Fan cable twisting platform cable wear monitoring system and method

Abstract

The invention provides a fan cable twisting platform cable wear monitoring system and method. The fan cable twisting platform cable wear monitoring system comprises a wear monitoring module, a data transmission module, a data processing and analyzing module and an intelligent early warning module. The wear monitoring module is used for monitoring cable wear data of the fan cable twisting platform; the data transmission module is used for transmitting data to the data processing and analyzing module; and the data processing and analyzing module is used for receiving and analyzing the data, judging a cable wear index, and controlling the intelligent early warning module to send out early warning information when the wear index is greater than a preset value. The guide rail is arranged around the cable, the sliding mechanism drives the probe to move circularly, 360-degree rotation monitoring is matched, multi-angle monitoring of the surface of the cable is achieved, and the problem that the traditional monitoring range is limited is solved; the four-dimension data of images, environment, time and motion are integrated, the wear index is calculated by adopting a weighting algorithm model, different land / sea working conditions are adapted, and the monitoring accuracy is improved compared with that of the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind turbine torsion cable platform cable wear monitoring, in particular to a wind turbine torsion cable platform cable wear monitoring system and method. BACKGROUND

[0002] The wind turbine torsion cable platform is a key structural component in the yaw system of a wind turbine generator unit, usually arranged below the nacelle at the top of the tower, used to support and manage the cables connecting the nacelle and the tower foundation. The platform plays a core role in the operation of the wind turbine generator unit. When the yaw system is working, the nacelle needs to rotate with the wind direction, and the torsion cable platform ensures that the power cables and communication cables remain stable during the twisting process, avoiding excessive winding or damage to the cables. The torsion cable platform is generally designed as a ring or frame structure, and multiple perforations may be provided on the surface for cable distribution.

[0003] As a key area for cable arrangement, the torsion cable platform of the wind turbine generator unit is prone to wear and tear due to repeated twisting and bending of the cables during operation of the wind turbine, as well as environmental factors such as temperature, humidity, and salt spray (for offshore wind turbines). If not detected and addressed in a timely manner, it may lead to damage to the cable insulation layer, short circuit, or even fire, seriously affecting the safe and stable operation of the wind turbine.

[0004] Existing cable wear monitoring methods rely heavily on manual inspection, which has the problems of long inspection cycle, high labor intensity, low detection accuracy, and inability to monitor in real time. Some automated monitoring equipment has limited monitoring range, poor environmental adaptability, single algorithm model, and cannot adapt to different specifications of cables, making it difficult to meet the monitoring needs of the complex working conditions of the wind turbine torsion cable platform. SUMMARY

[0005] The main purpose of the present application is to provide a wind turbine torsion cable platform cable wear monitoring system and method, which solves the problem of low detection accuracy and inconvenience of the prior art.

[0006] To solve the above technical problems, the technical solution adopted by the present application is: A wind turbine torsion cable platform cable wear monitoring system, comprising a wear monitoring module, a data transmission module, a data processing and analysis module, and an intelligent warning module. The wear monitoring module is used to monitor the cable wear data of the wind turbine torsion cable platform. The data transmission module is used to transmit data to the data processing and analysis module. The data processing and analysis module is used to receive and analyze data, determine the cable wear index, and control the intelligent warning module to issue warning information when the wear index is greater than the preset value.

[0007] In a preferred solution, the data processing and analysis module is built-in an algorithm model, which is used to process and calculate the obtained data to obtain the wear index, and obtain the warning level according to the wear index and the preset value; The warning level includes 0-level warning, 1-level warning, 2-level warning and 3-level warning; the 0-level warning represents no safety hidden danger, no alarm is needed, and normal use; the 1-level warning represents mild wear, and there is a safety hidden danger; the 2-level warning represents moderate wear, which represents a larger hidden danger and needs to be handled as soon as possible; and the 3-level warning represents severe wear and needs to be handled immediately.

[0008] In a preferred solution, the cable wear data monitored by the wear monitoring module includes image, environment, time and cable movement frequency data; The image data includes cable images, the environment data includes temperature and humidity, and the cable movement frequency data includes the angle and number of times of the cable following rotation.

[0009] In a preferred solution, the wear monitoring module includes a monitoring device arranged on the cable twisting platform, which is used to periodically monitor the wear data of the cable at multiple angles.

[0010] In a preferred solution, the wear monitoring module includes a guide rail connected to the fan cable twisting platform through a connecting column; A sliding mechanism is arranged on the guide rail and slides along the guide rail; The sliding mechanism is provided with a monitoring mechanism, which moves with the sliding mechanism and is used to monitor the cable wear data.

[0011] In a preferred solution, the guide rail includes a plurality of tracks, which are combined to surround the cable of the cable twisting platform; The side of the track is provided with a movable groove, and a rack is arranged in the movable groove; The racks inside the tracks are connected after the tracks are connected; The side surfaces of the two ends of the track are provided with threaded columns, the connecting portions of adjacent tracks are provided with connecting plates, and the two adjacent threaded columns pass through the same connecting plate; The threaded column is threadedly connected with a nut; The track includes an arc-shaped track, a special-shaped track and a straight track; The shape of the rack is adapted to the movable groove, and the shapes of the two ends are adapted to each other, and the connection is smooth after the other rack is connected; The connecting portions of adjacent tracks are connected smoothly.

[0012] In a preferred solution, the sliding mechanism includes a sliding seat, a connecting groove is arranged in the sliding seat, and a sliding rail passes through the connecting groove; The sliding seat slides along the sliding rail; A plurality of rollers are arranged in the sliding seat, the rollers are rotationally connected with the sliding seat, and the sliding rail is connected; A first motor is arranged at the top of the sliding seat; The output shaft of the first motor is provided with a gear, the gear extends to the inside of the connecting groove, and is engaged with the rack.

[0013] In the preferred scheme, the two sides of the sliding seat are provided with second fixing plates, and the second fixing plates are rotationally connected with the first fixing plates through rotation shafts; The first fixing plate is provided with a movable plate; A plurality of springs are arranged between the movable plate and the sliding seat, and the springs are located on the two sides of the first fixing plate; The bottom of the movable plate is provided with a plurality of guide wheels, and the guide wheels are located on the two sides of the track.

[0014] In the preferred scheme, the monitoring mechanism comprises a fixed column connected with the sliding mechanism; The top of the fixed column is rotationally connected with a rotating sleeve, and the top of the fixed column is provided with a connecting sleeve located in the rotating sleeve; A plurality of bearings are arranged between the connecting sleeve and the rotating sleeve; The fixed column is internally provided with a second motor, and the output shaft of the second motor is connected with the rotating sleeve; The top of the rotating sleeve is provided with a connecting piece, and the top of the connecting piece is provided with a probe.

[0015] A wind turbine twisted cable platform cable wear monitoring method, which refers to a wind turbine twisted cable platform cable wear monitoring system, comprising the following steps: S1, data acquisition: the monitoring probe of the wear monitoring module acquires real-time cable related data, including cable surface image , environmental temperature , environmental humidity , salt fog concentration , cable cumulative running time , single continuous running time , cable cumulative rotation angle , single rotation angle and rotation frequency ; S2, data transmission: the data transmission module first transmits the collected data to the local data collector through Bluetooth 5.0, and then uploads it to the remote data processing and analysis module through 4G / 5G or industrial Ethernet, and adopts AES-256 encryption processing in the transmission process; S3, data preprocessing: the data processing and analysis module pre-processes the collected data, including: The image adopts median filter denoising, histogram equalization enhancement and defect area extraction based on threshold segmentation; The environmental data , , and motion data , Adopt Kalman filter to eliminate abnormal values, and perform standardization on time data , , ; S4, wear index calculation: calculate the wear index W based on the wear evaluation algorithm model, and the algorithm formula is as follows: ; Wherein: is an image feature wear factor, which is calculated by weighting the defect area ratio, scratch depth and texture roughness, the defect area ratio weight is 0.6, the scratch depth weight is 0.3, and the texture roughness weight is 0.1, the value range is [0, 100]; is an environmental impact factor, , k is an environmental impact coefficient, k=0.02 for offshore wind turbine and k=0.015 for land wind turbine, the value range is [0, 100]; is a time loss factor, , A is the designed service life of the cable, the unit is h, B is the single continuous operation threshold, the default is 200h, the value range is [0, 100]; is a motion wear factor, , C is the design rotation threshold, the default is 10000 times, the value range is [0, 100]; , , , is a weight coefficient, which satisfies ; S5, trend analysis and warning level determination: the data processing and analysis module performs linear regression analysis on the wear index W of the last n times, n≥30, to obtain the wear trend slope When >0.5, it is determined that the wear is accelerated; Combined with the wear index W, the preset threshold and the wear trend, the warning level is determined: 0-level warning: W<20, no matter the trend, it is determined that there is no hidden danger; 1-level warning: 20≤W<45 and ≤0.5, it is determined that the wear is mild; 2-level warning: 45≤W<70 or (20≤W<45 and >0.5), it is determined that the wear is moderate; 3-level warning: W≥70 or (45≤W<70 and >1.0), it is determined that the wear is severe; S6, early warning and feedback: the intelligent early warning module sends early warning information to the operation and maintenance personnel through SMS, APP push or system pop-up window, marks the fault position, wear index W, early warning level, remaining safe operation time and targeted maintenance suggestion, and stores the data to the cloud database, supports historical tracing and trend analysis.

[0016] The application provides a wind turbine twisted cable platform cable wear monitoring system and method, and has the following beneficial effects by adopting the above scheme: The guide rail is arranged around the cable, the sliding mechanism drives the probe to move circularly, and the 360-degree rotation monitoring is matched to realize multi-angle monitoring of the surface of the cable and solve the problem of limited monitoring range of the traditional monitoring.

[0017] The four-dimensional data of images, environment, time and motion are comprehensively used, the wear index is calculated by using a weighted algorithm model, different working conditions on land and at sea are adapted, and the accuracy of monitoring is improved compared with the prior art.

[0018] Based on the wear index and the trend slope, four levels of early warning are divided, the processing priority of different early warning levels is clear, multi-channel early warning push and accurate fault positioning are matched, the operation and maintenance response time is shortened, and excessive maintenance or omission of hidden dangers is avoided.

[0019] The guide rail is formed by splicing arc-shaped rails, special-shaped rails and straight rails, can be flexibly combined according to different specifications of twisted cable platforms and on-site equipment, does not need to modify the original structure of the wind turbine, and is convenient to install.

[0020] Automatic monitoring reduces labor cost. DETAILED DESCRIPTION

[0021] The application will be further described below in combination with the drawings and embodiments: Figure 1 It is a system schematic diagram of the application; Figure 2 It is a structural schematic diagram of the application; Figure 3 It is a top view of the application; Figure 4 It is a sectional view of the sliding mechanism of the application; Figure 5 It is a structural schematic diagram of the track connection of the application; Figure 6 It is a structural schematic diagram of the sliding mechanism of the application; Figure 7 It is a top view of the embodiment of the application.

[0022] In the drawings: Guide rail 1, track 101, arc rail 111, irregular rail 112, straight rail 113, movable groove 102, rack 103, threaded column 104, connecting plate 105, nut 106, connecting column 2, sliding mechanism 3, sliding seat 301, connecting groove 302, roller 303, first motor 304, gear 305, monitoring mechanism 4, fixed column 401, rotating sleeve 402, second motor 403, connecting sleeve 404, bearing 405, connector 406, probe 407, stabilizing mechanism 5, first fixed plate 501, second fixed plate 502, spring 503, movable plate 504, guide wheel 505. Detailed Implementation

[0023] Example 1: like Figure 1 As shown, a cable wear monitoring system for a wind turbine cable twisting platform includes a wear monitoring module, a data transmission module, a data processing and analysis module, and an intelligent early warning module. These modules work together to achieve full-process monitoring and early warning of cable wear. Among them: Wear monitoring module: The core function is to collect multi-dimensional data related to cable wear, including image data (cable surface images), environmental data (temperature, humidity, salt spray concentration), time data (cumulative running time, single continuous running time), and cable movement frequency data (cumulative rotation angle, single rotation angle, and number of rotations); providing basic data support for subsequent analysis. Data transmission module: It includes a local data acquisition unit, Bluetooth module, 4G / 5G module and industrial Ethernet interface to achieve encrypted transmission of acquired data.

[0024] The preferred local data acquisition unit model is DATA-9200, preferably using an ARM Cortex-M4 core and supporting local data caching.

[0025] The Bluetooth module preferably uses Bluetooth 5.0 (model HC-08) to establish short-range communication with the monitoring probe 407 and achieve real-time data transmission; the 4G / 5G module preferably uses EC200S and supports full network compatibility; the industrial Ethernet interface preferably uses RJ45-8P8C and supports 10 / 100Mbps auto-negotiation. Both can be used simultaneously to upload data to the remote data processing and analysis module.

[0026] The data transmission process uses AES-256 encryption to ensure data transmission security and prevent tampering or theft.

[0027] Intelligent early warning module: The short message module, the APP push interface, and the system pop-up module are connected with the data processing and analysis module, and when the wear index exceeds the preset threshold, a graded early warning information is sent.

[0028] The short message module supports China Mobile, China Unicom, and China Telecom, and the delay of sending a short message is less than or equal to 10s; the APP push interface supports Android and iOS systems, and the response time of pushing is less than or equal to 3s; the system pop-up module is integrated in the operation and maintenance management platform, and the pop-up prompt is not less than 30s, ensuring that the operation and maintenance personnel receive the early warning information in time.

[0029] The data processing and analysis module: The cloud server is adopted, and the CPU is preferably Intel Xeon E5-2680v4, and the wear evaluation model based on the random forest algorithm is built in, and functions such as data preprocessing, trend analysis, early warning level determination, model updating, etc. are integrated.

[0030] The server is installed with a Linux operating system, and is equipped with a Python data analysis environment, and integrates libraries such as OpenCV, Scikit-learn, and NumPy, and supports multi-thread parallel processing.

[0031] The module is built-in with a system self-checking program, which automatically detects the state of each module every hour, and supports cloud iterative updating of the algorithm model, and when the cumulative number of new sample data exceeds 500 groups, the model is automatically triggered for retraining, and the updated model is distributed to the local system through OTA.

[0032] The data processing and analysis module is built-in with a wear evaluation algorithm model based on multi-factor fusion, and the wear index W is obtained through weighted calculation, and the early warning level is determined in combination with the wear trend slope, as follows: Early warning level division: 0-level early warning: no safety hazard, no alarm, normal use of cable wear index W < 20; 1-level early warning: mild wear, potential safety hazard exists 20≤W < 45 and wear trend slope ≤0.5; 2-level early warning: moderate wear, greater hidden danger, need to be handled as soon as possible 45≤W < 70 or 20≤W < 45 and > 0.5; 3-level early warning: severe wear, high risk, need to stop immediately W≥70 or 45≤W < 70 and > 1.0; Advantages of the algorithm model: comprehensive image features, environmental impact, time loss, and motion wear four dimensions, and the weight coefficient is calibrated through a large number of experiments, and is preferably: , , , , by the environmental impact coefficient k adjustment, adaptation of different conditions on land / sea.

[0033] Wear monitoring module hardware structure, such as Figures 2-7 shown: Wear monitoring module includes installed on the guide rail 1, sliding mechanism 3, monitoring mechanism 4, the realization of the cable periodic, multi-angle full coverage monitoring.

[0034] Specifically, the guide rail 1 is spliced by a plurality of tracks 101, combined after the cable around the cable platform, adapt to different specifications of the cable platform. Track 101 includes arc rail 111, profiled rail 112 and straight rail 113, can be combined according to the structure of the cable platform, frame structure and other equipment position in the fan, while ensuring the guidance path, will not affect the normal operation of the fan equipment inside, adjacent track 101 connection place connection smooth, no jam.

[0035] Track 101 side is provided with a movable slot 102, the movable slot 102 is fixed with a rack 103; track 101 connection, inside the rack 103 seamless docking, to ensure smooth transmission. The side of the track 101 both ends is welded with a threaded column 104, the connecting plate 105 is arranged at the connecting place of the adjacent track 101, the two adjacent threaded columns 104 pass through the reserved hole of the same connecting plate 105, and are locked and fixed through the nut 106, the splicing precision deviation is less than or equal to 2 mm.

[0036] The shape of the rack 103 is matched with the movable slot 102, the plane at both ends is in the same plane with the corresponding plane at both ends of the track 101, so that the two racks 103 are connected closely after the stable connection of the two tracks 101, to ensure smooth transmission when the sliding mechanism 3 moves.

[0037] Further, the sliding mechanism 3 includes a sliding seat 301, a connecting groove 302 is formed in the sliding seat 301, the track 101 passes through the connecting groove 302, and the sliding seat 301 can slide along the track 101.

[0038] Four rollers 303 are rotatably connected in the sliding seat 301 through bearings, the rollers 303 are symmetrically distributed on the upper and lower sides of the track 101, and are used to reduce the sliding friction force; a first motor 304 is fixed on the top of the sliding seat 301 through bolts, the first motor 304 is preferably a stepping motor, and the model is preferably 57HS22. The first motor 304 is connected and controlled in the existing mode, the output shaft of the first motor 304 is connected with a gear 305 through a shaft coupling, the gear 305 extends to the inside of the connecting groove 302, and the gear 305 is engaged with the rack 103; In use, the first motor 304 is started to drive the gear 305 to rotate, thereby driving the gear 305 to move along the rack 103, and finally driving the sliding seat 301 to move along the track 101.

[0039] The second fixed plate 502 is welded on both sides of the sliding seat 301, and the first fixed plate 501 is rotationally connected to the second fixed plate 502 through a rotating shaft; the side of the first fixed plate 501 away from the second fixed plate 502 is fixed with the movable plate 504 through a bolt; the rotating shaft is located at the center of the space between the movable plate 504 and the sliding seat 301; two springs 503 are arranged between the movable plate 504 and the sliding seat 301, and the springs 503 are symmetrically distributed on both sides of the first fixed plate 501, for resetting the position of the movable plate 504; the bottom of the movable plate 504 is rotationally connected with two to four guide wheels 505 through bearings, and the guide wheels 505 are located on both sides of the track 101, for stabilizing the position of the movable plate 504 and ensuring smooth movement.

[0040] The monitoring mechanism 4 is also arranged on the movable plate 504, so that the monitoring mechanism 4 is arranged on both the movable plate 504 and the sliding seat 301, and multiple monitoring mechanisms 4 are adapted, further ensuring the accuracy and comprehensiveness of monitoring.

[0041] In movement, the sliding seat 301 slides along the track 101, and simultaneously moves through the first fixed plate 501 and the second fixed plate 502, thereby driving the movable plate 504 to move, and finally driving the multiple monitoring mechanisms 4 to move, completing the inspection; when encountering a curve in the movement process, the guide wheels 505 guide the movable plate 504 to rotate around the rotating shaft, so that the movement of the whole device is not affected at the curve position, and the rotating and resetting of the movable plate 504 can be adapted to the curve change through the restoring force of the spring 503 after movement, ensuring the stability of the device movement.

[0042] The monitoring mechanism 4 comprises a fixed column 401 welded on the top of the sliding seat 301; the top of the fixed column 401 is rotationally connected with a rotating sleeve 402, and the top of the fixed column 401 is welded with a connecting sleeve 404 located inside the rotating sleeve 402; one to two bearings 405 are arranged between the connecting sleeve 404 and the rotating sleeve 402, to reduce the rotating friction and ensure smooth rotation.

[0043] A second motor 403 is fixed in the fixed column 401 by bolts, preferably a servo motor, and the model is preferably SGMAH-01AAA41. The output shaft of the second motor 403 is connected with the rotating sleeve 402 through a shaft coupling, driving the rotating sleeve 402 to rotate 360°. The top of the rotating sleeve 402 is welded with a connecting piece 406, and the top of the connecting piece 406 is fixed with a probe 407 by bolts. The probe 407 preferably includes a high-definition camera, a temperature and humidity sensor, a salt mist sensor, and an angle sensor. The camera pixel is ≥13 million, the temperature measurement range is -40℃-85℃, the humidity measurement range is 0-100%RH, the salt mist concentration measurement range is 0-20mg / m³, and the angle measurement accuracy is ±0.5°. The probe 407 moves with the sliding mechanism 3 and the rotating sleeve 402 rotates, realizing the monitoring of the cable surface.

[0044] Embodiment 2: A cable wear monitoring method for a wind turbine twisted cable platform, which refers to a wind turbine twisted cable platform cable wear monitoring system, comprising the following steps: S1: data acquisition: Start the wear monitoring module, and the sliding mechanism 3 moves along the guide rail 1 in a cycle. The moving period can be set to 1-24 hours / time, and the monitoring mechanism 4 is adjusted according to the running intensity of the wind turbine. The probe 407 of the monitoring mechanism 4 can rotate at multiple angles during the movement, and real-time multi-dimensional data is collected: image data : high-definition image of the cable surface, collection frequency 1 frame / s, resolution 1920×1080, covering the full circumference of the cable; environmental data , , : environmental temperature, accuracy ±0.5℃; relative humidity, accuracy ±2%RH; salt mist concentration, accuracy ±0.1mg / m³; collect once every 5-30 minutes; time data , : cumulative running time of the cable, accurate to seconds; single continuous running time, from the last shutdown to the current time; motion data , , : cumulative rotation angle of the cable; single rotation angle - twist angle of each yaw; rotation times - yaw times; Real-time synchronization of wind turbine yaw system data.

[0045] S2: data transmission: The data transmission module preliminarily sorts the collected raw data, and then transmits the raw data to a local data collector through Bluetooth 5.0 to realize offline storage backup, and then transmits the raw data to a remote data processing and analysis module through a 4G / 5G network or an industrial Ethernet, and an AES-256 encryption algorithm is used in the transmission process to prevent data leakage or tampering.

[0046] S3: Data preprocessing: After the data processing and analysis module receives the data, the data is preprocessed to improve the data quality: Image preprocessing: for cable images Median filtering is used to remove noise, and the window size is 3x3. The image contrast is enhanced by histogram equalization to highlight the wear defect area. Then, the area, scratch depth, and texture roughness of the cable surface defect area are extracted based on the adaptive threshold segmentation algorithm. Numerical preprocessing: for environmental data 、 、 and motion data 、 Kalman filtering is used to eliminate outliers such as sudden temperature changes and angle measurement errors. Time data 、 、 is standardized to ensure data consistency.

[0047] S4, wear index calculation: the wear index W is calculated based on the wear evaluation algorithm model, and the algorithm formula is as follows: ; wherein: is the image feature wear factor, which is calculated by weighting the defect area ratio, scratch depth, and texture roughness. The defect area ratio weight is 0.6, the scratch depth weight is 0.3, and the texture roughness weight is 0.1. The value range is [0, 100]; The defect area ratio = wear defect area in a single monitoring area / total surface area of the monitoring area x 100%. The defect area pixel area is calculated by the contour detection function (cv2.findContours) of OpenCV, and the actual area is converted by combining the camera calibration parameters; The scratch depth is estimated by image gray value gradient analysis, and the formula is: scratch depth = k_depth x (standard gray value - average gray value of defect area), where k_depth is the depth calibration coefficient (0.001 mm per gray level after experimental calibration), and the standard gray value is the average gray value of the non-worn cable surface, which is 220 by default. The texture roughness is obtained by calculating the energy value (ASM) and the entropy value (ENT) of the gray level co-occurrence matrix, and is normalized to obtain the formula: texture roughness = 0.5 * (1-ASM) + 0.5 * ENT, the value range is [0, 1], and then is mapped to the interval [0, 100]; The environmental impact factor is used to quantify the acceleration effect of temperature, humidity and salt spray on cable wear, and the formula is: ; In the formula, k is an environmental impact coefficient, when applied to offshore wind turbines, k = 0.02; when applied to land-based wind turbines, k = 0.015, is the environmental temperature, unit: ℃; is the relative humidity, unit: %RH; is the salt spray concentration, unit: mg / m³, when applied to land-based wind turbines = 0, the value range is [0, 100]; when the environmental parameters exceed the normal working range of the cable, such as temperature < -40℃ or > 85℃, humidity > 98%RH, 80 is automatically taken, triggering an environmental abnormality auxiliary warning; The time loss factor is used to reflect the cumulative effect of the cable running time on wear, and the formula is: ; In the formula, is the cumulative running time of the cable, unit: h; A is the designed service life of the cable, unit: h, which is input by the user, and the range is [20000, 80000], is the single continuous running time, unit: h, B is the single continuous running threshold, default 200h, and the adjustable range is [100, 500], and the value is limited to not more than 100 through the min function to avoid excessive amplification of time impact; The motion wear factor is used to quantify the mechanical wear caused by the twisting motion of the cable, and the formula is: ; In the formula, is the cumulative rotation angle, unit: °; is the single rotation angle, unit: °; is the cumulative rotation number, unit: times; C is the designed rotation number threshold, default 10000 times, and the adjustable range is [5000, 20000], and the value range is limited to [0, 100] through the min function; 36000° corresponds to 100 rotations of the cable, and 180° is the critical angle of single twisting, and the mechanical wear increases exponentially when the angle exceeds the critical angle; is a weight coefficient, which satisfies , dynamically adapt according to the cable specification, the specific adaptation rules are as follows:

[0048] The user can manually adjust the weight coefficient through the operation and maintenance management platform, the adjustment range is ±0.05, which meets the monitoring demand under special working conditions.

[0049] S5. Trend analysis and early warning level determination: Trend analysis: the data processing and analysis module performs linear regression analysis on the wear index W of the last n times, n≥30, and uses the least squares method to fit the wear trend curve to obtain the trend slope (unit: / day), the formula is: ; In the formula, is the time stamp of the i-th monitoring, unit: day, taking the first monitoring time as the starting point, is the wear index of the i-th monitoring; to avoid the influence of abnormal values, 3σ principle screening is performed on , and the abnormal data exceeding [μ-3σ, μ+3σ] (μ is the mean value and σ is the standard deviation) is removed before fitting; when >0.5, it is determined that the wear is accelerated, and the starting time and triggering factors of the accelerated wear, such as environmental changes and increased movement frequency, are recorded simultaneously; Early warning level determination: combined with the wear index W, the trend slope and the preset threshold, the early warning level is determined, and the specific determination rules are as follows: 0-level warning (green): W<20, regardless of the trend, it is determined that there is no safety hazard, no alarm is needed, and the cable is running normally; the system generates a regular operation report every 7 days to feedback the cable state; 1-level warning (yellow): 20≤W<45 and ≤0.5, it is determined that the wear is mild, and there is a potential safety hazard; the system shortens the inspection cycle to 15 minutes / time, and pushes the state tracking information every 24 hours, suggesting that the operation and maintenance personnel review it every 7 days; 2-level warning (orange): 45≤W<70 or (20≤W<45 and >0.5), it is determined that the wear is moderate, and the safety hazard is high; the system adjusts the inspection cycle to 5 minutes / time, real-time tracks the wear trend, and requires the operation and maintenance personnel to complete the repair plan within 72 hours, including spare parts preparation and repair scheme development; 3-level warning (red): W≥70 or (45≤W<70 and >1.0), it is determined that the wear is severe, and there is an immediate safety risk; the system immediately triggers an audible and light alarm, a field alarm is required, the warning information is forced to be sent to all related operation and maintenance personnel, and the fan is required to be shut down for processing within 24 hours, and the fan is prohibited from starting before processing.

[0050] After determining the warning level, the system automatically associates the historical maintenance records, and if the cable has similar wear failure records, the historical processing scheme is pushed synchronously as a reference.

[0051] Remaining safe operation time calculation: Based on the wear trend slope , the remaining safe operation time T of the cable is calculated, in days, and the formula is as follows: ; Wherein: is the critical wear index corresponding to the warning level, 1st warning =45, 2nd warning =70, 3rd warning =100; W is the current wear index; is the daily wear trend slope, which is converted from the linear regression slope of the last n wear indexes to the daily change value; Special case processing: When ≤0, it is determined that the wear is stable or slowed down, and the remaining safe operation time T=(A-a5) / 24, i.e. the remaining design service life is converted to days, if T>365, it is displayed as “more than 1 year”; When 45≤W<70 and >1.0, T_max is automatically taken as 100, and the severe wear risk is urgently evaluated; When the calculation result T<0, it is forced to display “0.0 days”, and the highest level of warning is triggered, and the fan start permission is locked, which needs to be manually unlocked by the operation and maintenance personnel; The output result of the remaining safe operation time T is kept to one decimal place, and the confidence is labeled simultaneously, based on the R² value of the regression analysis, R²≥0.8 is labeled as “high confidence”, 0.6≤R²<0.8 is labeled as “medium confidence”, and R²<0.6 is labeled as “low confidence”, and it is suggested to increase the monitoring frequency.

[0052] S6. Warning information sending and data storage: The intelligent warning module sends warning information to operation and maintenance personnel through multiple channels according to the determined warning level, and the information contains the following core content: Basic information: fan number, twisted cable platform location, cable number, fault location; fault location is based on guide rail positioning coordinates, accuracy ±5 cm; State data: wear index W, factor decomposition value (W1-W4), environmental parameters (a2-a4), motion parameters (a7-a9); Early warning details: early warning level, trend slope , remaining safe operation time T and confidence, processing time limit requirement; Maintenance suggestion: targeted scheme for different wear types, such as "image feature wear is the main, it is suggested to check the friction between cable and surrounding parts" "environmental impact is significant, it is suggested to strengthen sealing and protection"; Accessories: cable wear defect image, labeled defect area, size; the last 30 times wear index trend chart; Sending channel and rules: SMS notification: sent to the mobile phone numbers of 2-3 core operation and maintenance personnel, 1st level warning re-sent every 12 hours, 2nd level warning re-sent every 6 hours, 3rd level warning re-sent every 1 hour, until confirmed receipt; APP push: synchronized to the operation and maintenance management APP, supports online receipt, message feedback, work order creation, 3rd level warning pop-up window, which needs to be manually confirmed to have been read; System pop-up window: pop-up warning prompt in the operation and maintenance management platform-web, different levels correspond to different color marks, pop-up window cannot be manually closed until "confirmation processing" operation is completed; Data storage: the data processing and analysis module stores the full amount of data of this monitoring to the cloud database, using MySQL+MongoDB hybrid storage architecture: MySQL stores structured data: monitoring timestamp, parameter value, wear index, early warning level, remaining operation time, etc., supports quick query by fan number, cable number, and time range; MongoDB stores unstructured data: cable surface image, video clip, fault diagnosis report, etc., uses distributed storage, data replica number ≥3, ensures data safety; Data retention rules: raw monitoring data is retained for ≥5 years, trend analysis results and early warning records are permanently retained, supports historical data tracing and same type cable wear condition comparative analysis, provides data support for wind turbine operation and maintenance optimization.

[0053] System self-check and fault handling: System self-check: the data processing and analysis module automatically performs system self-check every hour, covering hardware status, communication link, and software function three dimensions, specific detection items are as follows: Wear monitoring module: Mechanical structure: detect the running current and temperature rise of the first motor 304 and the second motor 403, and the meshing state of the gear and the rack; Sensor and probe: detect the imaging clarity of the camera, the stability of temperature, humidity, and salt spray sensor data, and the zeroing accuracy of the angle sensor; Data transmission module: Bluetooth link: detect connection stability and transmission rate; Remote transmission: detect 4G / 5G signal strength and industrial Ethernet connectivity, and test data upload success rate; Software function: detect algorithm model running state, early warning information sending channel effectiveness, and database writing speed; Fault classification and processing: Minor fault, no impact on monitoring function: such as "occasional interruption of Bluetooth transmission" and "single sensor data fluctuation", the system automatically records fault log and sends a prompt through the APP, and suggests troubleshooting during subsequent maintenance; General fault, partial function affected: such as "camera imaging blur" and "weak 4G module signal", the system switches to the backup transmission channel (such as Bluetooth to industrial Ethernet) and reduces the precision of non-core functions, while sending fault alarm, which requires processing within 24 hours; Serious fault, monitoring function cannot be normally implemented: such as "first motor stuck" and "monitoring probe has no data output", the system immediately triggers fault warning, suspends regular inspection, starts emergency monitoring mode, and collects key data every 15 minutes at fixed points, which requires on-site processing within 4 hours; Fault repair verification: after the operation and maintenance personnel complete the processing, the "fault repair verification" can be initiated through the APP, the system automatically performs targeted detection, and after the detection is passed, the normal operation mode is restored, and if it is not passed, the warning is continued.

[0054] The above embodiments are only preferred technical solutions of the present application, and should not be regarded as a limitation of the present application. The protection scope of the present application should be based on the technical solutions claimed in the claims, including equivalent replacement solutions of the technical features claimed in the claims. That is, within this range, equivalent replacement improvements are also within the protection scope of the present application.

Claims

1. A cable wear monitoring system for a wind turbine cable twisting platform, characterized in that: It includes a wear monitoring module, a data transmission module, a data processing and analysis module, and an intelligent early warning module; The wear monitoring module is used to monitor cable wear data on the wind turbine cable twisting platform; The data transmission module is used to transmit data to the data processing and analysis module; The data processing and analysis module is used to receive and analyze data, determine the cable wear index, and control the intelligent early warning module to issue an early warning message when the wear index is greater than the preset value.

2. The cable wear monitoring system for a wind turbine cable twisting platform according to claim 1, characterized in that: The data processing and analysis module has a built-in algorithm model for processing and calculating the acquired data to obtain the wear index, and obtains the warning level based on the wear index and preset value. The warning levels include Level 0, Level 1, Level 2, and Level 3. Level 0 warning indicates no safety hazards, no alarm is needed, and normal use is permitted. A Level 1 warning indicates minor wear and tear, posing a potential safety hazard. Level 2 warning indicates moderate wear and tear, suggesting significant potential risks that require prompt action; Level 3 warning indicates severe wear and tear, requiring immediate action.

3. The cable wear monitoring system for a wind turbine cable twisting platform according to claim 1, characterized in that: The cable wear monitoring module monitors data including images, environmental data, time data, and cable movement frequency data. Image data includes cable images, environmental data includes temperature and humidity, and cable movement frequency data includes the angle and number of times the cable follows rotation.

4. The cable wear monitoring system for a wind turbine cable twisting platform according to claim 1, characterized in that: The wear monitoring module includes monitoring equipment located on the cable twisting platform, used for periodic monitoring of cable wear data from multiple angles.

5. The cable wear monitoring system for a wind turbine cable twisting platform according to claim 1, characterized in that: The wear monitoring module includes a guide rail (1) connected to the wind turbine cable twisting platform via a connecting column (2); A sliding mechanism (3) is provided on the guide rail (1), and the sliding mechanism (3) slides along the guide rail (1); The sliding mechanism (3) is equipped with a monitoring mechanism (4), which moves with the sliding mechanism (3) to monitor cable wear data.

6. The cable wear monitoring system for a wind turbine cable twisting platform according to claim 5, characterized in that: The guide rail (1) includes several rails (101), which are combined to surround the cable of the cable twisting platform; The track (101) has a movable groove (102) on its side, and a rack (103) is provided in the movable groove (102); After the track (101) is connected, the rack (103) inside the track (101) is connected; The sides of both ends of the track (101) are provided with threaded posts (104), and the connection of adjacent tracks (101) is provided with connecting plates (105). Two adjacent threaded posts (104) pass through the same connecting plate (105). The threaded column (104) is threaded with a nut (106). The track (101) includes an arc track (111), an irregular track (112), and a straight track (113). The shape of the rack (103) is adapted to the movable groove (102), and the shapes of both ends are adapted to each other, so that it can be smoothly transmitted after being connected to another rack (103); The connection between adjacent tracks (101) is smooth.

7. The cable wear monitoring system for a wind turbine cable twisting platform according to claim 6, characterized in that: The sliding mechanism (3) includes a sliding seat (301), a connecting groove (302) is provided in the sliding seat (301), and the slide rail (101) passes through the connecting groove (302); The sliding block (301) slides along the slide rail (101); The sliding seat (301) is provided with several rollers (303), which are rotatably connected to the sliding seat (301) and connected to the slide rail (101). A first motor (304) is provided on the top of the sliding seat (301); The output shaft of the first motor (304) is provided with a gear (305) that extends into the interior of the connecting groove (302) and meshes with the rack (103).

8. The cable wear monitoring system for a wind turbine cable twisting platform according to claim 7, characterized in that: The sliding seat (301) is provided with a second fixing plate (502) on both sides, and the second fixing plate (502) is rotatably connected to the first fixing plate (501) through a rotating shaft; The first fixed plate (501) is provided with a movable plate (504); Several springs (503) are provided between the movable plate (504) and the sliding seat (301); the springs (503) are located on both sides of the first fixed plate (501); The bottom of the movable plate (504) is provided with several guide wheels (505), which are located on both sides of the track (101); The top of the movable panel (504) is equipped with a monitoring mechanism (4).

9. A cable wear monitoring system for a wind turbine cable twisting platform according to any one of claims 5-8, characterized in that: The monitoring mechanism (4) includes a fixed column (401) connected to the sliding mechanism (3); The top of the fixed column (401) is rotatably connected to a rotating sleeve (402), and the top of the fixed column (401) is provided with a connecting sleeve (404) located inside the rotating sleeve (402). Several bearings (405) are provided between the connecting sleeve (404) and the rotating sleeve (402); A second motor (403) is installed inside the fixed column (401), and the output shaft of the second motor (403) is connected to the rotating sleeve (402); The top of the rotating sleeve (402) is provided with a connector (406), and the top of the connector (406) is provided with a probe (407).

10. A method for monitoring cable wear on a wind turbine cable twisting platform, characterized in that: A cable wear monitoring system for a wind turbine cable twisting platform according to any one of claims 1-9 includes the following steps: S1. Data Acquisition: Real-time acquisition of cable-related data, including cable surface images, is achieved through the monitoring probe (407) of the wear monitoring module. Ambient temperature Ambient humidity Salt spray concentration Cable cumulative operating time Single continuous run time The cumulative rotation angle of the cable Single rotation angle and number of rotations ; S2. Data transmission: The data transmission module first transmits the collected data to the local data acquisition unit via Bluetooth 5.0, and then uploads it to the remote data processing and analysis module via 4G / 5G or industrial Ethernet. AES-256 encryption is used during the transmission process. S3. Data Preprocessing: The data processing and analysis module preprocesses the collected data, including: For images Median filtering for noise reduction, histogram equalization for enhancement, and threshold-based segmentation for defect region extraction are employed. Environmental data , , and sports data , Kalman filtering is used to eliminate outliers in time data. , , Standardize the process; S4. Wear Index Calculation: The wear index W is calculated based on the wear assessment algorithm model. The algorithm formula is as follows: ； in: The wear factor is an image feature, which is calculated by weighting the defect area ratio, scratch depth and texture roughness. The defect area ratio has a weight of 0.6, the scratch depth has a weight of 0.3, and the texture roughness has a weight of 0.

1. The value range is [0,100]. As environmental impact factors, k is the environmental impact coefficient; The time loss factor, A represents the cable's design service life in hours (h), and B represents the single continuous operation threshold, which defaults to 200 hours and ranges from 0 to 100. For motion wear factor, C is the threshold for the number of rotations, which defaults to 10,000 and has a value range of [0, 100]. , , , Let be the weighting coefficient, satisfying ; S5. Trend Analysis and Early Warning Level Determination: The data processing and analysis module performs linear regression analysis on the wear index W for the most recent n times, where n≥30, to obtain the wear trend slope. ,when A value greater than 0.5 is considered accelerated wear. By combining the wear index W with the preset threshold and wear trend, the warning level is determined: Level 0 warning: W < 20, regardless of the trend, it is judged as no hidden danger; Level 1 warning: 20 ≤ W < 45 and ≤0.5, is considered slight wear; Level 2 warning: 45≤W<70 or (20≤W<45 and >0.5), which is considered moderate wear; Level 3 warning: W ≥ 70 or (45 ≤ W < 70 and >1.0), indicating severe wear; S6. Early Warning and Feedback: The intelligent early warning module sends early warning information to maintenance personnel via SMS, APP push or system pop-up, indicating the fault location, wear index W, early warning level, remaining safe operating time and targeted maintenance suggestions. At the same time, it stores the data in the cloud database, supporting historical traceability and trend analysis.

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