Stress monitoring and trend early warning method for tower drum flange connecting bolt and related device

Abstract

The invention discloses a stress monitoring and trend early warning method and related device for connecting bolts of a tower drum flange. The method comprises the following steps: collecting stress of each connecting bolt in the tower drum flange of a wind turbine generator; according to the collected stress of each connecting bolt, drawing a fatigue stress spectrum of the connecting bolts; processing the fatigue stress spectrum of the connecting bolt by adopting a rain flow counting method to obtain a cycle index corresponding to each sub-cycle stress amplitude and mean value in the fatigue stress spectrum of the bolt; drawing a stress-life curve of the bolt under preset cycle characteristics; analyzing the fatigue life of the connecting bolt according to the cycle index and the stress-life curve corresponding to each sub-cycle stress amplitude and mean value in the bolt fatigue stress spectrum; the stress trend analysis of the connecting bolts is carried out by adopting an autoregressive integral moving average model according to the stress of each connecting bolt in the tower flange of the wind turbine generator. The method and the related device can be used for stress monitoring and trend early warning of the connecting bolts.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind turbine maintenance, and relates to a stress monitoring and trend warning method for tower flange connecting bolts and a related device. Background Art

[0002] With the further deepening of power system reform, the continuous development and changes of power grid structure and the continuous advancement of power market construction, the proportion of wind power generation capacity has continued to increase, and wind turbines have developed rapidly in the past few decades.

[0003] The tower is a critical supporting component in a wind turbine. Typically, a tower consists of several sections, each directly connected by flange bolts. Reliable bolt connection is crucial for the proper operation of the wind turbine tower. Wind turbines operate in harsh environments and complex operating conditions for extended periods, subjecting the tower to a combination of lateral and longitudinal forces and moments. Maintaining the normal and safe condition of the wind turbine tower is essential for its proper operation. Under the influence of complex loads such as long-term vibration and bending moments, the tower flange bolts can suffer varying degrees of damage. If the stress on the tower bolts is concentrated on one side and reaches a certain level, it can lead to serious accidents such as tower collapse or breakage, endangering personnel safety and causing significant economic losses. Therefore, real-time online monitoring of the tower flange bolt status is essential.

[0004] Currently, manual sampling is mostly used for the operation and maintenance of wind turbine bolts, with operation and maintenance personnel selecting some bolts for inspection. However, this method cannot monitor the bolt status in real time and is time-consuming and labor-intensive. Online monitoring methods such as ultrasonic and displacement monitoring are also used, but these solutions have high requirements for measurement accuracy and require the use of precise measuring instruments. In addition, there are many bolts on the flange, so monitoring all of them would be costly. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a stress monitoring and trend warning method and related device for tower flange connecting bolts, which can perform stress monitoring and trend warning for connecting bolts.

[0006] To achieve the above-mentioned object, the present invention discloses a method for monitoring the stress of tower flange connecting bolts and providing a trend warning, comprising:

[0007] Collect the stress of each connecting bolt in the tower flange of the wind turbine;

[0008] According to the collected stress of each connecting bolt, the fatigue stress spectrum of the connecting bolt is drawn;

[0009] The fatigue stress spectrum of the connecting bolts is processed by the rain flow counting method to obtain the stress amplitude and the cycle number corresponding to the mean value of each sub-cycle in the fatigue stress spectrum of the bolts.

[0010] Draw the stress-life curve of the bolt under the preset cycle characteristics;

[0011] Analyze the fatigue life of the connecting bolts based on the stress amplitude and mean of each sub-cycle in the bolt fatigue stress spectrum, the number of cycles corresponding to the mean, and the stress-life curve;

[0012] According to the stress of each connecting bolt in the tower flange of the wind turbine, the autoregressive integral moving average model is used to analyze the stress trend of the connecting bolts.

[0013] The further improvement of the stress monitoring and trend warning method of the tower flange connecting bolts of the present invention is:

[0014] Furthermore, the power function form of the SN curve is expressed as:

[0015] σ m N=C

[0016] Where σ represents the stress generated by the average load on the bolt, m and C are material constants, and N is the strain.

[0017] Furthermore, based on the Palmgren-Miner theory, the rain flow counting method is used to process the fatigue stress spectrum of the connecting bolts, and the stress amplitude and the number of cycles corresponding to the mean value of each sub-cycle in the fatigue stress spectrum of the bolts are obtained.

[0018] Furthermore, the criteria for determining bolt fatigue are:

[0019]

[0020] Where D is the total fatigue damage accumulated by the bolt. If the total damage D is greater than 1, it is determined that the bolt has fatigue failure. i Indicates the number of cycles corresponding to each equivalent stress cycle, N i Represents the life of each equivalent stress cycle in the SN curve.

[0021] Furthermore, the autoregressive integrated moving average model is expressed as:

[0022]

[0023] Among them, x t represents time series data, x t with x t-i (i=1,2,…,p) correlation; ε t represents the residual term, ε t With ε t-jRelated, i=1,2,…,q, B represents the delay operator, satisfying B n x t =x t-n ; p represents the autoregressive order, q represents the moving average order, and d represents the difference order. represents the difference operator, Φ(B) represents the autoregressive coefficient polynomial, Θ(B) represents the sliding average coefficient polynomial, ε t Independent of x t-i and ε t-j white noise sequence.

[0024] Furthermore, it also includes:

[0025] Trend warning is issued based on the analysis results of the stress trend of the connecting bolts.

[0026] The present invention discloses a stress monitoring and trend warning system for tower flange connecting bolts, comprising:

[0027] A collection module is used to collect the stress of each connecting bolt in the tower flange of the wind turbine;

[0028] The first drawing module is used to draw the fatigue stress spectrum of the connecting bolts according to the collected stress of each connecting bolt;

[0029] A calculation module is used to process the fatigue stress spectrum of the connecting bolts using the rain flow counting method to obtain the stress amplitude and the number of cycles corresponding to the mean value of each sub-cycle in the fatigue stress spectrum of the bolts;

[0030] The second drawing module is used to draw the stress-life curve of the bolt under the preset cycle characteristics;

[0031] The first analysis module is used to analyze the fatigue life of the connecting bolts based on the stress amplitude and the number of cycles corresponding to the mean value of each sub-cycle in the fatigue stress spectrum of the bolts and the stress-life curve;

[0032] The second module is used to analyze the stress trend of the connecting bolts in the tower flange of the wind turbine using an autoregressive integral moving average model based on the stress of each connecting bolt in the tower flange of the wind turbine.

[0033] The further improvement of the tower flange connection bolt stress monitoring and trend warning system of the present invention is:

[0034] Furthermore, the power function form of the SN curve is expressed as:

[0035] σ m N=C

[0036] Where σ represents the stress generated by the average load on the bolt, m and C are material constants, and N is the strain.

[0037] The present invention discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the stress monitoring and trend warning method of the tower flange connecting bolts are implemented.

[0038] The present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the stress monitoring and trend warning method of the tower flange connecting bolts are implemented.

[0039] The present invention has the following beneficial effects:

[0040] The tower flange bolt stress monitoring and trend warning method and related devices described in the present invention monitor the bolt axial stress in real time during operation, generate a fatigue stress spectrum using the rainflow counting method, and estimate the bolt fatigue life based on the SN curve and cumulative damage theory. Furthermore, the present invention employs an ARIMA model for stress trend analysis, which can predict the fatigue damage trend of the bolts and provide threshold warnings on a cloud network server. This method does not require destroying the bolt structure, has high measurement accuracy and reliability, and improves the short-term prediction accuracy of non-stationary time series through differential stabilization processing. Application of this system can effectively prevent tower collapse caused by bolt stress concentration, reducing accident risks and operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0042] Figure 1 is a flow chart of the method of the present invention;

[0043] Figure 2 This is a working diagram of a bolt stress measuring device;

[0044] Figure 3 This is the installation position diagram of the bolt stress sensor;

[0045] Figure 4 This is the modeling flow chart of the autoregressive integrated moving average model. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0047] In the description of the present invention, it is to be understood that the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0048] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0049] It should be further understood that the term "and / or" as used in the present specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0050] It should be understood that although the terms "first," "second," and "third" may be used to describe preset ranges in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are merely used to distinguish one preset range from another. For example, without departing from the scope of embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0051] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0052] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0053] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0054] Example 1

[0055] refer to Figure 1 The method for monitoring the stress of the tower flange connecting bolts and providing a trend warning comprises the following steps:

[0056] 1) Collect the stress of each connecting bolt in the tower flange of the wind turbine.

[0057] refer to Figure 2 The bolt stress sensor collects the stress data of each connecting bolt in real time, summarizes the collected stress data by time, and then stores it in the data storage system. The schematic diagram and installation location of the bolt stress sensor are as follows: Figure 3 As shown in the figure, the bolt stress sensor selected adopts the strain measurement principle, which reflects the stress through the strain generated by the deformation under force, and can directly measure the axial stress of the connecting bolt. It is installed between the nut, gasket and flange without destroying the structure of the connecting bolt and changing the stiffness and strength of the bolt. It has the advantages of mature measurement principle and high measurement accuracy.

[0058] 2) Based on the collected stress data of each connecting bolt, a stress-time history curve of the connecting bolt, that is, a fatigue stress spectrum of the connecting bolt, is drawn.

[0059] 3) The fatigue stress spectrum of the connecting bolts is processed using the rain flow counting method. The specific process is as follows:

[0060] 31) According to the relationship between stress and time, the starting position of the rain flow is the inner side of the valley value of each wave trough or the inner side of the peak value of each wave crest;

[0061] 32) Rain flows down from the inside of each crest or trough, falling on the next crest or trough. It stops when the valley of the opposite trough meets a lower valley. Similarly, it stops when the opposite peak meets a higher peak.

[0062] 33) During the entire rain flow process, when it encounters rain flow from the roof above, a rain flow cycle stops;

[0063] 34) A closed rainflow trace (stress-strain hysteresis loop) constitutes a full cycle, and the amplitude and mean of each full cycle are recorded.

[0064] The rain flow counting method can be used to obtain the stress amplitude and the number of cycles corresponding to the mean value of each sub-cycle in the fatigue stress spectrum of the bolt.

[0065] In this embodiment, a stress-life curve of a bolt under a certain cycle characteristic, namely, an SN curve, is drawn. The power function form of the SN curve can be expressed as:

[0066] σ m N=C

[0067] Where σ represents the stress generated by the average load on the bolt, and m and C are material constants.

[0068] In order to observe the characteristics of the SN curve more intuitively, the SN curve is usually expressed in logarithmic form, and its expression is:

[0069] lgN=lgC-mlgσ

[0070] Most of the material SN curves used for engineering reference are measured under symmetrical cyclic loads. When the linear cumulative damage theory is actually applied, the mean stress of the material needs to be corrected. The most commonly used mean stress correction methods are the Goodman criterion and the Gerber criterion.

[0071] The Goodman mean stress correction formula is:

[0072]

[0073] The Gerber mean stress correction formula is:

[0074]

[0075] Among them, S eqv is the stress correction amplitude, S a is the stress calculation amplitude, S mis the mean value of stress calculation, σ b is the tensile strength of the material.

[0076] Based on the Palmgren-Miner theory, also known as the linear cumulative damage theory, the bolt stress spectrum is combined with the SN curve and the material fatigue limit. According to the material properties, relevant criteria are selected, the mean stress is corrected, and the proportional coefficient and strength factor are set to perform fatigue life analysis of the bolt.

[0077] The stress loads on the service bolts of wind turbines are random loads. For fatigue life prediction under such random load spectrum, it is generally considered that the random load spectrum is equivalent to the variable amplitude load spectrum or the constant amplitude load spectrum. Based on Miner's linear cumulative damage theory, the present invention regards the fatigue life of bolts under random stress loads as the result of the accumulation of axial constant amplitude stress. According to Miner's linear cumulative damage theory, the fatigue damage of bolts caused by various stresses can be regarded as independent of each other and can be linearly superimposed. Then the criterion for determining bolt fatigue is:

[0078]

[0079] Where D is the total fatigue damage accumulated by the bolt, which should be less than 1 during the design life cycle; if the total damage D is greater than 1, it is determined that the bolt has fatigue failure, n i Represents the number of cycles corresponding to each equivalent stress cycle, N i Represents the life of each equivalent stress cycle in the SN curve.

[0080] Taking maintenance reports, fault compilations, historical operation data and other offline data as references and real-time online monitoring data as basis, the autoregressive integrated moving average model (ARIMA) is used to analyze the bolt stress trend. The model structure is:

[0081]

[0082] Among them, x t represents time series data, x t with x t-i (i=1,2,…,p) correlation; ε t represents the residual term, ε t With ε t-j (i=1,2,…,q) related; B represents the delay operator, satisfying B n x t =x t-n ; p represents the autoregressive order, q represents the moving average order, and d represents the difference order. represents the difference operator,

[0083] Φ(B) represents the autoregressive coefficient polynomial:

[0084] Φ(B)=1-φ1B-φ2B 2 -...-φ p B p

[0085] Θ(B) represents the sliding mean coefficient polynomial:

[0086] Θ(B)=1-θ1B-θ2B 2 -...-θ q B q

[0087] ε t Independent of x t-i and ε t-j The white noise sequence satisfies:

[0088] ε t =θ1ε t-1 +θ2ε t-2 +...+θ q ε t-q -φ0-φ1x t-1 -φ2x t-2 -...-φ p x t-p -x t

[0089] like Figure 4 As shown, the steps for trend analysis using the ARIMA model are:

[0090] 21) By performing data transformation or differentiation on the existing time series data, the series is made to have zero mean and the variance does not change with time, and the differential order d is determined according to the number of differentials;

[0091] 22) After the sequence is stable, the order of the target sequence is determined by observing the autocorrelation coefficient and partial correlation coefficient graph of the sequence;

[0092] 23) From the estimated multiple models, select the best model for prediction. The ARIMA model is selected based on the Bayesian Information Criterion (BIC). The smaller the BIC, the better the model fit.

[0093] 24) Use the optimal model selected to predict the trend and obtain the development trend of the original time series. The prediction effect is evaluated based on the average prediction relative error between the predicted value and the actual value.

[0094] The data from the status monitoring module and the prediction module are summarized and uploaded to the cloud network server, and a trend warning value is set for the system. When the prediction value exceeds the preset threshold range, an early warning prompt is issued.

[0095] This invention utilizes a strain gauge-based stress monitor to reflect stress through the strain generated by deformation under load. This proven, reliable measurement principle offers high precision, requiring no data correction and enabling direct measurement of axial stress. The trend warning model employs an autoregressive integral moving average model that incorporates differential stabilization of the raw data, enhancing its predictive capabilities for non-stationary time series and achieving high short-term prediction accuracy. This effectively improves the accuracy of the trend warning system and prevents tower collapse caused by stress concentration on one side of the tower bolts.

[0096] Example 2

[0097] The tower flange connection bolt stress monitoring and trend warning system of the present invention comprises:

[0098] A collection module is used to collect the stress of each connecting bolt in the tower flange of the wind turbine;

[0099] The first drawing module is used to draw the fatigue stress spectrum of the connecting bolts according to the collected stress of each connecting bolt;

[0100] A calculation module is used to process the fatigue stress spectrum of the connecting bolts using the rain flow counting method to obtain the stress amplitude and the number of cycles corresponding to the mean value of each sub-cycle in the fatigue stress spectrum of the bolts;

[0101] The second drawing module is used to draw the stress-life curve of the bolt under the preset cycle characteristics;

[0102] The first analysis module is used to analyze the fatigue life of the connecting bolts based on the stress amplitude and the number of cycles corresponding to the mean value of each sub-cycle in the fatigue stress spectrum of the bolts and the stress-life curve;

[0103] The second module is used to analyze the stress trend of the connecting bolts in the tower flange of the wind turbine using an autoregressive integral moving average model based on the stress of each connecting bolt in the tower flange of the wind turbine.

[0104] The further improvement of the tower flange connection bolt stress monitoring and trend warning system of the present invention is:

[0105] Furthermore, the power function form of the SN curve is expressed as:

[0106] σ m N=C

[0107] Where σ represents the stress generated by the average load on the bolt, m and C are material constants, and N is the strain.

[0108] The division of modules in the embodiments of the present application is illustrative and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the present application may be integrated into a single processor, or may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0109] Example 3

[0110] A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the stress monitoring and trend warning method for tower flange connecting bolts are implemented. For example, the steps include: collecting the stress of each connecting bolt in the tower flange of a wind turbine generator set; drawing the fatigue stress spectrum of the connecting bolts based on the collected stress of each connecting bolt; processing the fatigue stress spectrum of the connecting bolts using a rain flow counting method to obtain the number of cycles corresponding to the stress amplitude and mean of each sub-cycle in the fatigue stress spectrum of the bolt; drawing the stress-life curve of the bolt under a preset cycle characteristic; analyzing the fatigue life of the connecting bolt based on the number of cycles corresponding to the stress amplitude and mean of each sub-cycle in the fatigue stress spectrum of the bolt and the stress-life curve; and performing stress trend analysis of the connecting bolts using an autoregressive integral sliding average model based on the stress of each connecting bolt in the tower flange of the wind turbine generator set. The memory may include internal memory, such as high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus. This internal bus may be an Industry Standard Architecture bus, a Peripheral Component Interconnect Standard bus, an Extended Industry Standard Architecture bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include program code, and the program code includes computer operating instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0111] Example 4

[0112] A computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a method for stress monitoring and trend warning of tower flange connecting bolts. The method includes, for example, collecting stresses experienced by each connecting bolt in a wind turbine tower flange; plotting a fatigue stress spectrum of the connecting bolts based on the collected stresses experienced by each connecting bolt; processing the fatigue stress spectrum of the connecting bolts using a rain flow counting method to obtain the number of cycles corresponding to the stress amplitude and mean of each sub-cycle in the bolt fatigue stress spectrum; plotting a stress-life curve of the bolts under preset cycle characteristics; analyzing the fatigue life of the connecting bolts based on the number of cycles corresponding to the stress amplitude and mean of each sub-cycle in the bolt fatigue stress spectrum and the stress-life curve; and analyzing the stress trend of the connecting bolts using an autoregressive integral moving average model based on the stress experienced by each connecting bolt in the wind turbine tower flange. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory and / or cache memory, etc. The non-volatile memory may include read-only memory, a hard disk, a flash memory, an optical disk, a magnetic disk, etc.

[0113] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0114] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0115] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0117] Those skilled in the art will readily identify other embodiments of the present invention after considering the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

[0118] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

[0119] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for monitoring the stress of tower flange connection bolts and providing a trend warning, characterized in that: include: Collect the stress of each connecting bolt in the tower flange of the wind turbine; According to the collected stress of each connecting bolt, the fatigue stress spectrum of the connecting bolt is drawn; The fatigue stress spectrum of the connecting bolts is processed by the rain flow counting method to obtain the stress amplitude and the cycle number corresponding to the mean value of each sub-cycle in the fatigue stress spectrum of the bolts. Draw the stress-life curve of the bolt under the preset cycle characteristics; Analyze the fatigue life of the connecting bolts based on the stress amplitude and mean of each sub-cycle in the bolt fatigue stress spectrum, the number of cycles corresponding to the mean, and the stress-life curve; According to the stress of each connecting bolt in the tower flange of the wind turbine, the autoregressive integral moving average model is used to analyze the stress trend of the connecting bolts.

2. The method for monitoring the stress and early warning trend of the tower flange connection bolts according to claim 1, characterized in that: The power function form of the SN curve is expressed as: s m N=C Where σ represents the stress generated by the average load on the bolt, m and C are material constants, and N is the strain.

3. The method for monitoring the stress and early warning trend of the tower flange connection bolts according to claim 1, characterized in that: Based on the Palmgren-Miner theory, the rain flow counting method is used to process the fatigue stress spectrum of the connecting bolts, and the stress amplitude and the cycle number corresponding to the mean value of each sub-cycle in the fatigue stress spectrum of the bolts are obtained.

4. The method for monitoring the stress and early warning trend of the tower flange connection bolts according to claim 3, characterized in that: The criteria for determining bolt fatigue are: Where D is the total fatigue damage accumulated by the bolt. If the total damage D is greater than 1, it is determined that the bolt has fatigue failure. i Indicates the number of cycles corresponding to each equivalent stress cycle, N i Represents the life of each equivalent stress cycle in the SN curve.

5. The method for monitoring the stress and early warning trend of tower flange connection bolts according to claim 1, characterized in that: The autoregressive integrated moving average model is expressed as: Among them, x t represents time series data, x t with x t-i (i=1,2,…,p) correlation; ε t represents the residual term, ε t With ε t-j Related, i=1,2,…,q, B represents the delay operator, satisfying B n x t =x t-n ; p represents the autoregressive order, q represents the moving average order, and d represents the difference order. represents the difference operator, Φ(B) represents the autoregressive coefficient polynomial, Θ(B) represents the sliding average coefficient polynomial, ε t Independent of x t-i and ε t-j white noise sequence.

6. The method for monitoring the stress and early warning trend of tower flange connection bolts according to claim 3, characterized in that: Also includes: Trend warning is issued based on the analysis results of the stress trend of the connecting bolts.

7. A tower flange connection bolt stress monitoring and trend warning system, characterized in that: include: A collection module is used to collect the stress of each connecting bolt in the tower flange of the wind turbine; The first drawing module is used to draw the fatigue stress spectrum of the connecting bolts according to the collected stress of each connecting bolt; A calculation module is used to process the fatigue stress spectrum of the connecting bolts using the rain flow counting method to obtain the stress amplitude and the number of cycles corresponding to the mean value of each sub-cycle in the fatigue stress spectrum of the bolts; The second drawing module is used to draw the stress-life curve of the bolt under the preset cycle characteristics; The first analysis module is used to analyze the fatigue life of the connecting bolts based on the stress amplitude and the number of cycles corresponding to the mean value of each sub-cycle in the fatigue stress spectrum of the bolts and the stress-life curve; The second module is used to analyze the stress trend of the connecting bolts in the tower flange of the wind turbine using an autoregressive integral moving average model based on the stress of each connecting bolt in the tower flange of the wind turbine.

8. The tower flange connection bolt stress monitoring and trend warning system according to claim 7, characterized in that: The power function form of the SN curve is expressed as: s m N=C Where σ represents the stress generated by the average load on the bolt, m and C are material constants, and N is the strain.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for stress monitoring and trend warning of tower flange connecting bolts as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for stress monitoring and trend warning of tower flange connecting bolts as described in any one of claims 1 to 6 are implemented.

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