Rain flow counting fatigue analysis method based on sliding window algorithm

Abstract

The invention provides a rain flow counting fatigue analysis method based on a sliding window algorithm. The method comprises the following steps: acquiring time sequence load data, and selecting sub-sequence data according to a preset sliding window value; and then hysteretic loop closing judgment is carried out on the sub-sequence data through a sliding window algorithm. And if the sub-sequence data accords with the hysteresis loop closing rule, extracting the sub-sequence data as a rain flow pair, moving the sliding window according to a first sliding window strategy, and carrying out next round of hysteresis loop closing judgment. And if the sub-sequence data does not conform to the hysteretic loop closing rule, moving the sliding window according to a second sliding window strategy, and carrying out next round of hysteretic loop closing judgment. In this way, the complexity of rain flow counting can be reduced, the extraction efficiency of the rain flow pairs is improved, and then the actual requirements of complex engineering application are met.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of engineering machinery, and in particular to a rainflow counting fatigue analysis method based on a sliding window algorithm. BACKGROUND

[0002] Structural fatigue failure is a serious challenge in the engineering field. In key fields such as aerospace, automotive engineering, wind power, engineering machinery, and structural health monitoring, it is crucial to accurately assess the fatigue life of components under complex alternating loads. Rainflow counting (RFC) is a core means of extracting equivalent cycles of fatigue damage, and has become an indispensable tool for modern fatigue life prediction. However, in the face of ultra-long time series data and real-time requirements generated by modern engineering, traditional RFC methods still have serious limitations. First, the loading of ultra-long load sequences increases the memory requirements of rainflow counting, far exceeding the resource limits of embedded systems or edge devices. Second, the traditional RFC method has high computational complexity, and in continuous monitoring scenarios that require frequent updates of analysis results, computational delay becomes a performance bottleneck, hindering the possibility of dynamic response. In addition, the batch processing mode in the traditional RFC method cannot adapt to continuous input data streams, and each update requires re-computation of the entire sequence, resulting in extremely low computational efficiency and serious resource waste.

[0003] Therefore, there is an urgent need for a rainflow counting fatigue analysis method that can efficiently process ultra-long sequences, significantly reduce memory dependence, and support real-time analysis. SUMMARY

[0004] According to embodiments of the present application, a rainflow counting fatigue analysis scheme based on a sliding window algorithm is provided, which can effectively reduce the complexity of rainflow counting and significantly improve the extraction efficiency of rainflow pairs.

[0005] In a first aspect of the present application, a rainflow counting fatigue analysis method based on a sliding window algorithm is provided. The method comprises: obtaining time series load data, and selecting subsequence data according to a preset sliding window value; performing hysteresis loop closure judgment on the subsequence data by a sliding window algorithm; if the subsequence data meets the hysteresis loop closure rule, extracting the subsequence data as a rainflow pair, moving the sliding window according to a first sliding window strategy, and performing the next round of hysteresis loop closure judgment; if the subsequence data does not meet the hysteresis loop closure rule, moving the sliding window according to a second sliding window strategy, and performing the next round of hysteresis loop closure judgment.

[0006] In a possible implementation, before obtaining the time series load data, the method further comprises: Obtaining multi-working condition load sequence data, performing extreme point monitoring and screening on the multi-working condition load sequence data; Removing non-peak-valley points in the multi-working condition load sequence data to obtain time sequence load data.

[0007] In a possible implementation, the hysteresis loop closing rule comprises, The amplitude between adjacent peak-valley points of the load data in the subsequence data is simultaneously smaller than the amplitude between adjacent peak-valley points of the load data in the two adjacent subsequence data.

[0008] In a possible implementation, the first sliding window strategy comprises, Moving the sliding window backward according to a preset sliding window value to obtain next subsequence data; The next subsequence data sequentially performs data covering on the subsequence data.

[0009] In a possible implementation, the second sliding window strategy comprises, Moving the sliding window by one unit backward, and performing data covering on the subsequence data by the in-situ updating strategy.

[0010] In a possible implementation, the method further comprises, After one round of traversal of the sliding window algorithm is completed, detecting and verifying residual values in the time sequence load data that are not included in the calculation of the sliding window algorithm, and determining whether there is end data missing.

[0011] Optionally, the detecting and verifying the residual values in the time sequence load data that are not included in the calculation of the sliding window algorithm comprises, Determining whether the sum of the number of residual values and the number of rainflow pairs extracted by the sliding window algorithm is equal to the length of the time sequence load data; If the sum of the number of residual values and the number of rainflow pairs extracted by the sliding window algorithm is equal to the length of the time sequence load data, ending the traversal of the sliding window algorithm; If the sum of the number of residual values and the number of rainflow pairs extracted by the sliding window algorithm is not equal to the length of the time sequence load data, supplementing the residual values to the end of the next round of traversal of the sliding window algorithm.

[0012] In a possible implementation, the method further comprises, After the subsequence data is extracted as rainflow pairs, merging the rainflow pairs to a global list, and updating fatigue damage information in real time according to the extracted rainflow pairs.

[0013] In a possible implementation, the method further comprises, After the sliding window algorithm completes all rounds of traversal, performing closed-loop verification on the obtained global fatigue damage information according to preset fatigue damage reference data, and verifying rainflow counting reliability.

[0014] In a possible implementation, the method further includes, The sub-sequence data is dynamically managed by the in-memory update mechanism.

[0015] The rainflow counting fatigue analysis method based on the sliding window algorithm provided by the embodiments of the application realizes efficient conversion from original time domain signals to fatigue damage evaluation, while reducing frequent data operations.

[0016] It should be understood that the content described in the summary section is not intended to limit or important features of the embodiments of the application, nor to limit the scope of the application. Other features of the application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0017] The above and other features, advantages, and aspects of the embodiments of the application will become more apparent by describing in detail the following embodiments with reference to the attached drawings. In the drawings, the same or similar reference numerals refer to the same or similar elements, and: Figure 1 A flowchart of the rainflow counting fatigue analysis method based on the sliding window algorithm according to the embodiments of the application; Figure 2 A schematic diagram of a random load stress spectrum according to the embodiments of the application; Figure 3 A schematic diagram of the first sliding window strategy according to the embodiments of the application; Figure 4 A schematic diagram of the second sliding window strategy according to the embodiments of the application; Figure 5 A flowchart of the rainflow counting fatigue analysis method based on the sliding window algorithm according to the embodiments of the application. DETAILED DESCRIPTION

[0018] In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only some but not all of the embodiments of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present disclosure.

[0019] In addition, the term "and / or" in this document is only used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent three cases of A alone, A and B together, and B alone. In addition, the character " / " in this document generally represents an "or" relationship between the front and rear associated objects.

[0020] Figure 1 A flow chart of a rainflow counting fatigue analysis method based on a sliding window algorithm according to an embodiment of the present disclosure is shown. Referring to Figure 1 , the method comprises: S101, obtaining time sequence load data, and selecting subsequence data according to a preset sliding window value.

[0021] In this embodiment, the sliding window ensures that the time sequence load data in each subsequence data is valid load data, which provides a basis for subsequent rainflow calculation.

[0022] Optionally, before obtaining the time sequence load data, the method further comprises: obtaining multi-condition load sequence data, and performing extreme point monitoring and screening on the multi-condition load sequence data; removing non-peak-valley points in the multi-condition load sequence data to obtain the time sequence load data.

[0023] Figure 2 A schematic diagram of a random load stress spectrum according to an embodiment of the present application is shown as Figure 2 . Wherein, the horizontal axis is time sequence (second), and the vertical axis is the load stress amplitude (MPa) of multi-condition load. From Figure 2It can be seen that the multi-condition load sequence data presents the characteristics of random distribution. In rain flow counting, only the maximum (peak) and minimum (valley) of the multi-condition load sequence data can represent the key turning points of the load borne by the structure. These turning points determine the direction and amplitude of the change of structural stress and strain. Therefore, it is also necessary to monitor and screen the extreme points of the multi-condition load data. In this application, the local extreme value detection algorithm is used to identify the maximum (peak) and minimum (valley) in the multi-condition load data. For example, there is a section of multi-condition load sequence data x=[xi-2, xi-1,xi,xi+1,xi+2]. If xi is greater than the value of all other data points in the sequence x, that is, xi>xi-2, xi>xi-1, xi>xi, xi>xi+1, xi>xi+2, then xi is determined to be a maximum (peak) point. If xi is less than the value of all other data points in the sequence x, that is, xi <xi-2,xi<xi-1,xi<xi,xi<xi+1,xi<xi+2,则判定xi为一个极小值(谷)点。最后,在遍历整个多工况载荷数据之后,去除多工况载荷序列数据中的非峰谷值点,就可以获取预处理后的时序载荷数据,从而减少后续雨流计数中不必要的计算量,提高雨流计数效率。

[0024] In addition, the amplitude between adjacent peaks and valleys between the maximum value (peak) and the minimum value (valley) can also be calculated to provide key data for subsequent rain flow counting. The specific calculation formula is as follows: , in, is an adjacent pair of peak-valley values The amplitude between is the i-th maximum value (peak), is the i-th minimum (valley), i <N,N为时序载荷的个数。

[0025] In this embodiment, by preprocessing the multi-condition load sequence data, the data processing process in the subsequent rain flow counting process is simplified, and the basis for subsequent rain flow counting can be further provided by setting the number of peak and valley value points in the time series load data to an odd number, and setting the beginning and end to the maximum value (peak) and minimum value (valley) at the same time.

[0026] S102: Perform hysteresis loop closure judgment on the subsequence data using a sliding window algorithm.

[0027] In this embodiment, the dynamic movement characteristics of the sliding window are combined to quickly switch calculations and analyses between different subsequence data segments, which can timely capture the hysteresis loop information existing in the data, effectively improving the efficiency and accuracy of hysteresis loop closure judgment.

[0028] Optionally, the hysteresis loop closing rule comprises, The amplitude between adjacent peak and valley of the load data in the subsequence data is less than the amplitude between adjacent peak and valley of the two load data before and after.

[0029] Specifically, the determination formula of the hysteresis loop closing rule is as follows: , Wherein, is the amplitude between adjacent peak and valley of the continuous load data. For example, the sliding window algorithm loads three continuous sliding windows, the pointer points to the i th load data, and the amplitude between adjacent peak and valley of the three continuous load data starting from the i th load data can be obtained by calculation, if the hysteresis loop closing rule is met, the i th and i+1 th load data can constitute a complete rainflow pair.

[0030] In the embodiment, the rainflow pair in the time sequence load data is identified by the hysteresis loop closing determination, which lays a foundation for subsequent counting.

[0031] S103, if the subsequence data meets the hysteresis loop closing rule, the subsequence data is extracted as a rainflow pair, the sliding window is moved according to the first sliding window strategy, and the next round of hysteresis loop closing judgment is performed.

[0032] In the embodiment, when the subsequence data meets the hysteresis loop closing rule, the rainflow pair is extracted in time, which provides a basis for subsequent fatigue analysis. In addition, the sliding window is moved by the first sliding window strategy, which ensures the continuity of data processing while avoiding repeated and invalid judgment area.

[0033] Optionally, the first sliding window strategy comprises, moving the sliding window backward according to the preset sliding window value to obtain the next subsequence data; The next subsequence data sequentially covers the subsequence data.

[0034] Wherein, the preset sliding window value can be set as the number of load in the rainflow pair identified before the current load data, or can be customized.

[0035] Figure 3 The schematic diagram of the first sliding window strategy according to the embodiment of the application is shown as follows: Figure 3 ​The 15 load data in the sub-sequence data are subjected to hysteresis loop closure judgment, and the load data B (time sequence 11) to the load data C (time sequence 14) meet the hysteresis loop closure rule, that is, constitute a complete fatigue cycle. After the rain flow pairs of the load data B to C are extracted by the sliding window algorithm and the corresponding amplitude data are recorded, the sliding window needs to be moved backward for the next hysteresis loop closure judgment. In order to avoid frequent memory allocation and release, the data coverage is adopted in the embodiment of the application, that is, the load data C is covered to the load data A (time sequence 10) before the next sub-sequence data is subjected to hysteresis loop closure judgment, and the next sub-sequence data after the load data C is subjected to corresponding data coverage in turn, so that the movement of the sliding window is completed.

[0036] In the embodiment, the first sliding window strategy is adopted, so that the overhead of dynamic memory management is eliminated, and the memory operation time is reduced.

[0037] In S104, if the sub-sequence data does not meet the hysteresis loop closure rule, the sliding window is moved according to the second sliding window strategy, and the next round of hysteresis loop closure judgment is performed.

[0038] In the embodiment, when the sub-sequence data does not meet the hysteresis loop closure rule, the sliding window is moved according to the second sliding window strategy, so that the data area that does not meet the requirement is quickly skipped, and the calculation resource is not wasted in the invalid area.

[0039] Optionally, the second sliding window strategy includes, The sliding window is moved backward by one unit, and the sub-sequence data is subjected to data coverage by the in-situ updating strategy.

[0040] Figure 4 The schematic diagram of the second sliding window strategy according to the embodiment of the application is shown in FIG. 3. Figure 4 As shown in FIG. 3, The load data DEF in the sliding window does not meet the hysteresis loop closure rule, and the next sub-sequence data is obtained by moving backward by one unit. The sub-sequence data is subjected to data coverage by the in-situ updating strategy, that is, the load data EFG is moved to the load data DEF in turn for corresponding data coverage, so that the movement of the sliding window is completed.

[0041] In the embodiment, the second sliding window strategy is adopted, so that the memory rearrangement overhead caused by the traditional deletion operation is avoided while the continuity of the load data is ensured.

[0042] In a possible implementation manner, the method further includes, When the sliding window algorithm completes a round of traversal, the residual values in the time sequence load data that are not included in the sliding window algorithm are detected and verified to determine whether there is missing end data.

[0043] In this embodiment, by detecting and verifying the residual values in the time sequence load data that are not included in the sliding window algorithm, the possible missing key data is found in time, and a basis is provided for subsequent data verification.

[0044] Optionally, the detecting and verifying the residual values in the time sequence load data that are not included in the sliding window algorithm comprises, determining whether the sum of the number of residual values and the number of rainflow pairs extracted by the sliding window algorithm is equal to the length of the time sequence load data; if the sum of the number of residual values and the number of rainflow pairs extracted by the sliding window algorithm is equal to the length of the time sequence load data, ending the traversal of the sliding window algorithm; if the sum of the number of residual values and the number of rainflow pairs extracted by the sliding window algorithm is not equal to the length of the time sequence load data, supplementing the residual values to the end of the next round of traversal of the sliding window algorithm.

[0045] For example, the time sequence load data contains 50 load data points, the sliding window size is set to 8, and after one round of sliding window algorithm traversal, 16 rainflow pairs are successfully extracted, that is, 48 load data points are extracted as rainflow pairs. At this time, there are 3 residual values that are not included in the calculation, and the sum of the number of residual values and the number of rainflow pairs extracted by the sliding window algorithm is not equal to the length of the time sequence load data. Therefore, the 3 residual values that are not included in the calculation need to be supplemented to the end of the next round of traversal of the sliding window algorithm to realize complete traversal of the load data. Compared with the scheme of deleting the load data points that have been counted one by one after identifying the cycle in the traditional rainflow counting method, the present application uses the data covering method to fill the untraversed load data points to the subsequent position of the time sequence load history, covers the invalid data, and shortens the data length of each traversal.

[0046] In this embodiment, through the rigorous detection and verification mechanism, the completeness and accuracy of the load data analysis are ensured, and the situation that the rainflow counting fatigue analysis result deviates due to partial data not being processed is avoided.

[0047] Optionally, the method further comprises, After the subsequence data is extracted as rainflow pairs, the rainflow pairs are merged into a global list, and the fatigue damage information is updated in real time according to the extracted rainflow pairs.

[0048] In the embodiment, the subsequence data is extracted and combined into a global list after rainflow counting, and the fatigue damage information is updated in real time, so that the load data can be efficiently integrated, the fatigue state can be dynamically reflected, and the efficiency of fatigue analysis is improved.

[0049] In a possible implementation, the method further includes, When the sliding window algorithm completes the entire cycle traversal, the obtained global fatigue damage information is closed-loop verified according to preset fatigue damage reference data, and the rainflow counting reliability is verified.

[0050] Table 1 shown below is a load data error example table, where element is the load data number, Damage is the cumulative fatigue damage value of the load data point after rainflow counting, Life is the fatigue life predicted according to the cycle number of the load data point and the Damage value, nCode is the fatigue damage value (i.e., fatigue damage reference data) calculated by a fatigue analysis software (such as nCode DesignLife), and error is the error value between Life and nCode.

[0051] Table 1 element load data number Damage accumulated fatigue damage value Life predicted fatigue life nCode calculated fatigue damage value error (%) error value 4719 0.957432 1.04446 0.9573 -0. 013788781 4666 0. 0601548 16.6238 0.06015 -0.00798005 4785 0. 0127267 78.5752 0. 01273 0. 025923016 4621 4.72E-05 21186. 7 0.0000472 0.001059322 4853 2. 91E-05 34382 0.00002909 0. 017188037 15105 7. 41E-07 1.35E+06 7. 406E-07 -0.000675128 4668 3. 00E-08 3.33E+07 3. 001E-08 -0.004665112 15108 2.39E-08 4.18E+07 2.394E-08 -0.010442774 4720 2.39E-08 4.18E+07 2.392E-08 -0.012541806 4733 7. 16E-09 1.40E+08 7. 155E-09 -0.001537386 15115 6.94E-09 1.44E+08 6.944E-09 0. 004176267 4815 4.47E-09 2.24E+08 4.466E-09 -0.009404389 4623 3.49E-09 2.87E+08 3.489E-09 -0.006305532 4786 2.03E-09 4.92E+08 2.03E-09 -0. 024137931 ... ... ... ... ... In the embodiment, the error value between the predicted fatigue life and the preset fatigue damage reference data is obtained through comparison and verification, so that the rainflow counting reliability is ensured.

[0052] Optionally, the method further includes, The subsequence data is dynamically managed by a memory in-place update mechanism.

[0053] First, the application adopts a hybrid layout combining columnar storage and cache line accurate alignment, and stores the amplitude, mean value and other key parameters of the load data in independent continuous memory areas. Moreover, the array starting address of each subsequence data is set according to an integer multiple of the processor cache line size (such as 64 bytes under x86 architecture), so as to improve the cache hit rate and avoid the performance loss caused by cross-cache line access.

[0054] Second, the memory in-place update mechanism pre-allocates a fixed-size ring buffer by pre-analyzing the characteristics of the load data, and realizes data in-place update by using a pointer sliding window counting. When the hysteresis loop is closed, instead of deleting data in the traditional method, the write pointer is moved, so that the new data directly covers the processed area, eliminating the overhead of dynamic memory management and reducing the memory operation time.

[0055] Finally, optimizations can be performed for the SIMD (Single Instruction Multiple Data) instruction set to ensure 128 / 256-bit alignment of key data structures, allowing the CPU's AVX / AVX2 (Advanced Vector Extensions) architecture to fully leverage its capabilities. Furthermore, data partitioning is performed on the payload data, dividing the sequence into multiple independently processable blocks. Each block is equipped with an independent buffer and state machine, enabling the sliding window algorithm to be ported to multi-core processors or GPU architectures for parallel acceleration.

[0056] In this embodiment, the cache hit rate is improved by precisely aligning columnar storage with cache lines, and the "memory in-place update" mechanism is adopted in the hysteresis loop processing algorithm to eliminate dynamic memory management overhead and improve system performance.

[0057] Figure 5 FIG. 1 is a flow chart of a method for fatigue analysis of rain flow counting based on a sliding window algorithm according to an embodiment of the present application, as shown in FIG. Figure 5 As shown: First, the time series load data is obtained, and the subsequence data is selected according to the pre-set sliding window. The hysteresis loop judgment is performed on the subsequence data in combination with the sliding window algorithm. If the current subsequence data meets the hysteresis loop closure rule, the current subsequence data is extracted as a rain flow pair, and the load data that meets the hysteresis loop closure rule, that is, its amplitude and other data, are saved in the fatigue damage global list. Then, the sliding window is moved according to the first sliding window strategy to perform the next round of hysteresis loop closure judgment. If the current subsequence data does not meet the hysteresis loop closure rule, the sliding window is moved according to the second sliding window strategy to perform the next round of hysteresis loop closure judgment. Then, after completing one process traversal, determine whether the sum of the number of residual values ​​and the number of rain flow pairs extracted by the sliding window algorithm is equal to the length of the time series load data. If the sum of the number of residual values ​​and the number of rain flow pairs extracted by the sliding window algorithm is equal to the length of the time series load data, end the traversal of the sliding window algorithm. If the sum of the number of residual values ​​and the number of rain flow pairs extracted by the sliding window algorithm is not equal to the length of the time series load data, add the residual value to the end of the next round of process traversal of the sliding window algorithm, and continue to participate in the next process traversal until the sum of the number of residual values ​​and the number of rain flow pairs extracted by the sliding window algorithm is equal to the length of the time series load data. End the traversal of the sliding window algorithm and complete the rain flow counting fatigue analysis of the entire time series load data.

[0058] According to the embodiments of the present disclosure, the following technical effects are achieved: 1) Combining the sliding window algorithm with the hysteresis loop closure determination in the rainflow counting method reduces the computational complexity and improves the efficiency of rainflow pair extraction; 2) Using data overlay instead of traditional dynamic memory allocation, reducing memory operation time and improving the processing speed of rainflow calculation; 3) Through residual closed loop verification, the integrity of the load sequence data is verified, the end missing problem of the traditional algorithm is solved, and the reliability of the fatigue analysis result in engineering application is ensured.

[0059] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the action sequence described, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0060] The above is the introduction of the method embodiment, and the scheme described in the present application will be further described through the device embodiment.

[0061] The flowcharts and block diagrams in the drawings illustrate the possible implementation architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the foregoing module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different order than that noted in the drawings. For example, two blocks indicated in succession can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the function involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0062] The units or modules involved in the embodiments described in the present application can be implemented in software or in hardware. The described units or modules can also be arranged in a processor. In some cases, the names of these units or modules do not constitute a limitation on the units or modules themselves.

[0063] The above description is only the preferred embodiment of the present application and the explanation of the technical principles. It should be understood by those skilled in the art that the application scope of the present application is not limited to the technical solutions with the specific combination of the above technical features, and should also cover other technical solutions formed by combining the above technical features or their equivalent features without departing from the concept of the application. For example, the technical solutions formed by replacing the above features with the technical features with similar functions disclosed in the present application (but not limited to) with each other.

Claims

1. A rainflow counting fatigue analysis method based on a sliding window algorithm, characterized in that: include: Obtain time series load data and select subsequence data based on the preset sliding window value; Performing hysteresis loop closure judgment on the subsequence data by using a sliding window algorithm; If the subsequence data meets the hysteresis loop closure rule, extracting the subsequence data as a rainflow pair, moving the sliding window according to the first sliding window strategy, and performing the next round of hysteresis loop closure judgment; If the subsequence data does not comply with the hysteresis loop closure rule, the sliding window is moved according to a second sliding window strategy to perform the next round of hysteresis loop closure judgment.

2. The rainflow counting fatigue analysis method based on sliding window algorithm according to claim 1 is characterized in that: Before acquiring the time series load data, the method further includes: Acquiring multi-condition load sequence data, and performing extreme point monitoring and screening on the multi-condition load sequence data; The non-peak and valley points in the multi-operating condition load sequence data are removed to obtain the time series load data.

3. The rainflow counting fatigue analysis method based on sliding window algorithm according to claim 1 is characterized in that: The hysteresis loop closure rules include: The amplitude between adjacent peaks and valleys of the load data in the subsequence data is simultaneously smaller than the amplitude between adjacent peaks and valleys of the two preceding and following load data.

4. The rainflow counting fatigue analysis method based on sliding window algorithm according to claim 1 is characterized in that: The first sliding window strategy includes: Moving the sliding window backward according to the preset sliding window value to obtain the next subsequence data; The next subsequence data sequentially overwrites the subsequence data.

5. The rainflow counting fatigue analysis method based on sliding window algorithm according to claim 1 is characterized in that: The second sliding window strategy includes: The sliding window moves backward by one unit, and the subsequence data is overwritten by an in-situ update strategy.

6. The method for rainflow counting fatigue analysis based on sliding window algorithm according to claim 1 is characterized in that: The method further comprises, After the sliding window algorithm completes one process traversal, the residual values ​​in the time series load data that are not included in the calculation of the sliding window algorithm are detected and verified to determine whether there is any end data omission.

7. The rainflow counting fatigue analysis method based on sliding window algorithm according to claim 6 is characterized in that: The detecting and verifying of the residual value in the time series load data that is not included in the calculation of the sliding window algorithm includes: Determining whether the sum of the number of residual values ​​and the number of rainflow pairs extracted by the sliding window algorithm is equal to the length of the time series payload data; If the sum of the number of residual values ​​and the number of rainflow pairs extracted by the sliding window algorithm is equal to the length of the time series payload data, the traversal of the sliding window algorithm is terminated; If the sum of the number of residual values ​​and the number of rain flow pairs extracted by the sliding window algorithm is not equal to the length of the time series load data, the residual value is added to the end of the next round of traversal of the sliding window algorithm.

8. The rainflow counting fatigue analysis method based on sliding window algorithm according to claim 6 is characterized in that: The method further comprises, After extracting the subsequence data into the rainflow pairs, the rainflow pairs are merged into a global list, and fatigue damage information is updated in real time according to the extracted rainflow pairs.

9. The method for rainflow counting fatigue analysis based on sliding window algorithm according to claim 6, characterized in that: The method further comprises, After the sliding window algorithm completes all process traversals, a closed-loop check is performed on the obtained global fatigue damage information according to the preset fatigue damage reference data to verify the reliability of the rainflow counting.

10. The rainflow counting fatigue analysis method based on sliding window algorithm according to claim 6 is characterized in that: The method further comprises, Dynamic memory management is performed on the subsequence data through a memory in-place update mechanism.

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