Intelligent flaw detection diagnosis method and equipment for tower crane steel wire rope

Abstract

The invention discloses intelligent flaw detection diagnosis equipment for a tower crane steel wire rope. The intelligent flaw detection diagnosis equipment comprises a flaw detection unmanned aerial vehicle; the scanning mechanism is arranged on one side of the flaw detection unmanned aerial vehicle and is used for scanning the steel wire rope; the flaw detection unmanned aerial vehicle is provided with a modeling system used for conducting three-dimensional modeling on the steel wire rope according to information collected by the scanning mechanism, and a calculation system used for conducting steel wire rope damage degree calculation based on modeling generated by the modeling system. The unmanned aerial vehicle is used as a carrier to scan and model the steel wire rope, the generated model is compared and calculated, the overall defect condition and the specific defect part of the steel wire rope are accurately analyzed, the analysis result is accurate, the whole process is short in time consumption, manual operation is not needed, and the complete coefficient is high.

Description

Technical Field

[0001] The present invention relates to the field of engineering construction technology and equipment, and particularly relates to an intelligent flaw detection and diagnosis method and equipment for tower crane steel ropes. Background Art

[0002] After the steel rope used in the tower crane has been in use for a long time, certain steel rope defects will occur (including: steel wire protrusion, core protrusion, diameter reduction, strand protrusion or distortion, positive and negative kinking, cage-like deformity, external wear, external corrosion, local magnification, broken wires at the top of the strand, broken wires in the groove, etc.). Generally, visual inspection is used for judgment, and the efficiency is low. In addition, according to the steel rope deterioration severity grading table in the national standard GB / T5972-2023, the comprehensive severity level of deterioration can be determined by the number of broken wires (calculated as a percentage), the amount of diameter reduction (calculated as a percentage), and external corrosion (calculated as a percentage) (the percentages of the three items are accumulated). Whether the steel rope is safe, whether it needs to be replaced, whether the inspection frequency needs to be increased, whether it needs to be scrapped, etc. are generally also judged by visual inspection, counting, and measurement, and the efficiency is low. Summary of the Invention

[0003] An intelligent flaw detection and diagnosis method and equipment for tower crane steel ropes of the present invention accurately analyze the overall defect situation and specific defect locations of the steel rope by scanning and modeling the steel rope and comparing and calculating the generated model.

[0004] An intelligent flaw detection and diagnosis method and equipment for tower crane steel ropes of the present invention

[0005] Includes: a flaw detection unmanned aerial vehicle; a scanning mechanism disposed on one side of the flaw detection unmanned aerial vehicle for scanning the steel rope; a modeling system for three-dimensionally modeling the steel rope according to the information collected by the scanning mechanism, the modeling system being data-connected to the scanning mechanism; and a calculation system for calculating the damage degree of the steel rope based on the model generated by the modeling system, the calculation system being data-connected to the modeling system.

[0006] A further improvement of the present invention lies in that the scanning mechanism includes: a flaw detection rod cantilevered on one side of the flaw detection unmanned aerial vehicle; a flaw detection loop for the steel rope to pass through disposed at the end of the flaw detection rod far from the flaw detection unmanned aerial vehicle; a camera mechanism and a laser sensor disposed on the inner wall of the flaw detection loop.

[0007] A further improvement of the present invention lies in that the camera mechanism includes at least three macro cameras evenly distributed on the inner wall of the flaw detection loop and fill lights disposed around each macro camera.

[0008] A further improvement of the present invention lies in that a balance rod extending in the opposite direction of the flaw detection rod is provided on the flaw detection unmanned aerial vehicle.

[0009] A further improvement of the present invention lies in that a notch is provided on the flaw detection ring, an opening and closing section is provided at the notch, one end of the opening and closing section is rotatably connected to the first end of the notch, and a locking member capable of being locked and connected to the second end of the notch is provided at the other end of the opening and closing section.

[0010] An intelligent flaw detection and diagnosis method for a tower crane steel wire rope of the present invention includes the following steps:

[0011] Step 1: Start the flaw detection unmanned aerial vehicle and use the scanning mechanism to scan the steel wire rope.

[0012] Step 2: Starting from one end of the steel wire rope, scan the steel wire rope in segments through the scanning mechanism, and control the flaw detection unmanned aerial vehicle to fly until the entire steel wire rope is scanned;

[0013] Step 3: Transmit the scanning result of the scanning mechanism to the modeling system in real time and perform segmental modeling on the steel wire rope;

[0014] Step 4: Perform real-time calculation and comparison on the results of the modeling through the calculation system, obtain the damage conditions of each segment of the steel wire rope, calculate the overall damage condition of the steel wire rope based on the damage conditions of each segment, judge the specific defects of each segment, and generate a steel wire rope damage condition report according to the overall damage condition of the steel wire rope and the specific defects judged for each segment.

[0015] A further improvement of the present invention lies in that the calculation process of the calculation system includes:

[0016] M algorithm: Compare and calculate the modeling of each segment with the modeling of the standard steel wire rope, respectively obtain the broken wire rate S1, diameter reduction rate S2, and external corrosion rate S3 of each segment, and calculate the overall deterioration value of the steel wire rope based on S1, S2, and S3 of each segment to reflect the overall damage condition of the steel wire rope;

[0017] N algorithm: Combine the S1, S2, and S3 values provided by the M algorithm and compare them with the preset standard values. If any one of the S1, S2, and S3 values in a segment is greater than the standard value, lock this segment from the modeling system and perform defect identification on the locked segment.

[0018] A further improvement of the present invention lies in that the specific calculation process of the M algorithm is:

[0019] Divide the steel wire into N segments, and the broken wire rate S1 = (the amount of broken wires in the detected segment / the total amount of steel wires in the standard model) * 100%;

[0020] The diameter reduction rate S2 = (1 - the average diameter of the detected segment / the nominal diameter) * 100%;

[0021] External corrosion rate S3: Calculate the percentage of corrosion depth through the Hausdorff distance;

[0022] Overall deterioration judgment:

[0023] (N is the number of wire rope segments);

[0024] Among them, the M algorithm is built-in for standard wire rope modeling to give the nominal diameter and the total amount of standard wires in the standard model.

[0025] A further improvement of the present invention is that the scanning mechanism includes: a flaw detection rod cantilevered on one side of the flaw detection UAV; a flaw detection loop for the wire rope to pass through provided at the end of the flaw detection rod far from the flaw detection UAV; a camera mechanism and a laser sensor provided on the inner wall of the flaw detection loop. The specific steps of step one include: starting the flaw detection UAV, putting the wire rope to be flaw detected into the flaw detection loop, and fine-tuning the flaw detection UAV to ensure that the wire rope is centered in the flaw detection loop.

[0026] The present invention uses a UAV as a carrier to scan and model a wire rope, compares and calculates the generated model, accurately analyzes the overall defect situation and specific defect parts of the wire rope, has accurate analysis results, short time consumption for the whole process, and does not require manual operation, with a high safety factor. Brief Description of the Drawings

[0027] Figure 1 It is a schematic diagram of the overall structure of the present invention;

[0028] Figure 2 It is a schematic diagram of the structure of the flaw detection loop of the present invention;

[0029] Figure 3 It is a schematic diagram of the scanning process of the present invention;

[0030] Figure 4 It is a flowchart of the method of the present invention;

[0031] Figure 5 It is a schematic diagram of the three-dimensional modeling result of the present invention;

[0032] Figure 6 It is a schematic diagram of the RCN model library built into the M algorithm of the present invention;

[0033] Figure 7 It is a schematic diagram of the defect situation model of the N algorithm of the present invention;

[0034] In the figure: 1, flaw detection rod; 2, flaw detection coil; 3, modeling system; 4, calculation system; 5, presetting and central control system; 6, balance rod; 7, camera mechanism; 7A, first camera mechanism; 7B, second camera mechanism; 7C, third camera mechanism; 8, macro camera; 9, fill light; 10, laser sensor; 11, opening and closing section. Specific implementation mode

[0035] An intelligent flaw detection and diagnosis equipment for tower crane steel wire ropes, comprising: a flaw detection drone; a scanning mechanism arranged on one side of the flaw detection drone; a modeling system 3 for three-dimensionally modeling the steel wire rope according to the information collected by the scanning mechanism, the modeling system 3 being data-connected to the scanning mechanism; a calculation system 4 for calculating the damage degree of the steel wire rope based on the model generated by the modeling system 3, the calculation system 4 being data-connected to the modeling system 3.

[0036] Preferably, the calculation system 4 in this embodiment is an edge calculation system.

[0037] Preferably, the modeling system in this embodiment adopts an embedded GPU (computing power 32TOPS).

[0038] As Figures 1 - 2 shown, the scanning mechanism includes: a flaw detection rod 1 cantilevered on one side of the flaw detection drone; a flaw detection coil 2 provided at the end of the flaw detection rod 1 far from the flaw detection drone for the steel wire rope to pass through; a camera mechanism 7 and a laser sensor 10 provided on the inner wall of the flaw detection coil 2.

[0039] As Figure 2 , Figure 3 shown, the camera mechanism 7 includes at least three macro cameras 8 evenly distributed on the inner wall of the flaw detection coil 2 and fill lights 9 provided around each macro camera 8.

[0040] In this embodiment, there are three camera mechanisms 7, namely the first camera mechanism 7A, the second camera mechanism 7B, and the third camera mechanism 7C.

[0041] Preferably, the resolution of the macro camera 8 in this embodiment is 50 million pixels, and the minimum focusing distance is 5 mm. The fill light 9 adopts a ring-shaped LED array, with adjustable brightness, supporting polarization filtering to eliminate metal reflection.

[0042] Preferably, the ranging accuracy of the laser sensor 10 in this embodiment is ±0.05 mm, and the sampling frequency is 100 Hz; by setting the laser sensor 10, the distance between the inner wall of the flaw detection coil 2 and the steel wire rope can be automatically detected.

[0043] As Figure 1As shown, a balance rod 6 is provided on the flaw detection drone and extends in the opposite direction of the flaw detection rod 1. In this embodiment, both the flaw detection rod 1 and the balance rod 6 are arranged horizontally.

[0044] As Figure 2 , Figure 3 shown, a notch is provided on the flaw detection coil 2 with an opening and closing section 11. One end of the opening and closing section 11 is rotatably connected to the first end of the notch, and a locking member that can be locked and connected to the second end of the notch is provided at the other end of the opening and closing section 11. The opening and closing section 11 is connected to the first section of the notch through a rotating member. Both the rotating member and the locking member are electromagnetically driven. When the steel wire rope is in the central position of the flaw detection coil 2, the opening and closing section 11 can be automatically closed, and the operator can also control it remotely.

[0045] As Figures 3 - 7 shown, an intelligent flaw detection and diagnosis method for a tower crane steel wire rope is characterized by including the following steps:

[0046] Step 1: Start the flaw detection drone and use the scanning mechanism to scan the steel wire rope.

[0047] Step 2: Starting from one end of the steel wire rope, scan the steel wire rope in segments through the scanning mechanism and control the flaw detection drone to fly until the entire steel wire rope is scanned. In this embodiment, as Figure 3 shown, the steel wire rope is divided into several segments, and each segment is scanned at three heights: upper, middle, and lower. There are three camera mechanisms 7, and each camera mechanism 7 takes one picture at each height, with a total of nine pictures at the three heights.

[0048] Step 3: Transmit the scanning results of the scanning mechanism to the modeling system 3 in real time and perform segmented modeling on the steel wire rope.

[0049] Step 4: Perform real-time calculation and comparison on the results of the modeling through the calculation system 4 to obtain the damage conditions of each segment of the steel wire rope, and generate a steel wire rope damage condition report based on the damage conditions of each segment and the overall damage condition of the steel wire rope.

[0050] Preferably, in this embodiment, the steel wire rope damage condition report also includes a three-dimensional model, a defect heat map, a statistical table, etc.

[0051] Preferably, the specific steps of Step 1 include: slipping the steel wire rope to be flaw detected into the flaw detection coil 2 and making fine adjustments to the drone to ensure that the steel wire rope is centered in the flaw detection coil 2 (allowing an error range of 5 mm).

[0052] In this embodiment, the specific method for modeling the wire rope is as follows: receiving image data from three macro cameras 7, with nine images for one standard segment, and generating a three-dimensional model (200 mm in length) with a point cloud density ≥ 1000 points / cm 2 by using the multi-view stereo (MVS) algorithm based on the OpenCV and PCL libraries.

[0053] As Figures 5 - 7 shown, the calculation process of the computing system 4 includes:

[0054] S41, M algorithm: Comparing and calculating the modeling of each segment, respectively obtaining the wire breakage rate S1, diameter reduction rate S2, and external corrosion rate S3 of each segment, and calculating the overall deterioration value of the wire rope based on S1, S2, and S3 of each segment to reflect the overall damage condition of the wire rope;

[0055] S42, N algorithm: Combining the S1, S2, and S3 values provided by the M algorithm, if any one of them in each segment is greater than 20%, this segment will be locked from the modeling system, and defect identification will be performed on the locked segment.

[0056] As Figure 6 shown, the specific calculation process of the M algorithm is as follows:

[0057] Dividing the steel wires into N segments, the wire breakage rate S1 = (the amount of broken wires in the detection segment / the total amount of steel wires in the standard model) * 100%;

[0058] Maximum wire breakage deterioration judgment: Taking the maximum value of S1 among the N segments;

[0059] The diameter reduction rate S2 = (1 - the average diameter of the detection segment / the nominal diameter) * 100%;

[0060] Maximum diameter reduction deterioration judgment: Taking the maximum value of S2 among the N segments;

[0061] The external corrosion rate S3: Calculating the percentage of corrosion depth through the Hausdorff distance;

[0062] Maximum external corrosion deterioration judgment: Taking the maximum value of S3 among the N segments;

[0063] Overall deterioration judgment:

[0064] (N is the number of segments of the wire rope);

[0065] Among them, the computing system 4 built-in all 36 RCN model libraries in GB / T 5972-2023 (covering structures such as 6×7 and 18×19S-WSC, 1:1 ratio, 200mm length three-dimensional model library), modeled based on the standard wire ropes in the RCN model library, obtained data such as nominal diameter and total amount of standard model wires, and then used the ICP algorithm to compare the modeling of the wire rope to be flaw-detected with it.

[0066] As Figure 7 shown, the specific calculation process of the N algorithm is as follows:

[0067] Combined with the S1, S2, and S3 values provided by the M algorithm, compare them with the preset standard values (the standard value in this embodiment is 20%). If any one is greater than the standard value, lock this section from the modeling system and perform defect identification on this section through the YOLOv5 model.

[0068] Preferably, in this embodiment, after locking this section, first generate three image snapshots of this section (three-dimensional to plane, three can cover the entire picture of this section of wire rope), and then perform distortion correction and contrast enhancement processing on the image snapshots of this section to ensure clear images and accurate defect identification results. The confidence threshold for the YOLOv5 model to perform defect identification on this section is ≥0.85.

[0069] Preferably, in this embodiment, as Figure 1 shown, the flaw-detection drone is also equipped with a preset and central control system 5. By setting the preset and central control system 5, the drone operator can preset parameters and create a new task before performing the detection task. The preset content includes: task height (maximum height, to prevent collision with the equipment at the top of the wire rope), category number (RCN), nominal diameter, core category, allowable visible external broken wire quantity, allowable wire diameter reduction amount, etc. After entering the category number (RCN), the corresponding standard model can be automatically loaded. The preset and central control system 5 combines with the laser sensor 10 for intelligent flight control to ensure that the flaw-detection drone performs horizontal attitude fine-tuning during vertical flight to avoid collision between the flaw-detection circle 2 and the wire rope.

[0070] The embodiments of the present invention include the following beneficial effects:

[0071] 1. Significantly improved efficiency:

[0072] Traditional manual inspection takes 4-6 hours / 100 meters. Through the automated inspection of the drone in the present invention, the time consumption is ≤1 hour / 100 meters, and the efficiency is increased by 4-6 times;

[0073] The detection time for each section is ≤10 seconds, supporting continuous operation and avoiding the risks of manual high-altitude operation.

[0074] 2. Breakthrough improvement in detection accuracy:

[0075] The broken wire recognition rate ≥ 99% (the error rate of traditional methods is 20% - 30%), and it can accurately identify internal broken wires (such as broken wires in the groin);

[0076] The diameter measurement error ≤ ±0.1mm (traditional ±0.5mm), fully meeting the requirements of the GB / T 5972 - 2023 standard;

[0077] The three - dimensional modeling accuracy reaches the millimeter level (point cloud density ≥ 1000 points / cm 2 ), and it can capture surface and internal subtle defects.

[0078] 3. Standardized dynamic adaptation:

[0079] Full - series automatic matching of RCN: Through the M algorithm, 36 RCN model libraries are built - in (such as 6×7, 18×19S - WSC), and the national standard thresholds are called in real - time to avoid manual misjudgment.

[0080] 4. Optimization of real - time performance and safety:

[0081] Edge - computing technology: 90% of the data processing is completed on the drone side, and the detection results are output within 10 seconds, without relying on cloud delays; Collision - risk avoidance: The laser - stabilized sensor monitors the distance in real - time, and the drone automatically adjusts its attitude to avoid collisions between the flaw - detection diagnostic circle and the wire rope.

[0082] 5. Full - process automation and data traceability:

[0083] An inspection report is automatically generated (including 3D models, defect heat maps, statistical tables, etc.), and the defect locations (such as broken wires in the groin) are accurately marked, facilitating subsequent maintenance decisions.

[0084] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent flaw detection and diagnosis equipment for tower crane steel wire ropes, characterized in that, Including: A flaw detection drone; A scanning mechanism disposed on one side of the flaw detection drone for scanning a wire rope; A modeling system for three-dimensionally modeling the wire rope based on the information collected by the scanning mechanism, the modeling system being data-connected to the scanning mechanism; and a calculation system for calculating the damage degree of the wire rope based on the modeling of the modeling system, the calculation system being data-connected to the modeling system.

2. The intelligent flaw detection and diagnosis equipment for tower crane steel wire ropes according to claim 1, characterized in that, The scanning mechanism includes: a flaw detection rod cantilevered on one side of the flaw detection drone; a flaw detection loop for the wire rope to pass through provided at the end of the flaw detection rod away from the flaw detection drone; a camera mechanism and a laser sensor provided on the inner wall of the flaw detection loop.

3. The intelligent flaw detection and diagnosis equipment for tower crane steel wire ropes according to claim 2, characterized in that, The camera mechanism includes at least three macro cameras evenly distributed on the inner wall of the flaw detection loop and fill lights provided around each macro camera.

4. The intelligent flaw detection and diagnosis equipment for tower crane steel wire ropes according to claim 2, characterized in that, A balance rod extending in the opposite direction of the flaw detection rod is provided on the flaw detection drone.

5. The intelligent flaw detection and diagnosis equipment for tower crane steel wire ropes according to claim 2, characterized in that, A notch is provided on the flaw detection loop, and an opening and closing section is provided at the notch. One end of the opening and closing section is rotatably connected to the first end of the notch, and a locking member capable of being locked and connected to the second end of the notch is provided at the other end of the opening and closing section.

6. An intelligent flaw detection and diagnosis method for tower crane steel ropes, characterized in that, Including the following steps: Step 1: Start the flaw detection drone and use the scanning mechanism to scan the wire rope; Step 2: Starting from one end of the wire rope, scan the wire rope in segments through the scanning mechanism and control the flaw detection drone to fly until the entire wire rope is scanned; Step 3: Transmit the scanning result of the scanning mechanism to the modeling system in real time and perform segmented modeling on the wire rope; Step 4: Perform real-time calculation and comparison on the results of the modeling through the calculation system to obtain the damage conditions of each segment of the wire rope, calculate the overall damage condition of the wire rope based on the damage conditions of each segment, judge the specific defects of each segment, and generate a wire rope damage condition report according to the overall damage condition of the wire rope and the judgment of the specific defects of each segment.

7. The intelligent flaw detection and diagnosis method for tower crane steel wire ropes according to claim 6, characterized in that, The calculation process of the calculation system includes: M algorithm: Compare and calculate the modeling of each segment with the modeling of the standard wire rope, respectively obtain the wire breakage rate S1, diameter reduction rate S2, and external corrosion rate S3 of each segment, and calculate the overall deterioration value of the wire rope based on S1, S2, and S3 of each segment to reflect the overall damage condition of the wire rope; N algorithm: Combine the S1, S2, and S3 values provided by the M algorithm and compare them with the preset standard values. If any one of the S1, S2, and S3 values in a segment is greater than the standard value, lock this segment from the modeling system and perform defect identification on the locked segment.

8. The intelligent flaw detection and diagnosis method for tower crane steel wire ropes according to claim 7, characterized in that, The specific calculation process of the M algorithm is: Divide the steel wire into N segments, wire breakage rate S1 = (number of broken wires in the detected segment / total amount of steel wires in the standard model) * 100%; Diameter reduction rate S2 = (1 - average diameter of the detected segment / nominal diameter) * 100%; External corrosion rate S3: Calculate the percentage of corrosion depth through the Hausdorff distance; Overall deterioration judgment: (where N is the number of wire rope segments); Among them, the calculation system internally has a standard wire rope modeling for giving the nominal diameter and the total amount of steel wires in the standard model.

9. The intelligent flaw detection and diagnosis method for tower crane wire ropes according to claim 6, characterized in that, The described scanning mechanism includes: a flaw detection rod cantilevered on one side of the flaw detection UAV; a flaw detection loop for the steel wire rope to pass through, provided at the end of the flaw detection rod away from the flaw detection UAV; a camera mechanism and a laser sensor provided on the inner wall of the flaw detection loop. The specific steps of Step 1 include: starting the flaw detection UAV, slipping the steel wire rope to be flaw detected into the flaw detection loop, and making fine adjustments to the flaw detection UAV to ensure that the steel wire rope is centered within the flaw detection loop.

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