A method for testing structural components of a low-noise fan

Abstract

The present application provides a method for testing the structural components of a low-noise fan, which relates to the technical field of component operation testing, including: obtaining the fan data of the target low-noise fan to form a set of fan structural components; then, using these components as indexes, collecting historical fault records, analyzing and determining the set of key structural components that have the greatest impact on the fan performance; identifying fatigue concentration locations; combining material characteristics to evaluate the fatigue life of components under multiple working conditions, and performing life consistency and compliance judgments. Finally, the process of the fan structural components is optimized based on the judgment results. This application can solve the problem that the existing technology has large errors and uncertainties in the fatigue life assessment of fan structural components due to the single testing method and poor accuracy. The structural process of the structural components of the target low-noise fan can be optimized based on the compliance judgment results, which helps to improve the overall performance of the fan, extend its service life and reduce the noise level.

Description

Technical Field

[0001] The present application relates to the technical field of component operation testing, and in particular to a structural component testing method for a low-noise fan. Background Art

[0002] Fans generate noise during operation, which is transmitted to the surrounding space through inlets and outlets, casings, pipes and other parts, not only damaging the physical and mental health of workers, but also seriously polluting the environment. However, the existing technology lacks accurate testing of low-noise fan structural components.

[0003] At present, the existing wind turbine fatigue life assessment technology mainly relies on empirical formulas and simple statistical analysis, which lacks systematicity and accuracy. These technologies often fail to fully consider the actual stress conditions of wind turbine structural components under multiple working conditions, as well as the impact of material properties on fatigue life.

[0004] In summary, due to the single testing method and poor accuracy of the existing technology, there are large errors and uncertainties in the fatigue life assessment of wind turbine structural components, which further affects the optimization and improvement of the overall performance of the wind turbine, as well as the safety and reliability of its operation. Summary of the invention

[0005] The purpose of this application is to provide a method for testing the structural components of a low-noise fan, so as to solve the problem that the existing technology has large errors and uncertainties in the fatigue life assessment of the fan structural components due to the single testing method and poor accuracy.

[0006] In view of the above problems, the present application provides a method for testing structural components of a low-noise fan.

[0007] The present application provides a method for testing structural components of a low-noise fan, wherein the method for testing structural components of a low-noise fan comprises: obtaining fan data of a target low-noise fan, parsing the fan structure according to the fan data, obtaining multiple fan structural components, and outputting them as a fan structural component set; using the fan structural component set as an index constraint, obtaining historical fault records, and analyzing and determining a key structural component set based on the historical fault records; performing multi-condition isolation operation tests on multiple fan structural components in the key structural component set to obtain an operation feature set, wherein the operation feature set includes strain features and vibration features; based on the operation feature set, identifying fatigue concentration locations, and evaluating component fatigue life sets under multiple conditions in combination with material features of multiple fan structural components; based on the component fatigue life set, evaluating life consistency and performing compliance judgment to obtain compliance judgment results; and based on the compliance judgment results, performing structural process optimization on the structural components of the target low-noise fan.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By acquiring fan data of a target low-noise fan, analyzing the fan structure according to the fan data, acquiring multiple fan structural components, and outputting them as a fan structural component set; using the fan structural component set as an index constraint, acquiring historical fault records, and analyzing and determining a key structural component set based on the historical fault records; performing multi-condition isolation operation tests on multiple fan structural components in the key structural component set to acquire an operation feature set, wherein the operation feature set includes strain features and vibration features; based on the operation feature set, identifying fatigue concentration locations, and evaluating component fatigue life sets under multiple conditions in combination with material features of multiple fan structural components; based on the component fatigue life set, evaluating life consistency and performing compliance judgment to obtain compliance judgment results; based on the compliance judgment results, performing structural process optimization on the structural components of the target low-noise fan, effectively solving the problem that the existing technology has large errors and uncertainties in fatigue life assessment of fan structural components due to the single testing method and poor accuracy, and can perform structural process optimization on the structural components of the target low-noise fan according to the compliance judgment results, which helps to improve the overall performance of the fan, extend its service life and reduce noise levels.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 A schematic flow chart of a structural component testing method for a low-noise fan of the present application;

[0013] Figure 2 This is a schematic flow chart of cumulative analysis of multiple fan structural components in a structural component testing method of a low-noise fan in the present application. DETAILED DESCRIPTION

[0014] The present application provides a method for testing the structural components of a low-noise fan, thereby solving the problem that the existing technology has large errors and uncertainties in the fatigue life assessment of the fan structural components due to the single testing method and poor accuracy. The structural process of the structural components of the target low-noise fan can be optimized according to the compliance judgment results, which helps to improve the overall performance of the fan, extend its service life and reduce the noise level.

[0015] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0016] Please see attached Figure 1 The present application provides a method for testing structural components of a low-noise fan, wherein the method for testing structural components of a low-noise fan specifically comprises the following steps:

[0017] S1: Obtain fan data of a target low-noise fan, analyze the fan structure according to the fan data, obtain a plurality of fan structural components, and output them as a fan structural component set.

[0018] Specifically, first, identify the fan model that needs to be analyzed, and obtain detailed information about the target fan by visiting the official website of the fan manufacturer or related industry forums. The information contains key information such as the fan's technical specifications, design features, working principles, and structural schematics. Based on the collected information, identify the main fan structural components, such as the casing, impeller, motor, bearing, air inlet, and air outlet. Divide the fan into different functional areas, such as the airflow channel area, power drive area, and support and fixing area. Based on the functional area division, list all the main structural components of the fan.

[0019] S2: using the wind turbine structural component set as an index constraint, obtaining historical fault records, and analyzing and determining a key structural component set based on the historical fault records.

[0020] Specifically, according to the fan structural component set, all relevant historical fault records are collected, including information such as the time of the fault, component name, fault description, maintenance measures and maintenance results. The collected fault data is cleaned and sorted to ensure the accuracy and completeness of the data, remove duplicate records, correct erroneous information and fill in missing values. The frequency of failure of each component is counted to identify components with higher failure frequencies, evaluate the impact of each fault on the overall performance and service life of the fan, analyze the mode and trend of the fault, and conduct a comprehensive evaluation of each component based on the results of the fault frequency, fault impact and fault mode analysis. According to the evaluation results, the key structural components that have a significant impact on the performance and service life of the fan are determined. The determined key components are sorted into a key structural component set and prioritized so that they can be given priority in maintenance and repair. The fan structural component set can be effectively used as an index constraint to obtain historical fault records, and the key structural component set can be determined based on these record analysis to provide strong support for the maintenance and repair of the fan.

[0021] S3: Performing a multi-condition isolation operation test on a plurality of wind turbine structural components in the key structural component set to obtain an operation feature set, wherein the operation feature set includes strain features and vibration features.

[0022] Specifically, according to the determined set of key structural components, the components that need to be tested in isolation under multiple working conditions are clearly identified. According to the actual operating conditions of the wind turbine, multiple representative working conditions are designed, such as different speeds, loads, ambient temperatures and humidity, etc., to identify the operating characteristics that need to be obtained for the test, including strain characteristics and vibration characteristics. Prepare test equipment, such as strain gauges, vibration sensors, data acquisition systems, etc. Install strain gauges and vibration sensors on key structural components to ensure that the installation position is accurate and can truly reflect the strain and vibration conditions of the components. Perform an isolated running test on each key structural component, that is, test the performance of the component under different working conditions separately while other components remain in normal state. During the test, use the data acquisition system to record the output data of the strain gauge and vibration sensor in real time to ensure the integrity and accuracy of the data. Switch one by one according to the working condition sequence set by the test plan, and collect data after stable operation under each working condition for a period of time. Preprocess the collected raw data, including denoising, filtering, standardization, etc., to improve data quality. Extract strain characteristics and vibration characteristics from the preprocessed data, including strain value, strain change rate, vibration acceleration, vibration frequency, etc.

[0023] S4: Based on the operation feature set, identify fatigue concentration locations, and evaluate component fatigue life sets under multiple operating conditions in combination with material features of multiple wind turbine structural components.

[0024] Specifically, identify areas with high strain and vibration levels that may be locations of fatigue concentration, associate high strain and high vibration characteristics with specific wind turbine structural components and operating conditions, and determine which components have the highest fatigue risk under which operating conditions. Collect material characteristic data for multiple wind turbine structural components, including material type, strength, toughness, hardness, etc., evaluate the collected material characteristic data, and understand the fatigue performance of different materials. Use material characteristics and operating characteristic sets to establish a fatigue life prediction model to predict the fatigue life of each key structural component under different operating conditions. Identify components with shorter fatigue life under different operating conditions, and organize the predicted fatigue life data into a fatigue life set, including component name, operating condition description, predicted fatigue life and other information.

[0025] S5: According to the component fatigue life set, the life consistency is evaluated and compliance determination is performed to obtain compliance determination results.

[0026] Specifically, the data on the fatigue life of components are classified and sorted according to the working conditions and structural parts. For each working condition, the fatigue life data of multiple structural parts are compared, and the standard deviation, coefficient of variation and other indicators of the life data are calculated to quantify the consistency of the life. According to the preset consistency standards, such as the standard deviation threshold, coefficient of variation threshold, etc., it is judged whether the life of multiple structural parts under each working condition is consistent. According to the design requirements and use environment of the wind turbine, the expected design life is set as the compliance standard, and the average fatigue life of multiple structural parts under each working condition is compared with the expected design life. If the average fatigue life is greater than or equal to the expected design life, it is judged to be up to standard. If the average fatigue life is less than the expected design life, it is judged to be not up to standard.

[0027] S6: According to the compliance determination result, structural process optimization is performed on the structural components of the target low-noise fan.

[0028] Specifically, based on the results of the compliance assessment, identify the structural components whose fatigue life does not reach the expected design life, and analyze the reasons why the fatigue life of these components does not meet the standards, including material selection, structural design, manufacturing process, etc. In response to the problem of insufficient material fatigue performance, higher strength materials or new materials with better fatigue properties can be used. Redesign the structural components to reduce stress concentration and increase overall stiffness, thereby extending fatigue life. Use simulation technology to verify and optimize the structural design to ensure that the structural strength is improved while meeting the low noise requirements.

[0029] Further, if Figure 2 As shown, step S2 of the present application also includes:

[0030] Based on big data and based on the set of fan structural components, the historical fault records are obtained, wherein the historical fault records include intrinsic historical fault records and homologous historical fault records; based on the historical fault records, a covariance matrix of multiple fan structural components and multiple fan faults is established; based on the fault frequency and fault severity, a cumulative analysis is performed on multiple fan structural components according to the covariance matrix to generate a fault severity sequence and a fault frequency sequence; the first N fan structural components of the fault severity sequence and the fault frequency sequence are respectively obtained, and stored as the set of key structural components.

[0031] Specifically, historical fault records are extracted from the wind turbine operation and maintenance database, including intrinsic historical fault records, i.e., faults that occurred in the component itself, and homologous historical fault records, i.e., faults of other components related to or caused by the component. Data is cleaned to remove duplicate, erroneous or incomplete records, and fault records are associated with the set of wind turbine structural components to ensure that each record can correspond to a specific component. Each wind turbine structural component is taken as a variable, and each wind turbine fault type is also taken as a variable. Using historical fault records, the covariance between each wind turbine structural component and each wind turbine fault is calculated to measure the degree of association between them. The calculated covariance values ​​are filled into the covariance matrix, with rows representing wind turbine structural components and columns representing wind turbine fault types. Weights are defined for each wind turbine fault according to fault frequency and fault severity. Weighted summation is performed on each row of the covariance matrix, i.e., each wind turbine structural component, to obtain the cumulative value of the fault severity of the component. At the same time, the cumulative value of the fault frequency of each component is calculated. According to the cumulative value of fault severity, the wind turbine structural components are arranged in descending order to generate a fault severity sequence. According to the cumulative value of fault frequency, a fault frequency sequence is also generated, and the first N wind turbine structural components are selected from the fault severity sequence and the fault frequency sequence respectively. The selected key structural components are stored as a key structural component set for subsequent analysis and optimization. Historical fault records can be obtained based on big data and the wind turbine structural component set, and a covariance matrix can be established for cumulative analysis to finally generate a key structural component set. This helps to identify the structural components that have the greatest impact on wind turbine failures, providing strong support for subsequent fault prediction, maintenance strategy formulation, and structural optimization.

[0032] Furthermore, step S2 of the present application also includes:

[0033] According to the operating characteristics of multiple wind turbine structural components, the key structural component set is classified and divided to obtain a simplified test component set and a composite test component set; based on the simplified test component set, a physical isolation operation test is performed (with a small amount of test, saving simulation modeling and parameter setting time), and the strain characteristics and the vibration characteristics are synchronously collected; based on the composite test component set, a parallel composite isolation operation test is performed, and according to the composite isolation operation test results, the strain characteristics and the vibration characteristics are calculated and obtained; the physical isolation operation test and the composite isolation operation test are combined to output the operation characteristic set.

[0034] Specifically, the operational characteristics of multiple wind turbine structural components are analyzed, including their functions, working modes, and interactions with other components. According to the similarity of operational characteristics, classification standards are formulated to divide key structural components into simplified test components and composite test components. According to the classification standards, the key structural component set is divided into a simplified test component set and a composite test component set. For the simplified test component set, a physical isolation operation test scheme is designed to ensure that the test volume is small and can effectively reflect the component performance. Physical isolation operation tests are performed to test the simplified test components separately to simulate their actual operation status. During the test, strain characteristics and vibration characteristics data are collected synchronously. For the composite test component set, a parallel composite isolation operation test scheme is designed to test the interaction of multiple components at the same time. Parallel composite isolation operation tests are performed to combine composite test components to simulate their composite status in actual operation. According to the composite isolation operation test results, strain characteristics and vibration characteristics data are calculated and obtained. The strain characteristics and vibration characteristics data obtained from the physical isolation operation test and the composite isolation operation test are integrated. The integrated data is analyzed to extract key characteristics reflecting the operation status of wind turbine structural components. The key features obtained from the analysis are organized into an operating feature set for subsequent wind turbine performance evaluation, fault diagnosis, predictive maintenance, etc.

[0035] Furthermore, the present application also includes:

[0036] The operating scenario of the interactive target low-noise fan is used to obtain historical scenario conditions; cluster analysis is performed on the historical scenario conditions, and multiple calibration condition intervals are determined based on the cluster analysis results; based on the multiple calibration condition intervals, combined with statistical analysis methods, the proportions of multiple condition intervals are obtained; based on the multiple cluster centers of the cluster analysis results, typical conditions of the multiple calibration condition intervals are determined to generate a typical condition set; a mapping relationship between the typical condition set and the proportions of the multiple condition intervals is established, and a multi-condition test sequence is constructed as a test condition for the isolated operation test.

[0037] Specifically, for the operation scenario of the interactive target low-noise fan, historical scenario operating data is collected from the operation and maintenance database, sensor records or manual records. The collected data is cleaned to remove duplicate, erroneous or incomplete records to ensure the accuracy and consistency of the data. According to the characteristics of the data and clustering requirements, a suitable clustering algorithm, such as K-means, DBSCAN, etc., is selected. The selected clustering algorithm is used to perform cluster analysis on the historical scenario operating data to identify groups of operating conditions with similar characteristics. According to the results of the cluster analysis, multiple calibration operating condition intervals are determined, and each interval represents a group of scenarios with similar operating condition characteristics. Combined with statistical analysis methods, such as frequency distribution and percentage calculation, the number of operating conditions in each calibration operating condition interval is counted. According to the statistical results, the proportion of each operating condition interval in the total number of operating conditions is calculated to reflect its frequency of occurrence in actual operations. According to the multiple cluster centers of the cluster analysis results, the typical operating condition of each calibration operating condition interval is determined, that is, the most representative operating condition in the interval. Summarize the typical working conditions of all calibrated working condition intervals to generate a typical working condition set for subsequent testing and analysis. Establish a mapping relationship between the typical working condition set and the proportion of multiple working condition intervals to reflect the probability of occurrence of different working conditions in actual operations. According to the mapping relationship, a multi-condition test sequence is constructed according to the proportion of the working condition interval. The sequence should contain working conditions with different proportions to ensure the comprehensiveness and representativeness of the test. According to actual test requirements and resource constraints, the test sequence is optimized, such as adjusting the order of working conditions, merging similar working conditions, etc.

[0038] Furthermore, the present application also includes:

[0039] Extract a first composite test component, obtain a first physical component, and deploy monitoring sensors on the first physical component, wherein the monitoring sensors include micro-strain sensors and vibration sensors; based on the multi-operating condition test sequence, obtain the operating condition test parameters with the highest proportion of the operating condition interval, and construct a physical isolation operation test environment; perform a physical isolation operation test according to the physical isolation operation test environment, synchronously collect the strain characteristics and the vibration characteristics, and obtain a first physical isolation operation test result; analyze the first physical isolation operation test result, identify multiple strain extreme value positions and vibration extreme value positions, and determine them as a key monitoring position set.

[0040] Specifically, a first composite test component is extracted from the composite test component set, and the corresponding first physical component is obtained according to the specifications and models of the first composite test component. Monitoring sensors, including micro strain sensors and vibration sensors, are arranged on the first physical component to ensure that strain and vibration data can be collected comprehensively and accurately. Based on the multi-condition test sequence, the condition with the highest proportion of the condition interval is selected, and its corresponding test parameters are obtained. According to the obtained test parameters, a physical isolation operation test environment is constructed to ensure that the test environment can simulate the working conditions in the actual operation scene. In the constructed physical isolation operation test environment, a physical isolation operation test is performed on the first physical component. During the test, the data of the micro strain sensor and the vibration sensor are synchronously collected to obtain the strain characteristics and vibration characteristics of the first physical component. After the test is completed, the collected data is sorted and analyzed to obtain the first physical isolation operation test results. The first physical isolation operation test results are analyzed, including the time domain and frequency domain characteristics of strain and vibration. In the analysis results, multiple strain extreme value positions and vibration extreme value positions are identified. These positions are the areas where the stress or vibration of the component is most concentrated during operation. The identified strain extreme value positions and vibration extreme value positions are summarized and determined as a set of key monitoring positions. A comprehensive physical isolation operation test can be performed on the first composite test component, and its key monitoring positions can be accurately identified, providing strong support for performance evaluation, fault diagnosis and predictive maintenance of the wind turbine.

[0041] Furthermore, the present application also includes:

[0042] According to the wind turbine data, a first physical model of the first composite test component is constructed; the shape characteristics of the first physical model are analyzed, adaptive meshing is performed, and the key monitoring position set is called to update the adaptive meshing results; according to the multi-condition test sequence, the load series and the boundary constraint series are initialized; in combination with the first physical model, the adaptive meshing results, the load series and the boundary constraint series, static analysis and modal analysis are performed to solve the strain characteristics and the vibration characteristics, and generate the operation characteristic set.

[0043] Specifically, detailed information about the first composite test component is collected, including size, material, structure, etc. Finite element analysis FEA software or other modeling tools are used to construct a first physical model of the first composite test component based on the collected information. The shape feature of the first physical model is analyzed to identify the geometric shape, boundary, hole and other features of the model. According to the shape feature, the physical model is initially meshed to generate a preliminary mesh model. The key monitoring position set determined by the analysis is imported into the meshing process to ensure that these positions are fully considered in the meshing. According to the key monitoring position set, the initial meshing result is adjusted and optimized to generate an updated adaptive meshing result. According to the multi-condition test sequence, the condition test parameters that need to be statically analyzed and modally analyzed are selected. According to the selected condition test parameters, the corresponding load series are initialized, including wind pressure, temperature, gravity, etc. At the same time, the boundary constraint series is initialized, including fixed constraints, sliding constraints, etc., to simulate the boundary conditions in the actual operation scene. Combined with the first physical model, the adaptive meshing result, the load series and the boundary constraint series, a static analysis is performed to solve the strain characteristics of the component under static load. Based on the static analysis, modal analysis is performed to solve the natural frequency and vibration mode of the components, that is, the vibration characteristics. The results of static analysis and modal analysis are sorted out, including strain distribution diagrams, vibration frequency tables, etc. Based on the sorted analysis results, an operating feature set containing strain characteristics and vibration characteristics is generated for subsequent fan performance evaluation, fault diagnosis and predictive maintenance.

[0044] Furthermore, the present application also includes:

[0045] Interact with the material data source to obtain material property data and extract the material characteristics; based on the operation characteristic set and according to the nominal stress operator, calculate the nominal stress of multiple wind turbine structural components under multiple working conditions to obtain the nominal stress set; establish a mapping relationship based on the material characteristics of the nominal stress set, perform fatigue life evaluation, and obtain the fatigue life set.

[0046] Specifically, the detailed data about the materials used in the wind turbine structural components are obtained by interacting with the material database. From the obtained data, key material property data, such as elastic modulus, yield strength, fatigue limit, etc., are extracted, and the extracted material property data are sorted to form a structured material feature set. Using the constructed physical model and the analysis performed, an operation feature set containing strain characteristics and vibration characteristics is obtained. According to the force characteristics and material characteristics of the wind turbine structural components, a suitable nominal stress operator is selected, such as the maximum principal stress equivalent stress. The strain characteristics and vibration characteristics in the operation feature set are combined with the nominal stress operator to calculate the nominal stress of multiple wind turbine structural components under multiple working conditions. The calculated nominal stress is sorted to form a nominal stress set for subsequent fatigue life evaluation. Based on the nominal stress set and the material feature set, a mapping relationship between the two is established, that is, the corresponding relationship between the nominal stress and fatigue life under different material characteristics is determined. According to the force characteristics and material characteristics of the wind turbine structural components, a suitable fatigue life evaluation model is selected, such as the SN curve method, the linear cumulative damage method, etc. The established mapping relationship and the selected fatigue life evaluation model are used to evaluate the fatigue life of multiple wind turbine structural components under multiple working conditions. The evaluated fatigue life is sorted to form a fatigue life set for subsequent wind turbine performance evaluation, fault diagnosis and predictive maintenance.

[0047] Furthermore, the present application also includes:

[0048] Construct the nominal stress operator, which is expressed as: ; Wherein, σ represents the nominal stress; , , They represent the axial stress, in-plane bending stress and out-of-plane bending stress of the fan structural components respectively; , , They respectively represent the stress concentration factors corresponding to the axial stress, the in-plane bending stress and the out-of-plane bending stress.

[0049] Specifically, the nominal stress comprehensively reflects the overall stress state of the wind turbine structural components under multi-directional stress. The axial stress, in-plane bending stress and out-of-plane bending stress of the wind turbine structural components respectively describe the stress distribution of the components in different directions. The stress concentration coefficients corresponding to the axial stress, in-plane bending stress and out-of-plane bending stress reflect the local stress amplification effect of the components under stress.

[0050] In summary, the structural component testing method of a low-noise fan provided in this application has the following technical effects:

[0051] By acquiring fan data of a target low-noise fan, analyzing the fan structure according to the fan data, acquiring multiple fan structural components, and outputting them as a fan structural component set; using the fan structural component set as an index constraint, acquiring historical fault records, and analyzing and determining a key structural component set based on the historical fault records; performing multi-condition isolation operation tests on multiple fan structural components in the key structural component set to acquire an operation feature set, wherein the operation feature set includes strain features and vibration features; based on the operation feature set, identifying fatigue concentration locations, and evaluating component fatigue life sets under multiple conditions in combination with material features of multiple fan structural components; based on the component fatigue life set, evaluating life consistency and performing compliance judgment to obtain compliance judgment results; based on the compliance judgment results, performing structural process optimization on the structural components of the target low-noise fan, effectively solving the problem that the existing technology has large errors and uncertainties in fatigue life assessment of fan structural components due to the single testing method and poor accuracy, and can perform structural process optimization on the structural components of the target low-noise fan according to the compliance judgment results, which helps to improve the overall performance of the fan, extend its service life and reduce noise levels.

[0052] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0053] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A method for testing structural components of a low-noise fan, characterized in that: The structural component testing method of a low-noise fan comprises: Obtain fan data of a target low-noise fan, analyze the fan structure according to the fan data, obtain a plurality of fan structural components, and output them as a fan structural component set; Taking the wind turbine structural component set as an index constraint, obtaining historical fault records, and analyzing and determining a key structural component set based on the historical fault records; Performing a multi-condition isolation operation test on a plurality of wind turbine structural components in the key structural component set to obtain an operation feature set, wherein the operation feature set includes strain features and vibration features; Based on the operating characteristic set, fatigue concentration locations are identified, and the fatigue life set of components under multiple operating conditions is evaluated in combination with material characteristics of multiple wind turbine structural components; According to the component fatigue life set, life consistency is evaluated and compliance determination is performed to obtain compliance determination results; According to the standard determination result, structural process optimization is performed on the structural components of the target low-noise fan; Among them, the material characteristics of multiple wind turbine structural components are combined to evaluate the component fatigue life set under multiple working conditions, including: Interacting with material data sources, acquiring material property data, and extracting said material characteristics; Based on the operating characteristic set, and according to the nominal stress operator, the nominal stresses of multiple wind turbine structural components under multiple working conditions are calculated to obtain a nominal stress set; Based on the nominal stress set and the material characteristics, a mapping relationship is established, and fatigue life evaluation is performed to obtain the fatigue life set; Among them, according to the nominal stress operator, the nominal stress of multiple wind turbine structural components under multiple working conditions is calculated, including: Construct the nominal stress operator, which is expressed as: ； Wherein, σ represents the nominal stress; , , They represent the axial stress, in-plane bending stress and out-of-plane bending stress of the fan structural components respectively; , , They respectively represent the stress concentration factors corresponding to the axial stress, the in-plane bending stress and the out-of-plane bending stress.

2. A method for testing structural components of a low-noise fan as claimed in claim 1, characterized in that: Taking the fan structural component set as an index constraint, obtaining historical fault records, and analyzing and determining a key structural component set based on the historical fault records, including: Based on big data and the set of wind turbine structural components, the historical fault records are obtained, wherein the historical fault records include intrinsic historical fault records and homologous historical fault records, the intrinsic historical fault records refer to faults occurring in the component itself, and the homologous historical fault records refer to faults caused by other components related to the component; According to the historical fault records, a covariance matrix of multiple fan structural components and multiple fan faults is established; Based on the fault frequency and fault severity, cumulative analysis is performed on multiple wind turbine structural components according to the covariance matrix to generate a fault severity sequence and a fault frequency sequence; The first N wind turbine structural components in the fault severity sequence and the fault frequency sequence are respectively obtained and stored as the key structural component set.

3. A method for testing structural components of a low-noise fan as claimed in claim 2, characterized in that: Performing a multi-condition isolation operation test on a plurality of wind turbine structural components in the key structural component set to obtain an operation feature set, including: According to the operation characteristics of the plurality of wind turbine structural components, the key structural component set is classified and divided to obtain a simplified test component set and a composite test component set; Based on the simplified test component set, a physical isolation operation test is performed, and the strain characteristics and the vibration characteristics are collected synchronously, wherein the physical isolation operation test refers to a separate test of the simplified test components; Based on the composite test component set, a parallel composite isolation operation test is performed, and according to the composite isolation operation test result, the strain characteristic and the vibration characteristic are calculated and obtained, wherein the composite isolation operation test refers to a combined test of the composite test components; The physical isolation operation test and the composite isolation operation test are combined to output the operation feature set.

4. A method for testing structural components of a low-noise fan as claimed in claim 3, characterized in that: Performing a multi-condition isolation operation test on a plurality of wind turbine structural components in the key structural component set to obtain an operation feature set, before that, the method further includes: Interactive target low-noise fan operation scene, obtain historical scene conditions; Performing cluster analysis on the historical scenario operating conditions, and determining a plurality of calibration operating condition intervals according to the cluster analysis results; Based on the multiple calibrated operating condition intervals, combined with a statistical analysis method, the proportions of the multiple operating condition intervals are obtained; Determine typical operating conditions of multiple calibration operating condition intervals according to multiple cluster centers of the cluster analysis results, and generate a typical operating condition set; A mapping relationship between the typical operating condition set and the proportion of multiple operating condition intervals is established, and a multi-operating condition test sequence is constructed as a test condition for the isolated operation test.

5. A method for testing structural components of a low-noise fan as claimed in claim 4, characterized in that: Based on the composite test component set, a parallel composite isolation operation test is performed, and according to the composite isolation operation test result, the strain characteristic and the vibration characteristic are calculated and obtained, including: Extracting a first composite test component, obtaining a first physical component, and arranging a monitoring sensor on the first physical component, wherein the monitoring sensor includes a micro strain sensor and a vibration sensor; Based on the multi-operating condition test sequence, the operating condition test parameter with the highest proportion in the operating condition interval is obtained, and a physical isolation operation test environment is constructed; Performing a physical isolation operation test according to the physical isolation operation test environment, synchronously collecting the strain characteristics and the vibration characteristics, and obtaining a first physical isolation operation test result; The first physical isolation operation test results are analyzed to identify multiple strain extreme value positions and vibration extreme value positions, and determine them as a key monitoring position set.

6. A method for testing structural components of a low-noise fan as claimed in claim 5, characterized in that: Based on the composite test component set, a parallel composite isolation operation test is performed, and according to the composite isolation operation test result, the strain characteristic and the vibration characteristic are calculated and obtained, further comprising: Constructing a first physical model of the first composite test component according to the wind turbine data; Analyzing the shape features of the first physical model, performing adaptive meshing, calling the key monitoring position set, and updating the adaptive meshing result; Initializing a load series and a boundary constraint series according to the multi-condition test sequence; In combination with the first physical model, the adaptive meshing result, the load series and the boundary constraint series, static analysis and modal analysis are performed to solve the strain characteristics and the vibration characteristics and generate the operation characteristic set.

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