Buckling judgment method and system for strain collection data of aircraft ground static test

Abstract

The present application belongs to the technical field of strain data processing for aircraft ground static tests, and specifically relates to a method and system for determining buckling of strain data collected for aircraft ground static tests, wherein the method for determining buckling of strain data collected for aircraft ground static tests comprises: a data acquisition step: acquiring strain data collected for aircraft ground static tests; a data preprocessing step: preprocessing the strain data collected, including repairing wild point data and intercepting valid data; a strain data feature recognition step: identifying features of the strain data collected; and a strain data identification step: dividing the strain data collected into undeterminable strain data, bad piece strain data, normal strain data, and buckling strain data based on the features of the strain data collected.

Description

Technical Field

[0001] The present application belongs to the technical field of aircraft ground static test strain data processing, and specifically relates to a method and system for determining buckling of aircraft ground static test strain collection data. Background Art

[0002] Aircraft ground static tests are used to verify whether the static strength of the aircraft structure meets the design requirements. They are mainly used to verify the load-bearing capacity and safety margin of the aircraft structure. They have the characteristics of high risk, high cost, and irreversible state.

[0003] With the extensive application of new materials and lightweight new configurations in the new generation of aircraft structures, the risks of ground static tests have increased dramatically. In order to avoid test risks, a large number of strain gauges are used in the test to collect strain data of the aircraft structure. By judging the rationality of the strain data, the strength characteristics of the aircraft structure are monitored, and it is determined whether the structure is in a buckling state to support test decisions.

[0004] Currently, whether the structure is in a buckling state is mostly determined by manually observing the changing patterns of various strain data curves. When the strain data is huge, the cost is high and it is affected by subjective factors. The judgment efficiency is low, the time is long, and omissions are prone to occur. The judgment accuracy is low, and it is difficult to effectively support the needs of real-time decision-making at the test site.

[0005] This application is proposed in view of the above-mentioned technical defects. Summary of the invention

[0006] The purpose of the present application is to provide a method and system for determining buckling of strain collection data from a ground static test of an aircraft, so as to overcome or alleviate at least one of the known technical deficiencies.

[0007] The technical solution of this application is:

[0008] On the one hand, a method for determining buckling of strain collection data of an aircraft ground static test is provided, comprising:

[0009] Data acquisition steps: obtaining strain acquisition data of aircraft ground static test;

[0010] Data preprocessing steps: preprocess the strain acquisition data, including repairing wild point data and intercepting valid data;

[0011] Strain acquisition data feature identification steps: identify the features of strain acquisition data, including the difference between the maximum and minimum values ​​Diff, the maximum strain absolute value MaxStrain, the strain values ​​strain are all 0 or all constant values, multiple continuous repetitions, the maximum single-step jump skip after normalization, the fifth-order fitting variance var5_up of loading, the fifth-order fitting variance var5_down of the unloading section, the variance Var5 of the fifth-order fitting, the variance var5_cut of the fifth-order fitting of effective data, the second-order fitting quadratic term coefficient a2 of effective data, the second-order fitting variance var2 of effective data, and the third-order fitting γ after effective data;

[0012] Strain acquisition data identification steps: Based on the characteristics of the strain acquisition data, the strain acquisition data is divided into unidentifiable strain data, bad piece strain data, normal strain data, and buckling strain data.

[0013] Optionally, in the above-mentioned method for determining buckling of strain collection data of aircraft ground static test, the field point data repair in the data preprocessing step specifically includes:

[0014] S11, normalizing the strain acquisition data, mapping the strain acquisition data to a range of 0 to 1;

[0015] S12, based on the jump size and direction, the normalized strain collection data is subjected to outlier judgment;

[0016] If the jump value of the i-th point data and the jump value of the i-1-th point data after it are greater than the maximum jump setting threshold, and the jump directions are opposite, then the i-th point is judged to be an outlier;

[0017] S13, repairing the wild point data in the strain acquisition data;

[0018] The linear interpolation method is used to repair the wild point data in the strain acquisition data.

[0019] Optionally, in the above-mentioned method for determining buckling of strain collection data of a ground static test of an aircraft, in the data preprocessing step, intercepting valid data specifically comprises intercepting data with a load percentage of 20% to 100% in the strain collection data as valid data.

[0020] Optionally, in the above-mentioned method for determining buckling of strain collection data from a static ground test of an aircraft,

[0021] The data preprocessing step also includes eliminating the same load test data, specifically, determining the repeated values ​​of the same test load, retaining only the first data, and eliminating the subsequent data of the same load test.

[0022] Optionally, in the above-mentioned method for determining buckling of strain collected data in a static ground test of an aircraft, the strain collected data identification step specifically includes:

[0023] S21, judging whether the difference Diff between the maximum and minimum values ​​is within the range of 0 to 100, if so, judging the strain acquisition data as strain data that cannot be judged;

[0024] S22, when the maximum-minimum difference Diff is not within the range of 0-100, determine whether the maximum-minimum difference Diff is greater than 21000, and whether the maximum strain absolute value MaxStrain is greater than 21000, if so, determine the strain acquisition data as bad sheet strain data;

[0025] If the difference between the maximum and minimum values ​​Diff is not greater than 21000, or the maximum strain absolute value MaxStrain is not greater than 21000, then it is determined whether the strain values ​​strain are all 0 or all constant values. If so, the strain acquisition data is determined to be bad film strain data;

[0026] If the strain value strain is not all 0 or a constant value, determine whether n consecutive values ​​are the same and occur more than m times, or the number of consecutive values ​​k is more than half. If so, the strain data is determined to be bad chip strain data. When the test average load interval is ≥5, n is 5, when the test average load interval is <5, n is 10, and m is greater than 1.

[0027] If there are no consecutive n values ​​that are the same and occur more than m times, or the number of consecutive values ​​k is more than half, then determine whether the normalized maximum single-step jump skip is greater than 0.5, and the fifth-order fitting variance var5_up of the loading section or the fifth-order fitting variance var5_down of the unloading section is greater than 0.1. If so, the strain acquisition data is determined to be bad sheet strain data;

[0028] S23, if the normalized maximum single-step jump skip is not greater than 0.5, or the fifth-order fitting variance var5_up of the loading section and the fifth-order fitting variance var5_down of the unloading section are not greater than 0.1, then determine whether the fifth-order fitting variance Var5 is greater than 0.1, if so, determine that the strain acquisition data cannot be determined strain data;

[0029] S24, when the variance Var5 of the 5th order fitting is not greater than 0.1, determine whether the coefficient a2 of the 2nd order fitting quadratic term of the valid data is less than or equal to 0.2, and whether the 2nd order fitting variance var2 of the valid data is less than or equal to 0.02. If so, determine that the strain acquisition data is normal strain data, and is normal strain data without hysteresis;

[0030] If the 2nd order fitting quadratic term coefficient a2 of the valid data is greater than 0.2, or the 2nd order fitting variance var2 of the valid data is greater than 0.02, then determine whether the 2nd order fitting quadratic term coefficient a2 of the valid data is less than or equal to 0.2, and whether the 3rd order fitting γ after the valid data is greater than or equal to 0.7. If so, the strain acquisition data is determined to be normal strain data, and is normal strain data with hysteresis;

[0031] S25, when the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than 0.2, or the third-order fitting γ after the valid data is less than 0.7, determine whether the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than or equal to 0.4, and whether the variance var5_cut of the fifth-order fitting of the valid data is less than or equal to 0.02. If so, determine that the strain acquisition data is buckling strain data, and is buckling strain data without hysteresis loop;

[0032] If the coefficient a2 of the second-order fitting quadratic term of the valid data is less than 0.4, or the variance var5_cut of the fifth-order fitting of the valid data is greater than 0.02, then determine whether the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than or equal to 0.4, and whether the variance var5_cut of the fifth-order fitting of the valid data is greater than 0.02. If so, the strain acquisition data is determined to be buckling strain data, and is buckling strain data with hysteresis loop. If not, the strain acquisition data is determined to be buckling strain data, and is uncertain buckling strain data.

[0033] On the other hand, a system for determining buckling of strain collected data from static ground test of aircraft is provided, which is used to implement the above-mentioned method for determining buckling of strain collected data from static ground test of aircraft, and is characterized by comprising:

[0034] A data acquisition module is used to obtain strain data collected during static ground tests of aircraft;

[0035] Data preprocessing module, used to preprocess strain acquisition data, including wild point data repair and effective data interception;

[0036] The strain acquisition data feature recognition module is used to identify the features of the strain acquisition data, including the difference between the maximum and minimum values ​​Diff, the maximum strain absolute value MaxStrain, the strain values ​​strain are all 0 or all constant values, multiple continuous repetitions, the maximum single-step jump skip after normalization, the fifth-order fitting variance var5_up of loading, the fifth-order fitting variance var5_down of the unloading section, the variance Var5 of the fifth-order fitting, the variance var5_cut of the fifth-order fitting of the effective data, the second-order fitting quadratic term coefficient a2 of the effective data, the second-order fitting variance var2 of the effective data, and the third-order fitting γ after the effective data;

[0037] The strain collection data identification module is used to classify the strain collection data into unidentifiable strain data, bad piece strain data, normal strain data, and buckling strain data based on the characteristics of the strain collection data.

[0038] Optionally, in the above-mentioned aircraft ground static test strain collection data buckling judgment system, the field point data repair in the data preprocessing step includes:

[0039] S11, normalizing the strain acquisition data, mapping the strain acquisition data to a range of 0 to 1;

[0040] S12, based on the jump size and direction, the normalized strain collection data is subjected to outlier judgment;

[0041] If the jump value of the i-th point data and the jump value of the i-1-th point data after it are greater than the maximum jump setting threshold, and the jump directions are opposite, then the i-th point is judged to be an outlier;

[0042] S13, repairing the wild point data in the strain acquisition data;

[0043] The linear interpolation method is used to repair the wild point data in the strain acquisition data.

[0044] Optionally, in the above-mentioned aircraft ground static test strain collection data buckling judgment system, in the data preprocessing step, intercepting valid data specifically includes intercepting data with a load percentage of 20% to 100% in the strain collection data as valid data.

[0045] Optionally, in the above-mentioned aircraft ground static test strain collection data buckling judgment system, the data preprocessing step also includes eliminating the same load test data, specifically, performing repeated value judgments with the same test load, retaining only the first data, and eliminating subsequent data of the same load test.

[0046] Optionally, in the above-mentioned aircraft ground static test strain collection data buckling judgment system, in the strain collection data identification module, based on the characteristics of the strain collection data, the strain collection data is divided into unidentifiable strain data, bad piece strain data, normal strain data, and buckling strain data, specifically including:

[0047] S21, judging whether the difference Diff between the maximum and minimum values ​​is within the range of 0 to 100, if so, judging the strain acquisition data as strain data that cannot be judged;

[0048] S22, when the maximum-minimum difference Diff is not within the range of 0-100, determine whether the maximum-minimum difference Diff is greater than 21000, and whether the maximum strain absolute value MaxStrain is greater than 21000, if so, determine the strain acquisition data as bad sheet strain data;

[0049] If the difference between the maximum and minimum values ​​Diff is not greater than 21000, or the maximum strain absolute value MaxStrain is not greater than 21000, then it is determined whether the strain values ​​strain are all 0 or all constant values. If so, the strain acquisition data is determined to be bad film strain data;

[0050] If the strain value strain is not all 0 or a constant value, determine whether n consecutive values ​​are the same and occur more than m times, or the number of consecutive values ​​k is more than half. If so, the strain data is determined to be bad chip strain data. When the test average load interval is ≥5, n is 5, when the test average load interval is <5, n is 10, and m is greater than 1.

[0051] If there are no consecutive n values ​​that are the same and occur more than m times, or the number of consecutive values ​​k is more than half, then determine whether the normalized maximum single-step jump skip is greater than 0.5, and the fifth-order fitting variance var5_up of the loading section or the fifth-order fitting variance var5_down of the unloading section is greater than 0.1. If so, the strain acquisition data is determined to be bad sheet strain data;

[0052] S23, if the normalized maximum single-step jump skip is not greater than 0.5, or the fifth-order fitting variance var5_up of the loading section and the fifth-order fitting variance var5_down of the unloading section are not greater than 0.1, then determine whether the fifth-order fitting variance Var5 is greater than 0.1, if so, determine that the strain acquisition data cannot be determined strain data;

[0053] S24, when the variance Var5 of the 5th order fitting is not greater than 0.1, determine whether the coefficient a2 of the 2nd order fitting quadratic term of the valid data is less than or equal to 0.2, and whether the 2nd order fitting variance var2 of the valid data is less than or equal to 0.02. If so, determine that the strain acquisition data is normal strain data, and is normal strain data without hysteresis;

[0054] If the 2nd order fitting quadratic term coefficient a2 of the valid data is greater than 0.2, or the 2nd order fitting variance var2 of the valid data is greater than 0.02, then determine whether the 2nd order fitting quadratic term coefficient a2 of the valid data is less than or equal to 0.2, and whether the 3rd order fitting γ after the valid data is greater than or equal to 0.7. If so, the strain acquisition data is determined to be normal strain data, and is normal strain data with hysteresis;

[0055] S25, when the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than 0.2, or the third-order fitting γ after the valid data is less than 0.7, determine whether the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than or equal to 0.4, and whether the variance var5_cut of the fifth-order fitting of the valid data is less than or equal to 0.02. If so, determine that the strain acquisition data is buckling strain data, and is buckling strain data without hysteresis loop;

[0056] If the coefficient a2 of the second-order fitting quadratic term of the valid data is less than 0.4, or the variance var5_cut of the fifth-order fitting of the valid data is greater than 0.02, then determine whether the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than or equal to 0.4, and whether the variance var5_cut of the fifth-order fitting of the valid data is greater than 0.02. If so, the strain acquisition data is determined to be buckling strain data, and is buckling strain data with hysteresis loop. If not, the strain acquisition data is determined to be buckling strain data, and is uncertain buckling strain data.

[0057] This application has at least the following beneficial technical effects:

[0058] Provided are a buckling judgment method and system for strain acquisition data of a ground static test of an aircraft. Based on preprocessing the strain acquisition data, by identifying the characteristics of the strain acquisition data, including the difference between the maximum and minimum values ​​Diff, the maximum strain absolute value MaxStrain, the strain values ​​strain all being 0 or all being constant values, multiple continuous repetitions, the maximum single-step jump skip after normalization, the fifth-order fitting variance var5_up of loading, the fifth-order fitting variance var5_down of the unloading section, the variance Var5 of the fifth-order fitting, the variance var5 of the fifth-order fitting of valid data, the second-order fitting quadratic term coefficient a2 of valid data, the second-order fitting variance var2 of valid data, and the third-order fitting γ after valid data, the strain acquisition data are classified and divided into undeterminable strain data, bad piece strain data, normal strain data, and buckling strain data, so as to achieve efficient, fast, and accurate judgment of the buckling of the strain acquisition data. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a schematic diagram of a buckling judgment method for aircraft ground static test strain collection data provided in an embodiment of the present application;

[0060] Figure 2 is a schematic diagram of the strain acquisition data identification steps provided in an embodiment of the present application;

[0061] Figure 3 It is a schematic diagram of a buckling judgment system for aircraft ground static test strain collection data provided in an embodiment of the present application.

[0062] In order to better illustrate the present embodiment, some contents of the drawings may be omitted, enlarged or reduced, which is only used for illustrative purposes and should not be construed as limiting the present application. DETAILED DESCRIPTION

[0063] In order to make the technical solution and advantages of the present application clearer, the technical solution of the present application will be described in further detail in detail and in detail with reference to the accompanying drawings. It can be understood that the specific embodiments described here are only partial embodiments of the present application, which are only used to explain the present application, not to limit the present application. It should be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, and other related parts can refer to the general design.

[0064] In addition, unless otherwise defined, the technical terms or scientific terms used in the description of this application should be the common meanings understood by those skilled in the art in the field to which this application belongs. The term "include" used in the description of this application means that the concepts appearing before the term include the concepts listed after the term and their equivalents, without excluding other related concepts.

[0065] In practice, the strain data collected during the aircraft ground static test can be divided into four categories, including unidentifiable strain data, bad piece strain data, normal strain data, and buckling strain data.

[0066] The inability to determine the strain data cannot be used to determine whether the strain gauge is damaged, nor can it be used to determine whether the aircraft structure is normal or buckled.

[0067] The bad gauge strain data corresponds to the situation of strain gauge detection failure, which is divided into invalid situations of disorder, zero value, constant value, and failure situation with no obvious characteristics. It cannot be used to judge whether the aircraft structure is normal or buckling.

[0068] The normal strain data has a good linear slope, and the corresponding aircraft structure is normal.

[0069] There is a sudden bend in the buckling strain data curve, and the corresponding aircraft structure buckles.

[0070] When judging whether the aircraft structure has buckled, the strain data that cannot be judged and the bad piece strain data can be identified first, and the strain data that cannot be judged and the bad piece strain data can be eliminated. Then the remaining strain data can be judged and divided into normal strain data and buckling strain data. The aircraft structure corresponding to the buckling strain data has buckled.

[0071] Research has shown that it is impossible to judge strain data, bad piece strain data, normal strain data, and buckling strain data. They can be accurately identified through the difference between the maximum and minimum values ​​Diff, the maximum strain absolute value MaxStrain, the strain values ​​strain are all 0 or all constant values, multiple continuous repetitions, the maximum single-step jump skip after normalization, the 5th order fitting variance var5_up of the loading section, the 5th order fitting variance var5_down of the unloading section, the variance Var5 of the 5th order fitting, the variance var5_cut of the 5th order fitting of the effective data, the 2nd order fitting quadratic term coefficient a2 of the effective data, the 2nd order fitting variance var2 of the effective data, and the 3rd order fitting γ after the effective data. For details, please refer to the following table:

[0072]

[0073] Based on the above, the present application provides a method for determining the buckling of strain collection data from a static ground test of an aircraft, such as Figure 1 shown.

[0074] Data acquisition steps: Obtain strain acquisition data of aircraft ground static test.

[0075] Specifically, the strain collection data can be imported from the Excel file or Txt file of the aircraft ground static test data.

[0076] Data preprocessing steps: Preprocess the strain acquisition data, including eliminating the same load test data, repairing wild point data, and intercepting valid data.

[0077] Eliminating the same load test data specifically involves determining repeated values ​​of the same test load, retaining only the first data, and eliminating subsequent data of the same load test to reduce data redundancy.

[0078] Wild point data repair specifically includes:

[0079] S11, normalize the strain acquisition data and map the strain acquisition data to a range of 0 to 1.

[0080] S12. Based on the jump size and direction, outlier determination is performed on the normalized strain acquisition data.

[0081] If the jump variable skip[i] of the i-th point data and the jump variable skip[i+1] of the subsequent i-1-th point data are greater than the maximum jump setting threshold max_skip, and the jump directions are opposite skip[i]•skip[i+1]<0, then the i-th point is judged to be an outlying point.

[0082] skip[i] = (strain_norm[i]-strain_norm[i-1]);

[0083] skip[i+1] = (strain_norm[i+1]-strain_norm[i]);

[0084] in,

[0085] strain_norm[i-1], strain_norm[i], and strain_norm[i+1] are the values ​​of the i-1th, i, and i+1th points in the normalized strain acquisition data, respectively.

[0086] The maximum jump setting threshold max_skip can be set according to specific actual conditions.

[0087] S13, repairing the wild point data in the strain acquisition data.

[0088] The linear interpolation method is used to repair the wild point data in the strain acquisition data.

[0089] If the i-th point in the strain acquisition data is an out-of-range point data, it is repaired as follows:

[0090] repair[i]=strain[i-1]+(strain[i+1]-strain[i-1])•((load[i]-load[i-1]) / (load[i+1]-load[i-1]) );

[0091] in,

[0092] repair[i] is the repair data of the i-th point in the strain acquisition data;

[0093] strain[i-1] and strain[i+1] are the values ​​of the i-1th and i+1th points in the strain acquisition data, respectively;

[0094] load[i-1], load[i], load[i+1] are the loads corresponding to the i-1th, i, and i+1th points in the strain acquisition data.

[0095] In order to eliminate the nonlinearity caused by the influence of assembly and equipment, the data with load percentage of 20%~100% in the strain acquisition data are intercepted as valid data.

[0096] Strain acquisition data feature identification steps: identify the features of strain acquisition data, including the difference between the maximum and minimum values ​​Diff, the maximum strain absolute value MaxStrain, strain values ​​strain all being 0 or all being constant values, multiple continuous repetitions, the maximum single-step jump skip after normalization, the fifth-order fitting variance var5_up of loading, the fifth-order fitting variance var5_down of the unloading section, the variance Var5 of the fifth-order fitting, the variance var5_cut of the fifth-order fitting of valid data, the quadratic term coefficient a2 of the second-order fitting of valid data, the variance var2 of the second-order fitting of valid data, and the third-order fitting γ after valid data.

[0097] Strain data identification steps: Based on the characteristics of strain data, the strain data is divided into unidentifiable strain data, bad piece strain data, normal strain data, and buckling strain data. Figure 2 As shown, including:

[0098] S21, judging whether the difference Diff between the maximum and minimum values ​​is within the range of 0 to 100, if so, judging the strain acquisition data as strain data that cannot be judged.

[0099] S22, when the maximum-minimum difference Diff is not within the range of 0-100, determine whether the maximum-minimum difference Diff is greater than 21000, and whether the maximum strain absolute value MaxStrain is greater than 21000, if so, determine the strain acquisition data as bad sheet strain data;

[0100] If the difference between the maximum and minimum values ​​Diff is not greater than 21000, or the maximum strain absolute value MaxStrain is not greater than 21000, then it is determined whether the strain values ​​strain are all 0 or all constant values. If so, the strain acquisition data is determined to be bad film strain data;

[0101] If the strain value strain is not all 0 or a constant value, determine whether n consecutive values ​​are the same and occur more than m times, or the number of consecutive values ​​k is more than half. If so, the strain data is determined to be bad chip strain data. When the test average load interval is ≥5, n is 5, when the test average load interval is <5, n is 10, and m is greater than 1. The specific value can be determined according to the actual situation.

[0102] If there are no consecutive n identical values ​​that occur more than m times, or the number of consecutive values ​​k is more than half, then determine whether the normalized maximum single-step jump skip is greater than 0.5, and the fifth-order fitting variance var5_up of the loading segment or the fifth-order fitting variance var5_down of the unloading segment is greater than 0.1. If so, the strain acquisition data is determined to be bad sheet strain data.

[0103] S23. If the normalized maximum single-step jump skip is not greater than 0.5, or the fifth-order fitting variance var5_up of the loading segment and the fifth-order fitting variance var5_down of the unloading segment are not greater than 0.1, then determine whether the fifth-order fitting variance Var5 is greater than 0.1. If so, the strain acquisition data is determined as strain data that cannot be determined.

[0104] S24, when the variance Var5 of the 5th order fitting is not greater than 0.1, determine whether the coefficient a2 of the 2nd order fitting quadratic term of the valid data is less than or equal to 0.2, and whether the 2nd order fitting variance var2 of the valid data is less than or equal to 0.02. If so, determine that the strain acquisition data is normal strain data, and is normal strain data without hysteresis;

[0105] If the 2nd order fitting quadratic term coefficient a2 of the valid data is greater than 0.2, or the 2nd order fitting variance var2 of the valid data is greater than 0.02, then determine whether the 2nd order fitting quadratic term coefficient a2 of the valid data is less than or equal to 0.2, and whether the 3rd order fitting γ after the valid data is greater than or equal to 0.7. If so, the strain acquisition data is determined to be normal strain data, and it is normal strain data with hysteresis.

[0106] S25, when the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than 0.2, or the third-order fitting γ after the valid data is less than 0.7, determine whether the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than or equal to 0.4, and whether the variance var5_cut of the fifth-order fitting of the valid data is less than or equal to 0.02. If so, determine that the strain acquisition data is buckling strain data, and is buckling strain data without hysteresis loop;

[0107] If the coefficient a2 of the second-order fitting quadratic term of the valid data is less than 0.4, or the variance var5_cut of the fifth-order fitting of the valid data is greater than 0.02, then determine whether the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than or equal to 0.4, and whether the variance var5_cut of the fifth-order fitting of the valid data is greater than 0.02. If so, the strain acquisition data is determined to be buckling strain data, and is buckling strain data with hysteresis loop. If not, the strain acquisition data is determined to be buckling strain data, and is uncertain buckling strain data.

[0108] The classification information of strain acquisition data can be saved as an Excel document and output as a graph.

[0109] The method for judging the buckling of strain collected data of a ground static test of an aircraft disclosed in the above-mentioned embodiment, based on preprocessing the strain collected data, classifies the strain collected data by identifying the characteristics of the strain collected data, including the difference between the maximum and minimum values ​​Diff, the maximum strain absolute value MaxStrain, the strain values ​​strain are all 0 or all constant values, multiple continuous repetitions, the maximum single-step jump skip after normalization, the fifth-order fitting variance var5_up of loading, the fifth-order fitting variance var5_down of the unloading section, the variance Var5 of the fifth-order fitting, the variance var5 of the fifth-order fitting of valid data, the coefficient a2 of the second-order fitting quadratic term of valid data, the second-order fitting variance var2 of valid data, and the third-order fitting γ after valid data, and classifies the strain collected data into strain data that cannot be judged, bad piece strain data, normal strain data, and buckling strain data, so as to judge the buckling of the strain collected data.

[0110] The method for determining buckling of strain collected data for a ground static test of an aircraft disclosed in the above-mentioned embodiment uses a feature-based classification model rule tree to perform buckling determination on the strain collected data, which facilitates automatic buckling determination on the strain collected data and can efficiently, quickly and accurately determine whether the aircraft structure is in a buckled state, thereby effectively supporting the need for real-time decision-making at the test site.

[0111] Based on the above, the present application provides a buckling judgment system for aircraft ground static test strain collection data, such as Figure 3 shown.

[0112] The data acquisition module is used to obtain the strain data of the aircraft ground static test.

[0113] Specifically, the strain collection data can be imported from the Excel file or Txt file of the aircraft ground static test data.

[0114] The data preprocessing module is used to preprocess the strain acquisition data, including eliminating the same load test data, repairing the wild point data, and intercepting the valid data.

[0115] Eliminating the same load test data specifically involves determining repeated values ​​of the same test load, retaining only the first data, and eliminating subsequent data of the same load test to reduce data redundancy.

[0116] Wild point data repair specifically includes:

[0117] S11, normalize the strain acquisition data and map the strain acquisition data to a range of 0 to 1.

[0118] S22. Based on the jump size and direction, outlier determination is performed on the normalized strain acquisition data.

[0119] If the jump variable skip[i] of the i-th point data and the jump variable skip[i+1] of the subsequent i-1-th point data are greater than the maximum jump setting threshold max_skip, and the jump directions are opposite skip[i]•skip[i+1]<0, then the i-th point data is judged to be wild point data.

[0120] skip[i] = (strain_norm[i]-strain_norm[i-1]);

[0121] skip[i+1] = (strain_norm[i+1]-strain_norm[i]);

[0122] in,

[0123] strain_norm[i-1], strain_norm[i], and strain_norm[i+1] are the values ​​of the i-1th, i, and i+1th points in the normalized strain acquisition data, respectively.

[0124] The maximum jump setting threshold max_skip can be set according to specific actual conditions.

[0125] S23, repairing the wild point data in the strain acquisition data.

[0126] The linear interpolation method is used to repair the wild point data in the strain acquisition data.

[0127] If the i-th point in the strain acquisition data is an out-of-range point data, it is repaired as follows:

[0128] repair[i]=strain[i-1]+(strain[i+1]-strain[i-1])•((load[i]-load[i-1]) / (load[i+1]-load[i-1]) );

[0129] in,

[0130] repair[i] is the repair data of the i-th point in the strain acquisition data;

[0131] strain[i-1] and strain[i+1] are the values ​​of the i-1th and i+1th points in the strain acquisition data, respectively;

[0132] load[i-1], load[i], load[i+1] are the loads corresponding to the i-1th, i, and i+1th points in the strain acquisition data.

[0133] In order to eliminate the nonlinearity caused by the influence of assembly and equipment, the data with load percentage of 20%~100% in the strain acquisition data are intercepted as valid data.

[0134] The strain acquisition data feature recognition module is used to identify the features of the strain acquisition data, including the difference between the maximum and minimum values ​​Diff, the maximum strain absolute value MaxStrain, the strain values ​​strain are all 0 or all constant values, multiple continuous repetitions, the maximum single-step jump skip after normalization, the fifth-order fitting variance var5_up of loading, the fifth-order fitting variance var5_down of the unloading section, the variance Var5 of the fifth-order fitting, the variance var5 of the fifth-order fitting of the effective data, the quadratic term coefficient a2 of the second-order fitting of the effective data, the variance var2 of the second-order fitting of the effective data, and the third-order fitting γ after the effective data.

[0135] The strain data identification module is used to classify the strain data into unidentifiable strain data, bad piece strain data, normal strain data, and buckling strain data based on the characteristics of the strain data, including:

[0136] S21, judging whether the difference Diff between the maximum and minimum values ​​is within the range of 0 to 100, if so, judging the strain acquisition data as strain data that cannot be judged.

[0137] S22, when the maximum-minimum difference Diff is not within the range of 0-100, determine whether the maximum-minimum difference Diff is greater than 21000, and whether the maximum strain absolute value MaxStrain is greater than 21000, if so, determine the strain acquisition data as bad sheet strain data;

[0138] If the difference between the maximum and minimum values ​​Diff is not greater than 21000, or the maximum strain absolute value MaxStrain is not greater than 21000, then it is determined whether the strain values ​​strain are all 0 or all constant values. If so, the strain acquisition data is determined to be bad film strain data;

[0139] If the strain value strain is not all 0 or a constant value, determine whether n consecutive values ​​are the same and occur more than m times, or the number of consecutive values ​​k is more than half. If so, the strain data is determined to be bad chip strain data. When the test average load interval is ≥5, n is 5, when the test average load interval is <5, n is 10, and m is greater than 1. The specific value can be determined according to the actual situation.

[0140] If there are no consecutive n identical values ​​that occur more than m times, or the number of consecutive values ​​k is more than half, then determine whether the normalized maximum single-step jump skip is greater than 0.5, and the fifth-order fitting variance var5_up of the loading segment or the fifth-order fitting variance var5_down of the unloading segment is greater than 0.1. If so, the strain acquisition data is determined to be bad sheet strain data.

[0141] S23. If the normalized maximum single-step jump skip is not greater than 0.5, or the fifth-order fitting variance var5_up of the loading segment and the fifth-order fitting variance var5_down of the unloading segment are not greater than 0.1, then determine whether the fifth-order fitting variance Var5 is greater than 0.1. If so, the strain acquisition data is determined as strain data that cannot be determined.

[0142] S24, when the variance Var5 of the 5th order fitting is not greater than 0.1, determine whether the coefficient a2 of the 2nd order fitting quadratic term of the valid data is less than or equal to 0.2, and whether the 2nd order fitting variance var2 of the valid data is less than or equal to 0.02. If so, determine that the strain acquisition data is normal strain data, and is normal strain data without hysteresis;

[0143] If the 2nd order fitting quadratic term coefficient a2 of the valid data is greater than 0.2, or the 2nd order fitting variance var2 of the valid data is greater than 0.02, then determine whether the 2nd order fitting quadratic term coefficient a2 of the valid data is less than or equal to 0.2, and whether the 3rd order fitting γ after the valid data is greater than or equal to 0.7. If so, the strain acquisition data is determined to be normal strain data, and it is normal strain data with hysteresis.

[0144] S25, when the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than 0.2, or the third-order fitting γ after the valid data is less than 0.7, determine whether the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than or equal to 0.4, and whether the variance var5_cut of the fifth-order fitting of the valid data is less than or equal to 0.02. If so, determine that the strain acquisition data is buckling strain data, and is buckling strain data without hysteresis loop;

[0145] If the coefficient a2 of the second-order fitting quadratic term of the valid data is less than 0.4, or the variance var5_cut of the fifth-order fitting of the valid data is greater than 0.02, then determine whether the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than or equal to 0.4, and whether the variance var5_cut of the fifth-order fitting of the valid data is greater than 0.02. If so, the strain acquisition data is determined to be buckling strain data, and is buckling strain data with hysteresis loop. If not, the strain acquisition data is determined to be buckling strain data, and is uncertain buckling strain data.

[0146] The classification information of strain acquisition data can be saved as an Excel document and output as a graph.

[0147] Regarding the aircraft ground static test strain collection data buckling judgment system disclosed in the above-mentioned embodiment, since it corresponds to the aircraft ground static test strain collection data buckling judgment method disclosed in the above-mentioned embodiment, the description is relatively simple. For specific related matters, please refer to the relevant description of the aircraft ground static test strain collection data buckling judgment method part. Its technical effects can also refer to the technical effects of the relevant parts of the aircraft ground static test strain collection data buckling judgment method, which will not be repeated here.

[0148] In addition, technicians in the field should also be able to realize that the various modules of the aircraft ground static test strain collection data buckling judgment system disclosed in the embodiments of the present application can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, this application generally describes them according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Technicians in the field can choose to adopt different methods to implement the described functions for each specific application and its actual constraints, but such implementation should not be considered to be beyond the scope of this application.

[0149] So far, the technical solution of the present application has been described in conjunction with the preferred embodiments shown in the accompanying drawings. Those skilled in the art should understand that the scope of protection of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the scope of protection of the present application.

Claims

1. A method for determining buckling of strain data collected from static ground tests on aircraft, characterized in that: include: Data acquisition steps: obtaining strain acquisition data of aircraft ground static test; Data preprocessing steps: preprocess the strain acquisition data, including repairing wild point data and intercepting valid data; Strain acquisition data feature identification steps: identify the features of strain acquisition data, including the difference between the maximum and minimum values ​​Diff, the maximum strain absolute value MaxStrain, the strain values ​​strain are all 0 or all constant values, multiple continuous repetitions, the maximum single-step jump skip after normalization, the fifth-order fitting variance var5_up of loading, the fifth-order fitting variance var5_down of the unloading section, the variance Var5 of the fifth-order fitting, the variance var5_cut of the fifth-order fitting of effective data, the second-order fitting quadratic term coefficient a2 of effective data, the second-order fitting variance var2 of effective data, and the third-order fitting γ after effective data; Strain data identification step: based on the characteristics of the strain data, the strain data is divided into unidentifiable strain data, bad piece strain data, normal strain data, and buckling strain data; The steps of strain data identification include: S21, judging whether the difference Diff between the maximum and minimum values ​​is within the range of 0 to 100, if so, judging the strain acquisition data as strain data that cannot be judged; S22, when the maximum-minimum difference Diff is not within the range of 0-100, determine whether the maximum-minimum difference Diff is greater than 21000, and whether the maximum strain absolute value MaxStrain is greater than 21000, if so, determine the strain acquisition data as bad sheet strain data; If the difference between the maximum and minimum values ​​Diff is not greater than 21000, or the maximum strain absolute value MaxStrain is not greater than 21000, then it is determined whether the strain values ​​strain are all 0 or all constant values. If so, the strain acquisition data is determined to be bad film strain data; If the strain value strain is not all 0 or a constant value, determine whether n consecutive values ​​are the same and occur more than m times, or the number of consecutive values ​​k is more than half. If so, the strain data is determined to be bad chip strain data. When the test average load interval is ≥5, n is 5, when the test average load interval is <5, n is 10, and m is greater than 1. If there are no consecutive n values ​​that are the same and occur more than m times, or the number of consecutive values ​​k is more than half, then determine whether the normalized maximum single-step jump skip is greater than 0.5, and the fifth-order fitting variance var5_up of the loading section or the fifth-order fitting variance var5_down of the unloading section is greater than 0.

1. If so, the strain acquisition data is determined to be bad sheet strain data; S23, if the normalized maximum single-step jump skip is not greater than 0.5, or the fifth-order fitting variance var5_up of the loading section and the fifth-order fitting variance var5_down of the unloading section are not greater than 0.1, then determine whether the fifth-order fitting variance Var5 is greater than 0.1, if so, determine that the strain acquisition data cannot be determined strain data; S24, when the variance Var5 of the 5th order fitting is not greater than 0.1, determine whether the coefficient a2 of the 2nd order fitting quadratic term of the valid data is less than or equal to 0.2, and whether the 2nd order fitting variance var2 of the valid data is less than or equal to 0.

02. If so, determine that the strain acquisition data is normal strain data, and is normal strain data without hysteresis; If the 2nd order fitting quadratic term coefficient a2 of the valid data is greater than 0.2, or the 2nd order fitting variance var2 of the valid data is greater than 0.02, then determine whether the 2nd order fitting quadratic term coefficient a2 of the valid data is less than or equal to 0.2, and whether the 3rd order fitting γ after the valid data is greater than or equal to 0.

7. If so, the strain acquisition data is determined to be normal strain data, and is normal strain data with hysteresis; S25, when the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than 0.2, or the third-order fitting γ after the valid data is less than 0.7, determine whether the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than or equal to 0.4, and whether the variance var5_cut of the fifth-order fitting of the valid data is less than or equal to 0.

02. If so, determine that the strain acquisition data is buckling strain data, and is buckling strain data without hysteresis loop; If the coefficient a2 of the second-order fitting quadratic term of the valid data is less than 0.4, or the variance var5_cut of the fifth-order fitting of the valid data is greater than 0.02, then determine whether the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than or equal to 0.4, and whether the variance var5_cut of the fifth-order fitting of the valid data is greater than 0.

02. If so, the strain acquisition data is determined to be buckling strain data, and is buckling strain data with hysteresis loop. If not, the strain acquisition data is determined to be buckling strain data, and is uncertain buckling strain data.

2. The method for determining buckling of strain collection data from aircraft ground static test according to claim 1, characterized in that: The outlier data repair in the data preprocessing step includes: S11, normalizing the strain acquisition data, mapping the strain acquisition data to a range of 0 to 1; S12, based on the jump size and direction, the normalized strain collection data is subjected to outlier judgment; If the jump value of the i-th point data and the jump value of the i-1-th point data after it are greater than the maximum jump setting threshold, and the jump directions are opposite, then the i-th point is judged to be an outlier; S13, repairing the wild point data in the strain acquisition data; The linear interpolation method is used to repair the wild point data in the strain acquisition data.

3. The method for determining buckling of strain collection data from aircraft ground static tests according to claim 2, characterized in that: In the data preprocessing step, the effective data is specifically intercepted by intercepting the data with a load percentage of 20% to 100% in the strain acquisition data as the effective data.

4. The method for determining buckling of strain collection data from aircraft ground static tests according to claim 3, characterized in that: The data preprocessing step also includes eliminating the same load test data, specifically, determining the repeated values ​​of the same test load, retaining only the first data, and eliminating the subsequent data of the same load test.

5. A system for determining buckling of strain data collected from static ground tests on aircraft, used to implement the method for determining buckling of strain data collected from static ground tests on aircraft as claimed in claim 1, characterized in that: include: A data acquisition module is used to obtain strain data collected during static ground tests of aircraft; Data preprocessing module, used to preprocess strain acquisition data, including wild point data repair and effective data interception; The strain acquisition data feature recognition module is used to identify the features of the strain acquisition data, including the difference between the maximum and minimum values ​​Diff, the maximum strain absolute value MaxStrain, the strain values ​​strain are all 0 or all constant values, multiple continuous repetitions, the maximum single-step jump skip after normalization, the fifth-order fitting variance var5_up of loading, the fifth-order fitting variance var5_down of the unloading section, the variance Var5 of the fifth-order fitting, the variance var5_cut of the fifth-order fitting of the effective data, the second-order fitting quadratic term coefficient a2 of the effective data, the second-order fitting variance var2 of the effective data, and the third-order fitting γ after the effective data; The strain data identification module is used to classify the strain data into unidentifiable strain data, bad piece strain data, normal strain data, and buckling strain data based on the characteristics of the strain data; In the strain data identification module, based on the characteristics of the strain data, the strain data is divided into unidentifiable strain data, bad piece strain data, normal strain data, and buckling strain data, including: S21, judging whether the difference Diff between the maximum and minimum values ​​is within the range of 0 to 100, if so, judging the strain acquisition data as strain data that cannot be judged; S22, when the maximum-minimum difference Diff is not within the range of 0-100, determine whether the maximum-minimum difference Diff is greater than 21000, and whether the maximum strain absolute value MaxStrain is greater than 21000, if so, determine the strain acquisition data as bad sheet strain data; If the difference between the maximum and minimum values ​​Diff is not greater than 21000, or the maximum strain absolute value MaxStrain is not greater than 21000, then it is determined whether the strain values ​​strain are all 0 or all constant values. If so, the strain acquisition data is determined to be bad film strain data; If the strain value strain is not all 0 or a constant value, determine whether n consecutive values ​​are the same and occur more than m times, or the number of consecutive values ​​k is more than half. If so, the strain data is determined to be bad chip strain data. When the test average load interval is ≥5, n is 5, when the test average load interval is <5, n is 10, and m is greater than 1. If there are no consecutive n values ​​that are the same and occur more than m times, or the number of consecutive values ​​k is more than half, then determine whether the normalized maximum single-step jump skip is greater than 0.5, and the fifth-order fitting variance var5_up of the loading section or the fifth-order fitting variance var5_down of the unloading section is greater than 0.

1. If so, the strain acquisition data is determined to be bad sheet strain data; S23, if the normalized maximum single-step jump skip is not greater than 0.5, or the fifth-order fitting variance var5_up of the loading section and the fifth-order fitting variance var5_down of the unloading section are not greater than 0.1, then determine whether the fifth-order fitting variance Var5 is greater than 0.1, if so, determine that the strain acquisition data cannot be determined strain data; S24, when the variance Var5 of the 5th order fitting is not greater than 0.1, determine whether the coefficient a2 of the 2nd order fitting quadratic term of the valid data is less than or equal to 0.2, and whether the 2nd order fitting variance var2 of the valid data is less than or equal to 0.

02. If so, determine that the strain acquisition data is normal strain data, and is normal strain data without hysteresis; If the 2nd order fitting quadratic term coefficient a2 of the valid data is greater than 0.2, or the 2nd order fitting variance var2 of the valid data is greater than 0.02, then determine whether the 2nd order fitting quadratic term coefficient a2 of the valid data is less than or equal to 0.2, and whether the 3rd order fitting γ after the valid data is greater than or equal to 0.

7. If so, the strain acquisition data is determined to be normal strain data, and is normal strain data with hysteresis; S25, when the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than 0.2, or the third-order fitting γ after the valid data is less than 0.7, determine whether the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than or equal to 0.4, and whether the variance var5_cut of the fifth-order fitting of the valid data is less than or equal to 0.

02. If so, determine that the strain acquisition data is buckling strain data, and is buckling strain data without hysteresis loop; If the coefficient a2 of the second-order fitting quadratic term of the valid data is less than 0.4, or the variance var5_cut of the fifth-order fitting of the valid data is greater than 0.02, then determine whether the coefficient a2 of the second-order fitting quadratic term of the valid data is greater than or equal to 0.4, and whether the variance var5_cut of the fifth-order fitting of the valid data is greater than 0.

02. If so, the strain acquisition data is determined to be buckling strain data, and is buckling strain data with hysteresis loop. If not, the strain acquisition data is determined to be buckling strain data, and is uncertain buckling strain data.

6. The aircraft ground static test strain collection data buckling judgment system according to claim 5, characterized in that: The data preprocessing step includes: S11, normalizing the strain acquisition data, mapping the strain acquisition data to a range of 0 to 1; S12, based on the jump size and direction, the normalized strain collection data is subjected to outlier judgment; If the jump value of the i-th point data and the jump value of the i-1-th point data after it are greater than the maximum jump setting threshold, and the jump directions are opposite, then the i-th point is judged to be an outlier; S13, repairing the wild point data in the strain acquisition data; The linear interpolation method is used to repair the wild point data in the strain acquisition data.

7. The aircraft ground static test strain collection data buckling judgment system according to claim 6, characterized in that: In the data preprocessing step, the effective data is specifically intercepted by intercepting the data with a load percentage of 20% to 100% in the strain acquisition data as the effective data.

8. The aircraft ground static test strain collection data buckling judgment system according to claim 7, characterized in that: The data preprocessing step also includes eliminating the same load test data, specifically, determining the repeated values ​​of the same test load, retaining only the first data, and eliminating the subsequent data of the same load test.

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