Method and device for determining multi-axial fatigue load spectrum

Abstract

The present invention discloses a method and device for determining a multi-axial fatigue load spectrum. The method obtains random load data of multiple axes of a target object, such as automotive parts, and automatically determines the damage values of the random load data of the multiple axes. Considering the multi-axial random load data of the target object, it can automatically determine the fatigue load spectrum of the target object according to the reference damage value and the damage values of the random load data of the multiple axes of the target object. Without high-demand bench equipment, it can obtain an accurate and reliable fatigue load spectrum of the target object at low cost, thereby improving the reliability and applicability of the fatigue load spectrum verification of the target object, and further ensuring the durability and lifespan of the vehicle; and when the target object is a part, by considering the multi-axial random load data of the part to determine the fatigue load spectrum of the part, it can also reduce the number of times of determining and verifying the fatigue load spectrum at the automotive system level and the automotive vehicle level.

Description

Technical Field

[0001] The present invention relates to the technical field of automobiles, and particularly to a method and device for determining a multi-axial fatigue load spectrum. Background Art

[0002] The durability of an automobile refers to the working period of the whole vehicle and assemblies of the automobile before reaching the limit wear value or becoming unusable. The durability of an automobile depends on the various performances of automobile parts, such as fatigue resistance. Therefore, accurately obtaining the fatigue resistance of automobile parts, that is, the fatigue load spectrum, has become one of the important concerns in the industry.

[0003] In practical applications, the general methods for determining the fatigue load spectrum of parts are as follows: First, measure the actual random load of the part on a test bench, and filter the measured actual random load to filter out the loads with larger damage values (for example, damage values greater than or equal to 90%), so as to obtain the fatigue load spectrum of the part; Second, measure the actual random load of each axis of the part, and respectively perform sine wave grading on the actual random load of each axis, and formulate an accelerated load block for each graded actual random load based on damage equivalence to obtain the fatigue load spectrum of the part. However, it is found in practice that in the first method, since it is necessary to determine the accelerated fatigue load spectrum for multiple axes, the required test bench equipment has relatively high requirements, that is, the cost is relatively high; in the second method, since the loads of each axis of the part are separately graded and accelerated, ignoring the coupling of the loads between each axis of the part, an accurate and reliable fatigue load spectrum cannot be obtained. Therefore, it is particularly important to propose a solution for obtaining an accurate and reliable fatigue load spectrum at low cost. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for determining a multi-axial fatigue load spectrum, which can obtain an accurate and reliable fatigue load spectrum at low cost.

[0005] To solve the above technical problem, in the first aspect of the embodiments of the present invention, a method for determining a multi-axial fatigue load spectrum is disclosed, and the method includes:

[0006] Obtain random load data of multiple axes of a target object, where the random load data is load data actually measured from the target object, and every two of the multiple axes are perpendicular to each other;

[0007] Determine the target damage value of the random load data of all the axes;

[0008] Based on the determined reference damage value and the target damage value, determine the fatigue load spectrum of the target object, where the reference damage value is the damage value of a standard object of the same type as the target object.

[0009] It can be seen that in the first aspect of the present invention, by obtaining the random load data of multiple axes of a target object, such as automotive parts, and automatically determining the damage values of the random load data of multiple axes of the target object, the multi-axis random load data of the target object is considered, and the multi-axis fatigue load spectrum of the target object can be automatically determined according to the reference damage value and the damage values of the random load data of multiple axes of the target object. Without high - requirement bench equipment, it can obtain an accurate and reliable fatigue load spectrum of the target object at low cost, thereby improving the reliability and applicability of the fatigue load spectrum verification of the target object, and further ensuring the durability and lifespan of the vehicle; and when the target object is a part, by considering the multi - axis random load data of the part to determine the fatigue load spectrum of the part, it can also reduce the number of determinations and verifications of the fatigue load spectrum at the vehicle system level and the vehicle whole - vehicle level.

[0010] In the second aspect of the embodiments of the present invention, a multi - axis fatigue load spectrum determination device is disclosed. The multi - axis fatigue load spectrum determination device includes:

[0011] An acquisition module, configured to acquire the random load data of multiple axes of a target object, where the random load data is the load data actually measured for the target object, and every two of the multiple axes are perpendicular to each other;

[0012] A first determination module, configured to determine the target damage values of the random load data of all the axes;

[0013] A second determination module, configured to determine the fatigue load spectrum of the target object based on the determined reference damage value and the target damage value, where the reference damage value is the damage value of a standard object of the same type as the target object.

[0014] It can be seen that in the second aspect of the present invention, by obtaining the random load data of multiple axes of a target object, such as automotive parts, and automatically determining the damage values of the random load data of multiple axes of the target object, the multi - axis random load data of the target object is considered, and the multi - axis fatigue load spectrum of the target object can be automatically determined according to the reference damage value and the damage values of the random load data of multiple axes of the target object. Without high - requirement bench equipment, it can obtain an accurate and reliable fatigue load spectrum of the target object at low cost, thereby improving the reliability and applicability of the fatigue load spectrum verification of the target object, and further ensuring the durability and lifespan of the vehicle; and when the target object is a part, by considering the multi - axis random load data of the part to determine the fatigue load spectrum of the part, it can also reduce the number of determinations and verifications of the fatigue load spectrum at the vehicle system level and the vehicle whole - vehicle level.

[0015] In the third aspect of the embodiments of the present invention, a multi - axis fatigue load spectrum determination device is disclosed. The multi - axis fatigue load spectrum determination device includes:

[0016] A memory storing executable program code;

[0017] A processor coupled to the memory;

[0018] The processor calls the executable program code stored in the memory and executes the multi-axial fatigue load spectrum determination method disclosed in the first aspect of the present invention.

[0019] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions which, when called, are used to execute the multi-axial fatigue load spectrum determination method disclosed in the first aspect of the present invention.

[0020] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0021] In the embodiments of the present invention, a multi-axial fatigue load spectrum determination method and apparatus are disclosed. The method obtains random load data of multiple axes of a target object, such as automotive parts, and automatically determines the damage values of the random load data of multiple axes of the target object. Considering the multi-axial random load data of the target object, it can automatically determine the multi-axial fatigue load spectrum of the target object based on the reference damage value and the damage values of the random load data of multiple axes of the target object. Without the need for high-demand bench equipment, it can obtain an accurate and reliable fatigue load spectrum of the target object at low cost, thereby improving the reliability and applicability of the fatigue load spectrum verification of the target object, and further ensuring the durability and lifespan of the vehicle; and when the target object is a part, by considering the multi-axial random load data of the part to determine the fatigue load spectrum of the part, it can also reduce the number of times of determining and verifying the fatigue load spectrum at the automotive system level and the automotive vehicle level. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 is a flowchart showing a multi-axial fatigue load spectrum determination method disclosed in an embodiment of the present invention;

[0024] Figure 2 is a flowchart showing another multi-axial fatigue load spectrum determination method disclosed in an embodiment of the present invention;

[0025] Figure 3It is a schematic structural diagram of a multi-axial fatigue load spectrum determination device disclosed in an embodiment of the present invention;

[0026] Figure 4 It is a schematic structural diagram of another multi-axial fatigue load spectrum determination device disclosed in an embodiment of the present invention;

[0027] Figure 5 It is a schematic structural diagram of yet another multi-axial fatigue load spectrum determination device disclosed in an embodiment of the present invention;

[0028] Figure 6 It is a schematic diagram of the projection direction of a two-dimensional random load data disclosed in an embodiment of the present invention;

[0029] Figure 7 It is a schematic diagram of the rain flow projection result of a two-dimensional random load data in two certain projection directions disclosed in an embodiment of the present invention;

[0030] Figure 8 It is a schematic diagram of the comparison result of a fatigue load spectrum disclosed in an embodiment of the present invention. Detailed implementation manners

[0031] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0032] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or equipment.

[0033] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0034] The present invention discloses a method and device for determining a multi-axial fatigue load spectrum. By obtaining the random load data of multiple axes of a target object, such as automotive parts, and automatically determining the damage values of the random load data of multiple axes of the target object, considering the multi-axial random load data of the target object, it can automatically determine the multi-axial fatigue load spectrum of the target object according to the reference damage value and the damage values of the random load data of multiple axes of the target object. Without high-demand bench equipment, it can obtain an accurate and reliable fatigue load spectrum of the target object at low cost, thereby improving the reliability and applicability of the fatigue load spectrum verification of the target object, and further ensuring the durability and lifespan of the vehicle; and when the target object is a part, by considering the multi-axial random load data of the part to determine the fatigue load spectrum of the part, it can also reduce the number of times of determining and verifying the fatigue load spectrum at the vehicle system level and the vehicle whole vehicle level. The following will be described in detail respectively.

[0035] Embodiment 1

[0036] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a method for determining a multi-axial fatigue load spectrum disclosed in an embodiment of the present invention. As Figure 1 shown, the method can be applied to a fatigue load spectrum determination system / fatigue load spectrum determination device / fatigue load spectrum determination terminal. As Figure 1 shown, the method for determining a multi-axial fatigue load spectrum may include the following operations:

[0037] 101. Obtain the random load data of multiple axes of the target object. The random load data is the load data actually measured for the target object, and every two axes among the multiple axes are perpendicular to each other.

[0038] In an embodiment of the present invention, the target object includes parts of any item (such as a vehicle).

[0039] In an embodiment of the present invention, the random load data of multiple axes includes at least the random load data of two axes, such as the random load data of the X-axis and the Y-axis.

[0040] In an embodiment of the present invention, the random load data of multiple axes may be pre-measured by the fatigue load spectrum determination system, or may be measured by the fatigue load spectrum determination system when the fatigue load spectrum of the target object needs to be determined, or may be obtained by the fatigue load spectrum determination system from other terminal devices storing the random load data of multiple axes of the target object. The embodiment of the present invention does not make a limitation.

[0041] 102. Determine the target damage values of the random load data of all axes.

[0042] In the embodiments of the present invention, the target damage value of the random load data includes the true damage value of the random load data and / or the pseudo damage value of the random load data. Correspondingly, the following reference damage value includes the true reference damage value and / or the reference pseudo damage value.

[0043] 103. Based on the determined reference damage value and the target damage value, determine the fatigue load spectrum of the target object, where the reference damage value is the damage value of a standard object of the same type as the target object.

[0044] It can be seen that implementing Figure 1 The described method for determining the multi-axial fatigue load spectrum obtains the random load data of multiple axes of a target object, such as automotive parts, and automatically determines the damage values of the random load data of multiple axes of the target object. Considering the multi-axial random load data of the target object, it can automatically determine the multi-axial fatigue load spectrum of the target object according to the reference damage value and the damage values of the random load data of multiple axes of the target object, without the need for high-demand bench equipment, and can obtain an accurate and reliable fatigue load spectrum of the target object at low cost, thereby improving the reliability and applicability of the verification of the fatigue load spectrum of the target object, and further ensuring the durability and lifespan of the vehicle; and when the target object is a part, by considering the multi-axial random load data of the part to determine the fatigue load spectrum of the part, it can also reduce the number of times of determining and verifying the fatigue load spectrum at the vehicle system level and the vehicle whole vehicle level.

[0045] In an optional embodiment, determining the target damage value of the random load data of all axes includes:

[0046] Obtain multiple projection directions of the random load data of all axes on the coordinate axes formed by all axes;

[0047] Based on each projection direction, perform a multi-axial rainflow projection operation on the random load data of all axes to obtain the rainflow random load data of all axes in each projection direction;

[0048] Perform a damage calculation operation on the rainflow random load data in each projection direction to obtain the damage value of the rainflow random load data in each projection direction, and determine the damage value of the rainflow random load data in all projection directions as the target damage value of the random load data of all axes.

[0049] In this optional embodiment, optionally, a multi-axial rainflow projection operation can be performed on the random load data of all axes through a multi-axial rainflow projection calculation method, where the multi-axial rainflow projection calculation method is as follows:

[0050]

[0051] In the formula, L βj(t) is the rain flow random load data in the j-th projection direction, L k (t) is the random load data of the k-th axis, β j is the projection direction of the j-th angle, d is the number of axes, that is, the dimension of the random load data, N is the number of cycles of the random load data, and M is the number of projection directions.

[0052] It can be seen that after obtaining the random load data of multiple axes, this optional embodiment further performs a multi-axis rain flow projection operation on the random load data of all axes in the obtained multiple projection directions, considering the multi-axis nature of the random load data, and calculates the damage value of the random load data after the multi-axis rain flow projection operation in each projection direction. It can not only determine the damage value of the random load data, but also improve the accuracy and comprehensiveness of the determination of the damage value of the random load data, thereby improving the accuracy, reliability and efficiency of the determination of the fatigue load spectrum of the target object.

[0053] In this optional embodiment, optionally, obtaining multiple projection directions of the random load data of all axes on the coordinate axes formed by all axes includes:

[0054] When the number of all axes is 2, one of the two axes is used as the first reference axis, and different angles are selected within the determined angle range (for example: 0° to 180°) to obtain the first angle set;

[0055] Determine the directions of all angles in the first angle set as multiple projection directions of the random load data of all axes on the coordinate axes formed by all axes.

[0056] In this optional embodiment, optionally, the angles in the first angle set are different from each other. Specifically, the angle sizes are different from each other and / or the angle directions are different from each other. Among them, the sizes of each angle in the first angle set can be in an arithmetic progression from small to large. For example: 15°, 30°, 45°, etc., and the arithmetic difference depends on the specific situation. Specifically, the more complex the structure of the target object, the smaller the difference between each two angles. For example: if the curvature of the component is greater than or equal to 0.9, the difference between each two angles is 10°, if the curvature of the component is less than 0.8, the difference between each two angles is 15°, etc.; or, the sizes of each angle in the first angle set are in a non-arithmetic progression from small to large. For example: 10°, 30°, 40°, etc. Preferably, the sizes of each angle in the first angle set are in an arithmetic progression from small to large.

[0057] In this optional embodiment, further optionally, the specific method for selecting different angles may be as follows: taking one of the two axes as the first reference axis, and within the determined angle range, selecting different angles in any selection manner to obtain a first angle set; or, starting from a certain clockwise or counterclockwise direction of a certain direction of one of the two axes as the first reference axis, successively selecting different angles to obtain a first angle set, where a certain direction includes the positive axis direction or the negative axis direction of the first reference axis, and a certain clockwise or counterclockwise direction includes the counterclockwise direction or the clockwise direction. Among them, when successively selecting different angles, the magnitudes of the selected angles increase successively and the directions are all different.

[0058] Now, taking the magnitudes of each angle in the first angle set as an arithmetic progression from small to large as an example, as Figure 6 shown, Figure 6 a schematic diagram of the projection directions of two-dimensional random load data on two axes is disclosed. As Figure 6 shown, taking the positive X-axis as the reference axis, different angles are equally spacedly selected within [0°, 180°], that is, starting from the positive X-axis, the magnitude of each angle increases by 15° successively, obtaining an angle set composed of 12 different angles, and determining the directions of the 12 different angles as the projection directions of the random load data. Further, each projection direction of the random load data is converted into a trigonometric function value to obtain 12 function values. As Figure 6 shown, the trigonometric function values corresponding to the projection directions of 0°, 15°, 30°, 45°, 60°, 75°, 90°, 105°, 120°, 135°, 150°, 165°, and 180° are (1, 0), (0.97, 0.26), (0.87, 0.50), (0.71, 0.71), (0.50, 0.87), (0.26, 0.97), (0, 1), (-0.26, 0.97), (-0.50, 0.87), (-0.71, 0.71), (-0.87, 0.50), (-0.97, 0.26), and (-1, 0) respectively. Further, as Figure 7 shown, Figure 7 a schematic diagram of the rainflow projection results of two-dimensional random load data in two certain projection directions is disclosed. It can be seen from Figure 7 that the two-dimensional random load data is basically the same in the two projection directions of (-71, 0.71) and (-0.5, 0.87).

[0059] It can be seen that after obtaining the random load data of all axes, and when the number of axes is 2, by taking one of the axes as a reference axis and selecting different angles within the determined angle range, and determining that the projection directions at different angles are multiple projection directions of the random load data on the coordinate axes formed by all axes, it is possible to determine multiple projection directions of the random load data in the plane dimension and improve the determination accuracy and efficiency of multiple projection directions of the random load data on the coordinate axes formed by all axes, thereby facilitating improving the determination accuracy and efficiency of the target damage value of the random load data, and thus improving the determination accuracy and efficiency of the fatigue load spectrum of the target object.

[0060] In this optional embodiment, optionally, obtaining multiple projection directions of the random load data of all axes on the coordinate axes formed by all axes includes:

[0061] When the number of all axes is 3, taking one of the three axes as a second reference axis and selecting different target angles within the determined angle range;

[0062] According to different target angles, taking the third axis that is not one of the three axes and not involved in the selection of different target angles as the determined angle range (for example: 0° to 90°) of the third reference axis, determining different angles, a second angle set;

[0063] Determining the directions of all angles in the second angle set as multiple projection directions of the random load data of all axes on the coordinate axes formed by all axes.

[0064] In this optional embodiment, optionally, the specific manner of selecting different target angles can refer to the specific description of the manner of selecting different angles in the first angle set described above, and will not be elaborated here.

[0065] In this optional embodiment, optionally, whenever a target angle is selected, different angles can be selected according to this target angle and in combination with the determined angle range taking the third axis as the third reference axis to obtain a second angle set; or, after all target angles are selected, different angles can be selected according to this target angle and in combination with the determined angle range taking the third axis as the third reference axis to obtain a second angle set.

[0066] For example, taking the positive X-axis as the reference axis, different angles between the X-axis and the Y-axis are selected respectively, such as: 0°, 45°, 90°, 135°, 180°, -135°, -90°, -45°. Then, in the plane formed by the X-axis and the Y-axis, different angles between this plane and the Z-axis are selected, such as: 0°, 35.3°, 45°, 62.6°, 90°. Then, according to the different angles between the X-axis and the Y-axis and the different angles between the plane and the Z-axis, the projection direction of the random load data is determined.

[0067] It can be seen that after obtaining the random load data of all axes in this optional embodiment, and when the number of axes is 3, by selecting the angle between two of the axes, and then combining the selected angle with the third axis, the selection of three-dimensional angles can be achieved. Then, determining the projection directions at different three-dimensional angles as the multiple projection directions of the random load data on the coordinate axes formed by all axes can realize the determination of multiple projection directions of the random load data in the three-dimensional dimension and improve the accuracy and efficiency of determining the multiple projection directions of the random load data on the coordinate axes formed by all axes, which is beneficial to improving the accuracy and efficiency of determining the target damage value of the random load data.

[0068] In another optional embodiment, before performing the multi-axis rain-flow projection operation on the random load data of all axes based on each projection direction to obtain the rain-flow random load data of all axes in each projection direction, this method for determining the multi-axial fatigue load spectrum may further include the following operations:

[0069] Assign corresponding load initial values to the random load data of each axis, and the load initial values include the initial amplitude of the random load data of each axis and the initial phase difference between the random load data of every two axes;

[0070] Among them, optionally, performing the multi-axis rain-flow projection operation on the random load data of all axes based on each projection direction to obtain the rain-flow random load data of all axes in each projection direction includes:

[0071] Performing the multi-axis rain-flow projection operation on the random load data of all axes based on each projection direction and the corresponding load initial values assigned to the random load data of each axis to obtain the rain-flow random load data of all axes in each projection direction.

[0072] In this optional embodiment, optionally, according to the type of the target object and / or the number of projection directions, assign corresponding load initial values to the random load data of each axis.

[0073] It can be seen that in this alternative embodiment, by assigning corresponding initial load values to the random load data of each axis and performing a multi-axis rainflow projection operation on the random load data according to the projection direction and the initial load values, the accuracy, reliability, and efficiency of the rainflow counting of the random load data can be improved, thereby improving the accuracy, reliability, and efficiency of determining the target damage value of the random load data, and further improving the accuracy, reliability, and efficiency of determining the fatigue load spectrum of the target object.

[0074] In yet another alternative embodiment, based on the determined reference damage value and the target damage value, determining the fatigue load spectrum of the target object includes:

[0075] Calculating the damage value difference between the determined reference damage value and the target damage value, and determining whether the damage value difference is less than or equal to the determined damage value difference threshold;

[0076] When it is determined that the damage value difference is less than or equal to the damage value difference threshold, the fatigue load spectrum of the target object is determined according to the initial load values corresponding to the random load data of all axes, and the initial load values include the initial amplitude of the random load data of each axis and the initial phase difference between the random load data of every two axes.

[0077] It can be seen that in this alternative embodiment, after determining the damage values of the random load data of all axes, by automatically comparing the damage value difference between the damage value and the reference damage value with the determined damage value difference threshold, and when it is not greater than the damage value difference threshold, determining the fatigue load spectrum of the target object based on the initial load values corresponding to the random load data, it can not only achieve the determination of the fatigue load spectrum of the target object, but also reduce the occurrence of inaccurate determination of the fatigue load spectrum caused by directly determining the fatigue load spectrum of the target object according to the initial load values corresponding to the random load data.

[0078] In this alternative embodiment, the number of target damage values is greater than or equal to 1, that is, different projection directions correspond to different target damage values. Optionally, calculating the damage value difference between the determined reference damage value and the target damage value includes:

[0079] Subtracting each target damage value from the determined reference damage value to obtain the damage value difference corresponding to each target damage value.

[0080] In this alternative embodiment, the damage value difference corresponding to each target damage value is calculated by the following damage value difference calculation formula;

[0081]

[0082]

[0083] In the formula, D jis the damage value difference corresponding to the target damage value of the random load data of all axes in the j-th projection direction; D βj is the target damage value of the random load data of all axes in the j-th projection direction; is the reference damage value of the reference random load data of the reference damage value in the j-th projection direction; n βji is the i-th amplitude of the random load data of all axes in the j-th projection direction is the number of cycles under the stress corresponding to; N βji is the i-th amplitude A of the random load data of all axes in the j-th projection direction i is the number of cycles to failure under the stress corresponding to.

[0084] In this optional embodiment, optionally, when it is determined that the proportion of the damage value differences less than or equal to the damage value difference threshold among all the damage value differences is greater than or equal to the determined proportion threshold, it is determined that the damage value difference is less than or equal to the damage value difference threshold, and the above operation of determining the fatigue load spectrum of the target object based on the reference fatigue load spectrum corresponding to the reference damage value and the damage value difference threshold is performed. Among them, when the number of target damage values is less than or equal to the determined number threshold (for example: 10), the determined proportion threshold can be 100%; when the number of target damage values is greater than the determined number threshold, the determined proportion threshold can be 95%.

[0085] In another optional embodiment, the method for determining the multi-axial fatigue load spectrum may further include the following operations:

[0086] When it is determined that the damage value difference is not less than or equal to the damage value difference threshold, perform a least squares operation on the reference damage value and the target damage value based on the determined least squares method to obtain the target damage value variable of the random load data of all axes;

[0087] Perform an iterative operation on the target damage value variable until the minimum target damage value of the target damage value variable is obtained, and determine the fatigue load spectrum of the target object based on the target load value corresponding to the minimum target damage value. The target load value includes the target load amplitude of the random load data of each axis and the target load phase difference between the random load data of every two axes.

[0088] In this optional embodiment, optionally, the minimum target damage value can be understood as less than or equal to the preset damage value, or can be understood as the minimum function value of the target damage value variable.

[0089] In this optional embodiment, perform a least squares operation on the reference damage value and the target damage value through the following least squares calculation formula to obtain the target damage value variable of the random load data of all axes;

[0090]

[0091] In the formula, is the target damage value variable of the random load data of all axes.

[0092] In this optional embodiment, optionally, an iterative operation is performed on the target damage value variable until the minimum target damage value of the target damage value variable is obtained, including:

[0093] Adjust the initial load value of the random load data of all axes, and re-obtain the target damage value variable of the random load data of all axes according to the adjusted initial load value until the minimum target damage value of the target damage value variable is obtained. Among them, the initial load value includes the initial amplitude of the random load data of each axis and the initial phase difference between the random load data of every two axes.

[0094] For example, the initial load value of the random load data of all axes is the A 1i , A 2i , θ i , where i≥0. When the damage value difference D j is greater than the damage value difference threshold, the initial load value A 11 , A 21 , θ1 of the random load data of all axes is adjusted for the first time, and the target damage value variable of the random load data of all axes is re-obtained When is still greater than the damage value threshold of 0.9, adjust A 12 , A 22 , θ2 again. When is still greater than the damage value threshold of 0.9, adjust A 13 , A 23 , θ3 again until the iteration stops, and determine as the minimum target damage value, and A 13 , A 23 , θ3 as the target load value. According to A 13 , A 23 , θ3, determine the fatigue load spectrum of the target object, that is, substitute A 13 , A 23 , θ3 into L βj (t) = cosβ j A 1i sin(2πt) + sinβ j A 2i sin(2πt + θ i ) to obtain the fatigue load spectrum of the target object, as shown in Figure 8 shown.Figure 8 A schematic diagram of the comparison result between the fatigue load spectrum of a target object and the reference fatigue load spectrum of the target object is disclosed. As Figure 8 shown, it can be seen that the pseudo-damage values in 12 projection directions of the equivalent load are basically the same as the original test load, that is, the fatigue load spectrum of the target object is basically the same as the reference fatigue load spectrum of the target object, and the damage values in each projection direction have little difference, which also means that the multi-axiality of the fatigue load spectrum of the target object is basically the same as that of the reference fatigue load spectrum of the target object.

[0095] It should be noted that this optional embodiment may not be based on the above optional embodiment. That is, when it is determined that the difference in damage values is not less than or equal to the damage value difference threshold, the least squares operation may be performed on the reference damage value and the target damage value based on the determined least squares method to obtain the target damage value variable of the random load data of all axes. Then, when it is determined that the difference in damage values is less than or equal to the damage value difference threshold, the fatigue load spectrum of the target object may be determined according to the initial load values corresponding to the random load data of all axes.

[0096] It can be seen that when the difference in damage values between the damage values of the random load data of all axes and the reference damage value is large, this optional embodiment can further perform the least squares operation on the damage value and the reference damage value to obtain the target damage value variable of the random load data of all axes, perform an iterative operation on the target damage value variable, and determine the fatigue load spectrum of the target object according to the target load value corresponding to the minimum target damage value obtained by iteration, realizing the determination of the fatigue load spectrum of the target object, improving the possibility and accuracy of determining the fatigue load spectrum of the target object, and further improving the adaptability and reliability of the accelerated fatigue verification of the target object.

[0097] Embodiment 2

[0098] Please refer to Figure 2 , Figure 2 which is a schematic flowchart of another method for determining a multi-axial fatigue load spectrum disclosed in an embodiment of the present invention. As Figure 2 shown, the method can be applied to a fatigue load spectrum determination system / fatigue load spectrum determination device / fatigue load spectrum determination terminal. As Figure 2 shown, the method for determining a multi-axial fatigue load spectrum may include the following operations:

[0099] 201. Obtain the random load data of multiple axes of a target object, where the random load data is the load data obtained by actually measuring the target object, and every two axes among the multiple axes are perpendicular to each other.

[0100] 202. Determine whether the random load data of all axes satisfy the determined damage value determination conditions; when it is determined that the damage value determination conditions are not satisfied, trigger step 203; when it is determined that the damage value determination conditions are satisfied, trigger the execution of step 204.

[0101] In an embodiment of the present invention, optionally, determining whether the random load data of all axes satisfy the determined damage value determination conditions includes:

[0102] Determine whether the noise of the random load data of all axes is greater than or equal to the determined noise threshold. When it is determined that the noise is greater than or equal to the noise threshold, it is determined that the random load data of all axes do not satisfy the determined damage value determination conditions; or,

[0103] Determine whether the data format of the random load data of all axes is the determined data format. When it is determined that the data format is not the determined data format, it is determined that the random load data of all axes do not satisfy the determined damage value determination conditions.

[0104] 203. Perform preprocessing operations on the random load data of all axes until the damage value determination conditions are satisfied.

[0105] In an embodiment of the present invention, optionally, performing preprocessing operations on the random load data of all axes until the damage value determination conditions are satisfied includes:

[0106] When it is determined that the noise of the random load data of all axes is greater than or equal to the noise threshold, perform a filtering operation on the random load data of all axes until the noise of the random load data of all axes satisfies the damage value determination conditions;

[0107] When it is determined that the data format of the random load data of all axes is not the determined data format, perform an equivalent operation on the random load data of each axis based on the determined data equivalence method to obtain the equivalent random load data of each axis. Among them, the determined data equivalence method includes a sine equivalence method or a cosine equivalence method. For example, when the number of all axes is 2, the equivalent random load data can be calculated by the following formula:

[0108] L eq,1 =A 10 sin(2πt), L eq,2 =A 20 sin(2πt + θ0);

[0109] In the formula, L eq,1 is the random load data of one of the equivalent axes, L eq,2 is the random load data of the other equivalent axis, A 10 and A 20They are respectively the initial amplitudes of the random load data of one of the axes after equivalence and the initial amplitudes of the random load data of the other axis after equivalence, and θ0 is the initial phase difference between L eq,1 and L eq,2 .

[0110] It can be seen that after obtaining the random load data of multiple axes in the embodiment of the present invention, it is first determined whether the random load data meets the damage value determination condition. If it meets, the subsequent operation of determining the target damage value of the random load data is directly executed. If it does not meet, a preprocessing operation is performed on the random load data, and after the random load data meets the damage value determination condition, the subsequent operation of determining the target damage value of the random load data is executed. This can reduce the occurrence of inaccurate target damage values caused by directly obtaining the target damage value of the random load data after obtaining the random load data, improve the accuracy of obtaining the target damage value, and thus improve the determination accuracy and efficiency of the fatigue load spectrum of the target object.

[0111] 204. Determine the target damage values of the random load data of all axes.

[0112] 205. Based on the determined reference damage value and target damage value, determine the fatigue load spectrum of the target object, where the reference damage value is the damage value of a standard object of the same type as the target object.

[0113] In the embodiment of the present invention, for the relevant descriptions of steps 201, 204, and 205, please refer to the detailed descriptions of steps 101-103 in Embodiment 1, and the embodiment of the present invention will not repeat them.

[0114] It can be seen that the implementation Figure 2The described method for determining a multi-axial fatigue load spectrum takes into account the multi-axial random load data of the target object by obtaining the random load data of multiple axes of the target object, such as automotive components, and automatically determining the damage values of the random load data of multiple axes of the target object. It can automatically determine the multi-axial fatigue load spectrum of the target object based on the reference damage value and the damage values of the random load data of multiple axes of the target object, without the need for high-demand bench equipment, and can obtain an accurate and reliable fatigue load spectrum of the target object at low cost, thereby improving the reliability and applicability of the verification of the fatigue load spectrum of the target object, and further ensuring the durability and lifespan of the vehicle; and when the target object is a component, by considering the multi-axial random load data of the component to determine the fatigue load spectrum of the component, it can also reduce the number of times of determining and verifying the fatigue load spectrum at the vehicle system level and the vehicle whole vehicle level; it can also reduce the occurrence of the situation where the accurate target damage value cannot be obtained by directly obtaining the target damage value of the random load data when the random load data is obtained, improve the accuracy of obtaining the target damage value, and thus improve the accuracy and efficiency of determining the fatigue load spectrum of the target object.

[0115] Embodiment III

[0116] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a multi-axial fatigue load spectrum determination device disclosed in an embodiment of the present invention. As Figure 3 shown, the device can be applied to a fatigue load spectrum determination system / fatigue load spectrum determination device / fatigue load spectrum determination terminal. As Figure 3 shown, the multi-axial fatigue load spectrum determination device may include: an acquisition module 301, a first determination module 302, and a second determination module 303, where:

[0117] The acquisition module 301 is configured to acquire the random load data of multiple axes of the target object, and the random load data is the load data obtained by actually measuring the target object, and every two axes among the multiple axes are perpendicular to each other.

[0118] The first determination module 302 is configured to determine the target damage values of the random load data of all axes.

[0119] The second determination module 303 is configured to determine the fatigue load spectrum of the target object based on the determined reference damage value and the target damage value, and the reference damage value is the damage value of a standard object of the same type as the target object.

[0120] It can be seen that in the implementation Figure 3The described multi-axial fatigue load spectrum determination device can obtain the random load data of multiple axes of a target object, such as automotive parts, and automatically determine the damage values of the random load data of multiple axes of the target object. Considering the multi-axial random load data of the target object, it can automatically determine the multi-axial fatigue load spectrum of the target object based on the reference damage value and the damage values of the random load data of multiple axes of the target object. Without high-demand bench equipment, it can obtain accurate and reliable fatigue load spectra of target objects at low cost, thereby improving the reliability and applicability of the fatigue load spectrum verification of target objects, and further ensuring the durability and lifespan of vehicles; and when the target object is a part, by considering the multi-axial random load data of the part to determine the fatigue load spectrum of the part, it can also reduce the number of times of fatigue load spectrum determination and verification at the automotive system level and the vehicle level.

[0121] In an alternative embodiment, as Figure 3 shown, the specific way for the first determination module 302 to determine the target damage values of the random load data of all axes is as follows:

[0122] Obtain multiple projection directions of the random load data of all axes on the coordinate axes formed by all axes;

[0123] Based on each projection direction, perform a multi-axial rainflow projection operation on the random load data of all axes to obtain the rainflow random load data of all axes;

[0124] Perform a damage calculation operation on the rainflow random load data in each projection direction to obtain the damage value of each projection direction, and determine the damage values of all projection directions as the target damage values of the random load data of all axes.

[0125] It can be seen that implementing Figure 3 the described device can, after obtaining the random load data of multiple axes, further perform a multi-axial rainflow projection operation on the random load data of all axes simultaneously in the obtained multiple projection directions, consider the multi-axiality of the random load data, and calculate the damage values of the random load data after the multi-axial rainflow projection operation in each projection direction. It can not only determine the damage values of the random load data, but also improve the accuracy and comprehensiveness of the determination of the damage values of the random load data, thereby improving the accuracy, reliability, and efficiency of the determination of the fatigue load spectrum of the target object.

[0126] In another alternative embodiment, as Figure 3 shown, the specific way for the first determination module 302 to obtain multiple projection directions of the random load data of all axes on the coordinate axes formed by all axes is as follows:

[0127] When the number of all axes is 2, one of the two axes is used as the first reference axis, and different angles are selected within the determined angle range to obtain the first angle set.

[0128] Determine the directions of all angles in the first angle set, which are the multiple projection directions of the random load data of all axes on the coordinate axes formed by all axes.

[0129] It can be seen that implementing Figure 3 the described device can, after obtaining the random load data of all axes and when the number of axes is 2, by using one of the axes as the reference axis, selecting different angles within the determined angle range, and determining the projection directions at different angles as the multiple projection directions of the random load data on the coordinate axes formed by all axes, realize the determination of multiple projection directions of the random load data in the plane dimension and improve the determination accuracy and efficiency of the multiple projection directions of the random load data on the coordinate axes formed by all axes, thereby being beneficial to improving the determination accuracy and efficiency of the target damage value of the random load data.

[0130] In another alternative embodiment, as Figure 3 shown, the specific manner in which the first determination module 302 obtains the multiple projection directions of the random load data of all axes on the coordinate axes formed by all axes is as follows:

[0131] When the number of all axes is 3, one of the three axes is used as the second reference axis, and different target angles are selected within the determined angle range;

[0132] According to different target angles, within the determined angle range with the third axis that is not one of the three axes and not involved in the selection of different target angles as the third reference axis, different angles are determined to obtain the second angle set;

[0133] Determine the directions of all angles in the second angle set, which are the multiple projection directions of the random load data of all axes on the coordinate axes formed by all axes.

[0134] It can be seen that implementing Figure 3 the described device can, after obtaining the random load data of all axes and when the number of axes is 3, by selecting the angle between two of the axes and then combining with the third axis according to the selected angle, realize the selection of three-dimensional angles, and then determine the projection directions at different three-dimensional angles as the multiple projection directions of the random load data on the coordinate axes formed by all axes, realize the determination of multiple projection directions of the random load data in the three-dimensional dimension and improve the determination accuracy and efficiency of the multiple projection directions of the random load data on the coordinate axes formed by all axes, thereby being beneficial to improving the determination accuracy and efficiency of the target damage value of the random load data.

[0135] In yet another alternative embodiment, as Figure 4 shown, the second determination module 303 includes: a calculation sub-module 3031, a judgment sub-module 3032, and a determination sub-module 3033, where:

[0136] The calculation sub-module 3031 is configured to calculate the damage value difference between the determined reference damage value and the target damage value.

[0137] The judgment sub-module 3032 is configured to judge whether the damage value difference is less than or equal to the determined damage value difference threshold.

[0138] The determination sub-module 3033 is configured to, when the judgment sub-module 3032 determines that the damage value difference is less than or equal to the damage value difference threshold, determine the fatigue load spectrum of the target object according to the load initial values corresponding to the random load data of all axes, where the load initial values include the initial amplitude of the random load data of each axis and the initial phase difference between the random load data of every two axes.

[0139] It can be seen that the Figure 4 described device can, after determining the damage values of the random load data of all axes, automatically compare the damage value difference between the damage value and the reference damage value with the determined damage value difference threshold, and when it is not greater than the damage value difference threshold, determine the fatigue load spectrum of the target object based on the load initial values corresponding to the random load data. It can not only realize the determination of the fatigue load spectrum of the target object, but also reduce the occurrence of inaccurate determination of the fatigue load spectrum caused by directly determining the fatigue load spectrum of the target object according to the load initial values corresponding to the random load data.

[0140] In yet another alternative embodiment, as Figure 4 shown, the second determination module 303 may further include a processing sub-module 3034 and an iteration sub-module 3035: where:

[0141] The processing sub-module 3034 is configured to, when the judgment sub-module 3032 determines that the damage value difference is not less than or equal to the damage value difference threshold, perform a least squares operation on the reference damage value and the target damage value based on the determined least squares method to obtain the target damage value variable of the random load data of all axes.

[0142] The iteration sub-module 3035 is configured to perform an iteration operation on the target damage value variable until the minimum target damage value of the target damage value variable is obtained.

[0143] The determination sub-module 3033 is further configured to determine the fatigue load spectrum of the target object based on the target load value corresponding to the minimum target damage value, where the target load value includes the target load amplitude of the random load data of each axis and the target load phase difference between the random load data of every two axes.

[0144] It can be seen that implementing Figure 4 the described device can, when it is determined that the difference in damage values between the damage values of the random load data of all axes and the reference damage value is large, further perform a least-squares operation on the damage value and the reference damage value to obtain the target damage value variable of the random load data of all axes, perform an iterative operation on the target damage value variable, and determine the fatigue load spectrum of the target object according to the target load value corresponding to the minimum target damage value obtained by iteration, realizing the determination of the fatigue load spectrum of the target object, improving the possibility and accuracy of determining the fatigue load spectrum of the target object, and thus further improving the adaptability and reliability of the accelerated fatigue verification of the target object.

[0145] In another alternative embodiment, as Figure 4 shown, the device further includes a judgment module 304 and a preprocessing module 305: where:

[0146] The judgment module 304 is configured to judge whether the random load data of all axes meet the determined damage value determination condition after the acquisition module 301 acquires the random load data of multiple axes of the target object and before the first determination module 302 determines the target damage value of the random load data of all axes. When it is judged that the damage value determination condition is met, the first determination module 302 is triggered to perform the above operation of determining the target damage value of the random load data of all axes.

[0147] The preprocessing module 305 is configured to, when the judgment module 304 judges that the damage value determination condition is not met, perform a preprocessing operation on the random load data of all axes until the damage value determination condition is met, and trigger the first determination module 302 to perform the above operation of determining the target damage value of the random load data of all axes.

[0148] It can be seen that implementing Figure 4 the described device can, after acquiring the random load data of multiple axes, first judge whether the random load data meet the damage value determination condition. If it meets, directly perform the subsequent operation of determining the target damage value of the random load data. If it does not meet, perform a preprocessing operation on the random load data, and after the random load data meet the damage value determination condition, perform the subsequent operation of determining the target damage value of the random load data, which can reduce the occurrence of the situation where the accurate target damage value cannot be obtained by directly obtaining the target damage value of the random load data after acquiring the random load data, improve the accuracy of obtaining the target damage value, and thus improve the determination accuracy and efficiency of the fatigue load spectrum of the target object.

[0149] Embodiment 4

[0150] Please refer to Figure 5 , Figure 5This is a multi-axial fatigue load spectrum determination device disclosed in an embodiment of the present invention. As Figure 5 shown, the multi-axial fatigue load spectrum determination device may include:

[0151] A memory 501 storing executable program code;

[0152] A processor 502 coupled to the memory 501;

[0153] Further, it may further include an input interface 503 and an output interface 504 coupled to the processor 502;

[0154] Wherein, the processor 502 calls the executable program code stored in the memory 501 to execute the steps of the multi-axial fatigue load spectrum determination method described in Embodiment 1 or Embodiment 2.

[0155] Embodiment 5

[0156] The embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the multi-axial fatigue load spectrum determination method described in Embodiment 1 or Embodiment 2.

[0157] Embodiment 6

[0158] The embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps of the multi-axial fatigue load spectrum determination method described in Embodiment 1 or Embodiment 2.

[0159] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0160] Through the specific descriptions of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solutions, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, and the storage medium includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disc memories, tape memories, or any other computer-readable medium capable of carrying or storing data.

[0161] Finally, it should be noted that: What is disclosed in a multi-axis fatigue load spectrum determination method and device disclosed in the embodiments of the present invention is only the preferred embodiments of the present invention, and is only used to illustrate the technical solutions of the present invention, rather than limiting it; Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: They can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining a multi-axial fatigue load spectrum, characterized in that The method includes: Obtaining random load data of multiple axes of a target object, where the random load data is load data actually measured for the target object, and every two of the multiple axes are perpendicular to each other; Determining a target damage value of the random load data of all the axes; Based on the determined reference damage value and the target damage value, determining a fatigue load spectrum of the target object, where the reference damage value is the damage value of a standard object of the same type as the target object; Wherein, the determining the fatigue load spectrum of the target object based on the determined reference damage value and the target damage value includes: Calculating a damage value difference between the determined reference damage value and the target damage value, and determining whether the damage value difference is less than or equal to a determined damage value difference threshold; When it is determined that the damage value difference is less than or equal to the damage value difference threshold, determining the fatigue load spectrum of the target object according to the load initial values corresponding to the random load data of all the axes, where the load initial values include the initial amplitude of the random load data of each axis and the initial phase difference between the random load data of every two axes; Wherein, the load initial values are determined according to the type of the target object, the target damage value includes the true damage value of the random load data, and the reference damage value includes the true reference damage value.

2. The method for determining a multi-axial fatigue load spectrum according to claim 1, wherein The determining the target damage value of the random load data of all the axes includes: Obtaining multiple projection directions of the random load data of all the axes on the coordinate axes formed by all the axes; Based on each projection direction, performing a multi-axis rainflow projection operation on the random load data of all the axes to obtain the rainflow random load data of all the axes in each projection direction; Performing a damage calculation operation on the rainflow random load data in each projection direction to obtain the damage value of the rainflow random load data in each projection direction, and determining the damage value of the rainflow random load data in all the projection directions as the target damage value of the random load data of all the axes.

3. The method for determining a multi-axial fatigue load spectrum according to claim 2, characterized in that, The obtaining multiple projection directions of the random load data of all the axes on the coordinate axes formed by all the axes includes: When the number of all the axes is 2, taking one of the two axes as a first reference axis, and selecting different angles within a determined angle range to obtain a first angle set; Determining the directions of all the angles in the first angle set as the multiple projection directions of the random load data of all the axes on the coordinate axes formed by all the axes.

4. The method for determining a multi-axial fatigue load spectrum according to claim 2, wherein The obtaining multiple projection directions of the random load data of all the axes on the coordinate axes formed by all the axes includes: When the number of all the axes is 3, taking one of the three axes as a second reference axis, and selecting different target angles within a determined angle range; According to different target angles, determining different angles within the determined angle range with the third axis that is not involved in the selection of different target angles among the three axes as a third reference axis to obtain a second angle set; Determine the directions of all the angles in the second angle set, which are the multiple projection directions of the random load data of all the axes on the coordinate axes formed by all the axes.

5. The method for determining a multi-axial fatigue load spectrum according to any one of claims 1-4, characterized in that, The method further includes: When it is determined that the damage value difference is not less than or equal to the damage value difference threshold, perform a least squares operation on the reference damage value and the target damage value based on the determined least squares method to obtain the target damage value variable of the random load data of all the axes; Perform an iterative operation on the target damage value variable until the minimum target damage value of the target damage value variable is obtained, and determine the fatigue load spectrum of the target object based on the target load value corresponding to the minimum target damage value, where the target load value includes the target load amplitude of the random load data of each axis and the target load phase difference between the random load data of every two axes.

6. The method for determining a multiaxial fatigue load spectrum according to any one of claims 1-4, characterized in that, After obtaining the random load data of multiple axes of the target object and before determining the target damage value of the random load data of all the axes, the method includes: Judge whether the random load data of all the axes meet the determined damage value determination condition; When it is determined that the damage value determination condition is met, trigger the operation of determining the target damage value of the random load data of all the axes; When it is determined that the damage value determination condition is not met, perform a preprocessing operation on the random load data of all the axes until the damage value determination condition is met, and trigger the operation of determining the target damage value of the random load data of all the axes.

7. A multi-axial fatigue load spectrum determination device, characterized in that, The device includes, An acquisition module, configured to acquire the random load data of multiple axes of a target object, where the random load data is the load data actually measured from the target object, and every two axes among the multiple axes are perpendicular to each other; A first determination module, configured to determine the target damage value of the random load data of all the axes; A second determination module, configured to determine the fatigue load spectrum of the target object based on the determined reference damage value and the target damage value, where the reference damage value is the damage value of a standard object of the same type as the target object; Wherein, determining the fatigue load spectrum of the target object based on the determined reference damage value and the target damage value includes: Calculate the damage value difference between the determined reference damage value and the target damage value, and judge whether the damage value difference is less than or equal to the determined damage value difference threshold; When it is determined that the damage value difference is less than or equal to the damage value difference threshold, determine the fatigue load spectrum of the target object according to the load initial values corresponding to the random load data of all the axes, where the load initial values include the initial amplitude of the random load data of each axis and the initial phase difference between the random load data of every two axes; Wherein, the load initial values are determined according to the type of the target object, the target damage value includes the true damage value of the random load data, and the reference damage value includes the true reference damage value.

8. The multi-axial fatigue load spectrum determination device according to claim 7, wherein the first determination module includes: An acquisition sub-module, configured to acquire a plurality of projection directions of the random load data of all the axes on the coordinate axes formed by all the axes; A projection sub-module, configured to perform a multi-axis rainflow projection operation on the random load data of all the axes based on each of the projection directions to obtain the rainflow random load data of all the axes; A calculation sub-module, configured to perform a damage calculation operation on the rainflow random load data to obtain a damage value for each of the projection directions; A determination sub-module, configured to determine that the damage values of all the projection directions are the target damage values of the random load data of all the axes.

9. A multi-axial fatigue load spectrum determination device, characterized in that, The apparatus includes: A memory storing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the method for determining a multi-axial fatigue load spectrum according to any one of claims 1-6.

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