Steel wire rope notch detection unit and detection method

Abstract

The invention relates to the field of nondestructive testing, and particularly discloses a steel wire rope notch detection unit and a detection method. Wherein the steel wire rope notch detection unit comprises a main body, a detection channel is arranged in the main body, and the detection channel penetrates through the main body; a magnetization section and a detection section are also arranged in the main body, and the magnetization section and the detection section are sequentially distributed along the detection channel; a steel wire magnetizing element is arranged in the magnetizing section, and a magnetic field signal acquisition element is arranged in the detection section. The steel wire rope notch detection unit can reliably detect the defects of the steel wire rope based on the notch opening angle parameter, and provides an important basis for evaluating the damage degree of the steel wire rope.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing, and in particular to a wire rope notch detection unit and detection method. Background Technology

[0002] Steel wire ropes are widely used in lifting equipment such as elevators, automated parking systems, and cranes due to their high strength and flexibility. However, under long-term alternating loads and contact friction, steel wire ropes are prone to defects such as broken wires, wear, and fatigue cracks, which can further develop into notch damage. Therefore, it is necessary to regularly inspect steel wire ropes for damage to improve the safety and reliability of equipment operation.

[0003] Magnetic flux leakage testing is a commonly used damage detection method for steel wire ropes. It mainly determines the degree and type of damage by the correspondence between damage characteristics and magnetic flux leakage signals. Among them, the magnetic flux leakage testing technology for notch defects with notch depth parameters is basically mature.

[0004] However, considering the specific working conditions of wire ropes, simply considering the notch depth parameter is still insufficient for a comprehensive and reliable assessment of the damage level. For example, research shows that the notch opening angle parameter also largely determines the degree of damage and remaining life of the wire rope. Therefore, there is an urgent need for a wire rope inspection method that can take into account the notch opening angle to improve inspection accuracy and reliability. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a wire rope notch detection unit and detection method, which can reliably detect wire rope defects based on the notch opening angle parameter, and provide an important basis for assessing the degree of damage to wire ropes.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a wire rope gap detection unit, comprising a main body, wherein a detection channel is provided inside the main body and the detection channel is disposed through the main body; the main body also comprises a magnetization zone and a detection zone, wherein the magnetization zone and the detection zone are distributed sequentially along the detection channel; a wire magnetization element is provided in the magnetization zone, and a magnetic field signal acquisition element is provided in the detection zone.

[0007] By setting a through-type detection channel within the main body and arranging magnetization and detection zones sequentially along the channel, the steel wire rope is first uniformly magnetized before entering the high-sensitivity detection area, effectively improving the stability and repeatability of the magnetic leakage signal. This provides a reliable hardware foundation for subsequent accurate identification of gap geometry features, while the overall layout is compact, facilitating integration into online or portable detection systems.

[0008] Preferably, the wire magnetizing element includes a permanent magnet, and the annular detection channel of the permanent magnet is configured as an annular structure.

[0009] Using a ring-shaped arrangement of permanent magnets as the magnetizing element for the steel wire can form a closed circumferential magnetic field without the need for an external power supply, achieving uniform magnetization of the cross-section of the steel wire rope. This not only simplifies the equipment structure and reduces power consumption, but also avoids problems such as electromagnetic coil overheating and complex control, significantly improving the stability and reliability of the detection unit in long-term field applications.

[0010] Preferably, the magnetic field signal acquisition element includes multiple Hall sensors, which are evenly distributed in a ring around the detection channel.

[0011] By arranging multiple Hall sensors evenly around the detection channel, the leakage magnetic field distribution of the steel wire rope in the entire circumference can be fully captured, effectively avoiding signal loss or angle misjudgment caused by local blind spots. This array design enhances the ability to perceive the directionality and shape of the gap, providing high-quality raw data for subsequent accurate extraction of spatial features related to the opening angle.

[0012] Preferably, the main body includes two separate parts, which are arranged face to face along the center of the detection channel and are fixedly connected in a detachable manner.

[0013] The main body consists of two separate parts facing each other along the center of the testing channel and connected in a detachable manner. This allows the testing unit to be quickly installed or removed from in-service equipment without cutting the wire rope, greatly improving its on-site applicability. It is particularly suitable for the periodic non-destructive testing of wire ropes already installed in elevators, cranes, etc., taking into account both ease of operation and engineering practicality.

[0014] A method for detecting wire rope notches based on the notch opening angle, employing the wire rope notch detection unit as described above, includes at least the following steps: S1. Sample preparation: Select a steel wire sample of the same specification as the target steel wire, and prepare multiple notch features with different opening angles on the steel wire sample; S2. Sample Detection: The steel wire sample is threaded into the detection channel, so that the steel wire sample passes through the magnetization zone and the detection zone in sequence; after the steel wire sample is magnetized in the magnetization zone, the magnetic field signal is collected and output by the magnetic field signal acquisition element in the detection zone; S3. Model Establishment: Based on the magnetic field signal of the steel wire sample, identify the leakage magnetic signal of each notch feature, and establish a notch correlation model between the notch geometric parameters and the leakage magnetic signal; S4. Target steel wire detection: The target steel wire to be detected is threaded into the detection channel, so that the target steel wire passes through the magnetization zone and the detection zone in sequence; after the target steel wire is magnetized in the magnetization zone, the magnetic field signal is collected and output by the magnetic field signal acquisition element in the detection zone; S5. Defect Feature Identification: Based on the magnetic field signal of the target steel wire, identify the leakage magnetic signal and match it with the notch-related model to obtain the defect information on the target steel wire rope and the opening angle parameters of each notch feature.

[0015] This application provides a wire rope detection method with the notch opening angle as the core parameter. Through a complete process of "standard sample calibration - leakage magnetic signal acquisition - related model establishment - target inversion identification", it breaks through the limitations of traditional methods that rely solely on depth parameters, realizes multi-dimensional quantitative assessment of notch damage, and significantly improves the scientific nature of damage degree judgment and the accuracy of remaining life prediction.

[0016] Preferably, step S3 further includes the following sub-steps: S31. Signal preprocessing: The magnetic flux leakage signal of the steel wire sample is subjected to noise reduction and filtering to obtain a smooth and stable magnetic flux leakage signal curve; S32. Signal Feature Extraction: Extract the peak amplitude and half-wave width from the leakage magnetic field signal curve for subsequent quantitative analysis. S33. Model Establishment: Based on the feature parameters extracted in S32 and the corresponding notch opening angle, establish a notch correlation model between the notch geometric parameters and the leakage magnetic signal.

[0017] In the model building stage, signal preprocessing and feature extraction steps are introduced. After noise reduction and filtering of the leakage magnetic signal, two key features, peak amplitude and half-wave width, are extracted and their mapping relationship with the opening angle is constructed. This not only greatly simplifies the complexity of subsequent analysis, but also ensures the stability and repeatability of the feature parameters, laying the foundation for efficient and accurate angle inversion.

[0018] Preferably, step S5 further includes the following sub-steps: S51. Signal preprocessing: The leakage magnetic signal of the target steel wire is subjected to noise reduction and filtering to obtain a smooth and stable leakage magnetic signal curve; S52. Signal Feature Extraction: Extract two characteristic parameters, peak amplitude and half-width, from the leakage magnetic signal curve; S53. Signal feature processing: Substitute the peak amplitude and half-wave width into the notch correlation model established in step S33 to calculate the opening angle of the corresponding notch.

[0019] During the target wire detection stage, the same signal preprocessing and feature extraction process as the modeling stage is adopted simultaneously. The obtained peak value and half-width are substituted into the established notch correlation model to achieve rapid and quantitative inversion of the actual defect opening angle, ensuring that the detection results are highly consistent with the calibration system and improving the engineering feasibility and reliability of the method.

[0020] Preferably, in steps S31 and S51, the signal preprocessing includes the following operations: First, wavelet transform is used to denoise the leakage magnetic signal. The Daubechies wavelet basis is selected for 4-6 level decomposition, and the high-frequency coefficients are denoised using the soft thresholding method to filter out random noise. Subsequently, the denoised signal is reconstructed to obtain a preliminary smoothed magnetic leakage curve; Next, the reconstructed signal is bandpass filtered with a frequency band of 5 Hz–2 kHz to preserve the notch characteristic frequency components, while high-pass filtering is used to eliminate the baseline shift caused by low-frequency drift. Finally, the amplitude of the filtered signal is normalized, and baseline correction is performed using the reference signal of the unnotched wire rope as a benchmark.

[0021] A multi-level signal processing strategy combining wavelet transform with soft threshold denoising, bandpass filtering, and high-pass baseline correction is adopted to effectively suppress random noise and low-frequency drift interference, eliminate residual jitter, and retain effective frequency components related to the notch geometry. This significantly improves the smoothness, stability, and feature fidelity of the leakage magnetic signal, ensuring the comparability of signals between different detection batches and providing reliable input for high-precision angle identification.

[0022] Preferably, in steps S32 and S52, the signal feature extraction includes the following steps: First, perform characteristic analysis on the leakage flux signal curve to determine the peak position and corresponding amplitude of the leakage flux signal curve; Subsequently, the positions where the signal amplitude drops to half of the peak value are found on both sides of the peak value, and the horizontal distance between the two points is calculated, which is the half-wavelength characteristic parameter.

[0023] A clearly defined half-width (WWHM) is the horizontal distance where the amplitude drops to half on both sides of the peak value of the magnetic flux leakage signal. This provides a standardized and quantifiable feature extraction method, avoiding subjective judgment bias, ensuring the consistency and comparability of feature parameters among different samples or batches, and enhancing the model's generalization ability and the objectivity of the detection results. By extracting the peak value and WWHM as two features, the mapping relationship of the notch geometry in the magnetic flux leakage signal can be effectively described, providing reliable data support for subsequent quantitative analysis based on the mechanomagnetic coupling model.

[0024] Preferably, in step S32, the leakage magnetic field signal of different opening angle notches is numerically calculated and simulated using the finite element simulation method, and a notch correlation model between the notch geometric parameters and the leakage magnetic field signal is established based on the simulation results.

[0025] Finite element simulation (FEM) is introduced into the model building process to numerically calculate the leakage magnetic response of notches with different opening angles, effectively expanding the training sample set and compensating for the high cost and limited coverage of physical calibration. By comparing the FEM simulation results with the actual acquired signals, the model can be corrected and calibrated, thereby forming a stable and reliable quantitative relationship, providing a theoretical basis for subsequent inversion calculation of notch parameters. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the wire rope gap detection unit in this embodiment; Figure 2 This is an exploded view of the wire rope gap detection unit in this embodiment; Figure 3 This is a waveform diagram of a 60° opening angle of a notch feature collected by the wire rope notch detection method in this embodiment; Figure 4 This is a waveform diagram of the notch feature at an 80° opening angle, collected by the wire rope notch detection method in this embodiment; Figure 5 This is a waveform diagram of the notch feature at a 100° opening angle, collected by the wire rope notch detection method in this embodiment; Figure 6 This is a waveform diagram of a 120° opening angle of a notch feature collected by the wire rope notch detection method in this embodiment; Figure 7 This is a waveform diagram of a 140° opening angle of a notch feature collected by the wire rope notch detection method in this embodiment; Figure 8 This is a waveform diagram of the 160° opening angle of the notch feature collected by the wire rope notch detection method in this embodiment; Figure 9 This is a leakage magnetic signal before noise reduction processing using the wire rope gap detection method of this embodiment; Figure 10 This is a segment of magnetic flux leakage signal after noise reduction processing using the wire rope gap detection method of this embodiment; Figure 11 This is a schematic diagram of the notch characteristics targeted by the wire rope notch detection method in this embodiment; Figure 12 This is a fitting curve showing the correspondence between the peak values ​​of axial leakage magnetic flux density at different notch opening angles obtained by the wire rope notch detection method in this embodiment; Figure 13 This is a fitted curve showing the correspondence between the axial leakage magnetic density and the half-wave width of different notch opening angles obtained by the wire rope notch detection method in this embodiment. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Example

[0028] like Figure 1 and Figure 2 As shown, a wire rope gap detection unit includes a main body 1, within which a detection channel is provided, penetrating the main body 1. The main body 1 also includes a magnetization zone and a detection zone, which are sequentially distributed along the detection channel. A wire magnetization element 2 is provided within the magnetization zone, and a magnetic field signal acquisition element 3 is provided within the detection zone. Specifically, to prevent demagnetization after magnetization, the distance between the wire magnetization element 2 and the magnetic field signal acquisition element 3 is 3-5 mm.

[0029] By setting a through-type detection channel within the main body 1, and arranging magnetization and detection zones sequentially along the channel, the wire rope is first uniformly magnetized before entering the high-sensitivity detection area, effectively improving the stability and repeatability of the magnetic leakage signal. This provides a reliable hardware foundation for subsequent accurate identification of gap geometry features, while the overall layout is compact, facilitating integration into online or portable detection systems.

[0030] Specifically, such as Figure 1 and Figure 2 As shown, the main body 1 comprises two separate parts, which are arranged face-to-face along the center of the detection channel and are fixedly connected in a detachable manner. The main body 1 uses two separate parts facing each other along the center of the detection channel and connected in a detachable manner, allowing the detection unit to be quickly installed or removed from in-service equipment without cutting the wire rope. This greatly improves on-site applicability and is particularly suitable for the periodic non-destructive testing of installed wire ropes in elevators, cranes, etc., balancing ease of operation and engineering practicality.

[0031] Specifically, such as Figure 1 and Figure 2 As shown, the wire magnetizing element 2 includes a permanent magnet, and the annular detection channel of the permanent magnet is arranged in an annular structure. Using an annularly arranged permanent magnet as the wire magnetizing element 2 can form a closed circumferential magnetic field without an external power supply, achieving uniform magnetization of the wire rope cross-section. This not only simplifies the equipment structure and reduces power consumption but also avoids problems such as electromagnetic coil overheating and complex control, significantly improving the stability and reliability of the detection unit in long-term field applications.

[0032] Specifically, such as Figure 1 and Figure 2As shown, the magnetic field signal acquisition element 3 includes multiple Hall sensors, which are evenly distributed in a ring around the detection channel. This uniform arrangement of multiple Hall sensors around the detection channel allows for comprehensive capture of the leakage magnetic field distribution throughout the entire circumference of the wire rope, effectively avoiding signal loss or angle misjudgment due to local blind spots. This array design enhances the perception of the direction and shape of the gap, providing high-quality raw data for subsequent accurate extraction of spatial features related to the opening angle.

[0033] A method for detecting wire rope notches based on the notch opening angle, employing the wire rope notch detection unit as described above, includes at least the following steps: S1. Sample Preparation: Select a steel wire sample of the same specification as the target steel wire, and prepare multiple notch features with different opening angles on the steel wire sample. Specifically, the opening angles of each notch feature are set in the form of an arithmetic sequence.

[0034] S2. Sample Detection: The steel wire sample is threaded into the detection channel, passing sequentially through the magnetization zone and the detection zone. After being magnetized in the magnetization zone, the steel wire sample is then monitored and output by a magnetic field signal acquisition element in the detection zone.

[0035] S3. Model Establishment: Based on the magnetic field signal of the steel wire sample, identify the leakage magnetic signal of each notch feature, and establish a notch correlation model between the notch geometric parameters and the leakage magnetic signal.

[0036] Specifically, step S3 also includes the following sub-steps: S31. Signal preprocessing: The magnetic flux leakage signal of the steel wire sample is subjected to noise reduction and filtering to obtain a smooth and stable magnetic flux leakage signal curve.

[0037] S32. Signal Feature Extraction: Extract the peak amplitude and half-width of the leakage magnetic field signal curve as two signal features for subsequent quantitative analysis.

[0038] S33. Model Establishment: Based on the feature parameters extracted in S32 and the corresponding notch opening angle, establish a notch correlation model between the notch geometric parameters and the leakage magnetic signal.

[0039] S4. Target Wire Detection: The target steel wire to be detected is threaded into the detection channel, allowing it to pass sequentially through the magnetization zone and the detection zone. After being magnetized in the magnetization zone, the target steel wire is then subjected to a magnetic field signal acquisition element in the detection zone, which acquires and outputs the magnetic field signal.

[0040] S5. Defect Feature Identification: Based on the magnetic field signal of the target steel wire, identify the leakage magnetic signal and match it with the notch-related model to obtain the defect information on the target steel wire rope and the opening angle parameters of each notch feature.

[0041] Specifically, step S5 also includes the following sub-steps: S51. Signal preprocessing: The leakage magnetic signal of the target steel wire is subjected to noise reduction and filtering to obtain a smooth and stable leakage magnetic signal curve; S52. Signal Feature Extraction: Extract two characteristic parameters, peak amplitude and half-width, from the leakage magnetic signal curve; S53. Signal feature processing: Substitute the peak amplitude and half-wave width into the notch correlation model established in step S33 to calculate the opening angle of the corresponding notch.

[0042] This application provides a wire rope detection method with the notch opening angle as the core parameter. Through a complete process of "standard sample calibration - leakage magnetic signal acquisition - related model establishment - target inversion identification", it breaks through the limitations of traditional methods that rely solely on depth parameters, realizes multi-dimensional quantitative assessment of notch damage, and significantly improves the scientific nature of damage degree judgment and the accuracy of remaining life prediction.

[0043] In one specific implementation, the signal preprocessing in steps S31 and S51 includes the following operations: First, wavelet transform is used to denoise the leakage magnetic signal. The Daubechies wavelet basis is selected for 4–6 level decomposition, and the high-frequency coefficients are denoised using the soft thresholding method to filter out random noise.

[0044] Subsequently, the denoised signal was reconstructed to obtain a preliminary smoothed magnetic leakage curve.

[0045] Next, the reconstructed signal is bandpass filtered with a frequency band of 5 Hz–2 kHz to preserve the notch characteristic frequency components, while high-pass filtering is used to eliminate baseline shift caused by low-frequency drift.

[0046] Finally, the amplitude of the filtered signal is normalized, and baseline correction is performed using the reference signal of the unnotched wire rope as a benchmark.

[0047] A multi-level signal processing strategy combining wavelet transform with soft threshold denoising, bandpass filtering, and high-pass baseline correction is adopted to effectively suppress random noise and low-frequency drift interference, eliminate residual jitter, and retain effective frequency components related to the notch geometry. This significantly improves the smoothness, stability, and feature fidelity of the leakage magnetic signal, ensuring the comparability of signals between different detection batches and providing reliable input for high-precision angle identification.

[0048] Specifically, in steps S32 and S52, the signal feature extraction includes the following steps: First, the leakage flux signal curve is analyzed to determine the peak position and corresponding amplitude.

[0049] Subsequently, the positions where the signal amplitude drops to half of the peak value are found on both sides of the peak value, and the horizontal distance between the two points is calculated, which is the half-wavelength characteristic parameter.

[0050] A clearly defined half-width (WWHM) is the horizontal distance where the amplitude drops to half on both sides of the peak value of the magnetic flux leakage signal. This provides a standardized and quantifiable feature extraction method, avoiding subjective judgment bias, ensuring the consistency and comparability of feature parameters among different samples or batches, and enhancing the model's generalization ability and the objectivity of the detection results. By extracting the peak value and WWHM as two features, the mapping relationship of the notch geometry in the magnetic flux leakage signal can be effectively described, providing reliable data support for subsequent quantitative analysis based on the mechanomagnetic coupling model.

[0051] Specifically, in step S32, the leakage magnetic field signal of different opening angle notches is numerically calculated and simulated using the finite element simulation method, and a notch correlation model between the notch geometric parameters and the leakage magnetic field signal is established based on the simulation results.

[0052] Finite element simulation (FEM) is introduced into the model building process to numerically calculate the leakage magnetic response of notches with different opening angles, effectively expanding the training sample set and compensating for the high cost and limited coverage of physical calibration. By comparing the FEM simulation results with the actual acquired signals, the model can be corrected and calibrated, thereby forming a stable and reliable quantitative relationship, providing a theoretical basis for subsequent inversion calculation of notch parameters.

[0053] The following example of a wire rope testing method illustrates the wire rope gap testing method of this application.

[0054] The target steel wire is a cold-drawn carbon steel material commonly used in lifting equipment, with a model of 6*19s+nf (6 strands, 19 wires, natural fiber core).

[0055] S1. Sample Preparation: Select a steel wire sample of the same specification as the target steel wire, and prepare six sets of notch features with different opening angles on the steel wire sample. The notch depth is 1.5 mm, and the opening angle ranges from 60° to 160°, with 20° intervals.

[0056] S2. Sample Detection: The steel wire sample is threaded into the detection channel, passing sequentially through the magnetization zone and the detection zone. After being magnetized in the magnetization zone, the steel wire sample is then monitored and output by a magnetic field signal acquisition element in the detection zone.

[0057] The magnetic field signal acquisition element is powered by a 5V DC power supply. The induced voltage signal is output to the multi-channel signal acquisition instrument, and the signal waveform is displayed on the host computer by the matching acquisition software.

[0058] The specific waveform is as follows: Figures 3-8As shown, where Figure 3 The waveform is a 60° opening angle, where Figure 4 The waveform is for an 80° opening angle, where Figure 5 The waveform is for a 100° opening angle, where Figure 6 The waveform is for an opening angle of 120°, where Figure 7 The waveform is for an opening angle of 1400°, where Figure 8 The waveform is for a 160° opening angle.

[0059] S3. Model Establishment: Based on the magnetic field signal of the steel wire sample, identify the leakage magnetic signal of each notch feature, and establish a notch correlation model between the notch geometric parameters and the leakage magnetic signal.

[0060] Specifically, step S3 also includes the following sub-steps: S31. Signal Preprocessing: The magnetic flux leakage signal of the steel wire sample is subjected to noise reduction and filtering to obtain a smooth and stable magnetic flux leakage signal curve. A segment of the magnetic flux leakage signal containing multiple notches is used to illustrate the noise reduction effect. Figure 9 The image shows the magnetic flux leakage signal before noise reduction processing. Figure 10 The image shows the leakage magnetic signal after noise reduction.

[0061] S32. Signal Feature Extraction: Extract the peak amplitude and half-width of the leakage magnetic field signal curve as two signal features for subsequent quantitative analysis. The table below shows the leakage magnetic field signal feature values ​​for each aperture angle.

[0062]

[0063] S33. Model Establishment: Based on the feature parameters extracted in S32 and the corresponding notch opening angle, establish a notch correlation model between the notch geometric parameters and the leakage magnetic signal.

[0064] Considering that the U-shaped notch is caused by the different opening angles due to the ratio of tensile and shear stresses in different directions, such as... Figure 11 As shown, assuming the notch is a symmetrical structure formed by the propagation of a central crack, with a notch width of 2l and a depth of H, the leakage magnetic flux signal components along the x and y directions caused by stress changes and magnetization effects can be expressed as:

[0065] The table below shows the parameters of the fitting curves for leakage flux, aperture angle, and half-wavewidth. Figure 12 Fitting curves showing the corresponding relationship between the peak values ​​of axial leakage magnetic flux density for different notch opening angles. Figure 13 Fitting curves showing the relationship between the half-wavewidth of axial leakage magnetic density for different notch opening angles.

[0066]

[0067] The results show that the notch opening angle is significantly negatively correlated with the peak value of the leakage magnetic field signal, meaning that the peak value decreases as the opening angle increases. Conversely, the notch opening angle is positively correlated with the half-wavelength, meaning that the half-wavelength increases as the opening angle increases. By comparing the finite element simulation results with the actual acquired signals, the model can be corrected and calibrated, thus establishing a stable and reliable quantitative relationship, providing a theoretical basis for subsequent inversion calculations of notch parameters.

[0068] S4. Target Wire Detection: The target steel wire to be detected is threaded into the detection channel, allowing it to pass sequentially through the magnetization zone and the detection zone. After being magnetized in the magnetization zone, the target steel wire is then subjected to a magnetic field signal acquisition element in the detection zone, which acquires and outputs the magnetic field signal.

[0069] S5. Defect Feature Identification: Based on the magnetic field signal of the target steel wire, identify the leakage magnetic signal and match it with the notch-related model to obtain the defect information on the target steel wire rope and the opening angle parameters of each notch feature.

[0070] The table below shows the test results for the target steel wire.

[0071]

[0072] The parameters in the table above demonstrate that this method can avoid misjudgments caused by relying solely on a single image feature, thus improving the robustness and accuracy of detection. Experimental verification shows that the method of this invention achieves an average accuracy of over 96% for both the opening angle and depth, enabling quantitative identification and reliable detection of gaps in hoisting wire ropes.

[0073] In summary, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A wire rope gap detection unit, characterized in that: The device includes a main body, within which a detection channel is provided, extending through the main body; the main body also includes a magnetization zone and a detection zone, which are sequentially distributed along the detection channel; the magnetization zone contains a steel wire magnetization element, and the detection zone contains a magnetic field signal acquisition element.

2. The wire rope gap detection unit according to claim 1, characterized in that: The steel wire magnetizing element includes a permanent magnet, and the annular detection channel of the permanent magnet is configured as an annular structure.

3. The wire rope gap detection unit according to claim 1, characterized in that: The magnetic field signal acquisition element includes multiple Hall sensors, which are evenly distributed in a ring around the detection channel.

4. The wire rope gap detection unit according to claim 1, characterized in that: The main body comprises two separate parts, which are arranged face to face along the center of the detection channel and are fixedly connected in a detachable manner.

5. A method for detecting wire rope notches based on the notch opening angle, characterized in that, The wire rope gap detection unit as described in any one of claims 1-4 includes at least the following steps: S1. Sample preparation: Select a steel wire sample of the same specification as the target steel wire, and prepare multiple notch features with different opening angles on the steel wire sample; S2. Sample Detection: The steel wire sample is threaded into the detection channel, so that the steel wire sample passes through the magnetization zone and the detection zone in sequence; after the steel wire sample is magnetized in the magnetization zone, the magnetic field signal is collected and output by the magnetic field signal acquisition element in the detection zone; S3. Model Establishment: Based on the magnetic field signal of the steel wire sample, identify the leakage magnetic signal of each notch feature, and establish a notch correlation model between the notch geometric parameters and the leakage magnetic signal; S4. Target steel wire detection: The target steel wire to be detected is threaded into the detection channel, so that the target steel wire passes through the magnetization zone and the detection zone in sequence; after the target steel wire is magnetized in the magnetization zone, the magnetic field signal is collected and output by the magnetic field signal acquisition element in the detection zone; S5. Defect Feature Identification: Based on the magnetic field signal of the target steel wire, identify the leakage magnetic signal and match it with the notch-related model to obtain the defect information on the target steel wire rope and the opening angle parameters of each notch feature.

6. The method for detecting notches in steel wire ropes according to claim 5, characterized in that, Step S3 also includes the following sub-steps: S31. Signal preprocessing: The magnetic flux leakage signal of the steel wire sample is subjected to noise reduction and filtering to obtain a smooth and stable magnetic flux leakage signal curve; S32. Signal Feature Extraction: Extract the peak amplitude and half-wave width from the leakage magnetic field signal curve for subsequent quantitative analysis. S33. Model Establishment: Based on the feature parameters extracted in S32 and the corresponding notch opening angle, establish a notch correlation model between the notch geometric parameters and the leakage magnetic signal.

7. The method for detecting notches in steel wire ropes according to claim 6, characterized in that, Step S5 also includes the following sub-steps: S51. Signal preprocessing: The leakage magnetic signal of the target steel wire is subjected to noise reduction and filtering to obtain a smooth and stable leakage magnetic signal curve; S52. Signal Feature Extraction: Extract two characteristic parameters, peak amplitude and half-width, from the leakage magnetic signal curve; S53. Signal feature processing: Substitute the peak amplitude and half-wave width into the notch correlation model established in step S33 to calculate the opening angle of the corresponding notch.

8. The method for detecting notches in steel wire ropes according to claim 7, characterized in that, In steps S31 and S51, the signal preprocessing includes the following operations: First, wavelet transform is used to denoise the leakage magnetic signal. The Daubechies wavelet basis is selected for 4-6 level decomposition, and the high-frequency coefficients are denoised using the soft thresholding method to filter out random noise. Subsequently, the denoised signal is reconstructed to obtain a preliminary smoothed magnetic leakage curve; Next, the reconstructed signal is bandpass filtered with a frequency band of 5 Hz–2 kHz to preserve the notch characteristic frequency components, while high-pass filtering is used to eliminate the baseline shift caused by low-frequency drift. Finally, the amplitude of the filtered signal is normalized, and baseline correction is performed using the reference signal of the unnotched wire rope as a benchmark.

9. The method for detecting notches in steel wire ropes according to claim 7, characterized in that, In steps S32 and S52, the signal feature extraction includes the following steps: First, perform characteristic analysis on the leakage flux signal curve to determine the peak position and corresponding amplitude of the leakage flux signal curve; Subsequently, the positions where the signal amplitude drops to half of the peak value are found on both sides of the peak value, and the horizontal distance between the two points is calculated, which is the half-wavelength characteristic parameter.

10. The method for detecting notches in steel wire ropes according to claim 6, characterized in that, In step S32, the leakage magnetic field signal of different opening angle notches is numerically calculated and simulated using the finite element simulation method, and a notch correlation model between the notch geometric parameters and the leakage magnetic field signal is established based on the simulation results.

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