A fan gear box wear evaluation method, device, equipment and storage medium

Abstract

This invention relates to the field of wind power generation technology and discloses a method, apparatus, equipment, and storage medium for assessing the wear of wind turbine gearboxes. Based on the collected vibration acceleration and oil metal filings content during wind turbine operation, and considering factors such as wind turbine load, vibration frequency, gear meshing frequency, ambient temperature, and gearbox age, the invention uses a first-parameter model to obtain the root mean square value of vibration acceleration, accurately determining the relationship between wind turbine vibration and gearbox wear. A second-parameter model is used to obtain the metal filings content growth rate, highlighting abnormal changes in metal filings content, thereby revealing the relationship between oil metal filings content changes and gearbox wear. Furthermore, using the current gearbox wear assessment model and the cumulative gearbox wear assessment model, the collected data is processed in depth to accurately calculate the root mean square value of vibration acceleration, the metal filings content growth rate, the wear function value, and the cumulative wear function value, achieving the goal of accurately assessing the degree of gearbox wear.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, specifically to a method, apparatus, equipment, and storage medium for assessing wear of wind turbine gearboxes. Background Technology

[0002] In the field of wind power generation, the gearbox of a wind turbine is a critical component, and its operating condition directly affects the efficiency and reliability of wind power generation. Because wind turbines typically operate in harsh natural environments, the gearbox is subjected to complex alternating loads over long periods, making it prone to wear and other failures. Therefore, monitoring the wear condition of the wind turbine gearbox helps avoid missing optimal maintenance opportunities and reduces the probability of serious gearbox failures that could affect the stable operation of the wind turbine generator set.

[0003] The wear monitoring of wind turbine gearboxes in related technologies mainly includes: judging gearbox wear by using gearbox vibration signals or judging gearbox wear by using the content of metal particles in the oil.

[0004] However, the operating environment of wind turbines is complex, and vibration signals are easily affected by various factors, such as changes in wind speed and resonance in the turbine structure. Judging gearbox wear solely based on vibration signals is prone to misjudgment; judging gearbox wear based on the content of metal particles in the oil is also inaccurate. In summary, the wind turbine gearbox wear monitoring methods disclosed in related technologies are all insufficient for accurately assessing the wear condition of wind turbine gearboxes. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and storage medium for assessing the wear of wind turbine gearboxes, thereby addressing the problem that existing wind turbine gearbox wear monitoring methods are difficult to accurately assess the wear condition of wind turbine gearboxes.

[0006] In a first aspect, the present invention provides a method for assessing wear of a wind turbine gearbox, comprising:

[0007] Based on the collected vibration acceleration, load, and gear meshing frequency data, the root mean square value of vibration acceleration is obtained using a pre-constructed first parameter processing model; the first parameter processing model includes a function of the root mean square value of vibration acceleration with respect to the vibration acceleration, load, and gear meshing frequency data;

[0008] Based on the collected data on oil iron filings content, load, and vibration frequency, the growth rate of iron filings content is obtained using a pre-constructed second-parameter processing model; the second-parameter processing model includes a function of the growth rate of iron filings content with respect to the data on oil iron filings content, load, and vibration frequency.

[0009] Based on the root mean square value of vibration acceleration and the growth rate of iron filings content, and combining the gear meshing frequency amplitude, load, and time data, the current wear value of the gearbox is obtained using a pre-built gearbox current wear assessment model. The gearbox current wear assessment model includes a function of the current wear value of the gearbox with respect to the root mean square value of vibration acceleration, the growth rate of iron filings content, the gear meshing frequency amplitude, load, and time data.

[0010] Based on the current wear value of the gearbox, combined with historical cumulative wear value, ambient temperature, load and gearbox age data, the cumulative wear value of the gearbox is obtained using a pre-built gearbox cumulative wear assessment model; the gearbox cumulative wear assessment model includes a function of the cumulative wear value of the gearbox with respect to the current wear value of the gearbox, historical cumulative wear value, ambient temperature, load and gearbox age data;

[0011] Based on the current wear function value and the cumulative wear function value, the wear assessment result of the wind turbine gearbox is obtained by using preset judgment conditions.

[0012] Through the above implementation method, based on the collected vibration acceleration and oil iron filings content during the operation of the fan, and taking into account factors such as the fan load, vibration frequency, gear meshing frequency, ambient temperature, and gearbox age, the root mean square value of vibration acceleration is obtained using the first parameter model, which facilitates a more accurate understanding of the relationship between fan vibration and gearbox wear. The growth rate of iron filings content is obtained using the second parameter model, highlighting abnormal changes in iron filings content and more accurately understanding the relationship between changes in oil iron filings content and gearbox wear. Furthermore, the current gearbox wear assessment model and the cumulative gearbox wear assessment model are used to perform in-depth processing of the collected data, accurately calculating the root mean square value of vibration acceleration, the growth rate of iron filings content, the wear function value, and the cumulative wear function value, thereby achieving the goal of accurately assessing the degree of gearbox wear.

[0013] In one alternative implementation, the construction of the current wear assessment model for the gearbox includes:

[0014] Based on the root mean square value of vibration acceleration, the relationship between vibration acceleration and the current wear of the gearbox is constructed using a Gaussian function, and the influencing factors of vibration acceleration are obtained.

[0015] Based on the growth rate of iron filings content, the relationship between the growth rate of iron filings content and the current wear of the gearbox is constructed using a Gaussian function, and the influencing factors of iron filings content are obtained.

[0016] Based on the gear meshing frequency amplitude, the relationship between the gear meshing frequency amplitude and the current wear of the gearbox is constructed using a Gaussian function, thereby obtaining the factors affecting gear meshing;

[0017] Based on the load, the relationship between the load and the current wear of the gearbox is constructed using the gamma function to obtain the factors affecting the load.

[0018] Based on time data, the relationship between time and current gearbox wear is constructed using an exponential function to obtain the factors influencing time.

[0019] Based on the combined effects of vibration acceleration, iron filings content growth rate, gear meshing, load, and time, a current wear assessment model for the gearbox is obtained.

[0020] Through the above implementation methods, the root mean square value of vibration acceleration, the amplitude of gear meshing frequency, and the growth rate of iron filings content are processed using a Gaussian function to explore the distribution characteristics of these values ​​around their respective mean values. This allows for a more comprehensive understanding of the impact of changes in these values ​​on the current wear of the gearbox. Simultaneously, a gamma function is used to better fit the complex relationship between the load and the current wear of the gearbox, more accurately considering the impact of the load on the current wear. Furthermore, an exponential function is used to reflect the characteristic that recent data has a greater impact on the current wear of the gearbox over time, effectively simulating time dependence and making the calculated results of the current wear of the gearbox more consistent with reality, thereby improving the accuracy of obtaining the current wear results.

[0021] In one optional implementation, the gearbox current wear assessment model includes:

[0022] ,

[0023] in, This indicates the current wear value of the gearbox; Indicates the total sampling time; Indicates the factors affecting vibration acceleration; This represents the root mean square value of the vibration acceleration; This represents the mean of the root mean square values ​​of vibration acceleration; The standard deviation of the root mean square value of vibration acceleration; Indicates factors affecting iron filings content; Indicates the growth rate of iron filings content; This represents the average growth rate of iron filings content; The standard deviation of the growth rate of iron filings content; Indicates factors affecting gear meshing; Indicates the amplitude of the gear meshing frequency; This represents the average amplitude of the gear meshing frequency; Standard values ​​representing the amplitude of gear meshing frequency; Indicates factors affecting load; express The load at any moment; Indicates shape parameters; Indicates the scale parameter; Indicates factors affecting time; Indicates the current time; This represents a parameter that controls the decay rate of the exponential function.

[0024] Through the above implementation methods, the root mean square value of vibration acceleration, the amplitude of gear meshing frequency, and the growth rate of iron filings content are processed using a Gaussian function to explore the distribution characteristics of these values ​​around their respective mean values. This allows for a more comprehensive understanding of the impact of changes in these values ​​on the current wear of the gearbox. Simultaneously, a gamma function is used to better fit the complex relationship between the load and the current wear of the gearbox, more accurately considering the impact of the load on the current wear. Furthermore, an exponential function is used to reflect the characteristic that recent data has a greater impact on the current wear of the gearbox over time, effectively simulating time dependence and making the calculated results of the current wear of the gearbox more consistent with reality, thereby improving the accuracy of obtaining the current wear results.

[0025] In one optional implementation, the construction of the gearbox cumulative wear assessment model includes:

[0026] Based on ambient temperature, the relationship between ambient temperature and cumulative wear of the gearbox is established using the Poisson function, and the influencing factors of ambient temperature are obtained.

[0027] Based on the load, the relationship between the load and the cumulative wear of the gearbox is established using the Weibull function, and the factors affecting the load are obtained.

[0028] Based on gearbox age data, a logical function is used to establish the relationship between gearbox age data and cumulative gearbox wear, thus obtaining the gearbox age data factor.

[0029] Based on the environmental temperature, load, and gearbox age data, and by combining the current wear value and historical cumulative wear value of the gearbox, a cumulative wear assessment model for the gearbox is obtained.

[0030] Through the above implementation methods, the influence of ambient temperature on the cumulative wear of the gearbox is quantified using the Poisson function, capturing the uncertainty and randomness of the effect of ambient temperature on the cumulative wear of the gearbox; the Weibull function can better fit the complex relationship between load and cumulative wear of the gearbox, accurately reflecting the influence of load on the cumulative wear of the gearbox during long-term operation; and the logic function is used to simulate the nonlinear accumulation process in which the influence of the gearbox on cumulative wear gradually increases with the age of the gearbox, more accurately reflecting the actual wear state of the gearbox and improving the accuracy of the generated cumulative wear function value.

[0031] In one optional implementation, the gearbox cumulative wear assessment model includes:

[0032] ,

[0033] in, This indicates the cumulative wear value of the gearbox; express Historical cumulative wear value up to this point in time; This indicates the current wear value of the gearbox; Indicates the wear accumulation coefficient; Indicates factors affecting ambient temperature; and All indicate relative to ambient temperature Relevant Poisson distribution parameters; express The ambient temperature at that moment; Indicates factors affecting load; express The load at any moment; This represents a scale parameter related to the load; Indicates the shape parameters related to the load; Indicates factors affecting gearbox age data; This indicates that the gearbox has used age data; Indicates the expected service life of the gearbox; Indicates the current moment.

[0034] Through the above implementation methods, the influence of ambient temperature on the cumulative wear of the gearbox is quantified using the Poisson function, capturing the uncertainty and randomness of the effect of ambient temperature on the cumulative wear of the gearbox; the Weibull function can better fit the complex relationship between load and cumulative wear of the gearbox, accurately reflecting the influence of load on the cumulative wear of the gearbox during long-term operation; and the logic function is used to simulate the nonlinear accumulation process in which the influence of the gearbox on cumulative wear gradually increases with the age of the gearbox, more accurately reflecting the actual wear state of the gearbox and improving the accuracy of the generated cumulative wear function value.

[0035] In one optional implementation, the first parameter processing model includes:

[0036] ,

[0037] in, This represents the root mean square value of the vibration acceleration; express The vibration acceleration at any given moment; Indicates time window Internal vibration acceleration The average value; Indicates time window Internal vibration acceleration Standard deviation; express The load at any moment; This represents the average load over a preset time period. This represents the standard deviation of the load within a preset time period; This indicates the gear meshing frequency.

[0038] Through the above implementation methods, the inverse hyperbolic tangent function can amplify minute abnormal changes in vibration acceleration, more accurately identifying potential wear risks. The logistic function maps the deviation of vibration acceleration after processing with the inverse hyperbolic tangent function, effectively adjusting the influence weight of abnormal vibration acceleration on the final result. This makes the algorithm more sensitive to abnormal vibrations and load changes, improving the accuracy of the calculation results in reflecting actual wear conditions. The error function normalizes the deviation between the load and the mean, transforming different ranges of deviation values ​​into comparable values, facilitating comprehensive calculations within a unified framework and improving the accuracy of subsequent wear assessment results.

[0039] In one optional implementation, the second parameter processing model includes:

[0040] ,

[0041] in, Indicates the growth rate of iron filings content; Indicates a time interval; express The iron filings content of the oil at any given time; This represents the average value of iron filings in the oil over a preset time period. This represents the standard deviation of the iron filings content in the oil over a preset time period. express The load at any moment; This represents the average load over a preset time period. This represents the standard deviation of the load within a preset time period; Indicates the control coefficient; This represents the vibration frequency data.

[0042] Through the above implementation methods, the deviation between the oil iron filings content and the mean is normalized using an error function, transforming deviations from different ranges into comparable values. This facilitates comprehensive calculations within a unified framework, improving the accuracy of subsequent wear assessment results. Furthermore, the deviation between the load and the mean is processed using an inverse hyperbolic tangent function, amplifying minute abnormal load changes and more accurately identifying potential wear risks. Finally, a cosine function is used to consider the impact of periodic vibration changes during unit operation on the iron filings content growth rate, accurately obtaining the influence of vibration frequency on gear wear rate and improving the accuracy of subsequent gearbox wear assessments using the iron filings content growth rate.

[0043] Secondly, the present invention provides a wind turbine gearbox wear assessment device, the device comprising:

[0044] The first data processing module is used to obtain the root mean square value of vibration acceleration based on the collected vibration acceleration, load, and gear meshing frequency data, using a pre-constructed first parameter processing model; the first parameter processing model includes a function of the root mean square value of vibration acceleration with respect to the vibration acceleration, load, and gear meshing frequency data;

[0045] The second data processing module is used to obtain the iron filings content growth rate based on the collected oil iron filings content, load and vibration frequency data, using a pre-built second parameter processing model; the second parameter processing model includes the iron filings content growth rate as a function of the oil iron filings content, load and vibration frequency data;

[0046] The current wear acquisition module is used to obtain the current wear value of the gearbox based on the root mean square value of vibration acceleration and the growth rate of iron filings content, combined with gear meshing frequency amplitude, load and time data, using a pre-built gearbox current wear assessment model; the gearbox current wear assessment model includes a function of the current wear value of the gearbox with respect to the root mean square value of vibration acceleration, the growth rate of iron filings content, gear meshing frequency amplitude, load and time data;

[0047] The cumulative wear acquisition module is used to obtain the cumulative wear value of the gearbox based on the current wear value of the gearbox, combined with historical cumulative wear values, ambient temperature, load and gearbox age data, using a pre-built gearbox cumulative wear assessment model; the gearbox cumulative wear assessment model includes a function of the cumulative wear value of the gearbox with respect to the current wear value of the gearbox, historical cumulative wear values, ambient temperature, load and gearbox age data;

[0048] The results output module is used to evaluate the wear of the wind turbine gearbox based on the current wear function value and the cumulative wear function value, using preset judgment conditions.

[0049] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the wind turbine gearbox wear assessment method of the first aspect or any corresponding embodiment described above.

[0050] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the wind turbine gearbox wear assessment method of the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0051] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0052] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention;

[0053] Figure 2 This is a schematic flowchart of a wind turbine gearbox wear assessment method according to an embodiment of the present invention;

[0054] Figure 3 This is a structural block diagram of a wind turbine gearbox wear assessment device according to an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0056] 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, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0058] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0059] As an optional application scenario of this invention, such as Figure 1 As shown, application 101 is installed in terminal device 110, and user 130 can interact with application 101 through terminal device 110 and / or access device of terminal device 110.

[0060] For example, application 101 can be any application that provides question-and-answer related services. For instance, application 101 could be a question-and-answer interactive application, such as a text-to-text application, an image-to-text application, etc. Figure 1 In the application scenario shown, if application 101 is active, the terminal device 110 can display the interface 102 of application 101. The interface 102 may include various pages that application 101 can provide, such as interactive pages, settings pages, query pages, etc.

[0061] In some embodiments, terminal device 110 is communicatively connected to server 120 to provide services to application 101. Terminal device 110 may be a mobile terminal, fixed terminal, or portable terminal, etc., including but not limited to mobile phones, desktop computers, laptop computers, multimedia tablets, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, terminal device 110 may also support any type of interface, and server 120 may be various types of computing systems or servers capable of providing computing power, including but not limited to mainframes, edge computing nodes, computing devices in cloud environments, etc.

[0062] It should be noted that, Figure 1 This is merely an example of an application scenario and does not limit the scope of protection of this invention.

[0063] The embodiments of the present invention will now be described with reference to the accompanying drawings. It should be understood that the pages shown in the drawings are merely examples, and various page designs are possible in practice. The various graphic elements on the page may have different arrangements and different visual representations; one or more elements may be omitted or replaced, and one or more other elements may also be present, without any limitation in the embodiments of the present invention. Furthermore, the embodiments described below primarily pertain to terminal device 110. It should be understood that the actions described relative to terminal device 110 can be performed by application 101 on terminal device 110, or can be performed by application 101 in conjunction with its server (e.g., server 120).

[0064] The wind turbine gearbox wear monitoring methods disclosed in the related technologies include: judging gearbox wear by using gearbox vibration signals or judging gearbox wear by using the content of metal particles in the oil.

[0065] However, the operating environment of wind turbines is complex, and vibration signals are easily affected by various factors, such as changes in wind speed and resonance in the turbine structure. Judging gearbox wear based on vibration signals is prone to misdiagnosis. For example, when a sudden change in wind speed causes increased overall turbine vibration, it might be mistakenly attributed to gearbox wear, when in reality the internal wear of the gearbox has not undergone any substantial change.

[0066] The sources of metal particles in the oil can be complex. Besides iron filings from the wear of the gearbox itself, they may originate from metal impurities left over from the equipment manufacturing process, or from wear on other components such as oil lines. Therefore, judging the degree of gearbox wear based solely on the metal particle content in the oil is not very accurate. For example, newly commissioned wind turbine gearboxes may have a high metal particle content in the oil due to residual impurities from the manufacturing process, even if the actual wear of the gearbox is not severe.

[0067] In summary, the gearbox wear monitoring methods disclosed in related technologies cannot accurately and comprehensively assess the wear condition of wind turbine gearboxes or predict their lifespan. In practical applications, this can cause wind turbines to miss optimal maintenance opportunities, leading to serious malfunctions, affecting the stable operation of wind power generation systems, increasing maintenance costs and downtime, and reducing power generation efficiency.

[0068] To address the aforementioned technical problems, this invention provides a method for assessing the wear of wind turbine gearboxes. It utilizes a Gaussian function to process the root mean square value of vibration acceleration, the amplitude of gear meshing frequency, and the growth rate of iron filings content. This process uncovers the distribution characteristics of these factors around their respective mean values, providing a more comprehensive understanding of their impact on the current wear of the gearbox. Simultaneously, a gamma function is used to better fit the complex relationship between load and current gearbox wear, more accurately considering the load's influence. Furthermore, an exponential function is employed to reflect the greater impact of recent data on current gearbox wear over time, effectively simulating time dependence and making the calculated current gearbox wear more consistent with reality, thereby improving the accuracy of the current gearbox wear results.

[0069] According to an embodiment of the present invention, a method for evaluating the wear of a wind turbine gearbox is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0070] This embodiment provides a method for assessing the wear of wind turbine gearboxes, which can be used in the aforementioned wind farm service terminal equipment. Figure 2 This is a flowchart of a wind turbine gearbox wear assessment method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0071] S201, based on the collected vibration acceleration, load and gear meshing frequency data, the root mean square value of vibration acceleration is obtained using a pre-constructed first parameter processing model; the first parameter processing model includes a function of the root mean square value of vibration acceleration with respect to the vibration acceleration, load and gear meshing frequency data.

[0072] Vibration acceleration is detected by high-precision vibration acceleration sensors installed in key components of the wind turbine gearbox, such as the bearing housing and gearbox body. These sensors sensitively reflect the vibration of internal gearbox components. Based on the piezoelectric effect, the sensors convert the mechanical energy generated by vibration into an electrical signal. Vibration acceleration of the wind turbine gearbox is acquired at a high sampling frequency of 10kHz to ensure the capture of detailed signal characteristics. Simultaneously, to reduce noise interference, a digital filter is used to filter the signal, obtaining filtered vibration acceleration, thus improving the accuracy of subsequent gearbox wear assessments.

[0073] The load is collected by a load sensor installed in the wind turbine drive system to obtain the load on the wind turbine gearbox in real time during operation.

[0074] Gear meshing frequency data is obtained through theoretical calculations or experimental measurements. Gear meshing frequency data is related to the number of gear teeth, rotational speed, etc., and is used to reflect the inherent frequency characteristics of gear meshing.

[0075] The first parameter processing model constructs a function of the root mean square value of vibration acceleration with respect to vibration acceleration, load, and gear meshing frequency data. By collecting vibration acceleration, load, and gear meshing frequency data, the model comprehensively considers the load and gear meshing frequency to optimize the vibration acceleration, making it easier to obtain a more accurate correlation between wind turbine vibration and gearbox wear.

[0076] For example, the first parameter processing model includes:

[0077] ,

[0078] in, This represents the root mean square value of the vibration acceleration; express The vibration acceleration at any given moment; Indicates time window Internal vibration acceleration The average value; Indicates time window Internal vibration acceleration Standard deviation; express The load at any moment; This represents the average load over a preset time period. This represents the standard deviation of the load within a preset time period; This indicates the gear meshing frequency.

[0079] The inverse hyperbolic tangent function amplifies minute anomalies in vibration acceleration, enabling more accurate identification of potential wear risks. Mapping the deviation in vibration acceleration after processing with the inverse hyperbolic tangent function using the logistic function effectively adjusts the weighting of anomalies in vibration acceleration on the final result, making the algorithm more sensitive to abnormal vibrations and load variations, thus improving the accuracy of the calculation results in reflecting actual wear conditions. Normalizing the deviation between the load and the mean using an error function transforms deviations from different ranges into comparable values, facilitating comprehensive calculations within a unified framework and improving the accuracy of subsequent wear assessment results. Finally, a sine function related to the gear meshing frequency is used to consider the influence of periodic variations during gear meshing on the calculation of the root mean square value of vibration acceleration, thereby integrating the factors affecting vibration characteristics based on gear meshing frequency.

[0080] The logistic function is expressed as:

[0081] ,

[0082] The logistic function is used to map the vibration acceleration deviation after processing by the inverse hyperbolic tangent function to the [0,1] interval, while adjusting the degree of influence of abnormal vibration on the calculation of the root mean square value of vibration acceleration.

[0083] The inverse hyperbolic tangent function is expressed as:

[0084] ,

[0085] The inverse hyperbolic tangent function handles the deviation between vibration acceleration and its mean, thereby highlighting the role of abnormal vibration in the calculation of the root mean square value of vibration acceleration.

[0086] The error function is expressed as:

[0087] ,

[0088] The error function normalizes the deviation between the load and its mean, reflecting the influence of the load on the calculation of the root mean square value of vibration acceleration.

[0089] S202, based on the collected data of oil iron filings content, load and vibration frequency, the iron filings content growth rate is obtained using a pre-constructed second parameter processing model; the second parameter processing model includes a function of the iron filings content growth rate with respect to the oil iron filings content, load and vibration frequency data.

[0090] The iron filings content in the oil was monitored by an iron filings sensor installed in the gearbox's return oil line. The iron filings sensor operates on the principle of electromagnetic induction; when oil containing iron filings flows through, the filings alter the magnetic field around the sensor, generating an induced electromotive force. This electromotive force is then detected and converted to obtain the iron filings content value. Furthermore, to ensure the accuracy of the measurement results, the sensor's readings are calibrated by using a temperature compensation device to eliminate the influence of oil temperature variations and by periodically sampling and analyzing the oil.

[0091] The vibration frequency data is obtained by analyzing the frequency domain of the collected vibration acceleration. It is used to reflect the inherent frequency characteristics of the gearbox vibration, thereby facilitating the acquisition of the influence relationship between the vibration frequency and the wear rate of the gearbox.

[0092] For example, the second parameter processing model includes:

[0093] ,

[0094] in, Indicates the growth rate of iron filings content; Indicates a time interval; express The iron filings content of the oil at any given time; This represents the average value of iron filings in the oil over a preset time period. This represents the standard deviation of the iron filings content in the oil over a preset time period. express The load at any moment; This represents the average load over a preset time period. This represents the standard deviation of the load within a preset time period; Indicates the control coefficient; This represents the vibration frequency data.

[0095] The deviation of the oil iron filings content from the mean is normalized using an error function, transforming deviations from different ranges into comparable values. This facilitates comprehensive calculations within a unified framework, improving the accuracy of subsequent gearbox wear assessments. Furthermore, the deviation of the load from the mean is processed using an inverse hyperbolic tangent function, amplifying minute abnormal load changes and more accurately identifying potential wear risks. Finally, a cosine function is used to consider the impact of periodic vibration changes during unit operation on the iron filings content growth rate, accurately obtaining the influence of vibration frequency on gear wear rate and improving the accuracy of subsequent gearbox wear assessments using the iron filings content growth rate.

[0096] S203, based on the root mean square value of vibration acceleration and the growth rate of iron filings content, and combining the gear meshing frequency amplitude, load, and time data, the current wear value of the gearbox is obtained using a pre-built gearbox current wear assessment model; the current wear assessment model of the gearbox includes a function of the current wear value of the gearbox with respect to the root mean square value of vibration acceleration, the growth rate of iron filings content, the gear meshing frequency amplitude, the load, and time data.

[0097] The amplitude of the gear meshing frequency can be obtained by performing spectral analysis on the acquired vibration acceleration. Since the vibration acceleration signal contains the vibration characteristics of various components inside the gearbox, by performing spectral analysis (such as Fourier transform) on the acquired vibration acceleration signal, the frequency components related to gear meshing can be separated from the complex frequency components, and the amplitude corresponding to that frequency can be obtained, i.e., the gear meshing frequency amplitude. This reflects the intensity of the vibration of the gears inside the gearbox during meshing and can be used to assess the wear condition of the gearbox.

[0098] The current wear assessment model for gearboxes constructs a function of the current wear value of the gearbox with respect to the root mean square value of vibration acceleration, the growth rate of iron filings content, the amplitude of gear meshing frequency, load, and time data. By comprehensively considering the root mean square value of vibration acceleration, the amplitude of gear meshing frequency, the growth rate of iron filings content, load, and time data, the current wear value of the gearbox is obtained for assessing the wear degree of the wind turbine gearbox, thus improving the accuracy of the assessment results for the current wear of the gearbox.

[0099] Specifically, the construction of the gearbox current wear assessment model in S203 above includes:

[0100] a1. Based on the root mean square value of vibration acceleration, the relationship between vibration acceleration and the current wear of the gearbox is constructed using a Gaussian function to obtain the influencing factors of vibration acceleration.

[0101] The Gaussian function is expressed as follows:

[0102] ,

[0103] In a1 above, Specifically, the root mean square value of vibration acceleration. ; Specifically, the mean value of the root mean square value of vibration acceleration. ; Specifically, the standard deviation of the root mean square value of vibration acceleration. .

[0104] By using the Gaussian function to obtain the distribution characteristics of the root mean square value of vibration acceleration near its mean, the accuracy of calculating the current wear value of the gearbox can be improved.

[0105] a2. Based on the growth rate of iron filings content, the relationship between the growth rate of iron filings content and the current wear of the gearbox is constructed using a Gaussian function, and the influencing factors of iron filings content are obtained.

[0106] In the above a2, the Gaussian function Specifically, the growth rate of iron filings content ; Specifically, the average growth rate of iron filings content. ; Specifically, the standard deviation of the growth rate of iron filings content. .

[0107] By using a Gaussian function to obtain the distribution characteristics of the iron filings content growth rate near its mean, the accuracy of calculating the current wear value of the gearbox can be further improved.

[0108] a3. Based on the gear meshing frequency amplitude, the relationship between the gear meshing frequency amplitude and the current wear of the gearbox is constructed using a Gaussian function to obtain the factors affecting gear meshing.

[0109] In the above a3, the Gaussian function Specifically, the amplitude of gear meshing frequency. ; Specifically, it refers to the average amplitude of the gear meshing frequency. ; Specifically, the standard deviation of the gear meshing frequency amplitude. .

[0110] By using the Gaussian function to obtain the distribution characteristics of the gear meshing frequency amplitude near its mean, the accuracy of calculating the current wear value of the gearbox can be further improved.

[0111] a4. Based on the load, the relationship between the load and the current wear of the gearbox is constructed using the gamma function to obtain the load influencing factors.

[0112] The gamma function is expressed as follows:

[0113] ,

[0114] ,

[0115] In a4 above, the gamma function Specifically Momentary load , Specifically, shape parameters It can be set to 2; while Specifically, scale parameters It can be set to 100.

[0116] The load is processed using a gamma function, which reflects the influence of the load on the current wear function value of the gearbox through a specific functional form.

[0117] a5, based on time data, uses an exponential function to construct the relationship between time and current gearbox wear, thus obtaining the factors influencing time.

[0118] The exponential function is expressed as follows:

[0119] ,

[0120] In a5 above, the exponential function Specifically , The parameter that controls the decay rate of the exponential function can be set to 2.

[0121] An exponential function is used to process time data to account for the influence of time on the calculation of the wear function. By controlling the decay rate of the exponential function, the influence of the wear function on the wear function gradually decreases as time approaches the end of the sampling time window.

[0122] a6. Combining the factors affecting vibration acceleration, the growth rate of iron filings, gear meshing, load, and time, a current wear assessment model for the gearbox is obtained.

[0123] For example, the current wear assessment model for the gearbox includes:

[0124] ,

[0125] in, This indicates the current wear value of the gearbox; Indicates the total sampling time; Indicates the factors affecting vibration acceleration; This represents the root mean square value of the vibration acceleration; This represents the mean of the root mean square values ​​of vibration acceleration; The standard deviation of the root mean square value of vibration acceleration; Indicates factors affecting iron filings content; Indicates the growth rate of iron filings content; This represents the average growth rate of iron filings content; The standard deviation of the growth rate of iron filings content; Indicates factors affecting gear meshing; Indicates the amplitude of the gear meshing frequency; This represents the average amplitude of the gear meshing frequency; Standard values ​​representing the amplitude of gear meshing frequency; Indicates factors affecting load; express The load at any moment; Indicates shape parameters; Indicates the scale parameter; Indicates factors affecting time; Indicates the current time; This represents a parameter that controls the decay rate of the exponential function.

[0126] By processing the root mean square value of vibration acceleration, the amplitude of gear meshing frequency, and the growth rate of iron filings content using a Gaussian function, the distribution characteristics of these values ​​around their respective mean values ​​are explored. This allows for a more comprehensive understanding of the impact of changes in these values ​​on the current wear of the gearbox. Simultaneously, a gamma function is used to better fit the complex relationship between load and current gearbox wear, more accurately considering the impact of load on current wear. Furthermore, an exponential function is employed to reflect the greater influence of recent data on current gearbox wear over time, effectively simulating time dependence and making the calculated current gearbox wear more consistent with reality, thereby improving the accuracy of the current gearbox wear results.

[0127] S204, based on the current wear value of the gearbox, combined with historical cumulative wear value, ambient temperature, load and gearbox age data, the cumulative wear value of the gearbox is obtained using a pre-built gearbox cumulative wear assessment model; the gearbox cumulative wear assessment model includes a function of the cumulative wear value of the gearbox with respect to the current wear value of the gearbox, historical cumulative wear value, ambient temperature, load and gearbox age data.

[0128] Historical cumulative wear value refers to the cumulative wear value of the gearbox during the operation of the wind turbine over a previous period, and is used to indicate the cumulative wear of the gearbox during past operation.

[0129] Ambient temperature is the external temperature in the fan's operating environment, collected by a temperature sensor. It is an environmental factor that affects the cumulative wear of the gearbox.

[0130] Gearbox age data reflects the service life of the wind turbine's gearbox, taking the cumulative wear and tear on the gearbox caused by the long-term operation of the wind turbine as an influencing factor.

[0131] Specifically, the construction of the gearbox cumulative wear assessment model in S204 above includes:

[0132] b1. Based on ambient temperature, the relationship between ambient temperature and cumulative wear of the gearbox is established using the Poisson function, and the influencing factors of ambient temperature are obtained.

[0133] The Poisson function is expressed as follows:

[0134] ,

[0135] In b1, Specifically, it relates to ambient temperature. Related Poisson distribution parameters ; Specifically, it relates to ambient temperature. Related Poisson distribution parameters .

[0136] By using the Poisson function to describe the frequency of random events within a certain time or space, the influence of ambient temperature on wear accumulation is quantified, and the probabilistic characteristics of ambient temperature and gearbox cumulative wear are linked, thereby obtaining a more accurate value of gearbox cumulative wear.

[0137] b2. Based on the load, the relationship between the load and the cumulative wear of the gearbox is established using the Weibull function to obtain the load influencing factors.

[0138] The Weibull function is expressed as follows:

[0139] ,

[0140] In b2 above, Specifically Momentary load ; Specifically, load-related dimensional parameters. ; Specifically, shape parameters related to the load. .

[0141] By utilizing the Weibull function to adapt to different types of reliability data distributions through parameter adjustment, the influence of load on cumulative gearbox wear is addressed, and the effect of load on cumulative gearbox wear at different levels is further reflected through a specific function form.

[0142] b3. Based on gearbox age data, a logical function is used to establish the relationship between gearbox age data and cumulative gearbox wear, thus obtaining the gearbox age data factor.

[0143] The logical function is expressed as follows:

[0144] ,

[0145] In b3 above, Specifically This indicates that the cumulative wear of the gearbox gradually increases with its age.

[0146] Since the wear of gearboxes does not change uniformly with age, the wear rate may differ between the initial and later stages of use. Processing gearbox age data using logic functions can accurately simulate this non-linear wear accumulation process with age, more accurately reflecting the actual cumulative wear state of the gearbox and providing a more reliable basis for gearbox life prediction.

[0147] b4. Based on the environmental temperature influencing factors, load influencing factors, and gearbox age data, and combining the current wear value and historical cumulative wear value of the gearbox, a gearbox cumulative wear assessment model is obtained.

[0148] For example, a gearbox cumulative wear assessment model includes:

[0149] ,

[0150] in, This indicates the cumulative wear value of the gearbox; express Historical cumulative wear value up to this point in time; This indicates the current wear value of the gearbox; Indicates the wear accumulation coefficient; Indicates factors affecting ambient temperature; and All indicate relative to ambient temperature Relevant Poisson distribution parameters; express The ambient temperature at that moment; Indicates factors affecting load; express The load at any moment; This represents a scale parameter related to the load; Indicates the shape parameters related to the load; Indicates factors affecting gearbox age data; This indicates that the gearbox has used age data; Indicates the expected service life of the gearbox; Indicates the current moment.

[0151] The Poisson function is used to quantify the impact of ambient temperature on the cumulative wear of the gearbox, capturing the uncertainty and randomness of this effect. The Weibull function is used to better fit the complex relationship between load and cumulative wear of the gearbox, accurately reflecting the impact of load on cumulative wear during long-term operation. Logistic functions are used to simulate the nonlinear accumulation process in which the impact of the gearbox on cumulative wear gradually increases with its age, more accurately reflecting the actual wear state of the gearbox and improving the accuracy of the generated cumulative wear function value.

[0152] S205, based on the current wear function value and the cumulative wear function value, an evaluation is performed using preset judgment conditions to obtain the wear evaluation result of the wind turbine gearbox.

[0153] The preset judgment conditions can be implemented as follows:

[0154] When the current wear value of the gearbox is less than or equal to 60%, it indicates that the gearbox is currently in normal working condition.

[0155] When the current wear value of the gearbox is greater than 60%, it indicates that there may be bad teeth inside the gearbox.

[0156] If the cumulative wear value of the gearbox is less than or equal to 20%, it indicates that the gearbox is currently functioning normally.

[0157] When the cumulative wear value of the gearbox is greater than 20% but less than 60%, it indicates that a small number of parts in the gearbox have already experienced cumulative wear.

[0158] When the cumulative wear value of the gearbox is greater than or equal to 60% and less than or equal to 80%, it indicates that there are some parts with a high degree of wear inside the gearbox.

[0159] When the cumulative wear value of the gearbox is greater than 80%, it indicates that most of the internal components of the gearbox are severely worn.

[0160] By comprehensively considering the current wear value and the cumulative wear value of the gearbox, it is easier for operation and maintenance personnel to judge the operating status of the gearbox in a timely and accurate manner, and take corresponding measures to ensure the stable operation of the wind turbine, reduce maintenance costs and failure risks.

[0161] Furthermore, when the current wear value of the gearbox exceeds 60%, the wind turbine should be shut down immediately, and a comprehensive inspection and repair of the wear degree of the gears inside the gearbox should be carried out. It can also analyze the various influencing factors in the current wear assessment model of the gearbox to determine the main reasons for the increase in the wear degree of the gearbox. This makes it convenient for maintenance personnel to carry out targeted repairs and adjustments to the wind turbine gearbox and reduce the probability of the same wear event occurring in the wind turbine gearbox in the future.

[0162] The root mean square of vibration acceleration more accurately reflects the relationship between vibration conditions and gearbox wear; the growth rate of iron filings content accurately reflects the relationship between changes in iron filings content and gearbox wear; the current wear value of the gearbox comprehensively and accurately assesses the wear degree of the wind turbine gearbox by integrating multiple key parameters; the cumulative wear value of the gearbox is used to assess the overall wear state and lifespan of the gearbox. By integrating the above parameters, the wear state of the gearbox can be evaluated more comprehensively and accurately, providing a scientific basis for operation and maintenance decisions.

[0163] The present invention provides a graded gearbox wear assessment method. Based on collected data of vibration acceleration and oil scrap content during fan operation, and considering factors such as fan load, vibration frequency, gear meshing frequency, ambient temperature, and gearbox age, the method uses a first-parameter model to obtain the root mean square value of vibration acceleration, facilitating a more accurate understanding of the relationship between fan vibration and gearbox wear. A second-parameter model is used to obtain the iron scrap content growth rate, highlighting abnormal changes in iron scrap content and further accurately determining the relationship between oil scrap content changes and gearbox wear. Finally, the current gearbox wear assessment model and the cumulative gearbox wear assessment model are used to perform in-depth data processing, accurately calculating the root mean square value of vibration acceleration, the iron scrap content growth rate, the wear function value, and the cumulative wear function value, thereby achieving a precise assessment of the gearbox wear degree.

[0164] This embodiment also provides a wind turbine gearbox wear assessment device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0165] This embodiment provides a device for assessing the wear of a wind turbine gearbox, such as... Figure 3 As shown, it includes:

[0166] The first data processing module 310 is used to obtain the root mean square value of vibration acceleration based on the collected vibration acceleration, load and gear meshing frequency data, using a pre-built first parameter processing model; the first parameter processing model includes a function of the root mean square value of vibration acceleration with respect to the vibration acceleration, load and gear meshing frequency data;

[0167] The second data processing module 320 is used to obtain the iron filings content growth rate based on the collected oil iron filings content, load and vibration frequency data, using a pre-built second parameter processing model; the second parameter processing model includes the iron filings content growth rate as a function of the oil iron filings content, load and vibration frequency data;

[0168] The current wear acquisition module 330 is used to obtain the current wear value of the gearbox based on the root mean square value of vibration acceleration and the growth rate of iron filings content, combined with gear meshing frequency amplitude, load and time data, using a pre-built gearbox current wear assessment model; the gearbox current wear assessment model includes a function of the current wear value of the gearbox with respect to the root mean square value of vibration acceleration, the growth rate of iron filings content, gear meshing frequency amplitude, load and time data;

[0169] The cumulative wear acquisition module 340 is used to obtain the cumulative wear value of the gearbox based on the current wear value of the gearbox, combined with historical cumulative wear values, ambient temperature, load and gearbox age data, using a pre-built gearbox cumulative wear assessment model; the gearbox cumulative wear assessment model includes a function of the cumulative wear value of the gearbox with respect to the current wear value of the gearbox, historical cumulative wear values, ambient temperature, load and gearbox age data;

[0170] The result output module 350 is used to evaluate the wear of the wind turbine gearbox based on the current wear function value and the cumulative wear function value using preset judgment conditions.

[0171] In some optional implementations, the first parameter processing model of the first data processing module 310 includes:

[0172] ,

[0173] in, This represents the root mean square value of the vibration acceleration; express The vibration acceleration at any given moment; Indicates time window Internal vibration acceleration The average value; Indicates time window Internal vibration acceleration Standard deviation; express The load at any moment; This represents the average load over a preset time period. This represents the standard deviation of the load within a preset time period; This indicates the gear meshing frequency.

[0174] In some optional implementations, the second parameter processing model of the second data processing module 320 includes:

[0175] ,

[0176] in, Indicates the growth rate of iron filings content; Indicates a time interval; express The iron filings content of the oil at any given time; This represents the average value of iron filings in the oil over a preset time period. This represents the standard deviation of the iron filings content in the oil over a preset time period. express The load at any moment; This represents the average load over a preset time period. This represents the standard deviation of the load within a preset time period; Indicates the control coefficient; This represents the vibration frequency data.

[0177] In some optional implementations, the construction of the gearbox current wear assessment model of the current wear acquisition module 330 specifically includes:

[0178] Based on the root mean square value of vibration acceleration, the relationship between vibration acceleration and the current wear of the gearbox is constructed using a Gaussian function, and the influencing factors of vibration acceleration are obtained.

[0179] Based on the growth rate of iron filings content, the relationship between the growth rate of iron filings content and the current wear of the gearbox is constructed using a Gaussian function, and the influencing factors of iron filings content are obtained.

[0180] Based on the gear meshing frequency amplitude, the relationship between the gear meshing frequency amplitude and the current wear of the gearbox is constructed using a Gaussian function, thereby obtaining the factors affecting gear meshing;

[0181] Based on the load, the relationship between the load and the current wear of the gearbox is constructed using the gamma function to obtain the factors affecting the load.

[0182] Based on time data, the relationship between time and current gearbox wear is constructed using an exponential function to obtain the factors influencing time.

[0183] Based on the combined effects of vibration acceleration, iron filings content growth rate, gear meshing, load, and time, a current wear assessment model for the gearbox is obtained.

[0184] In some optional implementations, the gearbox current wear assessment model of the current wear acquisition module 330 includes:

[0185] ,

[0186] in, This indicates the current wear value of the gearbox; Indicates the total sampling time; Indicates the factors affecting vibration acceleration; This represents the root mean square value of the vibration acceleration; This represents the mean of the root mean square values ​​of vibration acceleration; The standard deviation of the root mean square value of vibration acceleration; Indicates factors affecting iron filings content; Indicates the growth rate of iron filings content; This represents the average growth rate of iron filings content; The standard deviation of the growth rate of iron filings content; Indicates factors affecting gear meshing; Indicates the amplitude of the gear meshing frequency; This represents the average amplitude of the gear meshing frequency; Standard values ​​representing the amplitude of gear meshing frequency; Indicates factors affecting load; express The load at any moment; Indicates shape parameters; Indicates the scale parameter; Indicates factors affecting time; Indicates the current time; This represents a parameter that controls the decay rate of the exponential function.

[0187] In some optional implementations, the construction of the gearbox cumulative wear assessment model of the cumulative wear acquisition module 340 specifically includes:

[0188] Based on ambient temperature, the relationship between ambient temperature and cumulative wear of the gearbox is established using the Poisson function, and the influencing factors of ambient temperature are obtained.

[0189] Based on the load, the relationship between the load and the cumulative wear of the gearbox is established using the Weibull function, and the factors affecting the load are obtained.

[0190] Based on gearbox age data, a logical function is used to establish the relationship between gearbox age data and cumulative gearbox wear, thus obtaining the gearbox age data factor.

[0191] Based on the environmental temperature, load, and gearbox age data, and by combining the current wear value and historical cumulative wear value of the gearbox, a cumulative wear assessment model for the gearbox is obtained.

[0192] In some alternative implementations, the gearbox cumulative wear assessment model of the cumulative wear acquisition module 340 includes:

[0193] ,

[0194] in, This indicates the cumulative wear value of the gearbox; express Historical cumulative wear value up to this point in time; This indicates the current wear value of the gearbox; Indicates the wear accumulation coefficient; Indicates factors affecting ambient temperature; and All indicate relative to ambient temperature Relevant Poisson distribution parameters; express The ambient temperature at that moment; Indicates factors affecting load; express The load at any moment; This represents a scale parameter related to the load; Indicates the shape parameters related to the load; Indicates factors affecting gearbox age data; This indicates that the gearbox has used age data; Indicates the expected service life of the gearbox; Indicates the current moment.

[0195] The wind turbine gearbox wear assessment device provided in this embodiment of the invention can execute the wind turbine gearbox wear assessment method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0196] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0197] The following is a detailed reference. Figure 4This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0198] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0199] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the wind turbine gearbox wear assessment method of the embodiments of the present invention.

[0200] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0201] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the wind turbine gearbox wear assessment method shown in the above embodiments is implemented.

[0202] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0203] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for assessing wear of a wind turbine gearbox, characterized in that, The method includes: Based on the collected vibration acceleration, load, and gear meshing frequency data, the root mean square value of vibration acceleration is obtained using a pre-constructed first parameter processing model; the first parameter processing model includes a function of the root mean square value of vibration acceleration with respect to the vibration acceleration, load, and gear meshing frequency data; Based on the collected data on oil iron filings content, load, and vibration frequency, the growth rate of iron filings content is obtained using a pre-constructed second-parameter processing model; the second-parameter processing model includes a function of the growth rate of iron filings content with respect to the data on oil iron filings content, load, and vibration frequency. Based on the root mean square value of vibration acceleration and the growth rate of iron filings content, and combining the gear meshing frequency amplitude, load, and time data, the current wear value of the gearbox is obtained using a pre-built gearbox current wear assessment model. The gearbox current wear assessment model includes a function of the current wear value of the gearbox with respect to the root mean square value of vibration acceleration, the growth rate of iron filings content, the gear meshing frequency amplitude, load, and time data. Based on the current wear value of the gearbox, combined with historical cumulative wear value, ambient temperature, load and gearbox age data, the cumulative wear value of the gearbox is obtained using a pre-built gearbox cumulative wear assessment model; the gearbox cumulative wear assessment model includes a function of the cumulative wear value of the gearbox with respect to the current wear value of the gearbox, historical cumulative wear value, ambient temperature, load and gearbox age data; Based on the current wear function value and the cumulative wear function value, the wear assessment result of the wind turbine gearbox is obtained by using preset judgment conditions. The current wear assessment model for the gearbox includes: ， in, This indicates the current wear value of the gearbox; Indicates the total sampling time; Indicates the factors affecting vibration acceleration; This represents the root mean square value of the vibration acceleration; This represents the mean of the root mean square values ​​of vibration acceleration; The standard deviation of the root mean square value of vibration acceleration; Indicates factors affecting iron filings content; Indicates the growth rate of iron filings content; This represents the average growth rate of iron filings content; The standard deviation of the growth rate of iron filings content; Indicates factors affecting gear meshing; Indicates the amplitude of the gear meshing frequency; This represents the average amplitude of the gear meshing frequency; Standard values ​​representing the amplitude of gear meshing frequency; Indicates factors affecting load; express The load at any moment; Indicates shape parameters; Indicates the scale parameter; Indicates factors affecting time; Indicates the current moment; This represents a parameter that controls the decay rate of the exponential function. The gearbox cumulative wear assessment model includes: ， in, This indicates the cumulative wear value of the gearbox; express Historical cumulative wear value up to this point in time; This indicates the current wear value of the gearbox; Indicates the wear accumulation coefficient; Indicates factors affecting ambient temperature; and All indicate relative to ambient temperature Relevant Poisson distribution parameters; express The ambient temperature at that moment; Indicates factors affecting load; express The load at any moment; This represents a scale parameter related to the load; Indicates the shape parameters related to the load; Indicates factors affecting gearbox age data; This indicates that the gearbox has used age data; Indicates the expected service life of the gearbox; Indicates the current moment; The first parameter processing model includes: ， in, This represents the root mean square value of the vibration acceleration; express The vibration acceleration at any given moment; Indicates time window Internal vibration acceleration The average value; Indicates time window Internal vibration acceleration Standard deviation; express The load at any moment; This represents the average load over a preset time period. This represents the standard deviation of the load within a preset time period; Indicates the gear meshing frequency; The second parameter processing model includes: ， in, Indicates the growth rate of iron filings content; Indicates a time interval; express The iron filings content of the oil at any given time; This represents the average value of iron filings in the oil over a preset time period. This represents the standard deviation of the iron filings content in the oil over a preset time period. express The load at any moment; This represents the average load over a preset time period. This represents the standard deviation of the load within a preset time period; Indicates the control coefficient; This represents the vibration frequency data.

2. The method according to claim 1, characterized in that, The construction of the current wear assessment model for the gearbox includes: Based on the root mean square value of vibration acceleration, the relationship between vibration acceleration and the current wear of the gearbox is constructed using a Gaussian function, and the influencing factors of vibration acceleration are obtained. Based on the growth rate of iron filings content, the relationship between the growth rate of iron filings content and the current wear of the gearbox is constructed using a Gaussian function, and the influencing factors of iron filings content are obtained. Based on the gear meshing frequency amplitude, the relationship between the gear meshing frequency amplitude and the current wear of the gearbox is constructed using a Gaussian function, thereby obtaining the factors affecting gear meshing; Based on the load, the relationship between the load and the current wear of the gearbox is constructed using the gamma function to obtain the factors affecting the load. Based on time data, the relationship between time and current gearbox wear is constructed using an exponential function to obtain the factors influencing time. Based on the combined effects of vibration acceleration, iron filings content growth rate, gear meshing, load, and time, a current wear assessment model for the gearbox is obtained.

3. The method according to claim 1, characterized in that, The construction of the gearbox cumulative wear assessment model includes: Based on ambient temperature, the relationship between ambient temperature and cumulative wear of the gearbox is established using the Poisson function, and the influencing factors of ambient temperature are obtained. Based on the load, the relationship between the load and the cumulative wear of the gearbox is established using the Weibull function, and the factors affecting the load are obtained. Based on gearbox age data, a logical function is used to establish the relationship between gearbox age data and cumulative gearbox wear, thus obtaining the gearbox age data factor. Based on the environmental temperature, load, and gearbox age data, and by combining the current wear value and historical cumulative wear value of the gearbox, a cumulative wear assessment model for the gearbox is obtained.

4. A device for assessing wear of a wind turbine gearbox, characterized in that, The device includes: The first data processing module is used to obtain the root mean square value of vibration acceleration based on the collected vibration acceleration, load, and gear meshing frequency data, using a pre-constructed first parameter processing model; the first parameter processing model includes a function of the root mean square value of vibration acceleration with respect to the vibration acceleration, load, and gear meshing frequency data; The second data processing module is used to obtain the iron filings content growth rate based on the collected oil iron filings content, load and vibration frequency data, using a pre-built second parameter processing model; the second parameter processing model includes the iron filings content growth rate as a function of the oil iron filings content, load and vibration frequency data; The current wear acquisition module is used to obtain the current wear value of the gearbox based on the root mean square value of vibration acceleration and the growth rate of iron filings content, combined with gear meshing frequency amplitude, load and time data, using a pre-built gearbox current wear assessment model; the gearbox current wear assessment model includes a function of the current wear value of the gearbox with respect to the root mean square value of vibration acceleration, the growth rate of iron filings content, gear meshing frequency amplitude, load and time data; The cumulative wear acquisition module is used to obtain the cumulative wear value of the gearbox based on the current wear value of the gearbox, combined with historical cumulative wear values, ambient temperature, load and gearbox age data, using a pre-built gearbox cumulative wear assessment model; the gearbox cumulative wear assessment model includes a function of the cumulative wear value of the gearbox with respect to the current wear value of the gearbox, historical cumulative wear values, ambient temperature, load and gearbox age data; The result output module is used to evaluate the wear of the wind turbine gearbox based on the current wear function value and the cumulative wear function value using preset judgment conditions. The current wear assessment model for the gearbox includes: ， in, This indicates the current wear value of the gearbox; Indicates the total sampling time; Indicates the factors affecting vibration acceleration; This represents the root mean square value of the vibration acceleration; This represents the mean of the root mean square values ​​of vibration acceleration; The standard deviation of the root mean square value of vibration acceleration; Indicates factors affecting iron filings content; Indicates the growth rate of iron filings content; This represents the average growth rate of iron filings content; The standard deviation of the growth rate of iron filings content; Indicates factors affecting gear meshing; Indicates the amplitude of the gear meshing frequency; This represents the average amplitude of the gear meshing frequency; Standard values ​​representing the amplitude of gear meshing frequency; Indicates factors affecting load; express The load at any moment; Indicates shape parameters; Indicates the scale parameter; Indicates factors affecting time; Indicates the current moment; This represents a parameter that controls the decay rate of the exponential function. The gearbox cumulative wear assessment model includes: ， in, This indicates the cumulative wear value of the gearbox; express Historical cumulative wear value up to this point in time; This indicates the current wear value of the gearbox; Indicates the wear accumulation coefficient; Indicates factors affecting ambient temperature; and All indicate relative to ambient temperature Relevant Poisson distribution parameters; express The ambient temperature at that moment; Indicates factors affecting load; express The load at any moment; This represents a scale parameter related to the load; Indicates the shape parameters related to the load; Indicates factors affecting gearbox age data; This indicates that the gearbox has used age data; Indicates the expected service life of the gearbox; Indicates the current moment; The first parameter processing model includes: ， in, This represents the root mean square value of the vibration acceleration; express The vibration acceleration at any given moment; Indicates time window Internal vibration acceleration The average value; Indicates time window Internal vibration acceleration Standard deviation; express The load at any moment; This represents the average load over a preset time period. This represents the standard deviation of the load within a preset time period; Indicates the gear meshing frequency; The second parameter processing model includes: ， in, Indicates the growth rate of iron filings content; Indicates a time interval; express The iron filings content of the oil at any given time; This represents the average value of iron filings in the oil over a preset time period. This represents the standard deviation of the iron filings content in the oil over a preset time period. express The load at any moment; This represents the average load over a preset time period. This represents the standard deviation of the load within a preset time period; Indicates the control coefficient; This represents the vibration frequency data.

5. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the wind turbine gearbox wear assessment method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the wind turbine gearbox wear assessment method according to any one of claims 1 to 3.

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