Load spectrum similarity evaluation method based on overall characteristics

Abstract

The invention belongs to the technical field of aircraft load spectrum evaluation, and particularly relates to a load spectrum similarity evaluation method based on overall characteristics. The method comprises the following steps: firstly, selecting two load spectrums needing similarity evaluation, and preprocessing the two load spectrums; extracting a plurality of feature parameters of the load spectrum from a plurality of dimensions; finally, differentiated evaluation is conducted on all the parameters, and similarity judgment is conducted. The load spectrum similarity evaluation method based on absolute measurement does not depend on subjective experience, completely starts from objective analysis, improves the reliability and applicability of engineering application compared with a previous mode, and provides reliable means and basis for a proper and reasonable service life analogy analysis method.

Description

Technical Field

[0001] The invention belongs to the technical field of aircraft load spectrum evaluation, and in particular relates to a load spectrum similarity evaluation method based on overall characteristics. Background Art

[0002] At present, the service life analogy analysis method is widely used in engineering to predict fatigue life. This method can avoid the uncertain assumption that the cumulative damage reaching a certain critical value represents fatigue cracking, and introduces existing test life data as the basis or reference for life prediction, so it has a higher life prediction accuracy.

[0003] The premise of using the life analogy analysis method is the same or similar structural details (same materials) and similar load spectra. In this assumption, the same structural details and the same materials are easy to understand, but similar load spectra lack quantitative description. Industry insiders can only roughly divide them according to the approximate purpose and usage characteristics of the load spectra: it is generally believed that the uses are similar and the load spectra are similar. Such a division is vague and subjective, relying on the experience of practitioners, and it is impossible to objectively describe the similarity of the load spectra. In the early days when there were fewer aircraft models, such a division may have been able to meet the needs of engineering use, but now the multi-functional and multi-purpose characteristics of aircraft models make the previous "rough" division method no longer suitable for current engineering applications, so a more refined and accurate method is needed to replace the previous division method. Summary of the invention

[0004] Purpose of the present invention: At present, the similarity of load spectrum cannot be quantitatively evaluated in the use of life analogy method. The present invention analyzes the load spectrum comprehensively and in detail from multiple dimensions and multiple indicators, and proposes the criterion of each parameter indicator that satisfies the similarity of load spectrum, which provides a reliable means and basis for the appropriate and reasonable service life analogy method.

[0005] Technical solution of the present invention: In order to achieve the above-mentioned purpose, the present invention proposes a load spectrum similarity assessment method based on absolute measurement. First, two load spectra to be assessed for similarity are selected and preprocessed; then, multiple characteristic parameters of the load spectrum are extracted from multiple dimensions; finally, each parameter is differentially evaluated and similarity judgment is performed.

[0006] Furthermore, two load spectra to be judged for similarity are selected and recorded as [X i ](i=1,2...n) and [Y j ](j=1,2…m). Among them, X i is the data point in the load spectrum 1 sequence, and the length of the spectrum sequence is n; j is the data point in the load spectrum 2 sequence, and the length of the spectrum sequence is m.

[0007] Furthermore, for the two load spectra [X i ] and [Y j ] to perform normalization preprocessing; the formula is

[0008] [x i ]=[X i ] / NORM x (i=1,2…n)

[0009] [y j ]=[Y j ] / NORM y (j=1,2…m)

[0010] [x i ](i=1,2...n) and [y j ](j=1,2…m) is [X i ] and [Y j ] are respectively normalized spectral sequences;

[0011] NORM x and NORM y For their respective normalized denominators: When the load types of the two load spectra are the same, NORM x =NORM y =max{[X i ],[Y j ]}; When the load types of the two load spectra are inconsistent, NORM x =max{[X i ]}, NORM y =max{[Y j ]}.

[0012] Furthermore, the preprocessing process is to select two load spectra [X i ] and [Y j ] for normalization preprocessing; when the load types of the two load spectra are consistent, the normalized denominator is the maximum value of the two load spectra; when the load types of the two load spectra are inconsistent, the normalized denominator is the maximum value of their respective load spectra.

[0013] Furthermore, multiple characteristic parameters of the load spectrum are extracted from multiple dimensions, and the skewness ΔS of the amplitude is extracted from the statistical dimension. skew , the skewness of the stress ratio R skew and the mean value of the stress ratio R mean ;

[0014] The average overload ratio OL of each load spectrum is obtained by extracting the high-load hysteresis effect dimension mean and the unitized average maximum distance dis mean ;

[0015] The first-level broken line slope k1 and the slope change rate mean k' of the two load spectra are extracted from the shape dimension of the load exceedance curve mean , and the standard deviation of the slope change rate k' σ ;

[0016] From the probability pairing matrix correlation dimension, the probability pairing matrices of the two load spectra are operated to extract the correlation coefficient C;

[0017] The fractal dimension D is extracted from the self-similarity dimension of the load spectrum.

[0018] Furthermore, when extracting characteristic parameters from the high-load hysteresis impact dimension: average overload ratio OL mean It is the average of the ratios of several maximum values ​​in each load spectrum to the next adjacent peak value;

[0019] Unitized average maximum distance dis mean is the average maximum distance DIS of each load spectrum mean The ratio of the load spectrum length L to the average maximum distance DIS mean It is the average of the distances between two adjacent maximum values ​​in the load spectrum.

[0020] Furthermore, the first-level segment slope k1, the mean value k'mean of the slope change rate, and the standard deviation k' of the slope change rate of the two load spectra are extracted from the shape dimension of the load exceedance curve. σ ; The extraction method is:

[0021] First, the transcendence number-load curve of each load spectrum is drawn at each load level of 0.1, and the transcendence number-load curve is presented in the form of a broken line segment; wherein the ordinate is the transcendence number, which is displayed on a logarithmic scale with a base of 10, and the abscissa is the load level;

[0022] Calculate the slope of each broken line segment, the slope of the first broken line segment is recorded as k1; calculate the rate of change of the slope of any two adjacent broken line segments, and calculate their mean and standard deviation, which are recorded as the mean of the slope change rate k'mean and the standard deviation of the slope change rate k' respectively. σ .

[0023] Furthermore, from the probability pairing matrix correlation dimension, the probability pairing matrices of the two load spectra are operated to extract the correlation coefficient; the method is:

[0024] The probability pairing matrix of the peak and valley values ​​of each load spectrum is constructed according to each load level of 0.1. The two probability pairing matrices are aligned to the same dimension according to the envelope of the peak and valley values, and the correlation coefficient C of the two probability pairing matrices is calculated.

[0025] Furthermore, the fractal dimension D is extracted from the self-similarity dimension of the load spectrum, and each load spectrum sequence is regarded as a one-dimensional fractal, and the fractal dimension D of each load spectrum is calculated.

[0026] Furthermore, the extracted characteristic parameters are evaluated differentially;

[0027] For each characteristic parameter, an evaluation index parameter is set to evaluate whether the relationship between the corresponding characteristic parameters of the two load spectra meets the set conditions. When a certain number of index parameters meet the set conditions, the two load spectra are judged to be similar, otherwise they are not similar.

[0028] Beneficial effects of the present invention: The load spectrum similarity assessment method based on absolute measurement proposed in the present invention does not rely on subjective experience, but is completely based on objective analysis. Compared with previous methods, it improves the reliability and applicability of engineering applications and provides a reliable means and basis for appropriate and reasonable service life analogy analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a technical flow chart of the present invention;

[0030] Figure 2 This is a schematic diagram of the overload ratio and maximum value distance related to parameters 4 and 5 in step 4 of the solution;

[0031] Figure 3 This is a schematic diagram of the transcendence number-load curve related to parameters 6, 7, and 8 in step 5 of the solution;

[0032] Figure 4 This is a schematic diagram of the probability pairing matrix associated with parameter 9 in step 6 of the solution;

[0033] Figure 5 Schematic diagram of two overload spectrum parts (first 5000 points) selected in the embodiment;

[0034] Figure 6 Schematic diagram of load spectrum amplitude distribution in the embodiment;

[0035] Figure 7 Schematic diagram of load spectrum stress ratio distribution in the embodiment;

[0036] Figure 8 It is a schematic diagram of the exceedance number-load curve in the embodiment;

[0037] Fig. 9 It is a schematic diagram of a probability pairing matrix (part) in the embodiment;

[0038] Fig.10 Schematic diagram of the box counting method used in the embodiment; DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] A load spectrum similarity assessment method based on absolute measurement. First, two load spectra to be assessed for similarity are selected and preprocessed. Then, multiple characteristic parameters of the load spectra are extracted from multiple dimensions. Finally, each parameter is evaluated differentially to make a similarity judgment. The technical process is shown in Figure 1 .

[0041] The evaluation method proposed in the present invention specifically comprises the following steps:

[0042] Step 1: Select two load spectra to be judged for similarity, denoted as [X i ](i=1,2...n) and [Y j ](j=1,2…m). Among them, X i is the data point in the load spectrum 1 sequence, and the length of the spectrum sequence is n; j is the data point in the load spectrum 2 sequence, and the length of the spectrum sequence is m;

[0043] Step 2: i ] and [Y j ] to perform normalization preprocessing; the formula is

[0044] [x i ]=[X i ] / NORM x (i=1,2…n)

[0045] [y j ]=[Y j ] / NORM y (j=1,2…m)

[0046] [x i ](i=1,2...n) and [y j ](j=1,2…m) is [X i ] and [Y j ] are the normalized spectral sequences.

[0047] NORM x and NORM y For each normalized denominator: When the load types of the two load spectra are the same (such as both are overload spectra or both are bending moment spectra), NORMx =NORM y =max{[X i ],[Y j ]}; When the load types of the two load spectra are inconsistent (for example, one is an overload spectrum and the other is a moment spectrum), NORM x =max{[X i ]}, NORM y =max{[Y j ]};

[0048] Step 3: Based on the normalization process, multiple characteristic parameters of the load spectrum are extracted from multiple dimensions. These characteristic parameters have typical representative meanings, and the degree of similarity is objectively judged based on the differences between these typical characteristic parameter indicators.

[0049] In the specific implementation process, the skewness ΔS of the amplitude is extracted from the statistical dimension. skew , the skewness of the stress ratio R skew and the mean value of the stress ratio R mean ; respectively recorded as parameter 1, parameter 2, parameter 3;

[0050] The average overload ratio OL of each load spectrum is obtained by extracting the high-load hysteresis effect dimension mean and the unitized average maximum distance dis mean ; respectively recorded as parameter 4 and parameter 5;

[0051] The first-level broken line slope k1 and the slope change rate mean k' of the two load spectra are extracted from the shape dimension of the load exceedance curve mean , and the standard deviation of the slope change rate k' σ ; respectively recorded as parameter 6, parameter 7, parameter 8;

[0052] From the probability pairing matrix correlation dimension, the probability pairing matrix of the two load spectra is operated to extract the correlation coefficient C, which is recorded as parameter 9;

[0053] The fractal dimension D is extracted from the self-similarity dimension of the load spectrum and is recorded as parameter 10;

[0054] In the specific implementation process, the specific method for extracting characteristic parameters 1 to 3 is: respectively [x i ] and [y j ] to count the rain flow and calculate the amplitude ΔS of each pair of rain flow cycles for each load spectrum i (=S max,i -S min,i ) and stress ratio R i (=S min,i / S max,i ), where S max,i and Smin,i are the peak and valley values ​​of the ith pair of rainflow cycles, respectively. The distribution of amplitude and stress ratio is statistically analyzed to obtain the amplitude skewness ΔS skew (denoted as parameter 1) and stress ratio skewness R skew (denoted as parameter 2) and the mean stress ratio R mean (Parameter 3); The calculation formulas for sample skewness and sample mean can be found in general probability and statistics books;

[0055] In the specific implementation process, the specific method for extracting characteristic parameters 4 to 5 is: Calculate the average overload ratio OL of each load spectrum mean (the average of the ratios of the maximum value to the next peak in the spectrum, parameter 4), and the average maximum distance DIS mean The ratio of (the mean of the distances between the maximum values ​​in the spectrum) to the spectrum length L is the unitized average maximum distance dis mean (Parameter 5), L = n or m; the distance between the overload ratio and the maximum value is as follows Figure 2 As shown in the figure, x i and x j For [x i ] The maximum value of two closely connected spectra in the sequence, x i Overload ratio OL ni =x i / x i+1 , x j Overload ratio OL nj =x j / x j+1 , x i and x j The distance is DIS nij ; The calculation formula of sample mean can be found in general probability and statistics books;

[0056] In the specific implementation process, the specific method for extracting characteristic parameters 6 to 8 is: draw the exceedance number-load curve of each load spectrum at each load level 0.1 (only the peak value is counted), and the coordinates of each point in the curve segment are (P j ,N j ), j = 1, 2…p. Among them, P j represents the jth level load, P1 <P2<…<P j <… <P p ≤1.0,N j Represents the jth level load P j The corresponding exceedance number. Exceedance number-load curve shows Figure 3 . Calculate the slope k of each broken line segment in the curve j =(lgN j -lgN j+1 ) / (P j -P j+1), j = 1, 2…p-1, and the slope change rate of each segment k j '=k j+1 -k j , j = 1, 2 ... p-2; Statistical first-level broken line segment slope k1 (parameter 6) of the two load spectra, the mean value of the slope change rate k' mean (Parameter 7) and the standard deviation of the slope change rate k' σ (Parameter 8); The calculation formulas for sample mean and sample standard deviation can be found in general probability and statistics books;

[0057] In the specific implementation process, the specific method for extracting characteristic parameters 9 is: construct the peak / valley probability pairing matrix of each load spectrum according to each load level 0.1, which is recorded as A n1×m1 and B n2×m2 , the probability pairing matrix shows the opinion Figure 4 , f in the figure i,j Represents the number of occurrences of the pairing of the i-th level valley and the j-th level peak. Align the two probability pairing matrices to the same dimension according to the peak and valley envelopes, i.e., A n×m and B n×m , where n = max{n1,n2}, m = max{m1,m2}. Calculate the correlation coefficient C of the two matrices (parameter 9);

[0058] The calculation formula of correlation coefficient C is:

[0059]

[0060] Where tr() is the trace of the matrix, A is the probability pairing matrix of load spectrum 1, and B is the probability pairing matrix of load spectrum 2.

[0061] In the specific implementation process, the specific method of extracting the characteristic parameter 10 is: regard the load spectrum sequence as a one-dimensional fractal, and calculate [x i ](i=1,2...n) and [y j ](j=1,2…m) fractal dimension D (parameter 10). The calculation method of fractal dimension can refer to general fractal geometry books.

[0062] Step 4: Perform differential evaluation on the 10 characteristic parameters extracted as above. In order to obtain intuitive evaluation results, each parameter can be evaluated in the form of the following scores:

[0063] Parameter 1: When the amplitude skewness ΔS of load spectrum 1 skew,1 Amplitude skewness ΔS with load spectrum 2 skew,2 The absolute value of the difference satisfies the condition |ΔS skew,1 -ΔS skew,2 When |≤0.5, 1 point is given, otherwise 0 point is given;

[0064] Parameter 2: When the stress ratio skewness R of load spectrum 1 skew,1 Stress ratio skewness R of load spectrum 2 skew,2 The absolute value of the difference satisfies the condition |R skew,1 -R skew,2 When |≤0.5, 1 point is given, otherwise 0 point is given;

[0065] Parameter 3: When the stress ratio of load spectrum 1 is R mean,1 The stress ratio R of load spectrum 2 mean,2 The absolute value of the difference satisfies the condition |R mean,1 -R mean,2 When |≤0.1, 1 point is given, otherwise 0 point is given;

[0066] Parameter 4: When the average overload ratio OL of load spectrum 1 mean,1 Average overload ratio OL of load spectrum 2 mean,2 Satisfy the conditions (OL mean,1 -OL threshold )·(OL mean,2 -OL threshold )>0, 1 point is given, otherwise 0 point is given; threshold is the overload threshold value, which can be determined by test. For aluminum alloy, it is generally taken as 1.3 to 1.5;

[0067] Parameter 5: When the average maximum distance ratio DIS of load spectrum 1 mean,1 / n and the average maximum distance ratio DIS of load spectrum 2 mean,2 / m ratio satisfies the condition 0.1≤(DIS mean,1 / n) / (DIS mean,2 / m)≤10, 1 point is given, otherwise 0 point is given;

[0068] Parameter 6: When the initial broken line slope k of load spectrum 1 1,谱1 and the initial broken line slope k of load spectrum 2 1,谱2 The absolute value of the difference satisfies the condition |k 1,谱1 -k 1,谱2 When |≤0.5, 1 point is given, otherwise 0 point is given;

[0069] Parameter 7: When the mean value k' of the slope change rate of load spectrum 1 mean,谱1 and the mean value k' of the slope change rate of load spectrum 2 mean,谱2 The absolute value of the difference satisfies the condition |k' mean,谱1 -k' mean,谱2 When |≤0.5, 1 point is given, otherwise 0 point is given;

[0070] Parameter 8: When the standard deviation of the slope change rate of load spectrum 1 is k' σ,谱1 And the standard deviation k' of the slope change rate of load spectrum 2 σ,谱2The absolute value of the difference satisfies the condition |k' σ,谱1 -k' σ,谱2 When |≤0.5, 1 point is given, otherwise 0 point is given;

[0071] Parameter 9: When the correlation coefficient C satisfies the condition C ≥ 0.6, 1 point is given, otherwise 0 point is given;

[0072] Parameter 10: When the absolute value of the difference between the fractal dimension D1 of load spectrum 1 and the fractal dimension D2 of load spectrum 2 satisfies the condition |D1-D2|≤0.05, 1 point is given, otherwise 0 point is given;

[0073] In the above implementation manner, it is understandable to those skilled in the art that the setting condition of each typical characteristic parameter in the two load spectra may be other conditions. For load spectra of different types of aircraft, the above setting conditions may be determined by the following method:

[0074] For parameter 1, parameter 2, parameter 3, parameter 5, parameter 10;

[0075] These five characteristic parameters can be based on one of the load spectra, and all the peak and valley values ​​of the load spectrum are counted, which are randomly rearranged several times. After each rearrangement, a new load spectrum can be obtained, which is recorded as a test spectrum. The number of rearrangements is recommended to be no less than 1000 times; all the test spectra are extracted to their corresponding parameters 1, 2, 3, 5, and 10 according to the characteristic extraction method of the present invention; the maximum value a and the minimum value b of each parameter in all the test spectra are counted respectively, and (ab) / 2 is used as the setting condition of each parameter;

[0076] In the setting condition corresponding to parameter 4, the overload threshold value OL threshold It can be obtained from fatigue comparison test; a two-level equal amplitude spectrum test with a stress ratio of 0.06 is conducted, the first-level coefficient peak is S, S ≥ 1, and the frequency is recommended to be 10 times; the second-level coefficient peak is 1, and the frequency is recommended to be not less than 500 times; group fatigue tests with S = 1, S = 1.1, S = 1.2... are conducted in sequence. When an obvious high-load hysteresis effect appears, the current coefficient peak S is used as the overload threshold value OL threshold ;

[0077] The setting conditions corresponding to parameters 6, 7, and 8 can be based on one of the load spectra, and the maximum load level of its exceedance number-load curve is translated 10% to the left along the x-axis as the left boundary; the maximum load level is translated 10% to the right along the x-axis as the right boundary; for each level of exceedance number, the corresponding load level is randomly selected within the left and right boundaries to obtain a new test exceedance number curve, and repeated several times, and the number of repetitions is recommended to be not less than 1000 times; for all test exceedance number curves, the corresponding parameters 6, 7, and 8 are extracted according to the method proposed by the characteristics of the present invention; the maximum value c and the minimum value d of each parameter in all test exceedance number curves are respectively counted, and (cd) / 2 is used as the setting condition of each parameter;

[0078] For parameter 9, it is generally believed in the industry that when C ≥ 0.6, the two matrices are correlated; therefore, 0.6 is used as the setting condition.

[0079] Step 5: Make similarity judgment based on the differentiated evaluation scores.

[0080] The full score is 10 points: 8-10 points, highly similar; 6-7 points, similar; 3-5 points, not very similar; 0-2 points, almost no similarity.

[0081] When the score is ≥6, the life analogy analysis method is reliable and appropriate.

[0082] It should be noted that the above scoring is only a simple form of evaluation. Obviously, for those skilled in the art, the scoring is only one of the intuitive forms of expression and is not limited to this. There may be several other implementation forms, such as using color depth for evaluation. Finally, the color superposition depth can also intuitively reflect the similarity between the two load spectra. There may be more similar specific evaluation measures, which will not be elaborated in the present invention.

[0083] Example 1: Taking two overload spectra as an example, a load spectrum similarity assessment method based on absolute measurement proposed by the present invention is adopted. The technical process is shown in Figure 1 , one of the possible specific implementations includes the following steps:

[0084] The first step is to select two load spectra to be measured for similarity. In this embodiment, two overload spectra are selected as evaluation objects, which are denoted as [Nz 1i ](i=1,2...n) and [Nz 2j ](j=1,2…m); the first 5000 points of the spectrum sequence show opinions Figure 5 ;

[0085] The second step is to normalize the two load spectra in the first step. The load types of the two load spectra are both overload, so the denominator of the normalization of the two load spectra is Nz max=max{[Nz i ],[Nz j ]}; the normalized spectrum sequence is recorded as [nz 1i ](i=1,2...n) and [nz 2j ](j=1,2…m);

[0086] The third step is to extract feature parameters 1 to 3 from the two normalized sequences in the second step respectively:

[0087] Parameter 1 distribution see Figure 6 , we can get ΔS skew,1 =1.238, ΔS skew,2 =0.656;

[0088] Parameter 2 distribution see Figure 7 , we can get R skew,1 =-1.307, R skew,2 =0.446;

[0089] Parameter 3 distribution see Figure 7 , we can get R mean,1 =0.20, R mean,2 =0.42;

[0090] The fourth step is to extract feature parameters 4 to 5 from the two normalized sequences in the second step respectively:

[0091] Parameter 4, statistics can be obtained OL mean,1 =1.549,OL mean,2 =1.638;

[0092] Parameter 5, statistics can be obtained DIS mean,1 / n=0.0165,DIS mean,2 / m=0.1042;

[0093] Step 5: Extract feature parameters 6 to 8 from the two normalized sequences in the second step respectively:

[0094] Parameter 6 shows the opinion Figure 8 , we can get k 1,谱1 =-1.43, k 1,谱2 =-1.86;

[0095] Parameter 7 shows the opinion Figure 8 , we can get k' mean,谱1 =-0.70, k' mean,谱2 =-0.67;

[0096] Parameter 8 shows the opinion Figure 8 , we can get k' σ,谱1 =0.53, k' σ,谱2=0.85;

[0097] Step 6: Extract feature parameters 9 from the two normalized sequences in the second step:

[0098] Parameter 9 indicates Fig. 9 , we can calculate C = 0.72;

[0099] In the seventh step, for the two normalized sequences in the second step, feature parameters 10 are extracted respectively:

[0100] Parameter 10 is calculated using the box counting method in this embodiment. Fig.10 , the calculation formula is

[0101]

[0102] The calculation results show that D1 = 1.87, D2 = 1.89. Other methods can also be used to obtain them.

[0103] In the eighth step, a differentiated evaluation is performed based on the parameter statistical results of the third step, as shown in Table 1.

[0104] Table 1

[0105]

[0106] Step 9. According to Table 1, the similarity evaluation score of Spectrum 1 and Spectrum 2 is 7 points, which means that they are basically similar. Life analogy analysis can be carried out based on Spectrum 1 and Spectrum 2.

[0107] Starting from the overall characteristics of the load spectrum, the present invention proposes various characteristic parameters from multiple dimensions to describe the characteristics of the load spectrum, judges the degree of similarity based on the differences between parameter indicators, and establishes a quantitative evaluation method for the similarity between load spectra. Based on this method, a quantitative judgment of the degree of similarity between load spectra can be effectively given.

[0108] The above is only a specific embodiment of the present invention, and the present invention is described in detail. The unexplained part is a conventional technology. However, the protection scope of the present invention is not limited to this. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A load spectrum similarity evaluation method based on absolute measurement, characterized in that: The evaluation method first selects two load spectra to be evaluated for similarity and preprocesses them; then extracts multiple characteristic parameters of the load spectra from multiple dimensions; and finally performs differential evaluation on each parameter to make a similarity judgment.

2. A method for evaluating load spectrum similarity based on absolute measurement as claimed in claim 1, characterized in that: Select two load spectra to be judged for similarity, denoted as [X i ](i=1,2...n) and [Y j ](j=1,2…m); where X i is the data point in the load spectrum 1 sequence, and the length of the spectrum sequence is n; j is the data point in the load spectrum 2 sequence, and the length of the spectrum sequence is m.

3. A method for evaluating load spectrum similarity based on absolute measurement as claimed in claim 2, characterized in that: For two load spectra [X i ] and [Y j ] to perform normalization preprocessing; the formula is [x i ]=[X i ] / NORM x (i=1,2…n) [y j ]=[Y j ] / NORM y (j=1,2…m) [x i ](i=1,2...n) and [y j ](j=1,2…m) is [X i ] and [Y j ] are respectively normalized spectral sequences; NORM x and NORM y For their respective normalized denominators: When the load types of the two load spectra are the same, NORM x =NORM y =max{[X i ],[Y j ]}; When the load types of the two load spectra are inconsistent, NORM x =max{[X i ]}, NORM y =max{[Y j ]}.

4. A method for evaluating load spectrum similarity based on absolute measurement as claimed in claim 1, characterized in that: Extract multiple characteristic parameters of the load spectrum from multiple dimensions, and extract the skewness ΔS of the amplitude from the statistical dimension skew , the skewness of the stress ratio R skew and the mean value of the stress ratio R mean ; The average overload ratio OL of each load spectrum is obtained by extracting the high-load hysteresis effect dimension mean and the unitized average maximum distance dis mean ; The first-level broken line slope k1 and the slope change rate mean k' of the two load spectra are extracted from the shape dimension of the load exceedance curve mean , and the standard deviation of the slope change rate k' σ ; From the probability pairing matrix correlation dimension, the probability pairing matrices of the two load spectra are operated to extract the correlation coefficient C; The fractal dimension D is extracted from the self-similarity dimension of the load spectrum.

5. A method for evaluating load spectrum similarity based on absolute measurement as claimed in claim 4, characterized in that: When extracting characteristic parameters from the high-load hysteresis impact dimension: average overload ratio OL mean It is the average of the ratios of several maximum values ​​in each load spectrum to the next adjacent peak value; Unitized average maximum distance dis mean is the average maximum distance DIS of each load spectrum mean The ratio of the load spectrum length L to the average maximum distance DIS mean It is the average of the distances between two adjacent maximum values ​​in the load spectrum.

6. A method for evaluating load spectrum similarity based on absolute measurement as claimed in claim 4, characterized in that: The first-level segment slope k1, the mean value k'mean of the slope change rate, and the standard deviation k' of the slope change rate of the two load spectra are extracted from the shape dimension of the load exceedance curve. σ ; The extraction method is: First, the transcendence number-load curve of each load spectrum is drawn at each load level of 0.1, and the transcendence number-load curve is presented in the form of a broken line segment; wherein the ordinate is the transcendence number, which is displayed on a logarithmic scale with a base of 10, and the abscissa is the load level; Calculate the slope of each broken line segment, the slope of the first broken line segment is recorded as k1; calculate the rate of change of the slope of any two adjacent broken line segments, and calculate their mean and standard deviation, which are recorded as the mean of the slope change rate k'mean and the standard deviation of the slope change rate k' respectively. σ .

7. A method for evaluating load spectrum similarity based on absolute measurement as claimed in claim 4, characterized in that: From the probability pairing matrix correlation dimension, the probability pairing matrices of the two load spectra are operated to extract the correlation coefficient; The method is: The probability pairing matrix of the peak and valley values ​​of each load spectrum is constructed according to each load level of 0.

1. The two probability pairing matrices are aligned to the same dimension according to the envelope of the peak and valley values, and the correlation coefficient C of the two probability pairing matrices is calculated.

8. A method for evaluating load spectrum similarity based on absolute measurement as claimed in claim 4, characterized in that: The fractal dimension D is extracted from the self-similarity dimension of the load spectrum, and each load spectrum sequence is regarded as a one-dimensional fractal to calculate the fractal dimension D of each load spectrum.

9. A method for evaluating load spectrum similarity based on absolute measurement as claimed in claim 1, characterized in that: Conduct differential evaluation on the extracted feature parameters; For each characteristic parameter, an evaluation index parameter is set to evaluate whether the relationship between the corresponding characteristic parameters of the two load spectra meets the set conditions. When a certain number of index parameters meet the set conditions, the two load spectra are judged to be similar, otherwise they are not similar.

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