A synchronous detection system for intelligent identification of wire rope surface damage and diameter measurement

Abstract

The present invention discloses an intelligent recognition and diameter measurement synchronous detection system for surface damage of wire ropes, which relates to the technical field of wire rope monitoring. By deploying a waterproof sensor group on the mooring wire ropes of ocean buoys, including a laser scanning sensor, a waterproof industrial camera, and an ultrasonic sensor, this system can collect real-time images and defect data sets of the wire rope surface. The sensor module combines a communication unit and an image processing unit, which can transmit data in real time and perform image processing and analysis to extract the damage texture features and damage area on the wire rope surface. Through an efficient threshold segmentation algorithm, the coating damage area can be accurately located R ( x , y ) and the total damage area can be calculated A damage . This intelligent recognition and image processing technology can effectively avoid the errors and delays of traditional manual detection, greatly improve the detection efficiency and accuracy, thereby identifying potential damage and faults earlier and reducing the risk of accidents

Description

Technical Field

[0001] The present invention relates to the technical field of wire rope monitoring, and specifically to a wire rope surface damage intelligent identification and diameter measurement synchronous detection system. Background Art

[0002] As an important infrastructure industry, marine engineering widely involves the construction and maintenance of multiple key facilities such as offshore platforms, waterway facilities, and marine buoy systems. In these facilities, wire ropes, as core components of support and connection, are subject to huge tension and the test of the external environment. With the complexity of the marine environment, wire ropes are easily affected by multiple factors such as seawater corrosion, friction, and stretching during long-term use, resulting in surface coating damage and diameter changes, which in turn affects their bearing capacity and safety. Therefore, the inspection and maintenance of wire ropes have become a key issue that needs to be urgently addressed in marine engineering. Traditional wire rope inspection methods often rely on manual or periodic inspections, which have large blind spots and time lags, making it difficult to discover potential hidden dangers in the first place.

[0003] At present, marine buoy mooring wire ropes are prone to detection blind spots and misjudgments during actual use. The surface damage and diameter changes of the wire ropes are not identified in time, which often leads to a decrease in the bearing capacity of the wire ropes, or even a breakage accident, which directly threatens the safety and operational efficiency of marine engineering. At the same time, the lack of comprehensive and real-time health monitoring methods increases the cost and difficulty of wire rope maintenance, delays maintenance opportunities, and may lead to maintenance work requiring a wider range of intervention and higher costs. If the monitoring method of ultrasonic signal energy and attenuation rate is not effectively optimized, it may not be able to accurately assess the specific location and severity of the damage due to problems such as imprecise technology and poor real-time performance, thereby affecting the maintenance decision of the wire rope. Therefore, the limitations of the existing methods urgently need to be overcome by new detection systems to improve the safety and operational efficiency of marine buoy systems. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a wire rope surface damage intelligent identification and diameter measurement synchronous detection system to improve the accuracy and efficiency of wire rope detection, reduce the risk of accidents, and optimize the management and maintenance strategy of wire ropes.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a wire rope surface damage intelligent identification and diameter measurement synchronous detection system, comprising:

[0006] Sensor module: By installing a waterproof sensor group on the marine buoy mooring wire rope, the defect data set and wire rope surface image of the marine buoy mooring wire rope are collected in real time, and the collected defect data set and wire rope surface image are transmitted to the wire rope detection system through the communication module;

[0007] Data processing module: In the wire rope detection system, the defect data set and wire rope surface image are received in real time, and the wire rope surface image is processed and analyzed. At the same time, the defect data set is preprocessed to obtain grayscale smooth matrix images. and damage signature datasets;

[0008] Surface coating damage detection module: grayscale smoothing matrix image acquired Extract texture features and obtain the damaged texture feature vector T , and then use the threshold segmentation algorithm to locate the coating damage area R ( x , y ), and based on the coating damage area R ( x , y ) to calculate and output the total damage area A damage ;

[0009] Wire rope diameter change detection module: Preliminary calculation of diameter change amplitude △ based on damage feature data set D max , then build the linear regression algorithm model and calculate the output diameter change rate K d ;

[0010] Internal damage detection module: Calculates and outputs the total energy of the echo signal of the marine buoy mooring wire rope based on the damage feature data set E s and decay rate N s ;

[0011] Comprehensive evaluation and execution module: Construct a comprehensive algorithm formula, input the output results of the surface coating damage detection module, the wire rope diameter change detection module and the internal damage detection module into the comprehensive algorithm formula, and output the comprehensive health assessment value of the marine buoy mooring wire rope H , and set the first damage threshold F 1 and 2 damage thresholds F 2, and the first damage threshold F 1 and 2 damage thresholds F 2 and comprehensive health assessment value H Conduct comparative assessment and analyze damage to ocean buoy mooring wire ropes.

[0012] Preferably, the sensor module includes a collection unit and a communication unit;

[0013] Collection unit: by installing a waterproof sensor group at a fixed position on the mooring wire rope of the ocean buoy, the defect data set and wire rope surface image are collected in real time;

[0014] The waterproof sensor group includes a laser scanning sensor, a waterproof industrial camera and an ultrasonic sensor;

[0015] The defect data set includes laser reflection distance d and ultrasonic signal frequency f ;

[0016] Communication unit: Build a wire rope detection system based on the communication module of the waterproof sensor, use the satellite communication network to connect the waterproof sensor to the wire rope detection system, and transmit the defect data set and wire rope surface image to the wire rope detection system.

[0017] Preferably, the data processing module includes an image processing unit and a signal analysis unit;

[0018] Image processing unit: In the wire rope detection system, the defect data set and wire rope surface image are received in real time, and each wire rope surface image is represented as a two-dimensional matrix image to obtain the matrix image. I ( x , y , t ),in x Represents the horizontal coordinate of the pixel on the surface of the wire rope. y Represents the pixel vertical axis coordinate of the wire rope surface image, t Indicates the collection time;

[0019] Then the obtained matrix image I ( x , y , t ) to perform image processing and analysis to obtain a grayscale smooth matrix image , the image processing analysis includes graying and Gaussian filtering;

[0020] By using the grayscale method to convert the matrix image I ( x , y , t ) is converted into a grayscale matrix image I h ( x , y , t ), the specific algorithm formula is: I h ( x , y , t )=0.299 R ( x, y )+0.587 G ( x , y )+0.114 B ( x , y );

[0021] in, R Represents the red channel value, G Represents the green channel value, B Represents the blue channel value;

[0022] The Gaussian filter uses a convolution formula to smooth the grayscale image using a Gaussian kernel function and then outputs a grayscale smoothing matrix image. , the specific algorithm formula is: ,in, s represents the smoothing strength, p represents the circumference, and its value is 3.14. represents the Gaussian kernel function;

[0023] Signal analysis unit: extracts features based on the acquired defect data set to obtain a damage feature data set, which includes t Wire rope diameter at time D ( t )and t Internal damage echo signal at time S ( f , t );

[0024] Said t Wire rope diameter at time D ( t ) The laser sensor emits a laser beam to the surface of the wire rope, and the laser reflection distance on both sides of the mooring wire rope of the ocean buoy is d Calculate and obtain, the specific algorithm formula is:

[0025] D ( t )= d 1+ d 2, among which, d 1 indicates the distance between the left side of the wire rope and the laser sensor. d 2 indicates the distance between the right side surface of the wire rope and the laser sensor;

[0026] Said t Internal damage echo signal at time S ( f , t ) The ultrasonic sensor transmits the ultrasonic signal frequency into the wire rope f, receiving echo signals reflected from defects and boundaries.

[0027] Preferably, the surface coating damage detection module includes an image texture feature extraction unit, a damage region segmentation unit and a damage area calculation unit;

[0028] Image texture feature extraction unit: Use the gray level co-occurrence matrix GLCM to extract the gray level smooth matrix image The damage texture feature vector in T , the damage texture feature vector T Including energy t 1. Contrast t 2 and entropy t 3;

[0029] Based on the acquired damage texture feature vector T All feature vectors in the dynamic surface defect threshold are calculated and output T damage ;

[0030] The dynamic surface defect threshold T damage The output is calculated by the following algorithm formula;

[0031] T damage = T 0+ k 1× t 1+ k 2× t 2+ k 3× t 3;

[0032] In the formula, T 0 represents the initial global threshold, obtained through histogram analysis, k 1. k 2 and k 3 represents energy t 1. Contrast t 2 and entropy t The characteristic adjustment coefficient of 3.

[0033] Preferably, the damaged area segmentation unit is based on a dynamic surface defect threshold T damage Smooth matrix image with grayscale Perform threshold segmentation and then use the threshold segmentation algorithm to locate the coating damage area R ( x , y ), distinguishing damaged areas from non-damaged areas in grayscale images;

[0034] The coating damage area R (x , y ) is located by the following threshold segmentation algorithm;

[0035] ;

[0036] In the formula, 1 represents the damaged area and 0 represents the intact area;

[0037] Damage area calculation unit: traverse all grayscale smoothing matrix images , collect all coating damage areas R ( x , y ) Output the damage area with the result of 1, and calculate the total damage area of ​​the marine buoy mooring wire rope A damage ;

[0038] The total damaged area A damage The output is calculated by the following algorithm formula;

[0039] ;

[0040] In the formula, △ x Represents the physical interval of each pixel in the horizontal direction, △ y Indicates the physical spacing of each pixel in the vertical direction.

[0041] Preferably, the wire rope diameter change detection module includes a diameter change amplitude analysis unit and a diameter change trend analysis unit;

[0042] Diameter variation analysis unit: Based on the damage feature data set t Wire rope diameter at time D ( t ), calculate the limit value difference and obtain the diameter change range △ D max ;

[0043] The diameter variation range △ D max The output is calculated by the following algorithm formula;

[0044] ;

[0045] In the formula, max represents the upper limit function and min represents the lower limit function.

[0046] Preferably, the diameter change trend analysis unit: extracts the diameters at different time points by constructing a linear regression algorithm model. t Wire rope diameter at time D ( t), analyze the trend of wire rope diameter change over time, and calculate the output diameter change rate K d ;

[0047] The diameter change rate K d The output is calculated by the following linear regression algorithm model;

[0048]

[0049] In the formula, t i Indicates i a moment, n Represents the total number of all time points, D ( t i ) indicates that i The wire rope diameter at the time, represents the time average, Indicates the average value of wire rope diameter.

[0050] Preferably, the internal damage detection module includes an ultrasonic signal energy analysis unit and a signal attenuation rate analysis unit;

[0051] Ultrasonic signal energy analysis unit: internal damage echo signal at time t in the damage feature data set S ( f , t ), calculate and output the total energy of the echo signal E s ;

[0052] The total energy of the echo signal E s The output is calculated by the following algorithm formula;

[0053] ;

[0054] In the formula, f max Indicates the upper frequency limit of the ultrasonic signal. f min Indicates the lower limit of ultrasonic signal frequency, d f Indicates the frequency microvariable of ultrasonic signal;

[0055] Signal attenuation rate analysis unit: through the damage feature data set t Internal damage echo signal at time S ( f , t ), calculate the attenuation rate of the output ultrasonic signal N s, analyze the attenuation of ultrasonic signals transmitted in the mooring wire rope of ocean buoys;

[0056] The decay rate N s The output is calculated by the following algorithm formula;

[0057] ;

[0058] In the formula, S 0( f , t ) represents the internal damage echo signal in the lossless state, max( S ( f , t )) represents the upper limit amplitude of the current internal damage echo signal, max( S 0( f , t )) represents the upper limit amplitude of the echo signal in the lossless state.

[0059] Preferably, the comprehensive assessment and execution module includes a comprehensive health analysis unit and a health assessment unit;

[0060] Comprehensive health analysis unit: extract the total damage area obtained A damage , diameter change range △ D max , diameter change rate K d , total energy of echo signal E s and decay rate N s , input into the comprehensive algorithm formula, perform comprehensive calculation and output comprehensive health assessment value H , comprehensively analyze the damage of marine buoy mooring wire ropes;

[0061] The comprehensive health assessment value H The output is calculated by the following algorithm formula;

[0062] ;

[0063] In the formula, E s0 represents the total energy of the initial echo signal, A 1. A 2. A 3. A 4 and A 5 represents the total damaged area A damage , diameter change range △ D max , diameter change rateK d , attenuation rate N s and the total energy of the echo signal E s The weight value of A 1+ A 2+ A 3+ A 4+ A 5=1, and its specific value is set by the user.

[0064] Preferably, the health assessment unit: based on the historical comprehensive health assessment values ​​in normal state and damage state, performs standard deviation calculation to obtain the first damage threshold F 1 and 2 damage thresholds F 2. Then the first damage threshold F 1 and 2 damage thresholds F 2 and the comprehensive health assessment value obtained H Conduct real-time comparative assessment to analyze the comprehensive health status of marine buoy mooring wire ropes. The specific assessment contents are as follows;

[0065] When the comprehensive health assessment H >First damage threshold F 2:00, it is judged as first-level damage, and the wire rope detection system prompts to replace the wire rope immediately;

[0066] When the first damage threshold F 1<Comprehensive health assessment value H ≤First damage threshold F 2:00, it is judged as secondary damage, and the wire rope detection system prompts to maintain the surface damage of the wire rope;

[0067] When the comprehensive health assessment H ≤First damage threshold F 1:00, it is judged as normal and continues to be monitored.

[0068] The present invention provides a wire rope surface damage intelligent identification and diameter measurement synchronous detection system, which has the following beneficial effects:

[0069] (1) The system deploys a waterproof sensor group on the moored wire rope of the ocean buoy, including a laser scanning sensor, a waterproof industrial camera and an ultrasonic sensor, which can collect images and defect data sets on the surface of the wire rope in real time. The sensor module combines the communication unit and the image processing unit to transmit data in real time and perform image processing and analysis to extract the damaged texture characteristics and damaged area on the surface of the wire rope. Through an efficient threshold segmentation algorithm, the coating damage area can be accurately located R ( x ,y ), and calculate the total damaged area A damage This intelligent recognition and image processing technology can effectively avoid the errors and delays of traditional manual inspections, greatly improving the efficiency and accuracy of inspections, thereby identifying potential damage and failures earlier and reducing the risk of accidents.

[0070] (2) The system integrates the output results of multiple damage detection modules in the data processing module to build a comprehensive health assessment model. Through analysis based on the comprehensive algorithm formula, the system can output a comprehensive health assessment value H , and classify and evaluate the damage degree of the wire rope in real time according to the set damage threshold. H Exceeding the first damage threshold F 1, the system can immediately issue a prompt to remind the user to replace or repair the wire rope to ensure the safe use of the wire rope. This comprehensive evaluation method can conduct a comprehensive and quantitative analysis of the damage of the wire rope, provide a scientific basis for maintenance personnel, and avoid excessive or delayed maintenance.

[0071] (3) The system can dynamically set and adjust the first damage threshold by calculating the standard deviation of the historical comprehensive health assessment values ​​under normal and damaged conditions. F 1 and 2 damage thresholds F 2. To achieve adaptive damage prediction function. The system can continuously optimize the damage assessment model according to the actual damage of the wire rope, and accurately predict the damage that may occur in the future. On this basis, the system can provide timely treatment suggestions for different damage stages, such as prompting immediate replacement of the wire rope for level 1 damage, surface repair for level 2 damage, and continuous monitoring for normal conditions. This intelligent optimization mechanism not only improves the accuracy of wire rope management, but also effectively reduces unnecessary maintenance costs, extends the service life of the wire rope, and maximizes the long-term stable operation of the marine buoy mooring wire rope in harsh environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a schematic diagram of the module flow of a wire rope surface damage intelligent identification and diameter measurement synchronous detection system of the present invention;

[0073] Figure 2 It is a schematic diagram of a unit flow chart of a wire rope surface damage intelligent identification and diameter measurement synchronous detection system of the present invention;

[0074] Figure 3 It is a schematic diagram of the transmission layer of a unit of a wire rope surface damage intelligent identification and diameter measurement synchronous detection system of the present invention;

[0075] Figure 4 This is a schematic diagram of the evaluation output results of a synchronous detection system for intelligent identification of wire rope surface damage and diameter measurement according to the present invention. DETAILED DESCRIPTION

[0076] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0077] Example 1

[0078] The present invention provides a wire rope surface damage intelligent identification and diameter measurement synchronous detection system, please refer to Figure 1 and Figure 3 ,include:

[0079] Sensor module: By installing a waterproof sensor group on the marine buoy mooring wire rope, the defect data set and wire rope surface image of the marine buoy mooring wire rope are collected in real time, and the collected defect data set and wire rope surface image are transmitted to the wire rope detection system through the communication module;

[0080] Data processing module: In the wire rope detection system, the defect data set and wire rope surface image are received in real time, and the wire rope surface image is processed and analyzed. At the same time, the defect data set is preprocessed to obtain grayscale smooth matrix images. and damage signature datasets;

[0081] Surface coating damage detection module: grayscale smoothing matrix image acquired Extract texture features and obtain the damaged texture feature vector T , and then use the threshold segmentation algorithm to locate the coating damage area R ( x , y ), and based on the coating damage area R ( x , y ) to calculate and output the total damage area A damage ;

[0082] Wire rope diameter change detection module: Preliminary calculation of diameter change amplitude △ based on damage feature data set D max , then build the linear regression algorithm model and calculate the output diameter change rate K d ;

[0083] Internal damage detection module: Calculates and outputs the total energy of the echo signal of the marine buoy mooring wire rope based on the damage feature data set E s and decay rate N s ;

[0084] Comprehensive evaluation and execution module: Construct a comprehensive algorithm formula, input the output results of the surface coating damage detection module, the wire rope diameter change detection module and the internal damage detection module into the comprehensive algorithm formula, and output the comprehensive health assessment value of the marine buoy mooring wire rope H , and set the first damage threshold F 1 and 2 damage thresholds F 2, and the first damage threshold F 1 and 2 damage thresholds F 2 and comprehensive health assessment value H Conduct comparative assessment and analyze damage to ocean buoy mooring wire ropes.

[0085] In this embodiment, the sensor module constitutes the acquisition layer, the data processing module, the surface coating damage detection module, the wire rope diameter change detection module, and the internal damage detection module constitute the system layer, and the comprehensive evaluation and execution module constitutes the output layer. The sensor module of the system can collect the defect data set and the surface image of the wire rope in real time by setting a waterproof sensor group at the fixed position of the marine buoy mooring wire rope, and transmit the collected defect data set and surface image to the wire rope detection system through the communication module, realizing real-time, remote data transmission and monitoring. Compared with traditional manual detection, the present invention can realize all-weather monitoring, reduce interference from human factors, and improve the reliability and real-time performance of data. The data processing module receives and processes the transmitted defect data set and wire rope surface image in real time. By graying the surface image and performing Gaussian filtering, a grayscale smooth matrix image is obtained. , and preprocess the defect data set to obtain the damage feature data set. The surface coating damage detection module uses the gray level co-occurrence matrix GLCM to extract the texture feature vector in the image T , and combined with the dynamic surface defect threshold T damage , using threshold segmentation algorithm to locate coating damage area R ( x , y ), calculate the total damaged area A damage , accurately reflecting the scope and severity of wire rope surface damage. The wire rope diameter change detection module analyzes the wire rope diameter in the damage feature data set. D ( t ), first calculate the diameter change range △ D max , further construct a linear regression algorithm model, calculate and output the diameter change rate K d The module can timely reflect the diameter change trend of the wire rope and warn of possible breakage or excessive wear risks. The internal damage detection module is based on ultrasonic echo signals.S ( f , t ) Extract features and calculate the total energy of the echo signal E s and decay rate N s The comprehensive evaluation and execution module integrates the output results of the surface coating damage detection module, the wire rope diameter change detection module and the internal damage detection module, and inputs them into the comprehensive algorithm formula to calculate the comprehensive health assessment value. H By setting the first damage threshold F 1 and 2 damage thresholds F 2. Comprehensive health assessment value H Comparative evaluation is carried out to analyze the damage of the wire rope in real time. By combining comprehensive analysis of multiple sensors and data processing modules, it has shown significant advantages in real-time monitoring, intelligent diagnosis and maintenance decision-making.

[0086] Compared with traditional detection methods, this system can provide more accurate and real-time wire rope health assessment, automatically process a large amount of detection data, reduce manual intervention, improve the accuracy and efficiency of detection, and significantly improve the safety and life of wire ropes. At the same time, the real-time evaluation and early warning mechanism based on dynamic algorithms also greatly reduces the risk of accidents and optimizes the management and maintenance strategy of wire ropes.

[0087] Example 2

[0088] Based on Example 1, this example further optimizes the sensor module. Figure 2 and Figure 4 As shown, the sensor module includes a collection unit and a communication unit.

[0089] Collection unit: by installing a waterproof sensor group at a fixed position on the mooring wire rope of the ocean buoy, the defect data set and wire rope surface image are collected in real time;

[0090] The waterproof sensor group includes laser scanning sensors, waterproof industrial cameras and ultrasonic sensors;

[0091] Defect datasets include laser reflection distance d and ultrasonic signal frequency f ;

[0092] Communication unit: Build a wire rope detection system based on the communication module of the waterproof sensor, use the satellite communication network to connect the waterproof sensor to the wire rope detection system, and transmit the defect data set and wire rope surface image to the wire rope detection system.

[0093] In this embodiment, the acquisition unit of the system collects the defect data set and surface image of the wire rope in real time by installing laser scanning sensors, waterproof industrial cameras and ultrasonic sensors at fixed positions. This design not only ensures the continuous monitoring of the wire rope in harsh marine environments, but also provides accurate and comprehensive data support under various environmental conditions. Through the satellite communication network, the data is transmitted to the wire rope detection system in real time, realizing the effective combination of real-time monitoring and remote diagnosis. Compared with traditional detection methods, this module can comprehensively collect multi-dimensional data including surface damage, diameter changes and internal damage by introducing a variety of sensor fusion technologies. This multi-sensor collaborative working mode significantly improves the accuracy and reliability of the data, and can more comprehensively reflect the health status of the wire rope compared to a single sensor method. In addition, the use of satellite communication networks for data transmission breaks through the limitations of traditional detection methods on the monitoring range, enabling the monitoring system to be implemented globally, greatly improving the maintenance efficiency and safety of marine buoy mooring wire ropes.

[0094] Example 3

[0095] Based on Example 2, this example further optimizes the data processing module. Figure 2 As shown, the data processing module includes an image processing unit and a signal analysis unit.

[0096] Image processing unit: In the wire rope detection system, the defect data set and wire rope surface image are received in real time, and each wire rope surface image is represented as a two-dimensional matrix image to obtain the matrix image. I ( x , y , t ),in x Represents the horizontal coordinate of the pixel on the surface of the wire rope. y Represents the pixel vertical axis coordinate of the wire rope surface image, t Indicates the collection time;

[0097] Then the obtained matrix image I ( x , y , t ) to perform image processing and analysis to obtain a grayscale smooth matrix image ,Image processing and analysis include grayscale and Gaussian filtering;

[0098] By using the grayscale method to convert the matrix image I ( x , y , t ) is converted into a grayscale matrix image I h ( x , y, t ), the specific algorithm formula is: I h ( x , y , t )=0.299 R ( x , y )+0.587 G ( x , y )+0.114 B ( x , y );

[0099] in, R Represents the red channel value, G Represents the green channel value, B Represents the blue channel value;

[0100] Gaussian filtering uses the convolution formula to apply the Gaussian kernel function to the grayscale matrix image. I h ( x , y , t ) after smoothing to output grayscale smooth matrix image The specific algorithm formula is: ;in, s represents the smoothing strength, p represents the circumference, and its value is 3.14. represents the Gaussian kernel function;

[0101] Signal analysis unit: extracts features based on the acquired defect data set to obtain a damage feature data set. The damage feature data set includes t Wire rope diameter at time D ( t )and t Internal damage echo signal at time S ( f , t );

[0102] t Wire rope diameter at time D ( t ) The laser sensor emits a laser beam to the surface of the wire rope, and the laser reflection distance on both sides of the mooring wire rope of the ocean buoy is d Calculate and obtain, the specific algorithm formula is:

[0103] D ( t )= d 1+ d 2, among which, d1 indicates the distance between the left surface of the wire rope and the laser sensor. d 2 indicates the distance between the right side of the wire rope and the laser sensor;

[0104] t Internal damage echo signal at time S ( f , t ) The ultrasonic sensor transmits the ultrasonic signal frequency into the wire rope f , receiving echo signals reflected from defects and boundaries.

[0105] In this embodiment, the image processing unit of the system first converts the surface image of the wire rope into a two-dimensional matrix form. I ( x , y , t ), and grayscale and Gaussian filter processing are performed on it to eliminate the noise in the image, enhance the clarity of the image, and provide a reliable basis for subsequent damage identification. In this process, the grayscale process of the image uses the calculation of the red, green, and blue channels, and is converted into a grayscale matrix I h ( x , y , t ), optimizing the image processing efficiency. The Gaussian filter uses a smoothing algorithm to effectively remove high-frequency noise in the image, improving the image quality and the accuracy of subsequent analysis. The signal analysis unit provides more detailed wire rope health information by extracting features from the defect data set. The reflection distance measured by the laser sensor d , the diameter of the wire rope is accurately calculated D ( t ), and further analyzed its changes over time. The echo analysis of ultrasonic signals provides information on internal damage. By receiving echo signals reflected from internal defects and boundaries of the wire rope, potential internal damage can be effectively monitored. This combination of image and signal processing makes the damage identification of wire ropes more comprehensive, covering multiple aspects such as surface, diameter changes and internal defects. The introduction of this data processing module has brought significant improvements. Traditional methods often rely on a single sensor or manual monitoring, and it is difficult to comprehensively and real-timely identify multi-dimensional damage to wire ropes. The system integrates image processing and signal analysis technology to achieve efficient and real-time comprehensive monitoring, especially in terms of accuracy and processing speed. The deep integration of the efficiency of image processing and signal analysis makes the damage identification of wire ropes not only more accurate, but also has an intelligent trend.

[0106] Example 4

[0107] Based on Example 3, this example further optimizes the surface coating damage detection module, such as Figure 2 As shown, the surface coating damage detection module includes an image texture feature extraction unit, a damage region segmentation unit and a damage area calculation unit.

[0108] Image texture feature extraction unit: Use the gray level co-occurrence matrix GLCM to extract the gray level smooth matrix image The damage texture feature vector in T , the damage texture feature vector T Including energy t 1. Contrast t 2 and entropy t 3;

[0109] Based on the acquired damage texture feature vector T All feature vectors in the dynamic surface defect threshold are calculated and output T damage ;

[0110] Dynamic surface defect threshold T damage The output is calculated by the following algorithm formula;

[0111] T damage = T 0+ k 1× t 1+ k 2× t 2+ k 3× t 3;

[0112] In the formula, T 0 represents the initial global threshold, obtained through histogram analysis, k 1. k 2 and k 3 represents energy t 1. Contrast t 2 and entropy t The characteristic adjustment coefficient of 3 is used to control the energy t 1. Contrast t 2 and entropy t 3 pairs of dynamic surface defect thresholds T damage The contribution size is set based on the surface image defects of the wire rope.

[0113] Damage area segmentation unit: based on dynamic surface defect threshold T damage Smooth matrix image with grayscale Perform threshold segmentation and then use the threshold segmentation algorithm to locate the coating damage areaR ( x , y ), distinguishing damaged areas from non-damaged areas in grayscale images;

[0114] Coating damage area R ( x , y ) is located by the following threshold segmentation algorithm;

[0115] ;

[0116] In the formula, 1 represents the damaged area and 0 represents the intact area;

[0117] Damage area calculation unit: traverse all grayscale smoothing matrix images , collect all coating damage areas R ( x , y ) Output the damage area with the result of 1, and calculate the total damage area of ​​the marine buoy mooring wire rope A damage ;

[0118] Total damaged area A damage The output is calculated by the following algorithm formula;

[0119] ;

[0120] Among them, △ x Represents the physical interval of each pixel in the horizontal direction, △ y Indicates the physical spacing of each pixel in the vertical direction.

[0121] In this embodiment, the surface coating damage detection module of the system significantly improves the accuracy and automation level of wire rope surface damage detection through the coordinated work of the image texture feature extraction unit, the damage area segmentation unit and the damage area calculation unit. The image texture feature extraction unit uses the gray level co-occurrence matrix GLCM to extract the wire rope surface image. Analyze and extract the energy t 1. Contrast t 2 and entropy t 3 Damage texture feature vector T , and then calculate the dynamic surface defect threshold T damage The calculation of the dynamic threshold combines the actual defect situation of the wire rope surface image and adjusts the coefficient k 1. k 2. k 3 and k4 Accurately control the contribution of each feature to the threshold, achieving more sensitive damage identification. The damage area segmentation unit uses this dynamic surface defect threshold T damage Perform threshold segmentation with the grayscale smooth matrix image to distinguish the damaged area from the non-damaged area in the image and accurately locate the coating damaged area R ( x , y ). The threshold segmentation algorithm accurately identifies the damaged area, greatly improving the detection efficiency of the damaged area. The damage area calculation unit calculates all the coating damage areas by traversing all grayscale smooth matrix images, and obtains the total damage area of ​​the wire rope surface. A damage , further quantified the extent of damage and identified the corrosion damage to the surface coating of the ocean buoy mooring wire rope in the sea.

[0122] Example 5

[0123] Based on Example 3 or 4, this embodiment further optimizes the wire rope diameter change detection module, such as Figure 2 As shown, the wire rope diameter change detection module includes a diameter change amplitude analysis unit and a diameter change trend analysis unit.

[0124] Diameter variation analysis unit: Based on the damage feature data set t Wire rope diameter at time D ( t ), calculate the limit value difference and obtain the diameter change range △ D max ;

[0125] Diameter variation range D max The output is calculated by the following algorithm formula;

[0126] ;

[0127] In the formula, max represents the upper limit function and min represents the lower limit function.

[0128] Diameter change trend analysis unit: By constructing a linear regression algorithm model, the diameter change trend analysis unit can extract the diameter change trend at different time points. t Wire rope diameter at time D ( t ), analyze the trend of wire rope diameter change over time, and calculate the output diameter change rate K d ;

[0129] Diameter change rate K dThe output is calculated by the following linear regression algorithm model;

[0130]

[0131] In the formula, t i Indicates i a moment, n Represents the total number of all time points, D ( t i ) indicates that i The wire rope diameter at the time, represents the time average, Indicates the average value of wire rope diameter.

[0132] In this embodiment, the system first analyzes the diameter of the wire rope in the damage feature data set through the diameter variation amplitude analysis unit. D ( t ), by calculating the difference in limit values, the diameter change range △ D max This calculation method can accurately capture the local diameter fluctuation of the wire rope, providing an important basis for early detection of potential damage. This method can effectively identify the extreme changes in the wire rope diameter and promptly detect serious deformation caused by damage. The diameter change trend analysis unit further constructs a linear regression algorithm model to obtain the diameter change rate by analyzing the wire rope diameter change trend at different time points. K d , which can reveal the damage or aging trend of the wire rope that has gradually accumulated during long-term use. This dynamic monitoring method can not only track the health status of the wire rope in real time, but also predict the change trend in the future, helping maintenance personnel to take repair or replacement measures in advance to avoid unexpected failures.

[0133] Example 6

[0134] Based on one of the embodiments 3, 4, and 5, this embodiment further optimizes the internal damage detection module, such as Figure 2 As shown, the internal damage detection module includes an ultrasonic signal energy analysis unit and a signal attenuation rate analysis unit.

[0135] Ultrasonic signal energy analysis unit: through the damage feature data set t Internal damage echo signal at time S ( f , t ), calculate and output the total energy of the echo signal E s ;

[0136] Total echo signal energy E s The output is calculated by the following algorithm formula;

[0137] ;

[0138] In the formula, f max Indicates the upper frequency limit of the ultrasonic signal. f min Indicates the lower limit of ultrasonic signal frequency, d f Indicates the frequency microvariable of ultrasonic signal;

[0139] When ultrasonic waves encounter defects or uneven structures inside materials during propagation, they will be reflected and scattered. The echo signal of ultrasonic waves contains information about the internal state of the material, and the energy of the signal can help us determine whether there are defects in the material.

[0140] Signal attenuation rate analysis unit: through the damage feature data set t Internal damage echo signal at time S ( f , t ), calculate the attenuation rate of the output ultrasonic signal N s , analyze the attenuation of ultrasonic signals transmitted in the mooring wire rope of ocean buoys;

[0141] Decay rate N s The output is calculated by the following algorithm formula;

[0142] ;

[0143] In the formula, S 0( f , t ) represents the internal damage echo signal in the lossless state, max( S ( f , t )) represents the upper limit amplitude of the current internal damage echo signal, max( S 0( f , t )) represents the upper limit amplitude of the echo signal in the lossless state.

[0144] In this embodiment, the system uses an ultrasonic signal energy analysis unit to analyze the internal damage echo signal. S ( f , t ) total energy Es, which can quantify the energy in the ultrasonic echo signal and further reveal whether there are defects inside the wire rope. The propagation characteristics of ultrasonic signals make it possible to determine the location and severity of internal defects in the wire rope by the change in echo energy. This analysis can help identify potential internal damage in advance and avoid hidden faults that are difficult to detect with traditional detection methods. The signal attenuation rate analysis unit calculates the attenuation rate of the ultrasonic signal during transmission. N s , further verifying the damage inside the wire rope. When the wire rope is damaged, the propagation path of the ultrasonic signal will encounter different material densities and changes in the internal structure, resulting in signal attenuation. By comparing the current signal attenuation with the signal attenuation in the lossless state, the health of the wire rope can be accurately assessed and internal problems such as cracks and corrosion can be identified. This module accurately analyzes the energy and attenuation of the ultrasonic echo signal, not only providing more detailed internal status information than damage visible to the naked eye, but also enabling dynamic tracking of the health of the wire rope.

[0145] Example 7

[0146] Based on one of the embodiments 1 to 6, this embodiment further optimizes the comprehensive evaluation and execution module, such as Figure 2 and Figure 4 As shown, the comprehensive assessment and implementation module includes a comprehensive health analysis unit and a health assessment unit;

[0147] Comprehensive health analysis unit: extract the total damage area obtained A damage , diameter change range △ D max , diameter change rate K d , total energy of echo signal E s and decay rate N s , input into the comprehensive algorithm formula, perform comprehensive calculation and output comprehensive health assessment value H , comprehensively analyze the damage of marine buoy mooring wire ropes;

[0148] Comprehensive health assessment value H The output is calculated by the following algorithm formula;

[0149] ;

[0150] In the formula, E s0 represents the total energy of the initial echo signal, A 1. A 2. A 3. A4 and A 5 represents the total damaged area A damage , diameter change range △ D max , diameter change rate K d , attenuation rate N s and the total energy of the echo signal E s The weight value of A 1+ A 2+ A 3+ A 4+ A 5=1, and its specific value is set by the user.

[0151] Health assessment unit: Based on the historical comprehensive health assessment values ​​in normal and damaged states, standard deviation is calculated to obtain the first damage threshold F 1 and 2 damage thresholds F 2. Then the first damage threshold F 1 and 2 damage thresholds F 2 and the comprehensive health assessment value obtained H Conduct real-time comparative assessment to analyze the comprehensive health status of marine buoy mooring wire ropes. The specific assessment contents are as follows;

[0152] When the comprehensive health assessment H >First damage threshold F 2:00, it is judged as first-level damage, and the wire rope detection system prompts to replace the wire rope immediately;

[0153] When the first damage threshold F 1<Comprehensive health assessment value H ≤First damage threshold F 2:00, it is judged as secondary damage, and the wire rope detection system prompts to maintain the surface damage of the wire rope;

[0154] When the comprehensive health assessment H ≤First damage threshold F 1:00, it is judged as normal and continues to be monitored.

[0155] In this embodiment, the system uses the comprehensive health analysis unit to analyze the key indicators from each submodule, including the total damage area. A damage , diameter change range △ D max , diameter change rate K d , total energy of echo signal E sand decay rate N s , integrated into a comprehensive health assessment value H This evaluation value not only reflects the current damage state of the wire rope, but also takes into account the combined impact of different damage characteristics. By setting different weight values, the contribution of various indicators to the health evaluation value can be flexibly adjusted to ensure that the evaluation results are more accurate and meet the actual application needs. The health assessment module further compares and analyzes the historical health data with the real-time evaluation value, and determines the first damage threshold through standard deviation calculation. F 1 and 2 damage thresholds F 2. It realizes the hierarchical management of the health status of the wire rope. H Exceeding the first damage threshold F 2, the system will automatically determine that it is a first-level damage and remind you to replace the wire rope immediately; when the evaluation value is between the first damage threshold F 1 and 2 damage thresholds F 2, the system prompts to perform surface damage maintenance; and when the evaluation value is lower than the first damage threshold F 1, the wire rope is considered to be in normal condition and monitoring continues. This hierarchical evaluation mechanism makes maintenance decisions more scientific, avoids excessive or insufficient intervention, and effectively improves maintenance efficiency and safety.

[0156] Finally, it should be noted that the parts of the present invention that are not described in detail are all prior art. Those of ordinary skill in the art can understand that the above are only preferred examples of the invention and are not intended to limit the invention. Although the invention is described in detail with reference to the aforementioned examples, those of ordinary skill in the art can still modify the technical solutions recorded in the aforementioned examples, or replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, etc. made within the spirit and principles of the invention should be included in the scope of protection of the invention.

Claims

1. A wire rope surface damage intelligent identification and diameter measurement synchronous detection system, characterized by: include: Sensor module: By installing a waterproof sensor group on the marine buoy mooring wire rope, the defect data set and wire rope surface image of the marine buoy mooring wire rope are collected in real time, and the collected defect data set and wire rope surface image are transmitted to the wire rope detection system through the communication module; The defect data set includes laser reflection distance d and ultrasonic signal frequency f ; Data processing module: In the wire rope detection system, the defect data set and wire rope surface image are received in real time, and the wire rope surface image is processed and analyzed. At the same time, the defect data set is preprocessed to obtain grayscale smooth matrix images. and damage signature datasets; The damage feature dataset includes t Wire rope diameter at time D ( t )and t Internal damage echo signal at time S ( f , t ); Surface coating damage detection module: grayscale smoothing matrix image acquired Extract texture features and obtain the damaged texture feature vector T , and then use the threshold segmentation algorithm to locate the coating damage area R ( x , y ), and based on the coating damage area R ( x , y ) to calculate and output the total damage area A damage ; Based on the acquired damage texture feature vector T All feature vectors in the dynamic surface defect threshold are calculated and output T damage , the dynamic surface defect threshold T damage The output is calculated by the following algorithm formula; ； In the formula, T 0 represents the initial global threshold, obtained through histogram analysis, k 1. k 2 and k 3 represents energy t 1. Contrast t 2 and entropy t The characteristic adjustment coefficient of 3; The coating damage area R ( x , y ) is located by the following threshold segmentation algorithm; ； In the formula, 1 represents the damaged area and 0 represents the intact area; The total damaged area A damage The output is calculated by the following algorithm formula; ； In the formula, △ x Represents the physical interval of each pixel in the horizontal direction, △ y Indicates the physical interval of each pixel in the vertical direction; Wire rope diameter change detection module: Preliminary calculation of diameter change amplitude △ based on damage feature data set D max , then build the linear regression algorithm model and calculate the output diameter change rate K d ; Internal damage detection module: Calculates and outputs the total energy of the echo signal of the marine buoy mooring wire rope based on the damage feature data set E s and decay rate N s ; The diameter change rate K d The output is calculated by the following linear regression algorithm model; In the formula, t i Indicates i a moment, n Represents the total number of all time points, D ( t i ) indicates that i The wire rope diameter at the time, represents the time average, Indicates the average value of wire rope diameter; The total energy of the echo signal E s The output is calculated by the following algorithm formula; ； In the formula, f max Indicates the upper frequency limit of the ultrasonic signal. f min Indicates the lower limit of ultrasonic signal frequency, d f Indicates the frequency microvariable of ultrasonic signal; The decay rate N s The output is calculated by the following algorithm formula; ； In the formula, S 0( f , t ) represents the internal damage echo signal in the lossless state, max( S ( f , t )) represents the upper limit amplitude of the current internal damage echo signal, max( S 0( f , t )) represents the upper limit amplitude of the echo signal in the lossless state; Comprehensive evaluation and execution module: Construct a comprehensive algorithm formula, input the output results of the surface coating damage detection module, the wire rope diameter change detection module and the internal damage detection module into the comprehensive algorithm formula, and output the comprehensive health assessment value of the marine buoy mooring wire rope H , and set the first damage threshold F 1 and 2 damage thresholds F 2, and the first damage threshold F 1 and 2 damage thresholds F 2 and comprehensive health assessment value H Conduct comparative assessments and analyze damage to marine buoy mooring wire ropes; The comprehensive health assessment value H The output is calculated by the following algorithm formula; ； In the formula, represents the total energy of the initial echo signal, A 1. A 2. A 3. A 4 and A 5 represents the total damaged area A damage , diameter change range △ D max , diameter change rate K d , attenuation rate N s and the total energy of the echo signal E s The weight value of A 1+ A 2+ A 3+ A 4+ A 5=1, and its specific value is set by the user.

2. The wire rope surface damage intelligent identification and diameter measurement synchronous detection system according to claim 1 is characterized by: The sensor module includes a collection unit and a communication unit; Collection unit: by installing a waterproof sensor group at the fixed position of the marine buoy mooring wire rope, the defect data set and wire rope surface image are collected in real time; The waterproof sensor group includes a laser scanning sensor, a waterproof industrial camera and an ultrasonic sensor; Communication unit: Build a wire rope detection system based on the communication module of the waterproof sensor, use the satellite communication network to connect the waterproof sensor to the wire rope detection system, and transmit the defect data set and wire rope surface image to the wire rope detection system.

3. The wire rope surface damage intelligent identification and diameter measurement synchronous detection system according to claim 2 is characterized by: The data processing module includes an image processing unit and a signal analysis unit; Image processing unit: In the wire rope detection system, the defect data set and wire rope surface image are received in real time, and each wire rope surface image is represented as a two-dimensional matrix image to obtain the matrix image. I ( x , y , t ),in x Represents the horizontal coordinate of the pixel on the surface of the wire rope. y Represents the pixel vertical axis coordinate of the wire rope surface image, t Indicates the collection time; Then the obtained matrix image I ( x , y , t ) to perform image processing and analysis to obtain a grayscale smooth matrix image , the image processing analysis includes graying and Gaussian filtering; The grayscale method uses a grayscale method to convert the matrix image I ( x , y , t ) is converted into a grayscale matrix image I h ( x , y , t ), the specific algorithm formula is: I h ( x , y , t )=0.299 R ( x , y )+0.587 G ( x , y )+0.114 B ( x , y ); in, R Represents the red channel value, G Represents the green channel value, B Represents the blue channel value; The Gaussian filter uses the convolution formula to apply the Gaussian kernel function to the grayscale matrix image. I h ( x , y , t ) after smoothing to output grayscale smooth matrix image , the specific algorithm formula is: ;in, represents the smoothing strength, Represents pi, with a value of 3.

14. represents the Gaussian kernel function; Signal analysis unit: extracting features based on the acquired defect data set to obtain a damage feature data set; Said t Wire rope diameter at time D ( t ) The laser sensor emits a laser beam to the surface of the wire rope, and the laser reflection distance on both sides of the mooring wire rope of the ocean buoy is d Calculate and obtain, the specific algorithm formula is: D ( t )= d 1+ d 2, among which, d 1 indicates the distance between the left side of the wire rope and the laser sensor. d 2 indicates the distance between the right side surface of the wire rope and the laser sensor; Said t Internal damage echo signal at time S ( f , t ) The ultrasonic sensor transmits the ultrasonic signal frequency into the wire rope f , receiving echo signals reflected from defects and boundaries.

4. The wire rope surface damage intelligent identification and diameter measurement synchronous detection system according to claim 3 is characterized by: The surface coating damage detection module includes an image texture feature extraction unit, a damage region segmentation unit and a damage area calculation unit; Image texture feature extraction unit: Use the gray level co-occurrence matrix GLCM to extract the gray level smooth matrix image The damage texture feature vector in T , the damage texture feature vector T Including energy t 1. Contrast t 2 and entropy t 3.

5. The wire rope surface damage intelligent identification and diameter measurement synchronous detection system according to claim 4 is characterized by: Damage area segmentation unit: based on dynamic surface defect threshold T damage Smooth matrix image with grayscale Perform threshold segmentation and then use the threshold segmentation algorithm to locate the coating damage area R ( x , y ), distinguishing damaged areas from non-damaged areas in grayscale images; Damage area calculation unit: traverse all grayscale smoothing matrix images , collect all coating damage areas R ( x , y ) Output the damage area with the result of 1, and calculate the total damage area of ​​the marine buoy mooring wire rope A damage .

6. The wire rope surface damage intelligent identification and diameter measurement synchronous detection system according to claim 3 is characterized by: The wire rope diameter change detection module includes a diameter change amplitude analysis unit and a diameter change trend analysis unit; Diameter variation analysis unit: Based on the damage feature data set t Wire rope diameter at time D ( t ), calculate the limit value difference and obtain the diameter change range △ D max ; The diameter variation range △ D max The output is calculated by the following algorithm formula; ； In the formula, max represents the upper limit function and min represents the lower limit function.

7. The wire rope surface damage intelligent identification and diameter measurement synchronous detection system according to claim 6 is characterized by: Diameter change trend analysis unit: By constructing a linear regression algorithm model, the diameter change trend analysis unit can extract the diameter change trend at different time points. t Wire rope diameter at time D ( t ), analyze the trend of wire rope diameter change over time, and calculate the output diameter change rate K d .

8. The wire rope surface damage intelligent identification and diameter measurement synchronous detection system according to claim 3 is characterized by: The internal damage detection module includes an ultrasonic signal energy analysis unit and a signal attenuation rate analysis unit; Ultrasonic signal energy analysis unit: through the damage feature data set t Internal damage echo signal at time S ( f , t ), calculate and output the total energy of the echo signal E s ; Signal attenuation rate analysis unit: through the damage feature data set t Internal damage echo signal at time S ( f , t ), calculate the attenuation rate of the output ultrasonic signal N s , analyze the attenuation of ultrasonic signals transmitted in the mooring wire rope of ocean buoys.

9. The wire rope surface damage intelligent identification and diameter measurement synchronous detection system according to claim 1 is characterized by: The comprehensive assessment and execution module includes a comprehensive health analysis unit and a health assessment unit; Comprehensive health analysis unit: extract the total damage area obtained A damage , diameter change range △ D max , diameter change rate K d , total energy of echo signal E s and decay rate N s , input into the comprehensive algorithm formula, perform comprehensive calculation and output comprehensive health assessment value H , comprehensively analyze the damage of ocean buoy mooring wire ropes.

10. The wire rope surface damage intelligent identification and diameter measurement synchronous detection system according to claim 9, characterized in that: Health assessment unit: Based on the historical comprehensive health assessment values ​​in normal and damaged states, standard deviation is calculated to obtain the first damage threshold F 1 and 2 damage thresholds F 2. Then the first damage threshold F 1 and 2 damage thresholds F 2 and the comprehensive health assessment value obtained H Conduct real-time comparative assessment to analyze the comprehensive health status of marine buoy mooring wire ropes. The specific assessment contents are as follows; When the comprehensive health assessment H >First damage threshold F 2:00, it is judged as first-level damage, and the wire rope detection system prompts to replace the wire rope immediately; When the first damage threshold F 1<Comprehensive health assessment value H ≤First damage threshold F 2:00, it is judged as secondary damage, and the wire rope detection system prompts to maintain the surface damage of the wire rope; When the comprehensive health assessment H ≤First damage threshold F 1:00, it is judged as normal and continues to be monitored.

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