Fan variable pitch bearing fastening bolt fracture risk monitoring method and device

Abstract

The invention provides a fan variable pitch bearing fastening bolt fracture risk monitoring method and device, and relates to the technical field of fan bolt fracture risk detection.The fan variable pitch bearing fastening bolt fracture risk monitoring method comprises the steps that connecting pieces are installed on bolts of a detected object respectively, and all the connecting pieces are connected in series through a steel wire rope; obtaining a time sequence array of the real-time displacement of the steel wire rope; performing first data analysis according to the time-order array of the real-time displacement to judge whether a bolt fracture risk exists on the detection object or not; if the bolt fracture risk exists, a fracture risk alarm is given out; and if the bolt fracture risk does not exist, performing second data analysis on the time-order array of the real-time displacement to determine the time of next fracture risk detection. The method has the advantage of realizing rapid, reliable and high-cost-performance bolt fracture risk detection.

Description

Technical Field

[0001] This invention relates to the field of wind turbine bolt fracture risk detection technology, and more specifically, to a method and device for monitoring the fracture risk of wind turbine pitch bearing fastening bolts. Background Technology

[0002] In the wind power industry, high-strength bolts are widely used for connecting towers, nacelles, and blades.

[0003] High-strength bolts are fasteners specifically designed for connecting steel structures and are widely used in buildings, machinery, and other structures that need to withstand heavy loads. The bolts connecting the pitch bearing to the blades bear the weight of the entire blade and are subjected to aerodynamic and centrifugal loads during wind turbine operation, making them highly susceptible to bolt breakage. Currently, it is difficult to achieve intelligent bolt breakage risk monitoring that is computationally simple and efficient with minimal layout and calculation costs.

[0004] Therefore, there is an urgent need to optimize bolt fracture risk monitoring methods to achieve rapid, reliable, and cost-effective bolt fracture risk detection. Summary of the Invention

[0005] The purpose of this invention is to provide a method and device for monitoring the risk of bolt fracture in wind turbine pitch bearings, which can achieve rapid, reliable and cost-effective bolt fracture risk detection.

[0006] This invention is achieved through the following technical solution:

[0007] A method for monitoring the risk of fracture of fastening bolts in wind turbine pitch bearings includes the following steps:

[0008] Install connectors on the bolts of the object being inspected, and connect all the connectors together with a steel wire rope.

[0009] Obtain the time-series array of the real-time displacement of the wire rope;

[0010] Based on the time series array of the real-time displacement, a first data analysis is performed to determine whether there is a risk of bolt breakage on the detection object;

[0011] If there is a risk of bolt breakage, a breakage risk alarm will be issued;

[0012] If there is no risk of bolt breakage, a second data analysis is performed on the time series array of the real-time displacement to determine the time for the next breakage risk detection.

[0013] Preferably, the connector is a snap ring.

[0014] Preferably, the method for obtaining the time-series array of the real-time displacement of the wire rope is as follows:

[0015] During the monitoring period at that time, the real-time displacement of multiple steel wire ropes was periodically acquired using a wire displacement sensor.

[0016] The real-time displacement values ​​of multiple wire ropes are arranged in time sequence to form a time sequence array of the real-time displacement values.

[0017] Preferably, the method for performing a first data analysis based on the time series array of the real-time displacement to determine whether there is a risk of bolt breakage is as follows:

[0018] The fracture risk assessment parameters are obtained based on the maximum value of the array of real-time displacement time series and the fluctuation parameters.

[0019] The presence of bolt breakage risk is determined based on the magnitude of the fracture risk assessment parameter. If the fracture risk assessment parameter exceeds a preset threshold, the presence of bolt breakage risk is determined; otherwise, the presence of bolt breakage risk is determined.

[0020] Preferably, the method for obtaining fracture risk assessment parameters based on the maximum value of the array of real-time displacement time series and the fluctuation parameters is as follows:

[0021] Obtain the fluctuation parameter α:

[0022]

[0023] Among them, L k-1 L k These are the k-th and (k-1)-th real-time displacement values ​​in the array of real-time displacement time series, respectively, and N is the total number of real-time displacement values ​​in the array of real-time displacement time series.

[0024] Obtain the fracture risk assessment parameter β:

[0025]

[0026] Where e is the natural constant.

[0027] Preferably, the method for determining the time for the next fracture risk detection by performing a second data analysis on the time series array of the real-time displacement is as follows:

[0028] Sort the values ​​in the time series array of the real-time displacement in descending order;

[0029] Obtain the three largest real-time displacement values ​​from the time-series array of the real-time displacement values, and calculate the time evaluation parameter γ:

[0030] γ=A1*L max,1 +A2*L max,2 +A3*L max,3 ;

[0031] A1 + A2 + A3 = 1;

[0032] A1>A2>A3;

[0033] Among them, L max,1 L is the real-time displacement with the largest value in the time series array of the real-time displacements. max,2 L is the second largest real-time displacement value in the time series array of the real-time displacement values. max,3 A1, A2, and A3 are the third largest real-time displacement values ​​in the time series array of the real-time displacement values, where A1, A2, and A3 are all weights.

[0034] Calculate the time interval Δt until the next fracture risk detection:

[0035]

[0036] Among them, T st γ is the preset baseline time interval between two adjacent fracture risk detections. th is the preset time evaluation parameter threshold, e is the natural constant, and max is the function to find the maximum value.

[0037] Preferably, the reference time interval T st The method for obtaining it is as follows:

[0038] The average lifespan (lt) of the bolts used was obtained based on historical data.

[0039] Obtain the total number M of the bolts on the component that is fixed by bolts;

[0040] Calculate the reference time interval T st The range of values ​​for:

[0041]

[0042] Where ln is the logarithmic function.

[0043] Preferably, the method for obtaining the time evaluation parameter threshold is as follows:

[0044] The real-time displacement values ​​at multiple time points when no bolt breakage risk was obtained based on historical data, and the minimum value L′ among them was obtained. min and maximum value L′ max ;

[0045] Obtain the threshold γ of the time evaluation parameter th :

[0046]

[0047] Preferably, if there is a risk of bolt breakage, after replacing the bolt, at the reference time interval T st The first fracture risk test after replacement was then conducted.

[0048] This invention also provides a wind turbine pitch bearing fastening bolt fracture risk monitoring device, applied to the above-mentioned wind turbine pitch bearing fastening bolt fracture risk monitoring method, comprising:

[0049] The auxiliary testing component includes connectors and wire ropes. Connectors are installed on the bolts of the object being tested, and all connectors are connected in series by a single wire rope.

[0050] A data measurement device is used to acquire a time-series array of the real-time displacement of the wire rope;

[0051] The first data analysis module is used to perform first data analysis based on the time series array of the real-time displacement to determine whether the detected object has a risk of bolt breakage.

[0052] The alarm module will issue a breakage risk alarm if there is a risk of bolt breakage.

[0053] If there is no risk of bolt breakage, the second data analysis module performs a second data analysis on the time series array of the real-time displacement to determine the time for the next breakage risk detection.

[0054] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0055] This invention arranges multiple connectors in series with a single steel wire rope, enabling simultaneous monitoring of the status of multiple bolts. This avoids the installation complexity and increased costs associated with deploying sensors individually, thereby improving monitoring efficiency and system integration.

[0056] This invention analyzes the time series array of wire rope displacement to promptly detect sudden changes in wire rope displacement caused by the risk of bolt breakage, quickly issue a breakage risk alarm, improve fault response speed, and reduce the risk of fault escalation.

[0057] When no fracture risk is detected, this invention further analyzes displacement time series data and intelligently determines the optimal time for the next detection, thereby avoiding frequent invalid detections, optimizing monitoring frequency, saving system resources, and extending equipment lifespan.

[0058] This invention does not rely on high-precision sensors or complex electronic components, making it suitable for deployment in harsh operating environments such as wind turbines, which experience high vibration, high temperature differences, and wind and rain. It has good field adaptability and engineering feasibility, and the test results have high reliability.

[0059] This invention is reasonably designed, has a simple structure, involves inexpensive components, and consumes relatively little computing power, thus offering high cost-effectiveness. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating the method for monitoring the risk of fracture of wind turbine pitch bearing fastening bolts provided in Embodiment 1 of the present invention.

[0061] Figure 2 This is a schematic diagram of the wind turbine pitch bearing fastening bolt fracture risk monitoring device provided in Embodiment 2 of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0063] Example 1

[0064] This embodiment provides a method for monitoring the risk of fracture of fastening bolts in wind turbine pitch bearings. (See reference...) Figure 1 This includes the following steps:

[0065] Install connectors on the bolts of the object being inspected, and connect all the connectors together with a steel wire rope.

[0066] Obtain the time-series array of the real-time displacement of the wire rope;

[0067] Based on the time series array of the real-time displacement, a first data analysis is performed to determine whether there is a risk of bolt breakage on the detection object;

[0068] If there is a risk of bolt breakage, a breakage risk alarm will be issued;

[0069] If there is no risk of bolt breakage, a second data analysis is performed on the time series array of the real-time displacement to determine the time for the next breakage risk detection.

[0070] This embodiment first installs connectors on the bolts of the object to be tested and connects them in series with tensioned steel wire ropes. When any bolt breaks, indicating a risk of fracture—that is, when performance declines and stabilization becomes unstable—it will cause a displacement change in the entire steel wire rope. Therefore, timely and efficient bolt fracture risk detection can be achieved by monitoring the displacement changes of the steel wire rope. Since there are various interferences to the displacement of the steel wire rope during actual wind turbine operation, this embodiment does not choose to determine bolt fracture risk by simply comparing a single displacement amount with a corresponding threshold. Instead, it integrates a time-series array of real-time displacement data for the first data analysis, which helps to extract more accurate features for judging fracture risk based on the time series. If a bolt fracture risk is determined, the system can automatically issue a fracture risk alarm, such as an audible and visual alarm or sending a fault signal to the host computer system to prompt maintenance personnel to inspect and replace the bolt. If no abnormal displacement is found, the system continues to perform a second data analysis on the time-series array to evaluate the stability of the current state of the steel wire rope and the stress trend of the bolt. This allows for dynamic setting of the next monitoring interval, optimizing the detection frequency.

[0071] It is worth noting that when conducting fracture risk detection, this embodiment's detection scheme can be executed multiple times consecutively within a single detection, with the result of the last detection used as the basis for the next fracture risk detection. In other words, a single fracture risk detection involves setting multiple consecutive detection cycles covering a relatively long time period (e.g., 1-5 days). Each detection cycle performs data acquisition and first data analysis according to the technical solution of this embodiment. If no fracture risk is found throughout the entire process, a second data analysis is performed in the last detection cycle. If a fracture risk is found during this process, the detection is terminated and an alarm is issued directly.

[0072] In this embodiment, the connector is a snap ring.

[0073] As a preferred embodiment, the method for obtaining the time-series array of the real-time displacement of the wire rope is as follows:

[0074] During the monitoring period at that time, the real-time displacement of multiple steel wire ropes was periodically acquired using a wire displacement sensor.

[0075] The real-time displacement values ​​of multiple wire ropes are arranged in time sequence to form a time sequence array of the real-time displacement values.

[0076] Specifically, the method for performing a first data analysis based on the time-series array of the real-time displacement to determine whether there is a risk of bolt breakage is as follows:

[0077] The fracture risk assessment parameters are obtained based on the maximum value of the array of real-time displacement time series and the fluctuation parameters.

[0078] The presence of bolt breakage risk is determined based on the magnitude of the fracture risk assessment parameter. If the fracture risk assessment parameter exceeds a preset threshold, the presence of bolt breakage risk is determined; otherwise, the presence of bolt breakage risk is determined.

[0079] Furthermore, the method for obtaining fracture risk assessment parameters based on the maximum value of the array of real-time displacement time series and the fluctuation parameters is as follows:

[0080] Obtain the fluctuation parameter α:

[0081]

[0082] Among them, L k-1 L k These are the k-th and (k-1)-th real-time displacement values ​​in the array of real-time displacement time series, respectively, and N is the total number of real-time displacement values ​​in the array of real-time displacement time series.

[0083] Obtain the fracture risk assessment parameter β:

[0084]

[0085] Where e is the natural constant.

[0086] In the above scheme, the introduction of maximum displacement and fluctuation parameters comprehensively reflects the stress changes of the wire rope, avoiding misjudgments or omissions that may be caused by relying on a single indicator, thus improving the accuracy of fracture risk detection. Weighted summation of the maximum displacement and fluctuation parameters using a weighted natural exponential function yields fracture risk assessment parameters, demonstrating significant differences in response to minor fluctuations and obvious fracture risks, exhibiting stronger sensitivity. The fluctuation parameter is constructed by summing the differences between all adjacent values ​​in the time-series array, providing a global fluctuation profile and avoiding false alarms caused by wind vibration or minor deformation of the equipment itself, as is common in conventional monitoring methods. The maximum displacement directly reflects the peak displacement of the wire rope, providing an analytical basis for determining whether the wire rope displacement is caused by bolt fracture risk. This embodiment features simple calculations, facilitating rapid processing on embedded devices or edge computing, and is suitable for practical field deployment, offering high processing efficiency and cost-effectiveness.

[0087] Next, when there is no risk of fracture, the method for determining the time for the next fracture risk detection by performing a second data analysis on the time series array of the real-time displacement is as follows:

[0088] Sort the values ​​in the time series array of the real-time displacement in descending order;

[0089] Obtain the three largest real-time displacement values ​​from the time-series array of the real-time displacement values, and calculate the time evaluation parameter γ:

[0090] γ=A1*L max,1 +A2*L max,2 +A3*L max,3 ;

[0091] A1 + A2 + A3 = 1;

[0092] A1>A2>A3;

[0093] Among them, L max,1 L is the real-time displacement with the largest value in the time series array of the real-time displacements. max,2 L is the second largest real-time displacement value in the time series array of the real-time displacement values. max,3 A1, A2, and A3 are the third largest real-time displacement values ​​in the time series array of the real-time displacement values, where A1, A2, and A3 are all weights.

[0094] Calculate the time interval Δt until the next fracture risk detection:

[0095]

[0096] Among them, T st γ is the preset baseline time interval between two adjacent fracture risk detections. th is the preset time evaluation parameter threshold, e is the natural constant, and max is the function to find the maximum value.

[0097] As a further preferred option, the reference time interval T st The method for obtaining it is as follows:

[0098] The average lifespan (lt) of the bolts used was obtained based on historical data.

[0099] Obtain the total number M of the bolts on the component that is fixed by bolts;

[0100] Calculate the reference time interval T st The range of values ​​for:

[0101]

[0102] Where ln is the logarithmic function.

[0103] On the other hand, the method for obtaining the threshold of the time evaluation parameter is as follows:

[0104] The real-time displacement values ​​at multiple time points when no bolt breakage risk was obtained based on historical data, and the minimum value L′ among them was obtained. min and maximum value L′ max ;

[0105] Obtain the threshold γ of the time evaluation parameter th :

[0106]

[0107] It should be noted that if there is a risk of bolt breakage, after replacing the bolt, the reference time interval T should be used. st The first fracture risk test after replacement was then conducted.

[0108] Compared to traditional timed detection mechanisms, this embodiment dynamically adjusts the time interval for the next fracture risk detection based on the collected real-time displacement data. Specifically, the time interval is reduced to achieve more frequent detection when the risk is high, while detection is performed according to a preset baseline time interval between two adjacent fracture risk detections when the risk is low. For example, the calculated time evaluation parameter γ, which is the weighted sum of the three largest real-time displacements, does not exceed γ. th Then the value of Δt is T. st When the calculated time evaluation parameter γ exceeds γ th At this point, the greater the value exceeding the limit, the weaker the bolt's stability, meaning a higher risk of breakage. The larger the value of , the smaller Δt will be.

[0109] Based on this, the preset benchmark time interval between two adjacent fracture risk detections is determined by the bolt's lifespan and number. It is calculated by reducing the average lifespan (lt) based on the number of bolts; the more bolts, the shorter the preset benchmark time interval. Here, an exponential distribution is used to analyze the bolt fracture probability. That is, the average lifespan (lt) of a single bolt is first used to represent the time interval between two adjacent fracture risk detections. st The probability P' of the risk of fracture within a given time period is expressed using an exponential distribution as:

[0110]

[0111] Based on the above, assuming that in T st Within a given timeframe, at most one bolt out of M bolts can be at risk of fracture. Therefore:

[0112] M*P'≤1;

[0113] After solving the transformation, T can be obtained. st The ground value is not greater than Preferably, it can be directly set to

[0114] On the other hand, the time evaluation parameter threshold γ th This threshold is also determined based on historical data; directly using the maximum value as the threshold would reduce the fault tolerance during data analysis. This embodiment selects the maximum value L′ as the threshold. max and minimum value L′ minThe ratio was determined by the proportion, and this ratio was used as the fault tolerance ratio, which was then summed with the maximum value L′. max Multiplication, used as a threshold, enhances the tolerance for errors in judgment without excessive amplification, thus maintaining accuracy.

[0115] This embodiment, by reasonably setting the detection time interval, can avoid excessive monitoring that consumes system resources and helps extend the service life of sensors and acquisition equipment, while also avoiding the reduction in detection reliability caused by excessively long detection intervals.

[0116] Example 2

[0117] This embodiment provides a device for monitoring the risk of fracture of fastening bolts in wind turbine pitch bearings, applied to the aforementioned method for monitoring the risk of fracture of fastening bolts in wind turbine pitch bearings. (See reference...) Figure 2 ,include:

[0118] The auxiliary testing component includes connectors and wire ropes. Connectors are installed on the bolts of the object being tested, and all connectors are connected in series by a single wire rope.

[0119] A data measurement device is used to acquire a time-series array of the real-time displacement of the wire rope;

[0120] The first data analysis module is used to perform first data analysis based on the time series array of the real-time displacement to determine whether the detected object has a risk of bolt breakage.

[0121] The alarm module will issue a breakage risk alarm if there is a risk of bolt breakage.

[0122] If there is no risk of bolt breakage, the second data analysis module performs a second data analysis on the time series array of the real-time displacement to determine the time for the next breakage risk detection.

[0123] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring the risk of fracture of fastening bolts in wind turbine pitch bearings, characterized in that, Includes the following steps: Install connectors on the bolts of the object being inspected, and connect all the connectors together with a steel wire rope. Obtain the time-series array of the real-time displacement of the wire rope; Based on the time series array of the real-time displacement, a first data analysis is performed to determine whether there is a risk of bolt breakage on the detection object; If there is a risk of bolt breakage, a breakage risk alarm will be issued; If there is no risk of bolt breakage, a second data analysis is performed on the time series array of the real-time displacement to determine the time for the next breakage risk detection.

2. The method for monitoring the risk of fracture of wind turbine pitch bearing fastening bolts according to claim 1, characterized in that, The connector uses a snap ring.

3. The method for monitoring the risk of fracture of wind turbine pitch bearing fastening bolts according to claim 1, characterized in that, The method for obtaining the time-series array of the real-time displacement of the wire rope is as follows: During the monitoring period at that time, the real-time displacement of multiple steel wire ropes was periodically acquired using a wire displacement sensor. The real-time displacement values ​​of multiple wire ropes are arranged in time sequence to form a time sequence array of the real-time displacement values.

4. The method for monitoring the risk of fracture of wind turbine pitch bearing fastening bolts according to claim 1, characterized in that, The method for determining whether there is a risk of bolt breakage by performing a first data analysis based on the time series array of the real-time displacement is as follows: The fracture risk assessment parameters are obtained based on the maximum value of the array of real-time displacement time series and the fluctuation parameters. The presence of bolt breakage risk is determined based on the magnitude of the fracture risk assessment parameter. If the fracture risk assessment parameter exceeds a preset threshold, the presence of bolt breakage risk is determined; otherwise, the presence of bolt breakage risk is determined.

5. The method for monitoring the risk of fracture of wind turbine pitch bearing fastening bolts according to claim 4, characterized in that, The method for obtaining fracture risk assessment parameters based on the maximum value of the array of real-time displacement time series and fluctuation parameters is as follows: Obtain the fluctuation parameter α: Among them, L k-1 L k These are the k-th and (k-1)-th real-time displacement values ​​in the array of real-time displacement time series, respectively, and N is the total number of real-time displacement values ​​in the array of real-time displacement time series. Obtain the fracture risk assessment parameter β: Where e is the natural constant.

6. The method for monitoring the risk of fracture of wind turbine pitch bearing fastening bolts according to claim 1, characterized in that, The method for determining the time for the next fracture risk detection by performing a second data analysis on the time series array of the real-time displacement is as follows: Sort the values ​​in the time series array of the real-time displacement in descending order; Obtain the three largest real-time displacement values ​​from the time-series array of the real-time displacement values, and calculate the time evaluation parameter γ: γ=A1*L max,1 +A2*L max,2 +A3*L max,3 ; A1 + A2 + A3 = 1; A1>A2>A3; Among them, L max,1 L is the real-time displacement with the largest value in the time series array of the real-time displacements. max,2 L is the second largest real-time displacement value in the time series array of the real-time displacement values. max,3 A1, A2, and A3 are the third largest real-time displacement values ​​in the time series array of the real-time displacement values, where A1, A2, and A3 are all weights. Calculate the time interval Δt until the next fracture risk detection: Among them, T st γ is the preset baseline time interval between two adjacent fracture risk detections. th is the preset time evaluation parameter threshold, e is the natural constant, and max is the function to find the maximum value.

7. The method for monitoring the risk of fracture of wind turbine pitch bearing fastening bolts according to claim 6, characterized in that, The reference time interval T st The method for obtaining it is as follows: The average lifespan (lt) of the bolts used was obtained based on historical data. Obtain the total number M of the bolts on the component that is fixed by bolts; Calculate the reference time interval T st The range of values ​​for: Where ln is the logarithmic function.

8. The method for monitoring the risk of fracture of wind turbine pitch bearing fastening bolts according to claim 7, characterized in that, The method for obtaining the threshold of the time evaluation parameter is as follows: The real-time displacement values ​​at multiple time points when no bolt breakage risk was obtained based on historical data, and the minimum value L′ among them was obtained. min and maximum value L′ max ; Obtain the threshold γ of the time evaluation parameter th :

9. The method for monitoring the risk of fracture of wind turbine pitch bearing fastening bolts according to claim 8, characterized in that, If there is a risk of bolt breakage, the bolts will be replaced, and the reference time interval T will be used for further testing. st The first fracture risk test after replacement was then conducted.

10. A device for monitoring the risk of fracture of fastening bolts of wind turbine pitch bearings, applied to the method for monitoring the risk of fracture of fastening bolts of wind turbine pitch bearings as described in any one of claims 1-9, characterized in that, include: The auxiliary testing component includes connectors and wire ropes. Connectors are installed on the bolts of the object being tested, and all connectors are connected in series by a single wire rope. A data measurement device is used to acquire a time-series array of the real-time displacement of the wire rope; The first data analysis module is used to perform first data analysis based on the time series array of the real-time displacement to determine whether the detected object has a risk of bolt breakage. The alarm module will issue a breakage risk alarm if there is a risk of bolt breakage. If there is no risk of bolt breakage, the second data analysis module performs a second data analysis on the time series array of the real-time displacement to determine the time for the next breakage risk detection.

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