dimensional signal image with obvious wave patterns. Next, the wave regions of each image are marked and subjected to sliding window processing. Then, image equalization and edge detection based on the Canny algorithm are performed on each window region. Then, an improved Hough transform method is used to obtain the wave slope in the image to determine the direction of the steel wire rope's movement. Finally, the real-time velocity and moving distance of the steel wire rope are determined by the slope.

[0031] Meanwhile, the method for obtaining the velocity and displacement of a moving steel wire rope based on magnetic flux leakage image texture provided by the present invention also has the following beneficial effects:

[0032] (1) The present invention interpolates multiple leakage magnetic signals into two-dimensional image signals, analyzes the two-dimensional texture features of the stock wave image, and makes full use of the leakage magnetic signal information of the wire rope.

[0033] (2) The present invention uses leakage magnetic signal to determine the running speed and displacement of wire rope, avoiding the influence of speed encoder idling and shaking, simplifying the detection device and saving installation costs;

[0034] (3) The traditional Hough transform algorithm does not introduce connectivity judgment, and the resulting connecting line is the contour connecting line of each strand. In subsequent processing, the edge points of different strands that may be connected by the Hough transform are directly used. However, the improved Hough transform algorithm introduces connectivity judgment, which can identify the tilt angle of a single strand. Through the physical structure and mathematical relationship between the wire rope and the sensor, the speed of the wire rope can be detected in real time. Attached Figure Description

[0035] Figure 1 This is a flowchart of a method for obtaining the velocity and displacement of a moving steel wire rope based on magnetic flux leakage image texture;

[0036] Figure 2 This is a schematic diagram of the magnetic flux leakage sensor detection of the present invention;

[0037] Figure 3 This is a schematic diagram showing the relative positions of the Hall sensor and the steel wire rope;

[0038] Figure 4 It is a two-dimensional signal image after spline interpolation;

[0039] Figure 5 This is a schematic diagram of processing using the improved Hough transform;

[0040] Figure 6 This is a schematic diagram for judging the leakage magnetic signal wave.

[0041] Figure 7 This is a schematic diagram illustrating the change in the stock wave tilt angle;

[0042] Figure 8 This is a schematic diagram of the speed change of the wire rope. Detailed Implementation

[0043] The specific embodiments of the present invention will now be described with reference to the accompanying drawings to enable those skilled in the art to better understand the invention. It should be particularly noted that in the following description, detailed descriptions of known functions and designs that might obscure the main content of the invention will be omitted here.

[0044] Example

[0045] Figure 1 This is a flowchart of a method for obtaining the velocity and displacement of a moving steel wire rope based on magnetic flux leakage image texture.

[0046] In this embodiment, as Figure 1 As shown, the present invention provides a method for obtaining the velocity and displacement of a moving steel wire rope based on magnetic flux leakage image texture, comprising the following steps:

[0047] S1. Collect the magnetic leakage signal of the steel wire rope;

[0048] A schematic diagram of the sensor device and detection principle is shown below. Figure 2 As shown, in this embodiment, a permanent magnet and an armature are used to magnetize the wire rope, such as... Figure 3 As shown, Hall sensors are evenly distributed around the outer circumference of the wire rope, with each Hall sensor spaced 22.5° apart. In this embodiment, the leakage magnetic field sensor sampling frequency is 256Hz, and the sensor stops moving after passing through the entire length of the wire rope. In this embodiment, 16 Hall sensors are used to detect the leakage magnetic field signal of the saturated magnetized wire rope, and 16 leakage magnetic field signals S(x) are obtained after sampling over equal time, where x = 1, 2, ..., 16;

[0049] S2, Leakage signal preprocessing;

[0050] S2.1. The X-channel magnetic flux leakage signal S(x) is interpolated using spline interpolation to form 200 channels of magnetic flux leakage signal S(y), y = 1, 2, ..., 200. In this embodiment, after spline interpolation, the amount of data increases and the magnetic flux leakage signal can be read as a two-dimensional image signal.

[0051] S2.2. Denoising the magnetic flux leakage image signal S(y) to form an image signal with obvious wave ripples. In this embodiment, filtering methods including, but not limited to, two-dimensional wavelet transform are used to eliminate jitter and other noise interference in the two-dimensional image signal, such as... Figure 4 As shown, the wave region represents relative motion between the wire rope and the sensor, while the non-wave region represents the wire rope being relatively stationary.

[0052] S3, Mark each image signal The stock wave area;

[0053] S3.1, Transform the signal image Convert to grayscale image, denoted as

[0054] S3.2 Determine the region containing stock waves;

[0055] Set a grayscale range threshold K; for each grayscale image In the process, if the grayscale difference between any two pixels in each column is greater than K, then the column is determined to be a wave region.

[0056] S4. Grayscale image Edge detection is performed on the wave region within the data;

[0057] S4.1, in grayscale images In the middle, a sliding window with a size of M and a step size of 1 is used to slide across the wave region and capture multiple window regions S. y (n), where each window region contains M = 50 pixels, n = 1, 2, ..., where n represents the window region number;

[0058] S4.2, For each window region S y (n) First, perform image equalization, then use the Canny algorithm to extract edge points to obtain the edge detection map.

[0059] S5. Solve for the inclination angle α of the strand wave using the improved Hough transform;

[0060] S5.1, in each edge detection map In the process, all connected lines are found using the 8-neighborhood condition and marked as... Where x = 1, 2, ..., x is the number of the connecting line;

[0061] In this embodiment, finding the connected lines using the 8-neighborhood judgment condition is a prior art technique, and the specific processing method will not be elaborated further. The obtained connected lines are the contour connecting lines of each wavelet, avoiding the possibility of directly using the Hough transform to connect different wavelet edge points in subsequent processing. This clarifies the multiple wavelet contours existing within a single window, thereby utilizing the connected lines. The subsequent tilt angle is solved as the internal wave within the window.

[0062] S5.2, for each connected line Construct a blank image that matches the size of the sliding window. Then connect the lines Corresponding mapping to In the image Middle of the connecting line Perform the Hough transform to obtain the extrema and connect the lines. The coordinates of each pixel are then used to perform edge detection on the image. The middle part is displayed as a line segment;

[0063] S5.3 Traversing the Edge Detection Map Of all the displayed line segments, the three line segments with the largest corresponding extreme values ​​(q=3) are taken as the effective lines of the stock wave at that point.

[0064] In this embodiment, as Figure 5 As shown in the left image, the window area S y The edge points of the stock waves obtained within (n) show that multiple stock waves exist within the window, and the edge points of each stock wave are not on a straight line. To distinguish each stock wave and detect the line segment corresponding to each stock wave, a connectivity judgment is introduced, using an improved Hough transform to avoid misjudgments caused by connecting edge points of different stock waves. The right figure shows the window area S. y (n) The three line segments with the largest extreme values ​​within the region. After processing the entire signal, the stock wave judgment results within the stock wave region are as follows: Figure 6 As shown, Figure 6 The image below shows the identified stock wave region and the location of the window. Figure 6 The above figure shows the effective line segment with the largest extreme value inside all windows. By processing the effective line segment, the coordinates and slope of the corresponding stock wave can be obtained.

[0065] S5.4 Calculate the angle values ​​of all valid lines in a rectangular coordinate system, and then use the median of all angle values ​​as the wave tilt angle α of the window area;

[0066] In this embodiment, a connectivity judgment is introduced to avoid interference between connections between different wave edge points in determining the wave tilt angle. After calculating the angle values ​​of all valid lines, the following is obtained: Figure 7 The diagram shows the change in tilt angle.

[0067] S6. Use the relationship between slope and wire rope lay to solve for the speed change and travel distance of the wire rope;

[0068] Each window region is taken as a sampling moment, and the running speed v of the wire rope is calculated by using the mathematical relationship between the physical shape of the wire rope and the sensor sampling.

[0069]

[0070] Where L is the wire rope lay length, Y is the number of rows of the signal after spline interpolation, α is the strand tilt angle, and f is the sensor sampling frequency;

[0071] In this embodiment, the speed change trend of the wire rope is calculated through quantitative relationships, such as... Figure 8 As shown, the distance the wire rope moves, s = ∫vdt, can be solved by integrating the velocity v.

[0072] Although the illustrative specific embodiments of the present invention have been described above to enable those skilled in the art to understand the invention, it should be understood that the invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the invention as defined and determined by the appended claims, and all inventions utilizing the concept of the present invention are protected.

Claims

1. A method for obtaining the speed and displacement of a moving steel wire rope based on the texture of a magnetic flux leakage image, characterized in that, Includes the following steps: (1) Collect the leakage magnetic signal of the wire rope; Utilizing The Hall sensor detects the magnetic flux leakage signal of the saturated magnetization moving wire rope, and obtains the Road magnetic flux leakage signal , ; (2) Preprocessing of leakage magnetic field signal; (2.1), using spline interpolation method to the leakage magnetic signal interpolation processing, forming leakage magnetic signal , ; (2.2), for each leakage magnetic signal noise reduction processing is performed to form an image signal in which the crests of the corrugations are clearly visible ; (3) marking the region of the femoral wave in each image signal ; (3.1), the signal image is converted into a gray image, denoted as ; and ; (3.2) Determine the region containing stock waves; Setting a gray value difference threshold K; in each gray image If the gray value difference of any two pixel points in each column is greater than K, the column is determined as a stock wave region. (4) Grayscale image Edge detection is performed on the wave region within the data; (4.1) In grayscale images In the process, a sliding window with a size of M and a step size of 1 is used to slide across the stock wave region, capturing multiple window regions. Each window region contains M pixels. , Indicates the number of the window area; (4.2) For each window area First, image equalization is performed, then the Canny algorithm is used to extract edge points to obtain an edge detection map. ; (5) Solve for the wave tilt angle using the improved Hough transform. ; (5.1) In each edge detection map In the process, all connected lines are found using the 8-neighborhood condition and marked as... ,in, , Number the connected lines; (5.2) For each connecting line Construct a blank image that matches the size of the sliding window. Then connect the lines Corresponding mapping to In the image Middle of the connecting line Perform the Hough transform to obtain the extrema and connect the lines. The coordinates of each pixel are then used to perform edge detection on the image. The middle part is displayed as a line segment; (5.3) Traversing the edge detection map Of all the displayed line segments, the q line segments with the largest corresponding extreme values ​​are taken as the effective lines of the stock wave at that point. (5.4) Calculate the angle values ​​of all effective lines in a rectangular coordinate system, and then take the average of all angle values ​​as the wave tilt angle of the window area. ; (6) Use the relationship between slope and wire rope lay to solve for the speed change and moving distance of the wire rope; By treating each window region as a sampling moment, the running speed of the wire rope is calculated using the mathematical relationship between the physical shape of the wire rope and the sensor sampling. ; ； in, For wire rope lay length, The number of rows of the signal after spline interpolation. For the angle of stock wave tilt, The sampling frequency of the leakage magnetic field sensor; Integrating for the speed v, the running distance of the wire rope is solved .

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