Wind turbine assembly life evaluation method and system based on multi-physics field coupling

Abstract

The invention relates to the technical field of wind turbine generator management, in particular to a wind turbine generator assembly service life evaluation method and system based on multi-physics field coupling, and the method comprises the steps: dividing a plurality of evaluation units based on multi-physics field fine granularity according to a component function partitioning and physics field type matching principle for a wind turbine generator impeller gearbox system; obtaining multi-source sensing data of each evaluation unit; determining a multi-field coupling state threshold value of each evaluation unit based on geometrical characteristics and historical operation and maintenance fault data of the wind turbine impeller gearbox system; for each evaluation unit, determining a degradation candidate unit based on the multi-field coupling state threshold and the multi-source sensing data; historical data corresponding to the degradation candidate units are obtained, degradation trend analysis is carried out on the degradation candidate units according to the historical data, and the remaining service life of the wind turbine assembly corresponding to the degradation candidate units is obtained. The accuracy of life prediction can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind turbine management, and particularly relates to a wind turbine component life assessment method and system based on multi-physical field coupling. BACKGROUND

[0002] As the core equipment of a wind power system, the operation reliability of a wind turbine directly determines the power generation efficiency and economic benefits of a wind farm. The impeller gearbox system is a key transmission and energy conversion component of the wind turbine, and bears the core function of converting the wind energy captured by the impeller into mechanical energy and transmitting it to the generator. Since the wind power system is operated in a complex and harsh natural environment and working condition for a long time, it is necessary to assess the life of the wind turbine to ensure its safe and stable operation.

[0003] The existing wind turbine component life assessment methods mainly analyze a single physical field in isolation, such as using structural mechanics to assess impeller fatigue or using tribology to assess gearbox wear. The nonlinear coupling effects of multiple physical fields are ignored, for example, the fluctuation of impeller aerodynamic load changes the stress distribution of the gearbox, the increase of the oil temperature of the gearbox reduces the viscosity of the lubricating oil and accelerates wear, and the wear vibration unbalances the dynamic load of the impeller. The existing methods cannot quantify the influence of multiple fields on degradation, resulting in errors in life prediction and inaccurate assessment of the remaining life of the wind turbine. SUMMARY

[0004] The present application provides a wind turbine component life assessment method and system based on multi-physical field coupling, which can effectively solve the problems in the background art.

[0005] To achieve the above purpose, in a first aspect, the present application provides a wind turbine component life assessment method based on multi-physical field coupling, comprising: For the impeller gearbox system of a wind turbine, a plurality of multi-physical field fine-grained assessment units are divided according to the principle of matching component function zoning and physical field type, and multi-source sensing data of each assessment unit is obtained; Based on the geometric characteristics and historical operation and maintenance fault data of the impeller gearbox system of the wind turbine, the multi-field coupling state threshold of each assessment unit is determined; For each assessment unit, based on the multi-field coupling state threshold and the multi-source sensing data, a degradation candidate unit is determined; The historical data corresponding to each degradation candidate unit is obtained, and the degradation trend of the degradation candidate unit is analyzed based on the historical data, to obtain the remaining service life of the wind turbine component corresponding to the degradation candidate unit.

[0006] In a possible design, the assessment unit includes a physical field parameter mapping relationship, a sensing data interface specification, and a degradation characteristic benchmark library.

[0007] With reference to the first aspect, in a possible design, the degradation feature benchmark library is constructed based on component fatigue crack propagation rate and gear surface wear data obtained through multi-physical field coupling tests, and material damage rules obtained through micro-characterization techniques.

[0008] With reference to the first aspect, in a possible design, the evaluation unit is divided into a blade root stress evaluation unit, a blade tip vibration evaluation unit, and a leading edge corrosion evaluation unit on the impeller side, and a gear meshing evaluation unit, a bearing contact evaluation unit, and a lubricating oil film evaluation unit on the gearbox side.

[0009] With reference to the first aspect, in a possible design, the blade root stress evaluation unit and the leading edge corrosion evaluation unit are associated with a distributed optical fiber sensor, and the blade tip vibration evaluation unit is associated with an acceleration sensor; the gear meshing evaluation unit and the bearing contact evaluation unit are associated with a MEMS accelerometer, and the lubricating oil film evaluation unit is associated with an oil sensor and a temperature sensor.

[0010] With reference to the first aspect, in a possible design, based on the multi-field coupling state threshold and the multi-source sensing data, a degradation candidate unit is determined, including: The multi-field coupling state threshold and the multi-source sensing data are compared to screen a plurality of degradation risk units; For each of the degradation risk units, multi-physical field feature data is extracted; The multi-physical field feature data is matched with normal feature data of a corresponding unit in the degradation feature benchmark library, false positive units caused by data fluctuations are removed, and a degradation candidate unit is retained.

[0011] With reference to the first aspect, in a possible design, the multi-physical field feature data includes aerodynamic field parameters, structural field parameters, and temperature field parameters.

[0012] With reference to the first aspect, in a possible design, the multi-physical field feature data is matched with normal feature data of a corresponding unit in the degradation feature benchmark library, false positive units caused by data fluctuations are removed, and a degradation candidate unit is retained, including: In response to all key feature parameters of a risk unit exceeding normal feature data, the risk unit is determined as a degradation candidate unit; In response to some parameters of a risk unit exceeding normal feature data but other parameters being normal, the risk unit is determined as a false positive unit; The false positive unit is started in a short-time monitoring mode, and if the out-of-limit parameter returns to a normal range in the short-time monitoring process, the false positive unit is completely excluded; If the out-of-limit parameter continues to deteriorate, the false positive unit is re-included in the list of degradation candidate units.

[0013] With the first aspect, in a possible design, historical data corresponding to each of the degradation candidate units is acquired, and degradation trend analysis is performed on the degradation candidate units based on the historical data to obtain the remaining useful life of the wind turbine component corresponding to the degradation candidate unit, including: collecting historical operation and maintenance data and multi-source sensing data of each evaluation unit; for each degradation candidate unit, analyzing its degradation history, combining its historical operation and maintenance fault data, and using a hybrid model of a physical model and a data-driven model to construct a degradation trend analysis model; by the established degradation trend analysis model, the degradation speed of each degradation candidate unit is analyzed to determine the degradation mode; based on the degradation mode, the remaining useful life is calculated.

[0014] The second aspect, the application also provides a wind turbine component life assessment system based on multi-physical field coupling, comprising: a unit division and sensing data acquisition module, for a wind turbine impeller gearbox system, a plurality of multi-physical field fine-grained evaluation units are divided according to the principle of part function zoning and physical field type matching, and multi-source sensing data of each evaluation unit is acquired; a threshold determination module, based on the geometric characteristics and historical operation and maintenance fault data of the wind turbine impeller gearbox system, the multi-field coupling state threshold of each evaluation unit is determined; a degradation candidate unit determination module, for each evaluation unit, based on the multi-field coupling state threshold and the multi-source sensing data, a degradation candidate unit is determined; a remaining useful life prediction module, acquiring historical data corresponding to each of the degradation candidate units, and performing degradation trend analysis on the degradation candidate units based on the historical data to obtain the remaining useful life of the wind turbine component corresponding to the degradation candidate unit.

[0015] Through the technical scheme of the application, the following technical effects can be achieved: The method comprehensively considers the nonlinear coupling effect of multiple physical fields by dividing multiple multi-physical field fine-grained evaluation units, can accurately reflect the complex working conditions of the wind turbine in actual operation, avoids the limitation of single physical field evaluation, improves the accuracy of life prediction, and improves the pertinence and effectiveness of the evaluation. The evaluation unit is divided according to the matching principle of component function partition and physical field type, which is convenient for fine management and evaluation of different components and physical fields, improves the pertinence and effectiveness of the evaluation. The degradation feature benchmark library is constructed by obtaining multi-source sensing data of each evaluation unit and combining the material damage law obtained by the micro characterization technology, realizing the systematicness of data acquisition and processing. The fusion use of multi-source sensing data improves the richness and accuracy of the data, and the introduction of micro characterization technology enhances the scientificity and depth of data interpretation. Based on the geometric characteristics and historical operation and maintenance fault data of the wind turbine impeller gearbox system, the multi-field coupling state threshold of each evaluation unit is determined, combined with the actual operation experience, so that the threshold setting is more scientific and reasonable. At the same time, with the continuous accumulation of equipment operation data, the threshold can be dynamically adjusted to adapt to the change of equipment state, improving the flexibility and accuracy of the evaluation. By comprehensively considering the nonlinear coupling effect of multiple physical fields, combining the fine-grained evaluation unit division and the system data acquisition and processing process, the degradation characteristics of the equipment under different working conditions can be accurately captured, thereby improving the accuracy of life prediction, discovering potential faults in advance, reducing unplanned downtime, optimizing maintenance plan and reducing maintenance cost. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The flowchart of the present application is shown in the figure. Figure 2 The structure diagram of the wind turbine component life evaluation system based on multi-physical field coupling is shown in the figure. DETAILED DESCRIPTION

[0017] The present application will be described below in conjunction with the drawings in the present application.

[0018] As shown in the figure, Figure 1 The wind turbine component life evaluation method based on multi-physical field coupling of the present application specifically includes the following steps: S1, for the wind turbine impeller gearbox system, a plurality of multi-physical field fine-grained evaluation units are divided according to the matching principle of component function partition and physical field type, and multi-source sensing data of each evaluation unit is obtained; S2, based on the geometric characteristics and historical operation and maintenance fault data of the wind turbine impeller gearbox system, the multi-field coupling state threshold of each evaluation unit is determined; S3, for each evaluation unit, based on the multi-field coupling state threshold and the multi-source sensing data, the degradation candidate unit is determined; S4, acquire historical data corresponding to each of the degradation candidate units, and perform degradation trend analysis on the degradation candidate units based on the historical data to obtain the remaining service life of the wind turbine component corresponding to the degradation candidate unit.

[0019] In the embodiment, the method comprehensively considers the nonlinear coupling effects of multiple physical fields by dividing multiple evaluation units based on multiple physical fields and fine granularity, can accurately reflect the complex working conditions of the wind turbine in actual operation, avoids the limitations of single physical field evaluation, improves the accuracy of life prediction, and divides the evaluation units according to the matching principle of component function partition and physical field type, facilitating fine management and evaluation of different components and physical fields, improving the pertinence and effectiveness of evaluation, and realizing the systematicness of data acquisition and processing by acquiring multi-source sensing data of each evaluation unit and constructing a degradation feature benchmark library based on material damage rules obtained by micro characterization technology. The fusion of multi-source sensing data improves the richness and accuracy of data, and the introduction of micro characterization technology enhances the scientificity and depth of data interpretation. Based on the geometric characteristics and historical operation and maintenance fault data of the wind turbine impeller gearbox system, the threshold values of the multi-field coupling states of each evaluation unit are determined, and the threshold values are set more scientifically and reasonably in combination with actual operation experience. At the same time, with the continuous accumulation of equipment operation data, the threshold values can be dynamically adjusted to adapt to the changes of equipment state, improving the flexibility and accuracy of evaluation. By comprehensively considering the nonlinear coupling effects of multiple physical fields, combining the fine-grained evaluation unit division and the system data acquisition and processing process, the degradation characteristics of the equipment under different working conditions can be accurately captured, thereby improving the accuracy of life prediction, discovering potential faults in advance, reducing unplanned downtime, optimizing maintenance plans, and reducing maintenance costs.

[0020] In some embodiments of the application, for step S1, for the wind turbine impeller gearbox system, multiple evaluation units based on multiple physical fields and fine granularity are divided according to the matching principle of component function partition and physical field type, and multi-source sensing data of each evaluation unit is acquired. The evaluation unit includes a physical field parameter mapping relationship, a sensing data interface specification, and a degradation feature benchmark library. The wind turbine impeller gearbox system is affected by multiple physical fields, and there are nonlinear coupling effects between the physical fields. By establishing a physical field parameter mapping relationship, the interaction between different physical fields can be quantified. The fusion of multi-source sensing data requires a unified data interface standard to ensure that data from different types of sensors can be seamlessly integrated. Standardized interface design can reduce the complexity of data processing and improve the compatibility and scalability of the system. The degradation characteristic benchmark library is constructed based on the fatigue crack propagation rate and gear tooth surface wear data of the component obtained through multi-physical field coupling test and the material damage law obtained through micro-characterization technology; the fatigue crack propagation rate and gear tooth surface wear data of the component are obtained through simulation of different degradation stages on a multi-physical field coupling test platform; the correlation between the degradation characteristics and the microstructure change is established by analyzing the material damage law through micro-characterization technology; and the test data and the micro-analysis results are integrated into the degradation characteristic benchmark library to provide a comparison standard for real-time monitoring. The division of the evaluation unit includes: a blade root stress evaluation unit, a blade tip vibration evaluation unit and a leading edge corrosion evaluation unit on the impeller side; and a gear meshing evaluation unit, a bearing contact evaluation unit and a lubricating oil film evaluation unit on the gearbox side; The blade root stress evaluation unit is used for monitoring the stress distribution of the blade root position, and is associated with a distributed optical fiber sensor to obtain blade root stress data in real time; The blade tip vibration evaluation unit is used for monitoring the vibration of the blade tip position, and is associated with an acceleration sensor to capture the dynamic response of the blade tip; The leading edge corrosion evaluation unit is used for evaluating the corrosion degree of the leading edge of the impeller, and is associated with a distributed optical fiber sensor to monitor the strain and temperature change of the leading edge surface, thereby indirectly reflecting the corrosion condition; The gear meshing evaluation unit is used for monitoring the vibration and impact during gear meshing, and is associated with a MEMS accelerometer to capture the dynamic characteristics of gear meshing; The bearing contact evaluation unit is used for evaluating the stress and wear of the bearing contact area, and is associated with a MEMS accelerometer and a temperature sensor to monitor the running state of the bearing; The lubricating oil film evaluation unit is used for monitoring the thickness and temperature of the lubricating oil film, and is associated with an oil sensor and a temperature sensor to evaluate the lubrication effect and prevent wear caused by poor lubrication.

[0021] In this embodiment, by dividing a plurality of fine-grained evaluation units according to the physical field type and the component function, the monitoring of the wind turbine impeller-gearbox system can be more refined and targeted; each unit focuses on specific physical field parameters, enabling the system to better capture the dynamic changes and degradation trends of each component; by formulating a unified data interface standard, data from different types of sensors can be seamlessly integrated, thereby providing comprehensive and accurate system state information to ensure effective monitoring of the interaction between each physical field; the establishment of a degradation feature benchmark library, combined with test data and microscopic analysis, can provide a comparison standard for real-time monitoring; the system can compare real-time collected data with historical degradation features during actual operation to timely detect potential faults of the wind turbine; by dividing specific evaluation units, the system can comprehensively monitor the state changes of each key component of the wind turbine; each unit has a dedicated sensor and monitoring method, which can analyze and evaluate the health status of each component in a targeted manner, improving the monitoring accuracy of the entire system; by establishing a physical field parameter mapping relationship, the nonlinear coupling effect between different physical fields can be quantified; not only can the state of each component of the system be accurately captured, but also the degradation trend of each component under different working conditions can be predicted, enhancing the reliability and life prediction of the wind turbine; by monitoring the degradation features and abnormal changes of each component early, predictive information can be provided for the operation and maintenance of the wind turbine, thereby avoiding sudden failures, improving operation and maintenance efficiency, and reducing downtime and maintenance costs.

[0022] In some embodiments of the present application, for step S2, based on the geometric characteristics and historical operation and maintenance fault data of the wind turbine impeller-gearbox system, the multi-field coupling state threshold of each evaluation unit is determined to distinguish between the normal state and the degradation risk state of the evaluation unit. Collect the geometric characteristic parameters of the wind turbine impeller and gearbox, including the length, width, thickness, and twist angle of the impeller blade, the gear modulus, number of teeth, and meshing angle of the gearbox, the bearing diameter, and contact angle, etc. Extract historical fault records, including fault type, occurrence time, fault location, and maintenance records; obtain historical multi-source sensor data; Clean the collected data to remove noise and outliers, and align them according to the time stamp to ensure consistency and comparability of the data; Use finite element analysis software to establish a multi-physical field coupling model of the impeller and gearbox system; Use the geometric characteristics as model parameters to analyze the influence of geometric characteristics on the multi-physical field coupling effect through parameterized modeling; Simulate the dynamic changes of the multi-physical field under different working conditions to analyze how geometric characteristics affect the coupling relationship between physical fields; According to historical operation and maintenance fault data, main fault modes are identified, and the correlation between each fault mode and multi-physical field coupling effect is analyzed; By using the multi-physical field coupling model, the contribution of each physical field to the fault mode under different geometric characteristics is quantified; Degradation features related to the fault mode are extracted from historical multi-source sensing data; Based on historical operation and maintenance fault data and degradation features, the normal state and degradation risk state of the component are defined; In combination with the multi-physical field coupling model, the physical field parameter range of the normal state and the degradation risk state under different geometric characteristics is analyzed; Considering the influence of geometric characteristics, the threshold value of the component with different geometric parameters is determined.

[0023] In this embodiment, by combining the geometric characteristics of the impeller and the gear box with the historical operation and maintenance fault data, the main fault modes can be accurately identified and analyzed, and the accuracy of the wind turbine fault detection can be improved. By establishing a multi-physical field coupling model, the contribution of each physical field to the fault mode can be quantified, the influence of geometric characteristics on the coupling effect between physical fields can be deeply analyzed, and the comprehensiveness and accuracy of the component health state evaluation can be improved. Based on the fusion of the multi-physical field coupling model and the historical data, the normal state and the degradation risk state of the component can be clearly distinguished, and the possible failure of the equipment can be predicted in advance, thereby reducing downtime and maintenance cost. By considering the influence of different geometric characteristics on the state of the component, individualized state evaluation threshold values can be set for different wind turbines, avoiding overly rough judgments and improving the reliability of fault diagnosis. By collecting and integrating multi-dimensional information such as multi-source sensing data and historical fault records, multi-angle data support can be provided for fault diagnosis and risk assessment, and the operation and maintenance efficiency and safety of the wind turbine are improved.

[0024] In some embodiments of the present application, for step S3, for each evaluation unit, based on the multi-field coupling state threshold value and the multi-source sensing data, a degradation candidate unit is determined; The multi-field coupling state threshold value and the multi-source sensing data are compared to screen a plurality of degradation risk units; For each degradation risk unit, multi-physical field feature data are extracted; the multi-physical field feature data include aerodynamic field parameters, structural field parameters and temperature field parameters; The normal feature data of the corresponding unit in the degradation feature reference library are used to perform item-by-item matching on the multi-physical field feature data, to eliminate false positive units caused by data fluctuations, and to retain degradation candidate units; The real-time sensing data of each evaluation unit is compared with the preset multi-field coupling state threshold value to quickly mark the units exceeding the limit; If a single unit exceeds the limit, the correlation verification of the adjacent physical field data is triggered; When the blade root stress exceeds the limit, check the blade tip vibration data simultaneously. If the vibration amplitude increases synchronously, it indicates that the aerodynamic load fluctuation is causing stress concentration, thus confirming the risk. When the gearbox oil temperature exceeds the limit, check the gear meshing frequency components. If there is a sudden increase in energy in a specific frequency band, it indicates that the wear has intensified, leading to the temperature rise, and the risk is confirmed. The unit is marked as a degradation risk unit only when both layers of verification pass, to avoid misjudgment based on a single parameter; For each degradation risk unit, multiphysics feature data is extracted; the multiphysics feature data includes aerodynamic field parameters, structural field parameters, and temperature field parameters; The aerodynamic field parameters of the blade root stress assessment unit include impeller speed, angle of attack, and aerodynamic load fluctuation frequency; the structural field parameters include stress amplitude, stress cycle number, and stress-temperature coupling coefficient; and the temperature field parameters include the temperature gradient and thermal stress coefficient in the blade root region. The aerodynamic field parameters of the gear meshing evaluation unit include impeller output torque fluctuation; the structural field parameters include gear meshing force and contact fatigue damage index; and the temperature field parameters include gearbox oil temperature and tooth surface friction heat generation rate. Using the normal feature data of the corresponding unit in the degradation feature benchmark library, the multiphysics feature data is matched item by item to eliminate misjudged units caused by data fluctuations, and retain degradation candidate units, including: The degradation characteristic benchmark library contains the distribution range of characteristic parameters of each evaluation unit under normal and degradation conditions; A risk unit is identified as a degenerate candidate unit only when all of its key characteristic parameters exceed the normal range. If some parameters exceed the limits but other parameters are normal, it is judged as a misjudged unit; A short-term monitoring mode is activated for misjudged units. If the out-of-limit parameters return to the normal range, the misjudgment is completely eliminated. If the parameters exceeding the limit continue or worsen, the unit will be reinstated to the list of degraded candidate units and its risk level will be upgraded.

[0025] In the embodiment, by combining the multi-field coupling state threshold and the multi-source sensing data, the degradation candidate unit can be accurately identified, and a plurality of degradation risk units are gradually screened out according to data comparison, thereby improving the accuracy and reliability of system diagnosis; the correlation verification mechanism of adjacent physical field data is introduced, so that each risk unit not only meets the standard under a single physical field, but also needs to be verified in cooperation between multiple physical fields, thereby reducing the risk of single parameter misjudgment; by matching each item with the normal characteristic data in the degradation characteristic benchmark library, the misjudgment caused by data fluctuation can be effectively eliminated, and the real degradation candidate unit is retained; at the same time, the system automatically starts a short-time monitoring mode for the misjudged unit, further verifies and corrects the judgment result, and improves the flexibility and adaptability of intelligent diagnosis; the multi-physical field characteristic data of the pneumatic field, the structural field and the temperature field are used for comprehensive analysis, so that the degradation state of each evaluation unit can be comprehensively evaluated, and accurate diagnosis results are provided; by quickly marking the units exceeding the limit and verifying under the support of multi-physical field data, the misjudgment caused by single sensor data fluctuation is avoided, thereby improving the operation and maintenance efficiency of the wind turbine and ensuring the stability and safety of the wind power equipment.

[0026] In some embodiments of the application, for step S4, historical data corresponding to each degradation candidate unit is obtained, and the degradation trend of the degradation candidate unit is analyzed based on the historical data, to obtain the remaining service life of the wind power component corresponding to the degradation candidate unit. Historical operation and maintenance data and multi-source sensing data of each evaluation unit are collected to ensure the completeness and accuracy of the data; the data is preprocessed, including denoising, missing value filling, standardization and other steps, to ensure the data quality; For each degradation candidate unit, its degradation history is analyzed, and a hybrid model of physical model and data-driven model is used to construct a degradation trend analysis model in combination with historical operation and maintenance fault data; a regression model in machine learning, including random forest regression and support vector machine regression, is used to model the degradation trend, or a finite element analysis method is used to simulate the degradation process; different degradation models are established for the impeller, the gear box and other components according to their physical properties and historical data; The degradation speed of each degradation candidate unit is analyzed by the established degradation trend analysis model to determine the degradation mode. Based on the analyzed degradation mode, the remaining service life is calculated; for each degradation unit, the difference from the degradation threshold is calculated by comparing the historical data and the current state, and then the remaining service life of the wind turbine component is predicted.

[0027] In this embodiment, by combining the regression model in machine learning with the finite element analysis method, the degradation process of the wind turbine components can be more accurately modeled and simulated, thereby improving the accuracy of the remaining useful life prediction; the historical operation and maintenance data and multi-source sensor data are integrated to ensure the comprehensiveness and high quality of the data, which can better reflect the actual performance of the wind turbine components under different working conditions; through data preprocessing steps such as denoising, missing value filling and standardization, the integrity and accuracy of the data are ensured, thereby avoiding the influence of data quality problems on the analysis results; for different wind turbine components, specific degradation models are established to better adapt to the physical characteristics of each component and improve the analysis precision; through degradation trend analysis, the degradation speed and mode of each component can be deeply understood, and the accuracy of the fault prediction and maintenance decision of the wind turbine can be improved; accurate prediction of the remaining useful life and early warning can optimize the repair and replacement plan, avoid unnecessary downtime and sudden failures, ultimately prolong the service life of the equipment and reduce the operation and maintenance cost.

[0028] As shown in Figure 2 The application also provides a wind turbine component life evaluation system based on multi-physical field coupling, which specifically comprises the following modules: Unit division and sensor data acquisition module, for the wind turbine impeller gearbox system, multiple multi-physical field fine-grained evaluation units are divided according to the component function zoning and physical field type matching principle, and multi-source sensor data of each evaluation unit is acquired; Threshold determination module, based on the geometric characteristics and historical operation and maintenance fault data of the wind turbine impeller gearbox system, the multi-field coupling state threshold of each evaluation unit is determined; Degradation candidate unit determination module, for each evaluation unit, based on the multi-field coupling state threshold and the multi-source sensor data, the degradation candidate unit is determined; Remaining useful life prediction module, the historical data corresponding to each degradation candidate unit is acquired, and the degradation trend of the degradation candidate unit is analyzed based on the historical data, thereby obtaining the remaining useful life of the wind turbine component corresponding to the degradation candidate unit.

[0029] In the embodiment, the system considers the nonlinear coupling effect of multiple physical fields by dividing multiple multi-physical field fine-grained evaluation units, can accurately reflect the complex working conditions of the wind turbine in actual operation, avoids the limitation of single physical field evaluation, improves the accuracy of life prediction, and divides the evaluation units according to the matching principle of component function partition and physical field type, which facilitates fine management and evaluation of different components and physical fields, improves the pertinence and effectiveness of evaluation, and realizes the systematicness of data acquisition and processing by obtaining multi-source sensing data of each evaluation unit and combining the material damage law obtained by the micro characterization technology to construct a degradation feature benchmark library; the fusion use of multi-source sensing data improves the richness and accuracy of data, and the introduction of micro characterization technology enhances the scientificity and depth of data interpretation; based on the geometric characteristics and historical operation and maintenance fault data of the wind turbine impeller gearbox system, the threshold values of the multi-field coupling state of each evaluation unit are determined, combined with the actual operation experience, the threshold setting is more scientific and reasonable; at the same time, with the continuous accumulation of equipment operation data, the threshold value can be dynamically adjusted to adapt to the change of equipment state, improve the flexibility and accuracy of evaluation; by comprehensively considering the nonlinear coupling effect of multiple physical fields, combining the fine-grained evaluation unit division and the data acquisition and processing process of the system, the degradation characteristics of the equipment under different working conditions can be accurately captured, thereby improving the accuracy of life prediction, and potential faults can be found in advance, non-planned downtime can be reduced, maintenance plan can be optimized, and maintenance cost can be reduced.

[0030] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for assessing the lifespan of wind turbine components based on multiphysics coupling, characterized in that, include: For wind turbine impeller gearbox systems, multiple fine-grained evaluation units based on multi-physics are divided according to the principle of matching component functional zoning with physical field type, and multi-source sensor data of each evaluation unit are acquired. Based on the geometric characteristics and historical operation and maintenance fault data of the wind turbine rotor gearbox system, the multi-field coupling state threshold of each of the evaluation units is determined. For each of the evaluation units, a degradation candidate unit is determined based on the multi-field coupling state threshold and the multi-source sensing data; Historical data corresponding to each degradation candidate unit is obtained, and degradation trend analysis is performed on the degradation candidate unit to obtain the remaining service life of the wind turbine component corresponding to the degradation candidate unit.

2. The method for assessing the lifespan of wind turbine components based on multiphysics coupling according to claim 1, characterized in that, The evaluation unit includes physical field parameter mapping relationships, sensor data interface specifications, and a degradation feature benchmark library.

3. The method for assessing the lifespan of wind turbine components based on multiphysics coupling according to claim 2, characterized in that, The degradation feature benchmark library is constructed based on component fatigue crack propagation rate and tooth surface wear data obtained from multi-physics field coupling tests, combined with material damage laws obtained from microscopic characterization techniques.

4. The wind turbine component life assessment method based on multiphysics coupling according to claim 1, characterized in that, The evaluation units are divided as follows: on the impeller side, there are blade root stress evaluation units, blade tip vibration evaluation units, and leading edge corrosion evaluation units; on the gearbox side, there are gear meshing evaluation units, bearing contact evaluation units, and lubricating oil film evaluation units.

5. The wind turbine component life assessment method based on multiphysics coupling according to claim 4, characterized in that, The blade root stress assessment unit and the leading edge corrosion assessment unit are associated with distributed fiber optic sensors, and the blade tip vibration assessment unit is associated with an accelerometer; the gear meshing assessment unit and the bearing contact assessment unit are associated with a MEMS accelerometer, and the lubricating oil film assessment unit is associated with an oil sensor and a temperature sensor.

6. The method for assessing the lifespan of wind turbine components based on multiphysics coupling according to claim 2, characterized in that, Based on the multi-field coupling state threshold and the multi-source sensing data, degradation candidate units are determined, including: By comparing the multi-field coupling state threshold with the multi-source sensing data, multiple degradation risk units are obtained through screening. For each of the aforementioned degradation risk units, multiphysics feature data are extracted; Using the normal feature data of the corresponding unit in the degradation feature benchmark library, the multiphysics feature data is matched item by item to eliminate misjudged units caused by data fluctuations and retain degradation candidate units.

7. The wind turbine component life assessment method based on multiphysics coupling according to claim 6, characterized in that, The multiphysics field characteristic data includes aerodynamic field parameters, structural field parameters, and temperature field parameters.

8. The method for assessing the lifespan of wind turbine components based on multiphysics coupling according to claim 6, characterized in that, Using the normal feature data of the corresponding unit in the degradation feature benchmark library, the multiphysics feature data is matched item by item to eliminate misjudged units caused by data fluctuations, and retain degradation candidate units, including: If all key feature parameters of a risky unit exceed the normal feature data, it is determined to be a degenerate candidate unit. If some parameters of a risk unit exceed the normal feature data but other parameters are normal, it is judged as a misjudged unit. A short-term monitoring mode is activated for misjudged units. If the out-of-limit parameter returns to the normal range during the short-term monitoring process, the misjudgment is completely eliminated. If the out-of-limit parameters continue to deteriorate, they will be reinstated into the list of degraded candidate units.

9. The method for assessing the lifespan of wind turbine components based on multiphysics coupling according to claim 1, characterized in that, Historical data corresponding to each of the degradation candidate units is obtained, and degradation trend analysis is performed on the degradation candidate units based on this data to obtain the remaining service life of the wind turbine component corresponding to the degradation candidate unit, including: Collect historical operation and maintenance data and multi-source sensor data from various evaluation units; For each candidate unit of degradation, its degradation history is analyzed, and combined with its historical operation and maintenance failure data, a degradation trend analysis model is constructed using a hybrid model of physical model and data-driven approach. By establishing a degradation trend analysis model, the degradation rate of each degradation candidate unit is analyzed to determine the degradation mode; Calculate the remaining useful life based on the degradation mode.

10. A wind turbine component life assessment system based on multiphysics coupling, characterized in that, include: The module for unit division and sensor data acquisition, for wind turbine impeller gearbox system, divides multiple fine-grained evaluation units based on multi-physics field according to the principle of matching component functional zoning and physical field type, and acquires multi-source sensor data of each evaluation unit. The threshold determination module determines the multi-field coupling state threshold of each evaluation unit based on the geometric features of the wind turbine impeller gearbox system and historical operation and maintenance fault data. The degradation candidate unit determination module determines degradation candidate units for each of the evaluation units based on the multi-field coupling state threshold and the multi-source sensing data. The remaining service life prediction module acquires historical data corresponding to each of the degradation candidate units, and uses this data to perform degradation trend analysis on the degradation candidate units to obtain the remaining service life of the wind turbine component corresponding to the degradation candidate unit.

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