A fatigue analysis method, device, terminal and medium based on SAEMD

Abstract

The present application provides a fatigue analysis method, device, terminal and medium based on SAEMD, wherein the method comprises: collecting the load signal received by the structural member, recorded as x(t); performing SAEMD decomposition on x(t); calculating the coherence function between the IMF components of each order and x′(t), screening out a set of effective components and recording it as set B1; and calculating the sample entropy value of the IMF components of each order obtained in step S2, screening out a set of effective components and recording it as set B2; taking the intersection of sets B1 and B2, recorded as set B'; classifying the IMF components into x1′(t) and x2′(t); using the PSD method to calculate the fatigue damage caused by x1′(t) to the structural member, and using the quasi-static method to calculate the fatigue damage caused by x2′(t) to the structural member; accumulating the two fatigue damages obtained to obtain the total fatigue damage of the structural member. The present application adopts B-spline fitting for the original load signal, and utilizes the low-pass filtering property of B-spline fitting to restore the time scale of the signal from noise or intermittent interference, thereby improving the signal-to-noise ratio of the signal and better suppressing the influence of noise on the fatigue analysis results.

Description

Technical Field

[0001] The present application belongs to the technical field of fatigue simulation analysis of structural parts, and specifically relates to a fatigue analysis method, device, terminal and medium based on SAEMD. Background Art

[0002] Generally, the original load signals collected are noisy signals with modal aliasing and cannot be used directly. They need to be denoised. However, the denoising schemes commonly used at present often require some parameters to be manually specified according to the actual situation and have no adaptability.

[0003] Signal EMD decomposition is performed in the time domain and is local and adaptive. Using EMD for processing can ensure that the original signal is not distorted and avoid spectrum leakage caused by traditional Fourier transform. However, several-order IMF components generated by EMD often contain false components, making the judgment of fatigue damage inaccurate.

[0004] Therefore, how to provide a structural fatigue analysis method that can adaptively reduce noise and effectively eliminate false components after EMD decomposition is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of this, the present application proposes a fatigue analysis method, device, terminal and medium based on SAEMD to improve the calculation accuracy of fatigue damage of structural parts.

[0006] To achieve the above objectives, the technical solutions adopted in this application are as follows:

[0007] In a first aspect, the present application provides a fatigue analysis method based on SAEMD, comprising:

[0008] Step S1: collecting the load signal of the structural component, recorded as x(t);

[0009] Step S2: Perform SAEMD decomposition on x(t) to obtain the load signal x′(t) and its several-order IMF (eigenmode function) components, and record the set of each-order IMF components as set B;

[0010] Step S3: Calculate the coherence function between the IMF components of each order obtained in step S2 and x′(t), screen the IMF components according to the coherence function value, remove the false components, and record the set of screened valid components as set B1; and calculate the sample entropy value of the IMF components of each order obtained in step S2, screen the IMF components according to the sample entropy value, and record the set of screened valid components as set B2;

[0011] Step S4: Take the intersection of sets B1 and B2, recorded as set B′, classify the IMF components whose frequencies correspond to the IMF components and coincide with the modal frequencies of the structural components into the first category, and classify the remaining IMF components into the second category, and record the superposition of the first category IMF components as x1′(t), and record the superposition of the second category IMF components as x2′(t);

[0012] Step S5: using the PSD (power spectral density) method to calculate the fatigue damage caused by x1′(t) to the structural parts, and using the quasi-static method to calculate the fatigue damage caused by x2′(t) to the structural parts;

[0013] Step S6: Accumulate the two fatigue damages obtained in step S5 to obtain the total fatigue damage of the structural component.

[0014] Optionally, the specific method of performing SAEMD decomposition on x(t) in step S2 includes:

[0015] S2-1: Determine all extreme points of the original load signal x(t), denoted as t extr ;

[0016] S2-2: According to t extr Generate B-spline nodes t knot , and perform B-spline fitting on x(t), and the fitting result is recorded as d(t);

[0017] S2-3: Re-determine all extreme points of d(t) and update t extr , repeat step S2-2 until d(t) satisfies equation (1), then d i+1 (t) is the signal of the original load signal x(t) after B-spline fitting and filtering, and x′(t) = d i+1 (t);

[0018]

[0019] In the formula, L is the number of extreme points, and δ is the iteration termination parameter;

[0020] S2-4: Determine all extreme points of x′(t), use cubic spline function interpolation to form the upper envelope curve u1(t) and lower envelope curve u2(t) of the data, and take the average value m1(t) of u1(t) and u2(t), that is,

[0021] m1(t)=(u1(t)+u2(t)) / 2 (2)

[0022] S2-5: Let h1(t) = x′(t) - m1(t). If h1(t) satisfies both conditions of IMF: ① The number of zero crossings and the number of extreme points are equal or differ by 1 in the entire time history; ② It is locally symmetric about the time axis, that is, the mean of the upper envelope defined by the local maximum and the lower envelope defined by the local minimum is 0, then h1(t) is the first-order IMF component. If it does not meet the requirements, then h1(t) is regarded as the new x′(t), m 11 (t) is the mean of its upper and lower envelopes, and h 11 (t) = h1(t) - m 11 (t), if h 11 (t) is still not satisfied, repeat the process k times, and get

[0023] h 1k (t) = h 1(k-1) (t)-m 1k (t) (3)

[0024] If h 1k (t) and h 1(k-1) If the standard deviation SD of (t) is within the predetermined range, the process is stopped. 1k (t) is the first-order IMF component of the signal x′(t), and c1(t) = h 1k (t), where the standard deviation SD is calculated as:

[0025]

[0026] Where T is the total time length of the signal;

[0027] S2-6: Let r1(t) = x′(t) - c1(t), regard r1(t) as the new x′(t), repeat step S2-5 to obtain the second-order IMF component, denoted as c2(t), and then the other order IMF components are obtained by analogy, denoted as c3(t)...c i (t), until r i Until (t) is a monotonic function and no more IMFs can be separated, the set of all IMF components of each order is denoted as set B.

[0028] Optionally, in step S3, the coherence function between the IMF components of each order obtained in step S2 and x′(t) is calculated, the IMF components are screened according to the coherence function value, the false components are removed, and the set of screened valid components is recorded as set B1. The specific method is:

[0029] S3-1: Obtain the auto-power spectral density function G of the load signal x′(t) respectively x′x′ (f) and the auto-power spectral density function G of each order IMF component cc(f), G x′x′ (f) According to equations (5) and (6), replace x′(t) in equation (6) with the IMF component c i (t), and then substitute it into formula (5) to obtain G cc (f);

[0030]

[0031]

[0032] In the formula, N is the number of time series data, R x′x′ (τ) is the autocorrelation function of x′(t);

[0033] S3-2: Calculate the cross power spectral density function G of each order IMF component and the load signal x′(t) x′c (f), the calculation formula is

[0034]

[0035]

[0036] In the formula, R x′c (τ) is the value of x′(t) and c i (t) cross-correlation function;

[0037] S3-3: Calculate the coherence function ρ between each order IMF component and the load signal x′(t) x′c (f), the calculation formula is

[0038]

[0039] S3-4: All IMF components are screened according to the coherence function. If the value of the coherence function between a certain order IMF and x′(t) at a certain frequency point is greater than a first predetermined value, then the IMF is a valid component. If the values ​​at all frequency points are less than the first predetermined value, then it is a false component. The set of valid components is recorded as set B1.

[0040] Optionally, in step S3, the sample entropy values ​​of the IMF components of each order obtained in step S2 are calculated, the IMF components are screened according to the sample entropy values, and the screened valid component set is recorded as set B2. The specific method is:

[0041] S4-1: Calculate the sample entropy value of each order IMF component in set B respectively. The calculation method is as follows:

[0042] a. Assume that a time series is {c(1), c(2), …, c(N)}, and arrange the time series in the order of m-dimensional vectors to obtain the following vector:

[0043] C m (i)=[c(i),c(i+1),…,c(i+m-1)](1≤i≤N-m+1) (10)

[0044] This vector represents m consecutive values ​​of c starting from point i;

[0045] b. Define C m (i) With C m (j) The distance between is the absolute value of the maximum difference between the two contained elements, then

[0046]

[0047] Wherein, k = 0, 1, 2, ..., m-1;

[0048] c. Statistics m (i) With C m (j) Distance between The number A that is less than or equal to R i ,but

[0049]

[0050] In the formula, R represents a set threshold;

[0051] d. Find A i The mean The calculation formula is

[0052]

[0053] e. When the calculation reaches m+1 dimension, repeat steps ac, then

[0054]

[0055] Then the sample entropy SE is

[0056]

[0057] However, under practical conditions, N cannot be infinite, so

[0058]

[0059] S4-2: Screen the IMF components according to the sample entropy values ​​calculated in S4-1, remove the IMF components whose sample entropy values ​​are less than a second predetermined value, retain the remaining IMF components and record their set as set B2.

[0060] In a second aspect, the present invention provides a fatigue analysis device based on SAEMD, comprising:

[0061] The acquisition module is used to collect the load signal of the structural parts, which is recorded as x(t);

[0062] The decomposition module is used to perform SAEMD decomposition on x(t) to obtain the load signal x′(t) and its several-order IMF (eigenmode function) components, and the set of each-order IMF component is recorded as set B;

[0063] A first calculation module is used to calculate the coherence function between the IMF components of each order obtained by the decomposition module and x′(t), screen the IMF components according to the coherence function value, and record the set of screened valid components as set B1; and to calculate the sample entropy value of the IMF components of each order obtained by the decomposition module, screen the IMF components according to the sample entropy value, and record the set of screened valid components as set B2;

[0064] The classification module is used to take the intersection of sets B1 and B2, recorded as set B′, classify the IMF components whose frequencies coincide with the modal frequencies of the structural components into the first category, and the remaining IMF components into the second category. The first category IMF components are superimposed and recorded as x1′(t), and the second category IMF components are superimposed and recorded as x2′(t);

[0065] The second calculation module is used to calculate the fatigue damage caused by x1′(t) to the structural parts by using the PSD (power spectral density) method, and to calculate the fatigue damage caused by x2′(t) to the structural parts by using the quasi-static method;

[0066] The accumulation module is used to accumulate the two fatigue damages calculated by the second calculation module to obtain the total fatigue damage of the structural component.

[0067] Optionally, the decomposition module is specifically used to perform the following steps:

[0068] S2-1: Determine all extreme points of the original load signal x(t), denoted as t extr ;

[0069] S2-2: According to t extr Generate B-spline nodes t knot , and perform B-spline fitting on x(t), and the fitting result is recorded as d(t);

[0070] S2-3: Re-determine all extreme points of d(t) and update t extr , repeat step S2-2 until d(t) satisfies equation (1), then d i+1 (t) is the signal of the original load signal x(t) after B-spline fitting and filtering, and x′(t) = d i+1 (t);

[0071]

[0072] In the formula, L is the number of extreme points, and δ is the iteration termination parameter;

[0073] S2-4: Determine all extreme points of x′(t), use cubic spline function interpolation to form the upper envelope curve u1(t) and lower envelope curve u2(t) of the data, and take the average value m1(t) of u1(t) and u2(t), that is,

[0074] m1(t)=(u1(t)+u2(t)) / 2 (2)

[0075] S2-5: Let h1(t) = x′(t) - m1(t). If h1(t) satisfies both conditions of IMF: ① The number of zero crossings and the number of extreme points are equal or differ by 1 in the entire time history; ② It is locally symmetric about the time axis, that is, the mean of the upper envelope defined by the local maximum and the lower envelope defined by the local minimum is 0, then h1(t) is the first-order IMF component. If it does not meet the requirements, then h1(t) is regarded as the new x′(t), m 11 (t) is the mean of its upper and lower envelopes, and h 11 (t) = h1(t) - m 11 (t), if h 11 (t) is still not satisfied, repeat the process k times, and get

[0076] h 1k (t) = h 1(k-1) (t)-m 1k (t) (3)

[0077] If h 1k (t) and h 1(k-1) If the standard deviation SD of (t) is within the predetermined range, the process is stopped. 1k (t) is the first-order IMF component of the signal x′(t), and c1(t) = h 1k (t), where the standard deviation SD is calculated as:

[0078]

[0079] Where T is the total time length of the signal;

[0080] S2-6: Let r1(t) = x′(t) - c1(t), regard r1(t) as the new x′(t), repeat step S2-5 to obtain the second-order IMF component, denoted as c2(t), and then the other order IMF components are obtained by analogy, denoted as c3(t)...c i (t), until r iUntil (t) is a monotonic function and no more IMFs can be separated, the set of all IMF components of each order is denoted as set B.

[0081] Optionally, the first calculation module is specifically configured to perform the following steps:

[0082] S3-1: Obtain the auto-power spectral density function G of the load signal x′(t) respectively x′x′ (f) and the auto-power spectral density function G of each order IMF component cc (f), G x′x′ (f) According to equations (5) and (6), replace x′(t) in equation (6) with the IMF component c i (t), and then substitute it into formula (5) to obtain G cc (f);

[0083]

[0084]

[0085] In the formula, N is the number of time series data, R x′x′ (τ) is the autocorrelation function of x′(t);

[0086] S3-2: Calculate the cross power spectral density function G of each order IMF component and the load signal x′(t) x′c (f), the calculation formula is

[0087]

[0088]

[0089] In the formula, R x′c (τ) is the value of x′(t) and c i (t) cross-correlation function;

[0090] S3-3: Calculate the coherence function ρ between each order IMF component and the load signal x′(t) x′c (f), the calculation formula is

[0091]

[0092] S3-4: All IMF components are screened according to the coherence function. If the value of the coherence function between a certain order IMF and x′(t) at a certain frequency point is greater than a first predetermined value, then the IMF is a valid component. If the values ​​at all frequency points are less than the first predetermined value, then it is a false component. The set of valid components is recorded as set B1.

[0093] Optionally, the first calculation module is further configured to perform the following steps:

[0094] S4-1: Calculate the sample entropy value of each order IMF component in set B respectively. The calculation method is as follows:

[0095] a. Assume that a time series is {c(1), c(2), …, c(N)}, and arrange the time series in the order of m-dimensional vectors to obtain the following vector:

[0096] C m (i)=[c(i),c(i+1),…,c(i+m-1)](1≤i≤N-m+1) (10)

[0097] This vector represents m consecutive values ​​of c starting from point i;

[0098] b. Define C m (i) With C m (j) The distance between is the absolute value of the maximum difference between the two contained elements, then

[0099]

[0100] Wherein, k = 0, 1, 2, ..., m-1;

[0101] c. Statistics m (i) With C m (j) Distance between The number A that is less than or equal to R i ,but

[0102]

[0103] In the formula, R represents a set threshold;

[0104] d. Find A i The mean The calculation formula is

[0105]

[0106] e. When the calculation reaches m+1 dimension, repeat steps ac, then

[0107]

[0108] Then the sample entropy SE is

[0109]

[0110] However, under practical conditions, N cannot be infinite, so

[0111]

[0112] S4-2: Screen the IMF components according to the sample entropy values ​​calculated in S4-1, remove the IMF components whose sample entropy values ​​are less than a second predetermined value, retain the remaining IMF components and record their set as set B2.

[0113] In a third aspect, an embodiment of the present application further provides a terminal, including: a processor, a memory, and a communication unit;

[0114] The memory stores machine-readable instructions executable by the processor, and when the device is running, the processor communicates with the memory through the communication unit;

[0115] The processor executes the machine-readable instructions to perform the methods described in the above aspects.

[0116] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the methods described in the above aspects are executed.

[0117] Compared with the prior art, this application has the following beneficial technical effects:

[0118] 1. This application adopts B-spline fitting to the original load signal, and uses the low-pass filtering property of B-spline fitting to restore the time scale of the signal from noise or intermittent interference, thereby improving the signal-to-noise ratio of the signal and better suppressing the influence of noise on fatigue analysis results;

[0119] 2. This application uses EMD decomposition on the signal after B-spline fitting, and uses coherence function and sample entropy value to simultaneously screen IMF, so as to avoid energy leakage and waveform distortion of the signal after traditional Fourier transform, effectively eliminate false components, and well preserve the amplitude and frequency of useful signals;

[0120] 3. This application classifies the screened IMF components according to the modal frequency, and uses different analysis methods to calculate the fatigue damage of the structure for different types of input loads, so that the calculation results are more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0122] Figure 1 A flowchart of a fatigue analysis method based on SAEMD is provided for this application;

[0123] Figure 2 It is the signal B-spline fitting filtering flow chart;

[0124] Figure 3 It is the signal EMD flow chart;

[0125] Figure 4 This is a structural block diagram of a fatigue analysis device based on SAEMD in this application. DETAILED DESCRIPTION

[0126] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0127] Figure 1 A flowchart of a fatigue analysis method based on SAEMD is provided for this application. Figure 1 , the present application provides a fatigue analysis method based on SAEMD, comprising:

[0128] Step S1: collecting the load signal of the structural component, recorded as x(t);

[0129] The load signal x(t) can be collected through actual measurement or by using a multi-body dynamics model and virtual iteration.

[0130] Step S2: Perform SAEMD (Spline approximation Empirical mode decomposition) on x(t) to obtain the load signal x′(t) and its several-order IMF (eigenmode function) components, and record the set of each-order IMF components as set B;

[0131] Specifically, the specific method of performing SAEMD decomposition on x(t) in step S2 includes:

[0132] S2-1: Determine all extreme points of the original load signal x(t), denoted as t extr ;

[0133] S2-2: According to t extr Generate B-spline nodes t knot , and perform B-spline fitting on x(t), and the fitting result is recorded as d(t). Figure 2 This is the signal B-spline fitting filter flow chart. For the specific fitting process, please refer to Figure 2 ;

[0134] S2-3: Re-determine all extreme points of d(t) and update textr , repeat step S2-2 until d(t) satisfies equation (1), then d i+1 (t) is the signal of the original load signal x(t) after B-spline fitting and filtering, and x′(t) = d i+1 (t);

[0135]

[0136] Where L is the number of extreme points, δ is the iteration termination parameter, and takes a preset value close to zero;

[0137] S2-4: Determine all extreme points of x′(t), use cubic spline function interpolation to form the upper envelope curve u1(t) and lower envelope curve u2(t) of the data, and take the average value m1(t) of u1(t) and u2(t), that is,

[0138] m1(t)=(u1(t)+u2(t)) / 2 (2)

[0139] S2-5: Let h1(t) = x′(t) - m1(t). If h1(t) satisfies both conditions of IMF: ① The number of zero crossings and the number of extreme points are equal or differ by 1 in the entire time history; ② It is locally symmetric about the time axis, that is, the mean of the upper envelope defined by the local maximum and the lower envelope defined by the local minimum is 0, then h1(t) is the first-order IMF component. If it does not meet the requirements, then h1(t) is regarded as the new x′(t), m 11 (t) is the mean of its upper and lower envelopes, and h 11 (t) = h1(t) - m 11 (t), if h 11 (t) is still not satisfied, repeat the process k times, and get

[0140] h 1k (t) = h 1(k-1) (t)-m 1k (t) (3)

[0141] If h 1k (t) and h 1(k-1) If the standard deviation SD of (t) is within the predetermined range, the process is stopped. 1k (t) is the first-order IMF component of the signal x′(t), and c1(t) = h 1k (t), where the standard deviation SD is calculated as:

[0142]

[0143] Where T is the total time length of the signal;

[0144] The predetermined range of the standard deviation SD is generally 0.2≥SD≥0.3, but is not limited thereto. The predetermined range may also be adjusted differently according to actual conditions.

[0145] S2-6: Let r1(t) = x′(t) - c1(t), regard r1(t) as the new x′(t), repeat step S2-5 to obtain the second-order IMF component, denoted as c2(t), and then the other order IMF components are obtained by analogy, denoted as c3(t)...c i (t), until r i Until (t) is a monotonic function and no more IMFs can be separated, the set of all IMF components of each order is denoted as set B. Figure 3 This is the signal EMD flow chart. The specific process of the above EMD decomposition can be found in Figure 3 .

[0146] This application first performs B-spline fitting on the original load signal, and then uses EMD decomposition on the fitted signal. The B-spline least squares fitting has adaptive and time-varying filtering properties, and can eliminate modal aliasing according to the local characteristics of the signal without the need for any subjective parameters, thereby achieving the purpose of noise reduction.

[0147] Step S3: Calculate the coherence function between the IMF components of each order obtained in step S2 and x′(t), screen the IMF components according to the coherence function value, remove the false components, retain the valid components, and record the set of valid components as set B1; and calculate the sample entropy value of the IMF components of each order obtained in step S2, screen the IMF components according to the sample entropy value, remove the false components, retain the valid components, and record the set of valid components at this time as set B2;

[0148] Specifically, in step S3, the coherence function between the IMF components of each order obtained in step S2 and x′(t) is calculated, and the IMF components are screened according to the coherence function value, the false components are eliminated, and the valid components are retained, and the set of valid components is recorded as set B1 including:

[0149] S3-1: Obtain the auto-power spectral density function G of the load signal x′(t) respectively x′x′ (f) and the auto-power spectral density function G of each order IMF component cc (f), G x′x′ (f) According to equations (5) and (6), replace x′(t) in equation (6) with the IMF component c i (t), and then substitute it into formula (5) to obtain G cc (f);

[0150]

[0151]

[0152] In the formula, N is the number of time series data, R x′x′ (τ) is the autocorrelation function of x′(t);

[0153] S3-2: Calculate the cross power spectral density function G of each order IMF component and the load signal x′(t) x′c (f), the calculation formula is

[0154]

[0155]

[0156] In the formula, R x′c (τ) is the value of x′(t) and c i (t) cross-correlation function;

[0157] S3-3: Calculate the coherence function ρ between each order IMF component and the load signal x′(t) x′c (f), the calculation formula is

[0158]

[0159] S3-4: All IMF components are screened according to the coherence function. If the value of the coherence function between a certain order IMF and x′(t) at a certain frequency point is greater than a first predetermined value, then the IMF is a valid component. If the values ​​at all frequency points are less than the first predetermined value, then it is a false component. The set of valid components is recorded as set B1.

[0160] The first predetermined value may be 0.8, but is not limited thereto, and its value may also be adjusted according to actual conditions.

[0161] Specifically, in step S3, the sample entropy values ​​of the IMF components of each order obtained in step S2 are calculated, and the IMF components are screened according to the sample entropy values, false components are eliminated, and valid components are retained, and the valid component set at this time is recorded as set B2 including:

[0162] S4-1: Calculate the sample entropy value of each order IMF component in set B respectively. The calculation method is as follows:

[0163] a. Assume that a time series is {c(1), c(2), …, c(N)}, and arrange the time series in the order of m-dimensional vectors to obtain the following vector:

[0164] C m (i)=[c(i),c(i+1),…,c(i+m-1)](1≤i≤N-m+1) (10)

[0165] This vector represents m consecutive values ​​of c starting from point i;

[0166] b. Define C m (i) With C m (j) The distance between is the absolute value of the maximum difference between the two contained elements, then

[0167]

[0168] Wherein, k = 0, 1, 2, ..., m-1;

[0169] c. Statistics m (i) With C m (j) Distance between The number A that is less than or equal to R i ,but

[0170]

[0171] In the formula, R represents a set threshold;

[0172] d. Find A i The mean The calculation formula is

[0173]

[0174] e. When the calculation reaches m+1 dimension, repeat steps ac, then

[0175]

[0176] Then the sample entropy SE is

[0177]

[0178] However, under practical conditions, N cannot be infinite, so

[0179]

[0180] S4-2: Screen the IMF components according to the sample entropy values ​​calculated in S4-1, remove the IMF components whose sample entropy values ​​are less than a second predetermined value, retain the remaining IMF components and record their set as set B2.

[0181] In a specific implementation, the second predetermined value may be 0.01. In addition, other values ​​close to 0 may also be taken according to actual conditions.

[0182] The false component generally has less coherence with the original signal, and its sample entropy value is close to 0. The coherence function and sample entropy can be used to simultaneously identify the false component, and then remove the useless IMF, so that the interference signal can be suppressed, the signal-to-noise ratio can be improved, and an effective load signal can be obtained. The fatigue damage result obtained by using the signal after B-spline fitting filtering, EMD decomposition, coherence function and sample entropy value screening as the input load has smaller error and higher accuracy.

[0183] Step S4: Take the intersection of sets B1 and B2, recorded as set B′, classify the IMF components whose frequencies correspond to the IMF components and coincide with the modal frequencies of the structural components into the first category, and classify the remaining IMF components into the second category, and record the superposition of the first category IMF components as x1′(t), and record the superposition of the second category IMF components as x2′(t);

[0184] Specifically, step S4 further includes:

[0185] Take the intersection of sets B1 and B2, denoted as B′, then the elements in B′ are the effective IMF components finally screened out;

[0186] Perform Fourier transform on the screened effective IMF components respectively to obtain their respective spectra;

[0187] Establish a finite element model of the mechanical structure, perform modal analysis, and obtain modal frequencies;

[0188] The frequencies of the effective IMF components of each order are compared with the modal frequencies. The IMF components whose frequencies coincide with the modal frequencies of the structural parts are classified as the first category, and the others are classified as the second category. The two types of IMF components are superimposed respectively, and the obtained signals are recorded as x1′(t) and x2′(t), respectively.

[0189] Step S5: using the PSD (power spectral density) method to calculate the fatigue damage caused by x1′(t) to the structural parts, and using the quasi-static method to calculate the fatigue damage caused by x2′(t) to the structural parts;

[0190] Among them, x1′(t) is taken as the input load, and the PSD method is used to calculate the damage caused to the mechanical structure, which is recorded as D1; ​​x2′(t) is taken as the input load, and the quasi-static method is used to calculate the damage caused to the mechanical structure, which is recorded as D2.

[0191] Step S6: Accumulate the two fatigue damages obtained in step S5 to obtain the total fatigue damage of the structural component.

[0192] The mechanical structure damage calculated in step S5 is linearly accumulated to obtain the total fatigue damage D of the mechanical structure, that is,

[0193] D=D1+D2 (17)

[0194] This application classifies the screened IMF components according to the modal frequency, and uses different analysis methods to calculate the fatigue damage of the structure for different types of input loads. The calculation results are more accurate; and the fatigue damage of the structural parts can be accurately judged during the design stage, thereby improving the design quality and reducing R&D costs.

[0195] Figure 4 This is a structural block diagram of a fatigue analysis device based on SAEMD in this application. Figure 4 As shown, a fatigue analysis device based on SAEMD includes:

[0196] The acquisition module 410 is used to acquire the load signal of the structural member, which is recorded as x(t);

[0197] The decomposition module 420 is used to perform SAEMD decomposition on x(t) to obtain the load signal x′(t) and its several-order IMF (eigenmode function) components, and the set of each-order IMF component is recorded as set B;

[0198] The decomposition module 420 is specifically configured to perform the following steps:

[0199] S2-1: Determine all extreme points of the original load signal x(t), denoted as t extr ;

[0200] S2-2: According to t extr Generate B-spline nodes t knot , and perform B-spline fitting on x(t), and the fitting result is recorded as d(t);

[0201] S2-3: Re-determine all extreme points of d(t) and update t extr , repeat step S2-2 until d(t) satisfies equation (1), then d i+1 (t) is the signal of the original load signal x(t) after B-spline fitting and filtering, and x′(t) = d i+1 (t);

[0202]

[0203] In the formula, L is the number of extreme points, and δ is the iteration termination parameter;

[0204] S2-4: Determine all extreme points of x′(t), use cubic spline function interpolation to form the upper envelope curve u1(t) and lower envelope curve u2(t) of the data, and take the average value m1(t) of u1(t) and u2(t), that is,

[0205] m1(t)=(u1(t)+u2(t)) / 2 (2)

[0206] S2-5: Let h1(t) = x′(t) - m1(t). If h1(t) satisfies both conditions of IMF: ① The number of zero crossings and the number of extreme points are equal or differ by 1 in the entire time history; ② It is locally symmetric about the time axis, that is, the mean of the upper envelope defined by the local maximum and the lower envelope defined by the local minimum is 0, then h1(t) is the first-order IMF component. If it does not meet the requirements, then h1(t) is regarded as the new x′(t), m 11 (t) is the mean of its upper and lower envelopes, and h 11 (t) = h1(t) - m 11 (t), if h 11 (t) is still not satisfied, repeat the process k times, and get

[0207] h 1k (t) = h 1(k-1) (t)-m 1k (t) (3)

[0208] If h 1k (t) and h 1(k-1) If the standard deviation SD of (t) is within the predetermined range, the process is stopped. 1k (t) is the first-order IMF component of the signal x′(t), and c1(t) = h 1k (t), where the standard deviation SD is calculated as:

[0209]

[0210] Where T is the total time length of the signal;

[0211] The predetermined range of the standard deviation SD is generally 0.2≥SD≥0.3, but is not limited thereto. The predetermined range may also be adjusted differently according to actual conditions.

[0212] S2-6: Let r1(t) = x′(t) - c1(t), regard r1(t) as the new x′(t), repeat step S2-5 to obtain the second-order IMF component, denoted as c2(t), and then the other order IMF components are obtained by analogy, denoted as c3(t)...c i (t), until r i Until (t) is a monotonic function and no more IMFs can be separated, the set of all IMF components of each order is denoted as set B.

[0213] The first calculation module 430 is used to calculate the coherence function between the IMF components of each order obtained by the decomposition module and x′(t), screen the IMF components according to the coherence function value, remove the false components, retain the valid components, and record the set of valid components as set B1; and to calculate the sample entropy value of the IMF components of each order obtained by the decomposition module, screen the IMF components according to the sample entropy value, remove the false components, retain the valid components, and record the set of valid components at this time as set B2

[0214] The first calculation module 430 is specifically configured to perform the following steps:

[0215] S3-1: Obtain the auto-power spectral density function G of the load signal x′(t) respectively x′x′ (f) and the auto-power spectral density function G of each order IMF component cc (f), G x′x′ (f) According to equations (5) and (6), replace x′(t) in equation (6) with the IMF component c i (t), and then substitute it into formula (5) to obtain G cc (f);

[0216]

[0217]

[0218] In the formula, N is the number of time series data, R x′x′ (τ) is the autocorrelation function of x′(t);

[0219] S3-2: Calculate the cross power spectral density function G of each order IMF component and the load signal x′(t) x′c (f), the calculation formula is

[0220]

[0221]

[0222] In the formula, R x′c (τ) is the value of x′(t) and c i (t) cross-correlation function;

[0223] S3-3: Calculate the coherence function ρ between each order IMF component and the load signal x′(t) x′c (f), the calculation formula is

[0224]

[0225] S3-4: All IMF components are screened according to the coherence function. If the value of the coherence function between a certain order IMF and x′(t) at a certain frequency point is greater than a first predetermined value, then the IMF is a valid component. If the values ​​at all frequency points are less than the first predetermined value, then it is a false component. The set of valid components is recorded as set B1.

[0226] The first predetermined value may be 0.8, but is not limited thereto, and its value may also be adjusted according to actual conditions.

[0227] The first calculation module 430 is further configured to perform the following steps:

[0228] S4-1: Calculate the sample entropy value of each order IMF component in set B respectively. The calculation method is as follows:

[0229] a. Assume that a time series is {c(1), c(2), …, c(N)}, and arrange the time series in the order of m-dimensional vectors to obtain the following vector:

[0230] C m (i)=[c(i),c(i+1),…,c(i+m-1)](1≤i≤N-m+1) (10)

[0231] This vector represents m consecutive values ​​of c starting from point i;

[0232] b. Define C m (i) With C m (j) The distance between is the absolute value of the maximum difference between the two contained elements, then

[0233]

[0234] Wherein, k = 0, 1, 2, ..., m-1;

[0235] c. Statistics m (i) With C m (j) Distance between The number A that is less than or equal to R i ,but

[0236]

[0237] In the formula, R represents a set threshold;

[0238] d. Find A i The mean The calculation formula is

[0239]

[0240] e. When the calculation reaches m+1 dimension, repeat steps ac, then

[0241]

[0242] Then the sample entropy SE is

[0243]

[0244] However, under practical conditions, N cannot be infinite, so

[0245]

[0246] S4-2: Screen the IMF components according to the sample entropy values ​​calculated in S4-1, remove the IMF components whose sample entropy values ​​are less than a second predetermined value, retain the remaining IMF components and record their set as set B2.

[0247] In a specific implementation, the second predetermined value may be 0.01. In addition, other values ​​close to 0 may also be taken according to actual conditions.

[0248] The classification module 440 is used to obtain the intersection of the sets B1 and B2, recorded as the set B′, classify the IMF components whose frequencies correspond to the IMF components and coincide with the modal frequencies of the structural components into the first category, and classify the remaining IMF components into the second category, and record the superposition of the first category IMF components as x1′(t), and record the superposition of the second category IMF components as x2′(t);

[0249] The second calculation module 450 is used to calculate the fatigue damage caused by x1′(t) to the structural component by using a PSD (power spectral density) method, and to calculate the fatigue damage caused by x2′(t) to the structural component by using a quasi-static method;

[0250] The accumulation module 460 is used to accumulate the two fatigue damages calculated by the second calculation module to obtain the total fatigue damage of the structural component.

[0251] The embodiment of the present application also provides a terminal, including: a processor, a memory and a communication unit;

[0252] The memory stores machine-readable instructions executable by the processor, and when the device is running, the processor communicates with the memory through the communication unit;

[0253] The processor executes the machine-readable instructions to perform the methods described in the above aspects.

[0254] The memory can be used to store the execution instructions of the processor, and the memory can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. When the execution instructions in the memory are executed by the processor, the device is enabled to perform some or all of the steps in the following method embodiments.

[0255] The processor is the control center of the storage terminal, which uses various interfaces and lines to connect various parts of the entire electronic terminal, and executes various functions and / or processes data of the electronic terminal by running or executing software programs and / or modules stored in the memory, and calling data stored in the memory. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor can include only a central processing unit (CPU). In the embodiment of the present application, the CPU can be a single computing core or multiple computing cores.

[0256] The communication unit is used to establish a communication channel so that the storage device can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.

[0257] The embodiment of the present application also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided in the present application. The storage medium may be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0258] In the embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the node embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0259] The modules described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0260] In addition, each functional module in the embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0261] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A fatigue analysis method based on SAEMD, characterized in that: include: Step S1: Collect the load signal of the structural component, recorded as ; Step S2: Perform SAEMD decomposition to obtain the load signal and its several-order characteristic mode function IMF components, and the set of each-order IMF components is denoted as set B; Step S3: Calculate the IMF components of each order obtained in step S2 and The coherence function of is used to screen the IMF components according to the coherence function value, remove the false components, and record the set of screened valid components as set B1; and calculating the sample entropy values ​​of the IMF components of each order obtained in step S2, screening the IMF components according to the sample entropy values, and recording the screened valid component set as set B2; Step S4: Take the intersection of sets B1 and B2, recorded as set B', then the elements in set B' are the effective IMF components finally screened out; perform Fourier transform on the screened effective IMF components respectively to obtain their respective spectra; establish a finite element model of the mechanical structure, perform modal analysis, and obtain the modal frequency; compare the frequency of each order of effective IMF components with the modal frequency, classify the IMF components whose frequencies correspond to the IMF components and coincide with the modal frequency of the structural parts into the first category, and classify the remaining IMF components into the second category, and superimpose the first category of IMF components and record them as , after superimposing the second type of IMF components, it is recorded as ; Step S5: Calculate using the power spectral density PSD method The fatigue damage caused to the structural parts is calculated by quasi-static method Fatigue damage to structural parts; Step S6: Accumulate the two fatigue damages obtained in step S5 to obtain the total fatigue damage of the structural component.

2. A fatigue analysis method based on SAEMD as claimed in claim 1, characterized in that: In step S2, The specific methods for SAEMD decomposition include: S2-1: Determine the original load signal All extreme points of ; S2-2: According to Generate B-spline nodes , and Perform B-spline fitting, and the fitting result is recorded as ; S2-3: Re-determine All extreme points of and update , repeat step S2-2 until If equation (1) is satisfied, then is the original load signal The signal after B-spline fitting filtering is recorded as ; （1） In the formula, L is the number of extreme points, It is the iteration termination parameter; S2-4: Confirmed All extreme points, all maximum points and minimum points are interpolated using cubic spline functions to form the upper envelope curve of the data and the lower envelope curve , and take and The average ,Right now （2） S2-5: Order ,like At the same time, it meets the two conditions of IMF: ① The number of zero crossings and the number of extreme points are equal or differ by 1 in the entire time history; ② The local symmetry about the time axis, that is, the mean of the upper envelope defined by the local maximum and the lower envelope defined by the local minimum is 0, then is the first-order IMF component. If it is not satisfied, See as new , is the mean of its upper and lower envelopes, ,like Still not satisfied, repeat the process k times, and get （3） like and If the standard deviation SD is within the predetermined range, the process is stopped. For signal The first-order IMF component of , where the standard deviation SD calculation formula is: （4） Where T is the total time length of the signal; S2-6: Order ,Will See as new , repeat steps S2-5 to obtain the second-order IMF component, denoted as , and then the other IMF components of each order are obtained by analogy, which are recorded as … ,until Until it becomes a monotonic function and no more IMF can be separated, the set of all IMF components of each order is recorded as set B.

3. A fatigue analysis method based on SAEMD as claimed in claim 1, characterized in that: In step S3, the IMF components of each order obtained in step S2 are calculated and The specific method of filtering IMF components according to the coherence function value, removing false components, and recording the set of filtered valid components as set B1 is as follows: S3-1: Obtain load signals separately The autopower spectral density function And the autopower spectral density function of each order IMF component , According to formula (5) and (6), we can obtain Replaced with IMF component , and then substitute into formula (5) to obtain ; （5） （6） In the formula, N is the number of time series data, for The autocorrelation function of S3-2: Calculate each order IMF component and load signal The cross power spectral density function , the calculation formula is （7） （8） In the formula, for and The cross-correlation function of S3-3: Calculate each order IMF component and load signal The coherence function , the calculation formula is （9） S3-4: All IMF components are screened according to the coherence function. If a certain order IMF is If the value of the coherence function at a certain frequency point is greater than the first predetermined value, the IMF is a valid component. If the value at all frequency points is less than the first predetermined value, it is a false component. The set of valid components is recorded as set B1.

4. A fatigue analysis method based on SAEMD as claimed in claim 1, characterized in that: In step S3, the sample entropy values ​​of the IMF components of each order obtained in step S2 are calculated, the IMF components are screened according to the sample entropy values, and the screened valid component set is recorded as set B2. The specific method is: S4-1: Calculate the sample entropy value of each order IMF component in set B respectively. The calculation method is as follows: a. Assume a time series is , arrange the time series in the order of m-dimensional vectors to obtain the following vector: （10） This vector represents the Click to start Continuous values ; b. Definition and The distance between is the absolute value of the maximum difference between the two contained elements, then （11） in, =0, 1, 2, ..., m-1; c. Statistics and Distance Less than or equal to Number of ,but （12） In the formula, Indicates a set threshold; d. Ask for The mean , the calculation formula is （13） e. When the calculation reaches m+1 dimension, repeat steps ac, then （14） Then the sample entropy for （15） However, under practical conditions, N cannot be infinite, so （16） S4-2: Screen the IMF components according to the sample entropy values ​​calculated in S4-1, remove the IMF components whose sample entropy values ​​are less than a second predetermined value, retain the remaining IMF components and record their set as set B2.

5. A fatigue analysis device based on SAEMD, characterized in that: include: The acquisition module is used to collect the load signal of the structural parts, which is recorded as ; Decomposition module for Perform SAEMD decomposition to obtain the load signal and its several-order characteristic mode function IMF components, and the set of each-order IMF components is denoted as set B; The first calculation module is used to calculate the IMF components of each order obtained by the decomposition module and The coherence function is used to filter the IMF components according to the coherence function value, and the set of filtered valid components is recorded as set B1; and the sample entropy values ​​of each order IMF components obtained by the decomposition module are calculated, the IMF components are filtered according to the sample entropy values, and the set of filtered valid components is recorded as set B2; The classification module is used to take the intersection of sets B1 and B2, recorded as set B', and the elements in set B' are the effective IMF components finally screened out; Fourier transform is performed on the screened effective IMF components to obtain their respective spectra; a finite element model of the mechanical structure is established, modal analysis is performed, and modal frequencies are obtained; the frequencies of the effective IMF components of each order are compared with the modal frequencies, and the IMF components whose frequencies correspond to the IMF components and coincide with the modal frequencies of the structural parts are classified into the first category, and the remaining IMF components are classified into the second category, and the first category of IMF components are superimposed and recorded as , after superimposing the second type of IMF components, it is recorded as ; The second calculation module is used to calculate using the power spectrum density PSD method The fatigue damage caused to the structural parts is calculated by quasi-static method Fatigue damage to structural parts; The accumulation module is used to accumulate the two fatigue damages calculated by the second calculation module to obtain the total fatigue damage of the structural component.

6. A fatigue analysis device based on SAEMD as claimed in claim 5, characterized in that: The decomposition module is specifically used to perform the following steps: S2-1: Determine the original load signal All extreme points of ; S2-2: According to Generate B-spline nodes , and Perform B-spline fitting, and the fitting result is recorded as ; S2-3: Re-determine All extreme points of and update , repeat step S2-2 until If equation (1) is satisfied, then is the original load signal The signal after B-spline fitting filtering is recorded as ; （1） In the formula, L is the number of extreme points, It is the iteration termination parameter; S2-4: Confirmed All extreme points, all maximum points and minimum points are interpolated using cubic spline functions to form the upper envelope curve of the data and the lower envelope curve , and take and The average ,Right now （2） S2-5: Order ,like At the same time, it meets the two conditions of IMF: ① The number of zero crossings and the number of extreme points are equal or differ by 1 in the entire time history; ② The local symmetry about the time axis, that is, the mean of the upper envelope defined by the local maximum and the lower envelope defined by the local minimum is 0, then is the first-order IMF component. If it is not satisfied, See as new , is the mean of its upper and lower envelopes, ,like Still not satisfied, repeat the process k times, and get （3） like and If the standard deviation SD is within the predetermined range, the process is stopped. For signal The first-order IMF component of , where the standard deviation SD calculation formula is: （4） Where T is the total time length of the signal; S2-6: Order ,Will See as new , repeat steps S2-5 to obtain the second-order IMF component, denoted as , and then the other IMF components of each order are obtained by analogy, which are recorded as … ,until Until it becomes a monotonic function and no more IMF can be separated, the set of all IMF components of each order is recorded as set B.

7. A fatigue analysis device based on SAEMD as claimed in claim 5, characterized in that: The first calculation module is specifically configured to perform the following steps: S3-1: Obtain load signals separately The autopower spectral density function And the autopower spectral density function of each order IMF component , According to formula (5) and (6), we can obtain Replaced with IMF component , and then substitute into formula (5) to obtain ; （5） （6） In the formula, N is the number of time series data, for The autocorrelation function of S3-2: Calculate each order IMF component and load signal The cross power spectral density function , the calculation formula is （7） （8） In the formula, for and The cross-correlation function of S3-3: Calculate each order IMF component and load signal The coherence function , the calculation formula is （9） S3-4: All IMF components are screened according to the coherence function. If a certain order IMF is If the value of the coherence function at a certain frequency point is greater than the first predetermined value, the IMF is a valid component. If the value at all frequency points is less than the first predetermined value, it is a false component. The set of valid components is recorded as set B1.

8. A fatigue analysis device based on SAEMD as claimed in claim 5, characterized in that: The first calculation module is further configured to perform the following steps: S4-1: Calculate the sample entropy value of each order IMF component in set B respectively. The calculation method is as follows: a. Assume a time series is , arrange the time series in the order of m-dimensional vectors to obtain the following vector: （10） This vector represents the Click to start A continuous The value of b. Definition and The distance between is the absolute value of the maximum difference between the two contained elements, then （11） in, =0, 1, 2, ..., m-1; c. Statistics and Distance Less than or equal to Number of ,but （12） In the formula, Indicates a set threshold; d. Ask for The mean , the calculation formula is （13） e. When the calculation reaches m+1 dimension, repeat steps ac, then （14） Then the sample entropy for （15） However, under practical conditions, N cannot be infinite, so （16） S4-2: Screen the IMF components according to the sample entropy values ​​calculated in S4-1, remove the IMF components whose sample entropy values ​​are less than a second predetermined value, retain the remaining IMF components and record their set as set B2.

9. A terminal, characterized in that: include: Processor, memory and communication unit; The memory stores machine-readable instructions executable by the processor, and when the device is running, the processor communicates with the memory through the communication unit; The processor executes the machine-readable instructions to perform the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is executed.

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