A method for compiling stochastic multi-parameter load spectra based on linear combination

Abstract

The invention discloses a method for compiling a random multi-parameter load spectrum based on linear combination. The method takes the multi-parameter measured load spectrum of a component as basic compilation data, performs data compression, i.e., equal value judgment, peak-valley value detection, and non-critical point elimination processing on the multi-parameter random load to obtain a multi-parameter simplified load history, obtains a multi-parameter composite load history through a linear combination of the multi-parameter simplified load history, performs peak-valley value extraction processing on the multi-parameter simplified load history to obtain a composite load peak-valley value sequence and a non-peak-valley value point set, performs rain flow cycle counting to obtain a rain flow cycle matrix of the composite load history, reconstructs the load history based on the rain flow cycle matrix and randomly inserts non-peak-valley value points to obtain a composite load reconstruction history, performs linear solution of the composite load to obtain a new multi-parameter random load spectrum. The invention effectively solves the problems in the prior art of lack of consideration of multi-parameter load phase relationship in the compilation of multi-parameter load spectrum of components and loss of multi-axis damage information.

Description

Technical Field

[0001] The invention relates to the technical field of multi-parameter random load spectrum compilation of complex mechanical components, and mainly to a random multi-parameter load spectrum compilation method based on linear combination. Background Art

[0002] At present, most of the research on load spectrum at home and abroad is focused on the compilation of single-parameter load spectrum, and it has been widely used in aerospace, vehicle, engineering machinery and other industrial fields. However, in actual engineering applications, most mechanical components are subjected to random multi-parameter loads for a long time, that is, the components are subjected to more than one load, and the components are often very prone to multi-axial fatigue damage and then failure. For example, complex components such as aircraft engine casing components, automobile universal joints and front suspensions are subjected to typical random non-proportional multi-axial loads during actual service. Although the application of single-parameter load statistical methods and load spectrum compilation methods has been relatively mature, they are no longer suitable for such components subjected to complex multi-parameter loads. In order to conduct a full and scientific life assessment of such components, a multi-parameter load spectrum compilation method must be developed.

[0003] When conducting life assessment tests on complex mechanical components, the load spectrum must reflect the actual working characteristics of the components to a certain extent. In addition, if the fatigue damage information contained in the measured load spectrum is not fully considered, this will lead to certain differences in load characteristics and fatigue life performance between the compiled load spectrum and the actual service load. For the compilation of multi-parameter load spectra, not only the damage information of uniaxial loads should be retained, but also the phase relationship and multi-axial damage information between multi-parameter loads should be fully considered. At present, there is no clear and generally recognized method for compiling multi-parameter load spectra for components at home and abroad. Only a few institutions and scholars have conducted research on this to varying degrees. The Fraunhofer Society in Germany has studied the multi-parameter loads borne by automotive structures and developed the CARLOS multi-parameter standard load sequence, but has not published specific methods; while the research on multi-parameter load spectra in China started late, mainly using a given matching principle to randomly combine the loads and cycles, lacking a theoretical basis and not considering the phase relationship between loads.

[0004] In summary, the existing methods for compiling multi-parameter load spectra still have certain limitations. There is no clear and generally recognized method for compiling multi-parameter load spectra for components, and the requirements for compiling multi-parameter load spectra that retain fatigue damage information are also met to varying degrees. Under the action of multi-parameter loads, compiling multi-parameter load spectra for components that fully consider the phase relationship between multi-parameter loads and multi-axial damage information is still a key engineering problem that urgently needs to be solved. This is also of great significance to the research on fatigue damage analysis and fatigue testing of complex mechanical components in engineering practice.

[0005] Therefore, it is necessary to develop a random multi-parameter load spectrum compilation method that can consider the phase relationship of multi-parameter loads and retain multi-axis damage information, laying the foundation for life determination of complex engineering machinery and its components. Summary of the invention

[0006] Purpose of the invention: In view of the problems existing in the above-mentioned background technology, the present invention provides a method for compiling a random multi-parameter load spectrum based on linear combination, which is used to solve the problems in the prior art that the compilation of multi-parameter load spectrum of components lacks consideration of the phase relationship of multi-parameter loads and multi-axis damage information is lost.

[0007] Technical solution: To achieve the above purpose, the technical solution adopted by the present invention is:

[0008] A method for compiling a random multi-parameter load spectrum based on linear combination includes the following steps:

[0009] Step S1: perform peak-valley detection on the random multi-parameter load spectrum of the complex mechanical component to obtain a random multi-parameter load history {F1′(t) F2′(t) ... F n ′(t)};

[0010] Step S2: the random multi-parameter load history {F1′(t) F2′(t) ... F n ′(t)} to obtain the multi-parameter composite load history {F1″(t) F2″(t) ... F″ m (t)}; further extract the peak and valley values ​​of the multi-parameter composite load history, and obtain the peak and valley value sequence of the multi-parameter composite load history {F″ RF,1 (t) F″ RF,2 (t) ...F″ RF,m (t)} and non-peak and valley point set F″ R,j (j=1,2,...,m);

[0011] Step S3: Count the rain flow cycles of the peak-valley value sequence obtained in step S2 to obtain the rain flow cycle matrix {RFM1 RFM2 ... RFM m}; Based on the rain flow cycle matrix, random load history is reconstructed to obtain the composite load reconstruction history {F″ RFR,1 (t) F″ RFR,2 (t) ... F″ RFR,m (t)};

[0012] Step S4: The non-peak and valley value point set F″ obtained in step S2 R,j (j=1, 2, ..., m) are randomly inserted into the reconstruction history of the composite load peak and valley values ​​obtained in step S3 {F″RFR,1 (t) F″ RFR,2 (t) ... F″ RFR,m (t)}, obtain the full data reconstruction process of the composite load with consistent data volume {F′ RFR,1 (t) F′ RFR,2 (t) ... F′ RFR,m (t)};

[0013] Step S5: Reconstruct the composite load full data obtained in step S4 {F′ RFR,1 (t) F′ RFR,2 (t) ...F′ RFR,m (t)} is linearly solved to obtain the compiled random multi-parameter random load spectrum based on linear combination.

[0014] Furthermore, the specific steps of obtaining the random multi-parameter load history with simplified data volume in step S1 are as follows:

[0015] Step S1.1, using the multi-parameter measured load spectrum of complex mechanical components as the basic spectrum compilation data, for random multi-parameter load data {F1(t) F2(t) ... F n (t)} each load F i (t)(i=1,2,...,n) respectively make equal value judgment and mark; select two adjacent load points F in turn i (j), F i (j+1) is used to judge. When the load values ​​are equal, F i (j), F i (j+1) is recorded as the equivalent point and marked as "Y" respectively; repeat the selection and judgment until the load values ​​of the two selected load points are not equal, then F i (j+1) is marked as “N”, and the other non-equivalent points are all marked as “N”;

[0016] Step S1.2: for random multi-parameter load data {F1(t) F2(t) ... F n (t)} each load F i (t)(i=1,2,...,n) respectively judge the peak and valley values ​​and mark them; select three adjacent non-equivalent data points F in turn i (1) F i (2) F i (3) Perform peak-valley value judgment. When an equal value point appears, select the next non-equivalue point for judgment in turn. When no equal value point appears, the following conditions are satisfied:

[0017] [F i (2)-F i (1)][F i(3)-F i (2)]>0

[0018] Then F i (2) is the peak point or valley point. i (2) When there are equivalent points, then F i (2) and its equivalent points are marked as “-1”. i (2) When there is no equivalent point, the point is marked as "1"; all non-peak and valley value points are marked as "0";

[0019] Step S1.3: According to the peak and valley value marking results, the random multi-parameter load F i (t)(i=1,2,...,n) load points are selected synchronously in turn; when any load is marked as "1", that is, any load is a non-equivalent peak-valley point, the load point must be retained; when all loads are marked as "0", that is, all loads are non-peak-valley points, the load point must be removed; when any load is marked as "-1" and no load is marked as "1", that is, the loads are only marked by "0" and "-1" and at least one load is marked by "-1", if all the load points marked as "-1" have equal load points before and after them, that is, equivalent points, then the load point must be removed; based on the above rules, the random multi-parameter load history with simplified data volume {F1′(t) F2′(t) ... F n ′(t)}.

[0020] Furthermore, the specific steps of obtaining the peak-valley value sequence and the non-peak-valley value point set of the multi-parameter composite load history in step S2 include:

[0021] Step S2.1: Based on {F1′(t) F2′(t) ... F obtained in step S1 n ′(t)}, for each load F i ′(t)(i=1,2,...,n) is linearly combined, that is, F j ″(t)=F1′(t)±F2′(t)±...±F n ′(t), from which we can obtain the multi-parameter composite load history {F1″(t) F2″(t) ... F″ m (t)}, where m = 2 n-1 ;

[0022] Step S2.2: for the random multi-parameter composite load history {F1″(t) F2″(t) ...F″ obtained in step S2.1 m (t)}, the load time history F j″(t)(j=1,2,...,m) respectively judge the peak and valley points, retain the peak and valley points, and move the non-peak and valley points to the set F″ R,j (j=1,2,...,m); Specifically, the three-point method is used to judge the data points, that is, three adjacent data points F are read in sequence. j ″(i-1), F j ″(i), F j ″(i+1), if it satisfies:

[0023] [F j ″(i)-F j ″(i-1)][F j ″(i+1)-F j ″(i)]≥0 and F j ″(i)-F j ″(i-1)≠0

[0024] Then F(i) is a peak point or a valley point; thus we can get the set of non-peak and valley points F″ R,j (j=1,2,...,m) and multi-parameter composite load history peak-to-valley value sequence {F″ RF,1 (t) F″ RF,2 (t) ... F″ RF,m (t)}.

[0025] Furthermore, the specific method of obtaining the composite load reconstruction history in step S3 includes:

[0026] Step S3.1: The peak-to-valley value sequence of the multi-parameter composite load history obtained in step S2 {F″ RF,1 (t) F″ RF,2 (t) ... F″ RF,m (t)} count the rain flow cycles and obtain the rain flow cycle matrix {RFM1 RFM2 ... RFM m}; Specifically,

[0027] Specifically, based on the rain flow cycle counting, the peak and valley value sequence of the multi-parameter principal component load is extracted for the full cycle of the load; four points in the load history are read continuously, and the full cycle selection is based on the following:

[0028]

[0029] If the above full cycle selection criteria are met, the full cycle data point (F″) is extracted RF,j (i+1), F″ RF,j (i+2)), and delete the full cycle data point in the peak-to-valley value sequence; then read four points in the load history in sequence, and repeat the above selection until there are no four consecutive points that meet the criteria.

[0030] Step S3.2, based on the above rain flow cycle matrix {RFM1 RFM2 ... RFM m}, respectively, the time history reconstruction based on rain flow statistics is performed, and the cyclic loads in the rain flow cycle matrix are randomly inserted and connected to form a new random load spectrum, thereby obtaining the reconstruction history of the peak and valley values ​​of the composite load {F″ RFR,1 (t) F″ RFR,2 (t) ... F″ RFR,m (t)}; the peak-to-valley values ​​of the inserted load cycle must be able to contain the peak-to-valley values ​​of the pre-inserted load cycle, that is, the peak-to-valley value line segment inserted in the load sequence must have a peak value greater than the peak value of the full cycle to be inserted, and a valley value less than the valley value of the full cycle to be inserted.

[0031] Furthermore, the specific method for obtaining the full data reconstruction process of the composite load with consistent data volume in step S4 is as follows:

[0032] The non-peak and valley value point set F″ obtained in step S2.2 R,j (j=1, 2, ..., m) are randomly inserted into the composite load peak-valley value reconstruction history obtained in step S3.2 {F″ RFR,1 (t) F″ RFR,2 (t) ... F″ RFR,m (t)}, where non-peak and valley points are randomly inserted into the peak and valley half-cycle containing the load value at that point, thereby obtaining the full data reconstruction history of the composite load with consistent data volume {F′ RFR,1 (t) F′ RFR,2 (t) ... F′ RFR,m (t)}.

[0033] Furthermore, the specific method for obtaining the random multi-parameter random load spectrum based on linear combination in step S5 includes:

[0034] The whole data reconstruction process of the composite load in step S4 {F′ RFR,1 (t) F′ RFR,2 (t) ... F′ RFR,m (t)} to perform linear solution of the composite load history, that is, to solve the equation group F′ RFR,j (t) = F RFR,1 (t)±F RFR,2 (t)±...±F RFR,n (t), of which there are 2 n-1 A set of linear combination equations is randomly selected from which n linear independent equations are obtained to obtain the compiled random multi-parameter random load spectrum based on linear combination {F RFR,1 (t) F RFR,2 (t) ... F RFR,n(t)}.

[0035] Beneficial effects:

[0036] The random multi-parameter load spectrum compilation method based on linear combination provided by the present invention is based on the idea of ​​single-parameter load spectrum compilation, and performs rain flow cycle and load history reconstruction for the multi-parameter load of linear combination, so as to compile a random multi-parameter load spectrum with load phase and multi-axis damage information as comprehensive considerations. Compared with the existing multi-parameter load spectrum compilation method, it has the following advantages:

[0037] (1) Taking the random multi-parameter component load spectrum as the basic compilation data, the rain flow cycle counting and load history reconstruction are performed through the linear combination of the multi-parameter load history, and non-peak and valley points are randomly inserted to obtain the composite load reconstruction history. The linear solution of the composite load is then performed to obtain a new multi-parameter random load spectrum. The load phase and multi-axial damage information are comprehensively considered, which is more reasonable than the single-parameter load spectrum compilation method.

[0038] (2) The multi-parameter load spectrum compilation method proposed in the present invention is simple and has strong versatility. Therefore, under existing technical conditions, the present invention can be used to reasonably compile random multi-parameter load spectra of complex mechanical components that are consistent with the load characteristics under actual load conditions, which has a wide range of engineering application value.

[0039] (3) The random multi-parameter load spectrum compilation method compiled by the present invention can reflect the actual load characteristics and damage characteristics of the component, and can provide a basis for load spectrum compilation for studying multi-axial damage and multi-axial fatigue life analysis methods under actual service conditions of specific mechanical components. In addition, the multi-parameter fatigue test spectrum compiled by the present invention can be used to preliminarily perform multi-axial fatigue tests at the material level to reduce design and development costs and time.

[0040] In summary, the present invention provides a basis for multi-axial fatigue damage analysis of complex mechanical components in actual engineering under random multi-parameter loads, and provides a basis for multi-parameter fatigue test evaluation of such complex mechanical components. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of a random multi-parameter load spectrum compilation method based on linear combination provided by the present invention;

[0042] Figure 2 is a multi-parameter load spectrum composed of two load paths in an embodiment of the present invention;

[0043] Figure 3 It is the result of marking equivalent points of multi-parameter load spectrum in the embodiment of the present invention;

[0044] Figure 4 It is the peak and valley value point marking result of the multi-parameter load spectrum in the embodiment of the present invention;

[0045] Figure 5 It is a random multi-parameter simplified load history in an embodiment of the present invention;

[0046] Figure 6 is a random multi-parameter composite load history in an embodiment of the present invention;

[0047] Figure 7 It is a non-peak and valley value point of the random multi-parameter composite load in the embodiment of the present invention;

[0048] Figure 8 is a random multi-parameter composite load peak-valley value sequence in an embodiment of the present invention;

[0049] Fig. 9 is a random multi-parameter composite load history rain flow cycle matrix in an embodiment of the present invention;

[0050] Fig.10 is a peak-to-valley value sequence reconstructed by a random multi-parameter composite load in an embodiment of the present invention;

[0051] Fig.11 This is the full data reconstruction process of the random multi-parameter composite load in the embodiment of the present invention;

[0052] Fig.12 It is a random multi-parameter random load spectrum in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present invention is further described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, rather than all 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.

[0054] The random multi-parameter load spectrum compilation method based on linear combination provided by the present invention uses the multi-parameter measured load spectrum of the component as the basic compilation data, performs data compression, i.e., equivalence judgment, peak-valley value detection, and non-critical point elimination processing on the multi-parameter random load to obtain a multi-parameter simplified load history, obtains a multi-parameter composite load history through a linear combination of the multi-parameter simplified load history, performs peak-valley value extraction processing on it to obtain a composite load peak-valley value sequence and a non-peak-valley value point set, performs rain flow cycle counting to obtain a rain flow cycle matrix of the composite load history, reconstructs the load history based on the rain flow cycle matrix and randomly inserts non-peak-valley value points to obtain a composite load reconstructed history, and performs linear solution of the composite load to obtain a new multi-parameter random load spectrum. The specific process is as follows Figure 1 The technical solution is described in detail below in conjunction with specific embodiments.

[0055] This embodiment uses a multi-parameter load spectrum composed of two load paths of a component for example analysis, and the specific steps are as follows:

[0056] Step S1, performing peak and valley detection on a random multi-parameter load spectrum of a complex mechanical component to obtain a random multi-parameter load history {F1′(t) F2′(t)} with a simplified data volume.

[0057] The multi-parameter measured load spectrum of complex mechanical components is used as the basic spectrum compilation data. For each load F of the random multi-parameter load data {F1(t) F2(t)} in this embodiment, i (t)(i=1,2) are judged as equal value and marked respectively, that is, two adjacent load points F are selected in turn. i (j), F i (j+1) to make a judgment, such as Figure 2 The multi-parameter load spectrum of the two load paths in this embodiment is formed. If the load values ​​are equal, they are equal value points and are marked as "Y" respectively. Until the two selected load points are not equal, then F i (j+1) is marked as "N", and the other non-equivalent points are marked as "N", such as Figure 3 This is the result of marking equivalent points of the multi-parameter load spectrum of this embodiment.

[0058] For each load F of the random multi-parameter load data {F1(t) F2(t)} i (t)(i=1,2) respectively judge the peak and valley values ​​and mark them. Select three adjacent non-equivalent data points F in turn. i (1) F i (2) F i (3) Perform peak-valley value judgment. When an equal value point appears, select the next non-equivalue point for judgment. When no equal value point appears, it satisfies:

[0059] [F i (2)-F i (1)][F i (3)-F i (2)]>0

[0060] Then F i (2) is the peak point or valley point. i (2) When there are equivalent points, then F i (2) and its equivalent points are marked as “-1”. i (2) If there is no equal value point, the point is marked as "1". All non-peak and valley value points are marked as "0". Figure 4 This is the marking result of the peak and valley value points of the multi-parameter load spectrum of this embodiment, where non-equivalent peak and valley value points are marked with "five-pointed stars", equivalent peak and valley value points are marked with "diamonds", and non-peak and valley value points are marked with "inverted triangles".

[0061] According to the peak and valley value marking results, the random multi-parameter load F i The load points of (t)(i=1,2) are selected synchronously in sequence. When any load is marked as "1", that is, any load is a non-equivalent peak-valley point, the load point must be retained. When all loads are marked as "0", that is, all loads are non-peak-valley points, the load point must be removed. When any load is marked as "-1" and no load is marked as "1", that is, the loads are only marked by "0" and "-1" and at least one load is marked by "-1", if all the load points marked as "-1" have equal load points before and after them, that is, equivalent points, then the load point must be removed. Here is a further example. When the loads are only marked with 0 and -1 at a certain moment, the load marked as 0 should be removed, and for the load point marked as -1, it is necessary to further determine whether it is the head and tail of the continuous equivalent points. Only when the load point is only in the middle of the continuous equivalent points (that is, not the head and tail points), the load point can be removed.

[0062] Based on the above rules, the random multi-parameter load history {F1′(t) F2′(t)} with simplified data volume is obtained. Figure 5 This is a random multi-parameter simplified load history for this embodiment.

[0063] Step S2: linearly combine the random multi-parameter load history {F1′(t) F2′(t)} obtained in step S1 to obtain a multi-parameter composite load history {F″1(t) F″2(t)}. Further extract the peak and valley values ​​of the multi-parameter composite load history to obtain the peak and valley value sequences of the multi-parameter composite load history {F″ RF,1 (t) F″ RF,2 (t)} and non-peak and valley point set {F″ RF,1 (t)F″ RF,2 (t)}.

[0064] Based on {F1′(t)F2′(t)} obtained in step S1, for each load F i ′(t)(i=1,2) is linearly combined, that is, F1″(t)=F1′(t)+F2′(t), F2″(t)=F1′(t)+F2′(t), thereby obtaining the multi-parameter composite load history {F1″(t) F2″(t)}. Figure 6 This is the random multi-parameter composite load history of this embodiment.

[0065] For each load time history F in the above multi-parameter composite load history {F1″(t)F2″(t)} j″(t)(j=1,2) respectively judge the peak and valley points, retain the peak and valley points, and move the non-peak and valley points to set F″ R,j (j=1,2). Specifically, the three-point method is used to judge the data points, that is, three adjacent data points F are read in sequence. j ″(i-1), F j ″(i), F j ″(i+1), if it satisfies:

[0066] [F j ″(i)-F j ″(i-1)][F j ″(i+1)-F j ″(i)]≥0 and F j ″(i)-F j ″(i-1)≠0

[0067] Then F j (i) is the peak point or valley point. Figure 7 The non-peak and valley point set F″ R,1 , F″ R,2 He Ru Figure 8 The peak-to-valley sequence of the multi-parameter composite load history {F″ RF,1 (t) F″ RF,2 (t)}.

[0068] Step S3: Count the rain flow cycles of the peak-valley value sequence obtained in step S2 to obtain the rain flow cycle matrix {RFM1 RFM2} of the multi-parameter composite load history. Reconstruct the random load history based on the rain flow cycle matrix to obtain the peak-valley value sequence {F″ RFR,1 (t) F″ RFR,2 (t)}.

[0069] First, the multi-parameter composite load history peak-valley value sequence {F″ RF,1 (t) F″ RF,2 (t)} to count the rain flow cycles and obtain Fig. 9 The rain flow cycle matrix {RFM1 RFM2} is shown. Specifically,

[0070] Specifically, based on the material stress-strain hysteresis loop principle, the peak-valley value sequence of the multi-parameter principal component load is extracted for the full load cycle. Four points in the load history are read continuously, and the full cycle selection is based on the following:

[0071]

[0072] If the above full cycle selection criteria are met, the full cycle data point (F″) is extracted RF,j (i+1), F″RF,j (i+2)), and delete the full cycle data point in the peak-to-valley value sequence. Then read four points in the load history in sequence, and repeat the above selection until there are no four consecutive points that meet the criteria.

[0073] Based on the above rain flow cycle matrix {RFM1 RFM2}, the time history reconstruction based on rain flow statistics is performed respectively, and the cyclic loads in the rain flow cycle matrix are randomly inserted and connected to form a new random load spectrum, thereby obtaining the following: Fig.10 The reconstruction history of the peak and valley values ​​of the composite load is shown in RFR,1 (t) F″ RFR,2 (t)}. The peak and valley values ​​of the inserted load cycle must be able to contain the peak and valley values ​​of the pre-inserted load cycle, that is, the peak and valley value line segment inserted in the load sequence must have a peak value greater than the peak value of the full cycle to be inserted, and a valley value less than the valley value of the full cycle to be inserted.

[0074] Step S4: The non-peak and valley value point set F″ obtained in step S2 R,j (j=1,2) are randomly inserted into the reconstruction history of the composite load peak and valley values ​​obtained in step S3 {F″ RFR,1 (t) F″ RFR,2 (t)}, where after the non-peak-valley point is randomly inserted, the point is still not a peak-valley point, that is, the non-peak-valley point is only randomly inserted into the peak-valley half cycle containing the load value of the point, thereby obtaining the full data reconstruction process of the random multi-parameter composite load of this embodiment with consistent data volume {F′ RFR,1 (t)F′ RFR,2 (t)}, such as Fig.11 shown.

[0075] Step S5: Reconstruct the composite load full data obtained in step S4 {F′ RFR,1 (t) F′ RFR,2 (t)} is linearly solved to obtain the compiled random multi-parameter random load spectrum based on linear combination.

[0076] The full data reconstruction process of the composite load obtained in step S4 {F′ RFR,1 (t) F′ RFR,2 (t)} to perform linear solution of the composite load history, that is, to solve the linear equation system

[0077]

[0078] Right now

[0079] Thus, the random multi-parameter random load spectrum of this embodiment based on linear combination can be obtained. RFR,1 (t) F RFR,2(t)}, such as Fig.12 shown.

[0080] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A random multi-parameter load spectrum compilation method based on linear combination, characterized in that: The following steps are involved: Step S1: perform peak-valley detection on the random multi-parameter load spectrum of the complex mechanical component to obtain a random multi-parameter load history {F1′(t) F2′(t) ... F′ n (t)}; Step S2: the random multi-parameter load history {F1′(t) F2′(t) ... F′ n (t)} to obtain the multi-parameter composite load history {F1″(t) F2″(t) ... F″ m (t)}; further extract the peak and valley values ​​of the multi-parameter composite load history, and obtain the peak and valley value sequence of the multi-parameter composite load history {F″ RF,1 (t) F″ RF,2 (t) ...F″ RF,m (t)} and non-peak and valley point set F″ R,j (j=1,2,...,m); Step S3: Count the rain flow cycles of the peak-valley value sequence obtained in step S2 to obtain the rain flow cycle matrix {RFM1 RFM2 ... RFM m }; Based on the rain flow cycle matrix, the random load history is reconstructed to obtain the composite load peak and valley value reconstruction history {F″ RFR,1 (t) F″ RFR,2 (t) ... F″ RFR,m (t)}; Step S4: The non-peak and valley value point set F″ obtained in step S2 R,j (j=1, 2, ..., m) are randomly inserted into the reconstruction history of the composite load peak and valley values ​​obtained in step S3 {F″ RFR,1 (t) F″ RFR,2 (t) ... F″ RFR,m (t)}, obtain the full data reconstruction process of the composite load with consistent data volume {F′ RFR,1 (t) F′ RFR,2 (t) ... F′ RFR,m (t)}; Step S5: Reconstruct the composite load full data obtained in step S4 {F′ RFR,1 (t) F′ RFR,2 (t) ... F′ RFR,m (t)} perform linear solution to obtain the compiled random multi-parameter random load spectrum based on linear combination; The specific steps of obtaining the random multi-parameter load history with simplified data volume in step S1 are as follows: Step S1.1, using the multi-parameter measured load spectrum of complex mechanical components as the basic spectrum compilation data, for random multi-parameter load data {F1(t) F2(t) ... F n (t)} each load F i (t)(i=1,2,...,n) respectively make equal value judgment and mark; select two adjacent load points F in turn i (j), F i (j+1) is used to judge. When the load values ​​are equal, F i (j), F i (j+1) is recorded as the equivalent point and marked as "Y" respectively; repeat the selection and judgment until the load values ​​of the two selected load points are not equal, then F i (j+1) is marked as "N", and the other non-equivalent points are marked as "N"; Step S1.2: for random multi-parameter load data {F1(t) F2(t) ... F n (t)} each load F i (t)(i=1,2,...,n) respectively judge the peak and valley values ​​and mark them; select three adjacent non-equivalent data points F in turn i (1) F i (2) F i (3) Perform peak-valley value judgment. When an equal value point appears, select the next non-equivalue point for judgment in turn. When no equal value point appears, the following conditions are satisfied: [F i (2)-F i (1)][F i (3)-F i (2)]>0 Then F i (2) is the peak point or valley point. i (2) When there are equivalent points, then F i (2) and its equivalent points are marked as "-1", when F i (2) When there is no equal value point, the point is marked as "1"; all non-peak and valley value points are marked as "0"; Step S1.3: According to the peak and valley value marking results, the random multi-parameter load F i (t)(i=1,2,...,n) load points are selected synchronously in sequence; when any load is marked as "1", that is, any load is a non-equivalent peak-valley point, the load point must be retained; when all loads are marked as "0", that is, all loads are non-peak-valley points, the load point must be removed; when any load is marked as "-1" and no load is marked as "1", that is, the loads are only marked by "0" and "-1" and at least one load is marked by "-1", if all the load points marked as "-1" have equal load points before and after them, that is, equivalent points, then the load point must be removed; based on the above rules, the random multi-parameter load history {F1′(t) F2′(t) ... F n ′(t)}; The specific steps of obtaining the peak-valley value sequence and non-peak-valley value point set of the multi-parameter composite load history in step S2 include: Step S2.1: Based on {F1′(t) F2′(t) ... F obtained in step S1 n ′(t)}, for each load F i ′(t)(i=1,2,...,n) is linearly combined, that is, F″ j (t)=F1′(t)±F2′(t)±...±F n ′(t), and thus obtain the multi-parameter composite load history {F1″(t) F2″(t) ... F m ″(t)}, where m = 2 n-1 ; Step S2.2: for the random multi-parameter composite load history {F1″(t) F″2(t) ... F″ obtained in step S2.1 m (t)}, each load time history F″ j (t)(j=1,2,...,m) respectively judge the peak and valley points, retain the peak and valley points, and move the non-peak and valley points to the set F″ R,j (j=1,2,...,m); Specifically, the three-point method is used to judge the data points, that is, three adjacent data points F″ are read in sequence. j (i-1), F″ j (i) F″ j (i+1), if: [F″ j (i) - F″ j (i - 1)][F″ j (i + 1) - F″ j (i)] ≥ 0 and F″ j (i) - F″ j (i - 1) ≠ 0 Then F(i) is a peak point or a valley point; thus we can get the set of non-peak and valley points F″ R,j (j=1,2,...,m) and multi-parameter composite load history peak-to-valley value sequence {F″ RF,1 (t) F″ RF,2 (t) ... F″ RF,m (t)}; The specific method for obtaining the composite load reconstruction history in step S3 includes: Step S3.1: The peak-to-valley value sequence of the multi-parameter composite load history obtained in step S2 {F″ RF,1 (t) F″ RF,2 (t)... F″ RF,m (t)} count the rain flow cycles and obtain the rain flow cycle matrix {RFM1 RFM2 ... RFM m }; Specifically, Based on the rain flow cycle count, the peak and valley value sequence of the multi-parameter principal component load is extracted for the full cycle of the load; four points in the load history are read continuously, and the full cycle selection is based on the following: If the above full cycle selection criteria are met, the full cycle data point (F″) is extracted RF,j (i+1), F″ RF,j (i+2)), and delete the full cycle data point in the peak-to-valley value sequence; then read four points in the load history in sequence, and repeat the above selection until there are no four consecutive points that meet the criteria; Step S3.2, based on the above rain flow cycle matrix {RFM1 RFM2 ... RFM m }, respectively, the time history reconstruction based on rain flow statistics is performed, and the cyclic loads in the rain flow cycle matrix are randomly inserted and connected to form a new random load spectrum, thereby obtaining the reconstruction history of the peak and valley values ​​of the composite load {F″ RFR,1 (t) F″ RFR,2 (t) ... F″ RFR,m (t)}; the peak-to-valley values ​​of the inserted load cycle must be able to contain the peak-to-valley values ​​of the pre-inserted load cycle, that is, the peak-to-valley value line segment inserted in the load sequence must have a peak value greater than the peak value of the full cycle to be inserted, and a valley value less than the valley value of the full cycle to be inserted.

2. A method for compiling a random multi-parameter load spectrum based on linear combination according to claim 1, characterized in that: The specific method of obtaining the full data reconstruction process of the composite load with consistent data volume in step S4 is as follows: The non-peak and valley value point set F″ obtained in step S2.2 R,j (j=1, 2, ..., m) are randomly inserted into the composite load peak-valley value reconstruction history obtained in step S3.2 {F″ RFR,1 (t) F″ RFR,2 (t) ... F″ RFR,m (t)}, where non-peak and valley points are randomly inserted into the peak and valley half-cycle containing the load value at that point, thereby obtaining the full data reconstruction history of the composite load with consistent data volume {F′ RFR,1 (t) F′ RFR,2 (t) ... F′ RFR,m (t)}.

3. The method for compiling a random multi-parameter load spectrum based on linear combination according to claim 2, characterized in that: The specific method of obtaining the random multi-parameter random load spectrum based on linear combination in step S5 includes: The full data reconstruction process of the composite load in step S4 {F′ RFR,1 (t) F′ RFR,2 (t) ... F′ RFR,m (t)} to perform linear solution of the composite load history, that is, to solve the equation group F′ RFR,j (t) = F RFR,1 (t)±F RFR,2 (t)±...±F RFR,n (t), of which there are 2 n-1 A set of linear combination equations is randomly selected from which n linear independent equations are obtained to obtain the compiled random multi-parameter random load spectrum based on linear combination {F RFR,1 (t) F RFR,2 (t) ... F RFR,n (t)}.

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