Vibration detection and analysis system in transportation process of wind driven generator

Abstract

The invention relates to a wind driven generator blade detection system, and relates to the technical field of transportation safety monitoring, and the system comprises a vibration sensing module which is disposed on a blade, a cylinder, and a motor of a wind driven generator, and is used for collecting the vibration information of the blade, the cylinder, and the motor in real time in a transportation process; the vibration data analysis module is used for receiving the vibration information, analyzing the vibration information, outputting an analysis result and outputting an instruction A or an instruction B according to the analysis result; the camera linkage module is mounted on the transport vehicle and is used for receiving the instruction A, collecting image information and video information, receiving the instruction B and keeping closing; the storage module is used for receiving and storing the vibration information, the analysis result, the image information and the video information; and the cloud platform module is used for receiving the analysis result, the image information and the video information, generating a related report, receiving historical vibration information and generating a transportation vibration thermodynamic diagram.

Description

Technical Field

[0001] This application relates to the field of transportation safety monitoring technology, and in particular to a vibration detection and analysis system for wind turbines during transportation. Background Technology

[0002] As large-scale new energy equipment, wind turbine generators have key components such as blades, nacelles, generators, and towers that are large in size and heavy in weight. During long-distance transportation, they are often affected by factors such as uneven road surfaces, sudden braking, turning, and insecure loading, resulting in vibration and impact. Since the transportation routes often involve mountainous areas, urban and rural roads, or even a combination of sea and land transportation, the transportation environment is complex and changeable, which can easily cause collisions, friction, or abnormal stress on support points between equipment, leading to blade edge damage, shell cracking, or even structural hazards.

[0003] In existing technologies, simple vibration recording is typically achieved using accelerometers, or observation relies on manual inspections and fixed monitoring devices. However, these methods have the following shortcomings: First, simple vibration data lacks intuitiveness, making it difficult to accurately determine the impact point and damage location; second, traditional surveillance cameras cannot be linked to vibration events to continuously record when no abnormalities occur, resulting in wasted storage and energy consumption, and also making it difficult to efficiently retrieve the moment of the incident; third, existing data is mostly stored locally, lacking cloud synchronization and unified management, which is not conducive to post-incident tracing and liability determination.

[0004] Based on the above problems, there is an urgent need for a system that can collect vibration data in real time during the transportation of wind turbine generator sets, actively trigger image acquisition when abnormalities occur, realize the association and storage of data and images, and upload them to a cloud platform, so as to effectively reduce transportation losses and provide a complete and reliable chain of evidence for accident tracing. Summary of the Invention

[0005] To address the limitations of the aforementioned technologies, the present invention aims to provide a vibration detection and analysis system for wind turbines during transportation. This system can detect vibrations during transportation in real time using a portable vibration detector. When the vibration data exceeds a set threshold, it automatically triggers a camera to capture images or videos, achieving data and image binding and recording, and uploading to a cloud platform. This reduces equipment damage caused by collisions during transportation and forms a complete chain of accident evidence, providing strong support for subsequent tracing and liability determination.

[0006] To solve the above problems, the technical solution adopted by the present invention is: to provide a vibration detection and analysis system during the transportation of wind turbines, comprising: The vibration sensing module is installed on the blades, cylinder, and motor of the wind turbine to collect vibration information of the blades, cylinder, and motor in real time during transportation. The vibration data analysis module is connected to the data output terminal of the vibration sensing module. It is used to receive vibration information, analyze vibration information and output analysis results, and output command A or command B according to the analysis results. The camera linkage module is installed on the transport vehicle and is connected to the data output terminal of the vibration data analysis module. It is used to receive instruction A and collect image and video information, and to receive instruction B and keep it off. The storage module is connected to the data output terminals of the vibration sensing module, vibration data analysis module, and camera linkage module, and is used to receive and store the vibration information, analysis results, image information, and video information. The cloud platform module is connected to the data output terminals of the vibration sensing module, vibration data analysis module, and camera linkage module. It is used to receive the analysis results, image information, and video information and generate relevant reports, as well as to receive historical vibration information and generate a transportation vibration heat map.

[0007] Preferably, the vibration sensing module includes a triaxial vibration detector fixedly installed on the blade, cylinder, and motor, and the data output terminal of the triaxial vibration detector is connected to the data input terminal of the vibration data analysis module, storage module, and cloud platform module.

[0008] Preferably, the camera linkage module includes two fixed cameras, which are respectively fixedly installed on the inner walls of both sides of the transport vehicle; The data input terminals of both fixed cameras are connected to the data output terminals of the vibration data analysis module, and the data output terminals of both fixed cameras are connected to the data input terminals of the storage module and the cloud platform module.

[0009] Preferably, the analysis method of the vibration data analysis module includes the following steps: Step S11: Collect vibration information of blades, cylinder or motor according to the set frequency. The vibration information includes amplitude data and vibration duration data. Step S12: Preset a first amplitude threshold, a second amplitude threshold, and a duration threshold. The second amplitude threshold is greater than the first amplitude threshold. Analyze the vibration information. If the amplitude data is greater than the first amplitude threshold and less than the second amplitude threshold, and the vibration duration data is greater than the duration threshold, then output command A. If the amplitude data is greater than the second amplitude threshold, output command A regardless of the vibration duration data. If the amplitude data is less than the first amplitude threshold, output command B. If the amplitude data is greater than the first amplitude threshold and the vibration duration data is less than the duration threshold, then output command B.

[0010] Preferably, the method by which the cloud platform module outputs a transportation vibration heat map based on vibration data includes the following steps: Step S21: Data preprocessing, including data cleaning and data distribution analysis; Step S22: Define the risk assessment function; Step S23: Calculate the risk value for each vibration data point according to the risk assessment function; Step S24: Data binning and risk value aggregation, dividing the continuous space into cells and calculating the aggregated risk value in each cell; Step S25: Generate a heat map; Step S26: Compare the heat map with the recorded image and video information to check whether the heat map display matches the actual vibration situation.

[0011] Preferably, step S21 includes the following steps: Step S211, data cleaning, including: Missing value handling: Check whether there are missing values ​​in the amplitude data and vibration duration data. If there are missing values, perform appropriate interpolation. Outlier handling: Identify and handle extremely large or small outliers caused by errors in the triaxial vibration meter; Step S212: Data distribution analysis, draw histograms of amplitude data and vibration duration data respectively.

[0012] Preferably, step S22 includes the following steps: Step S221: Perform Min-Max normalization on the amplitude data and vibration duration data to obtain the normalized amplitude value A_norm and the normalized vibration duration value D_norm. Step S222: Through function model Calculate the vibration risk value, where R represents the risk value of the vibration data, α represents the weighting index of the amplitude, and β represents the weighting index of the vibration duration. Step S223: Determine the function parameters and determine the values ​​of α and β based on prior knowledge.

[0013] Preferably, step S24 includes the following steps: Step S241: Establish a coordinate system in a continuous space. The X-axis of the coordinate system represents the duration of vibration, and the Y-axis represents the amplitude. Divide the X-axis into N_d intervals and the Y-axis into N_a intervals. Determine the values ​​of N_d and N_a according to the number of bins, and divide the vibration data into bins according to the strategy of equal-interval bins. Step S242: Risk value aggregation. For each cell in the coordinate system, find all data points that fall into the cell, aggregate the risk values ​​of these data points, and take the average risk value of the data points in the cell as the cell risk value. Step S243: Establish the risk matrix H_risk based on the risk values ​​of the cells in the coordinate system.

[0014] Preferably, step S25 includes the following steps: Step S251: Color mapping. The aggregated risk matrix H_risk is mapped to colors. A divergent color band is used to map each cell. The direction of red-yellow-blue indicates the level of risk, with red being high risk and green being low risk. Step S252: Smooth the H_risk matrix using Gaussian filtering to make the heatmap more aesthetically pleasing; Step S253: Draw a heatmap using pcolormesh, add color bars, and add titles and axis labels to clearly explain the correspondence between colors and risk values, ensuring the completeness of the chart information. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of Embodiment 1 of this application, illustrating the data transmission between the vibration sensing module, vibration data analysis module, camera linkage module, storage module, and cloud platform module.

[0016] Figure 2 This is a logic diagram of Embodiment 1 of this application used to illustrate the analysis of vibration information during the transportation of wind turbines. Detailed Implementation

[0017] The following combination Figures 1-2 This application will be described in further detail.

[0018] Example 1 This application discloses a vibration detection and analysis system for wind turbines during transportation. (Refer to...) Figure 1 It mainly includes a vibration sensing module, a vibration data analysis module, a camera linkage module, a storage module, and a cloud platform module.

[0019] The vibration sensing module includes multiple triaxial vibration detectors, which are fixedly installed on the blades, casing, and motor of the wind turbine. These detectors can collect vibration information from the blades, casing, and motor in real time during the transport of the wind turbine. The vibration information includes amplitude data and vibration duration data.

[0020] The data input terminal of the vibration data analysis module is wirelessly connected to the data output terminal of the vibration sensing module. It is used to receive vibration information, analyze vibration information, output analysis results, and output command A or command B based on the analysis results.

[0021] The camera linkage module consists of two fixed cameras, which are respectively fixedly installed on the inner walls of both sides of the vehicle. The data input terminals of the two fixed cameras are connected to the data output terminals of the vibration data analysis module. When the vibration data analysis module outputs command A, the two fixed cameras start and collect image and video information of the wind turbine blades, cylinder, or motor; when the vibration data analysis module outputs command B, both fixed cameras remain in the off state.

[0022] The data input end of the storage module is wirelessly connected to the data output ends of the vibration sensing module, vibration data analysis module, and camera linkage module to receive and store vibration information, analysis results, image information, and video information. Storing vibration information, analysis results, image information, and video information during transportation through the storage module facilitates subsequent data traceability.

[0023] The data input end of the cloud platform module is connected to the data output ends of the vibration sensing module, vibration data analysis module, and camera linkage module via wireless data connection. It is used to receive analysis results, image information, and video information and generate relevant reports, receive vibration information and generate transportation vibration heat map, and provide decision-making basis for transportation route optimization and protection measure improvement.

[0024] In this embodiment, the analysis method of the vibration data analysis module includes the following steps: Step S11: Collect vibration information of blades, cylinder or motor according to the set frequency. The vibration information includes amplitude data and vibration duration data. Step S12: Preset a first amplitude threshold, a second amplitude threshold, and a duration threshold. The second amplitude threshold is greater than the first amplitude threshold. Analyze the vibration information. If the amplitude data is greater than the first amplitude threshold and less than the second amplitude threshold, and the vibration duration data is greater than the duration threshold, then output command A. If the amplitude data is greater than the second amplitude threshold, output command A regardless of the vibration duration data. If the amplitude data is less than the first amplitude threshold, output command B. If the amplitude data is greater than the first amplitude threshold and the vibration duration data is less than the duration threshold, then output command B.

[0025] Reference Figure 2During actual transportation, the vibration sensing module uses a triaxial vibration detector to sense the vibration information of the wind turbine blades, cylinder, and motor, and outputs amplitude data and vibration duration data. The vibration data analysis module compares the amplitude data with a first amplitude threshold and a second amplitude threshold, and compares the vibration duration data with a duration threshold. When the amplitude data is greater than the first amplitude threshold, less than the second amplitude threshold, and the vibration duration data is greater than the duration threshold, the vibration data analysis module outputs command A. When the amplitude data is greater than the second amplitude threshold, regardless of the vibration duration data value, the vibration data analysis module outputs command A. When the amplitude data is greater than the first amplitude threshold, and the vibration duration data is less than the duration threshold, the vibration data analysis module outputs command B. When the amplitude data is less than the first amplitude threshold, regardless of the vibration duration value, the vibration data analysis module outputs command B.

[0026] The above settings can reduce the false alarm rate and improve the sensitivity of identifying abnormal vibrations. When used with two fixed cameras, image and video information can be collected only when the blades, cylinder and / or motor of the wind turbine are vibrating abnormally. When there is no abnormal vibration in the blades, cylinder and motor of the wind turbine, the two fixed cameras are kept off, which can significantly reduce the amount of data and save the memory occupied by storing image and data information.

[0027] Example

[0028] In this embodiment, the method for the cloud platform module to transport vibration thermal maps based on vibration data includes the following steps: Step S21: Data preprocessing, including data cleaning and data distribution analysis; Step S22: Define the risk assessment function; Step S23: Calculate the risk value for each vibration data point according to the risk assessment function; Step S24: Data binning and risk value aggregation, dividing the continuous space into cells and calculating the aggregated risk value in each cell; Step S25: Generate a heat map; Step S26: Compare the heat map with the recorded image and video information to check whether the heat map display matches the actual vibration situation.

[0029] Step S21 includes the following steps: Step S211, data cleaning, including: Missing value handling: Check whether there are missing values ​​in the amplitude data and vibration duration data. If there are missing values, perform appropriate interpolation. Outlier handling: Identify and handle extremely large or small outliers caused by errors in the triaxial vibration meter; Step S212: Data distribution analysis, draw histograms of amplitude data and vibration duration data respectively.

[0030] By cleaning the amplitude and vibration duration data, missing data can be interpolated, and outliers caused by errors in the triaxial vibration meter can be identified and removed, ensuring the accuracy of subsequent data processing. Data distribution analysis of the amplitude and vibration duration data reveals the data's range and central tendency, providing a basis for subsequent binning.

[0031] Step S22 includes the following steps: Step S221: Perform Min-Max normalization on the amplitude data and vibration duration data to obtain the normalized amplitude value A_norm and the normalized vibration duration value D_norm. Step S222: Through function model Calculate the vibration risk value, where R represents the risk value of the vibration data, α represents the weighting index of the amplitude, and β represents the weighting index of the vibration duration. Step S223: Determine the function parameters. Based on prior knowledge, determine the values ​​of α and β. If there is no prior knowledge, historical failure data can be analyzed, and a regression model can be used to fit the parameters α and β that best assess the risk. This will not be limited or elaborated here.

[0032] The risk assessment function can be used to merge amplitude data and vibration duration data into a risk value with uniform dimensions. Since the unit difference between amplitude data and vibration duration data can be huge, normalization of the amplitude data and vibration duration data can prevent data explosion and data loss.

[0033] Step S24 includes the following steps: Step S241: Establish a coordinate system in a continuous space. The X-axis of the coordinate system represents the duration of vibration, and the Y-axis represents the amplitude. Divide the X-axis into N_d intervals and the Y-axis into N_a intervals. Determine the values ​​of N_d and N_a according to the number of bins, and divide the vibration data into bins according to the strategy of equal-interval bins. By dividing the X-axis into N_d intervals and the Y-axis into N_a intervals, an N_d*N_a grid can be established in the coordinate system, with each grid cell corresponding to a colored pixel block in the heatmap; Step S242: Risk value aggregation. For each cell in the coordinate system, find all data points that fall into the cell, aggregate the risk values ​​of these data points, and take the average risk value of the data points in the cell as the cell risk value. Step S243: Establish a risk matrix H_risk based on the risk values ​​of cells in the coordinate system; H_risk is a two-dimensional matrix that stores the aggregated risk values ​​of all vibration data points in each grid cell divided by the Y-axis and X-axis.

[0034] Step S25 includes the following steps: Step S25 includes the following steps: Step S251: Color mapping. The aggregated risk matrix H_risk is mapped to colors. A divergent color band is used to map each cell. The direction of red-yellow-blue indicates the level of risk, with red being high risk and green being low risk. Step S252: Smooth the H_risk matrix using Gaussian filtering to make the heatmap more aesthetically pleasing; Step S253: Draw a heatmap using pcolormesh, add color bars, and add titles and axis labels to clearly explain the correspondence between colors and risk values, ensuring the completeness of the chart information.

[0035] In some other embodiments, contour lines can be overlaid on the heatmap to clearly delineate the boundaries of different risk levels. Continuous color bands can also be used to represent continuous risk values, which is not limited or elaborated upon here.

[0036] By analyzing vibration information through a cloud platform module to generate a heat map, vibration data can be visualized, with colors used to indicate the risk of vibration signals. Connecting the cloud platform module to a display in the control room of the transport vehicle (or vessel) helps personnel monitor the status of the wind turbine blades, casing, and motor during transport. This data visualization allows personnel to determine the location of faults based on road conditions, facilitating avoidance of high-risk areas or timely inspection and maintenance of the positioning mechanisms of various components on the fixed wind turbine.

[0037] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A vibration detection and analysis system for wind turbines during transportation, characterized in that, The vibration sensing module is installed on the blades, the cylinder and the motor of the wind driven generator, and is used for collecting vibration information of the blades, the cylinder and the motor in real time during transportation. The vibration data analysis module is connected with the data output end of the vibration sensing module, and is used for receiving the vibration information, analyzing the vibration information and outputting the analysis result, and outputting instruction A or instruction B according to the analysis result. The camera linkage module is installed on the transportation vehicle, and is connected with the data output end of the vibration data analysis module, and is used for receiving instruction A and collecting image information and video information, and receiving instruction B and keeping closed. The storage module is connected with the data output ends of the vibration sensing module, the vibration data analysis module and the camera linkage module, and is used for receiving and storing the vibration information, the analysis result, the image information and the video information. The cloud platform module is connected with the data output ends of the vibration sensing module, the vibration data analysis module and the camera linkage module, and is used for receiving the analysis result, the image information and the video information and generating a related report, and receiving historical vibration information and generating a transportation vibration heat map. The vibration sensing module includes a three-axis vibration detector fixedly installed on the blades, the cylinder and the motor, and the data output end of the three-axis vibration detector is connected with the data input ends of the vibration data analysis module, the storage module and the cloud platform module.

2. A vibration detection and analysis system for wind turbines during transportation according to claim 1, characterized in that The camera linkage module includes two fixed cameras fixedly installed on the inner walls of the two sides of the transportation vehicle.

3. A vibration detection and analysis system for wind turbines during transportation according to claim 2, characterized in that The data input ends of the two fixed cameras are connected with the data output end of the vibration data analysis module, and the data output ends of the two fixed cameras are connected with the data input ends of the storage module and the cloud platform module. The analysis method of the vibration data analysis module includes the following steps:

4. A vibration detection and analysis system for wind turbines during transportation according to claim 3, characterized in that Step S11, vibration information of the blades, the cylinder or the motor is collected at a set frequency, and the vibration information includes amplitude data and vibration duration data; Step S12, a first amplitude threshold, a second amplitude threshold and a duration threshold are preset, the second amplitude threshold is greater than the first amplitude threshold, the vibration information is analyzed, if the amplitude data is greater than the first amplitude threshold and less than the second amplitude threshold, and the vibration duration data is greater than the duration threshold, instruction A is output; if the amplitude data is greater than the second amplitude threshold, instruction A is output regardless of the vibration duration data; if the amplitude data is less than the first amplitude threshold, instruction B is output; if the amplitude data is greater than the first amplitude threshold, and the vibration duration data is less than the duration threshold, instruction B is output. The method for outputting a transportation vibration heat map by the cloud platform module according to vibration data includes the following steps:

5. A vibration detection and analysis system for wind turbines during transportation according to claim 4, characterized in that Step S21, data preprocessing, data cleaning and data distribution analysis are performed on the data; Step S22, a risk assessment function is defined; Step S23, the risk value of each vibration data point is calculated according to the risk assessment function; Step S24, data binning and risk value aggregation, the continuous space is divided into cells, and the aggregated risk value in each cell is calculated; Step S25, a heat map is generated. ​ Step S26, compare the thermal map with the recorded image information and video information to check whether the thermal map display matches the actual vibration condition.

6. A vibration detection and analysis system for wind turbines during transportation according to claim 5, characterized in that The step S21 includes the following steps: Step S211, data cleaning, including: Missing value processing: check whether there are missing values in the amplitude data and vibration duration data, and if there are missing values, perform reasonable interpolation processing; Abnormal value processing: identify and process extremely large or small abnormal values caused by errors of the three-axis vibration detector; Step S212, data distribution analysis, draw amplitude data histogram and vibration duration data histogram respectively.

7. A vibration detection and analysis system for wind turbines during transportation according to claim 6, characterized in that The step S22 includes the following steps: Step S221, Min-Max normalization processing is performed on the amplitude data and vibration duration data to obtain normalized amplitude value A_norm and normalized vibration duration value D_norm; Step S222, passing the function model The vibration risk value is calculated, wherein R represents the risk value of the vibration data, a represents the weight index of the amplitude, and b represents the weight index of the vibration duration. Step S223, determine the function parameters, determine the values of α and β according to prior knowledge.

8. A vibration detection and analysis system for wind turbines during transportation according to claim 7, characterized in that, The step S24 includes the following steps: Step S241, establish a coordinate system in continuous space, the X-axis of the coordinate system represents the vibration duration, and the Y-axis represents the amplitude, divide the X-axis into N_d intervals and the Y-axis into N_a intervals; determine the values of N_d and N_a according to the number of bins, and perform binning on the vibration data according to the equal interval binning strategy; Step S242, risk value aggregation, for each cell in the coordinate system, find all data points falling into the cell, aggregate the risk values of these data points, and take the average of the risk values of the data points in the cell as the cell risk value; Step S243, establish a risk matrix H_risk based on the cell risk values in the coordinate system.

9. A vibration detection and analysis system for wind turbines during transportation according to claim 8, characterized in that The step S25 includes the following steps: Step S251, color mapping, map the aggregated risk matrix H_risk to color, map each cell using a diverging color band, and represent the risk level through the direction of red-yellow-blue, with red indicating high risk and green indicating low risk; Step S252, smooth the H_risk matrix through Gaussian filtering to make the thermal map more beautiful; Step S253, draw the thermal map through pcolormesh, add color bar, and add title and axis labels to clearly indicate the correspondence between color and risk value, and ensure the completeness of the chart information.

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