Hot end disc life test spectrum compilation method based on actual measurement spectrum

Abstract

The invention discloses a hot end disc life test spectrum compilation method based on an actual measurement spectrum. The method comprises the following specific steps: firstly, carrying out peak valley value identification and small load removal on all rotating speed and temperature spectrums of an engine; secondly, the rotating speed peak value and temperature peak value data and the rotating speed valley value and temperature valley value data are matched, and the correlation between the temperature and the rotating speed in the engine actual measurement spectrum is fitted through a least square method and a one-dimensional linear function; the peak value and the valley value of the rotating speed spectrum and the load holding time of the peak value and the valley value are extracted and counted, and the counted rotating speed extreme value is clustered so as to compile an eight-level life test spectrum. And finally, based on the counted rotating speed spectrum grade, comprehensively considering the correlation between the temperature and the rotating speed in the operation process of the hot end disc, and fully considering the fatigue and creep damage of the hot end disc and the material attribute change caused by the temperature. And a hot end disc multi-parameter life test spectrum containing temperature and rotating speed is compiled.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft engine load spectrum compilation, in particular to a method for compiling a hot end disk life test spectrum based on a measured spectrum. Background Art

[0002] The aircraft engine load spectrum serves as the load basis for engine structural life studies and provides a basis for decision-making in aircraft engine design. To assess the reliability and durability of aircraft engines and determine their service life, a life-cycle test must be conducted before finalization. During the aircraft engine's service life, further work on determining and extending the aircraft engine's service life is conducted in conjunction with the field service load spectrum. As can be seen, a series of test runs are required throughout the life of an aircraft engine. Therefore, it is particularly important to compile experimental spectra based on measured spectra that closely resemble the damage modes and service life of various aircraft engine components.

[0003] With the continuous improvement of engine performance, the turbine inlet temperature is also increasing. The turbine disc (hot end disc) operates at high speed for a long time in an extreme environment of high temperature and high pressure, and is subjected to complex mechanical and thermal loads. A destructive failure can lead to serious consequences. At present, the life assessment of the hot end disc in China is based on the life test evaluation spectrum determined in accordance with the industry standard "Low Cycle Fatigue Test Method for Aviation Gas Turbine Engine Discs" (HB20362-2016). The temperature state selected for the experiment is the highest temperature of the hot end disc, and the load cycle is usually the engine 0-maximum-0 cycle. However, due to the large number of other types of load cycles in the actual working load spectrum of the hot end disc, and the fact that temperature changes can cause changes in the thermal stress and material mechanical properties of the hot end disc, the original life test evaluation method has a poor effect on the damage mode comparison of the hot end disc.

[0004] To predict the life of a hot-end disk, it's necessary to compile a life test spectrum based on the measured spectrum that better reflects the damage patterns of various engine components. Since speed and temperature are the two parameters that most significantly impact the hot-end disk, it's necessary to compile a hot-end disk life test spectrum based on the temperature and speed data in the engine's measured spectrum. Summary of the Invention

[0005] In order to solve the problem of insufficient assessment of the influence of temperature on the life of the hot end disk in the existing hot end disk life test spectrum compilation process, the present invention proposes a hot end disk life test spectrum compilation method based on the measured spectrum.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0007] The present invention is a method for compiling a hot end disk life test spectrum based on a measured spectrum, comprising the following steps:

[0008] Step 1: Extracting the temperature extreme value and speed extreme value data based on the engine's measured temperature spectrum and speed spectrum, that is, obtaining the peak-valley values ​​of the engine's measured speed spectrum and the peak-valley values ​​of the engine's measured temperature spectrum;

[0009] Step 2: Match the peak-valley values ​​of the engine's measured speed spectrum with the peak-valley values ​​of the engine's measured temperature spectrum, and use the least squares method to fit the matched peak-valley values ​​of the engine's measured speed spectrum and the engine's measured temperature spectrum to obtain the correlation between the speed and temperature;

[0010] Step 3: Perform cluster analysis on the rising and falling sections of the speed cycle in the engine's measured speed spectrum to determine the load level in the compiled life test spectrum;

[0011] Step 4: Identify the load-holding segment in the engine's measured speed spectrum, and calculate the duration and load information of the load-holding segment;

[0012] Step 5: Prepare a speed block spectrum based on the load level determined according to the load cycle in step 3 and the holding load size and duration determined in step 4;

[0013] Step 6: Based on the effect of temperature on the life damage of the hot end disk, the correlation between speed and temperature, and the speed program block spectrum compiled in step 5, compile a hot end disk life test spectrum based on the measured spectrum.

[0014] A further improvement of the present invention is that the step 1 extracts the temperature extreme value and speed extreme value data based on the engine's measured temperature spectrum and speed spectrum, specifically including:

[0015] Step 1.1: Determine the speed and temperature deletion thresholds based on the damage and the trend of the distribution of the measured engine speed spectrum and the measured engine temperature spectrum as the deletion threshold changes;

[0016] Step 1.2: Perform a small cycle deletion on the engine speed spectrum and the engine temperature spectrum using the deletion threshold determined in step 1.1;

[0017] Step 1.3: Use the rain flow counting algorithm to perform cycle counting on the engine speed spectrum and the engine temperature spectrum, and determine the peak and valley point data of the engine speed spectrum and the engine temperature spectrum based on the cycle counting results.

[0018] A further improvement of the present invention is that the step 1.1 specifically includes the following operations:

[0019] Step 1.11, converting the engine temperature spectrum and the engine speed spectrum into percentage values;

[0020] Step 1.12, binning and counting the peak and valley values ​​of the engine speed spectrum and the engine temperature spectrum, which are percentage values;

[0021] Step 1.13: Based on the bin counting results, analyze the statistical data of the bin segments with high bin counts as the deletion threshold changes to determine the optimal deletion thresholds for the rotation speed and temperature.

[0022] A further improvement of the present invention is that the specific operations of step 1.2 include:

[0023] Step 1.21, extracting peak and valley values ​​from the measured engine speed spectrum and the measured engine temperature spectrum to obtain data including corresponding speed extreme points and temperature extreme points, wherein the corresponding speed extreme points include speed peak extreme points and speed valley extreme points, and the corresponding temperature extreme points include temperature peak extreme points and temperature valley extreme points; Step 1.22, determining the initial speed extreme point and the initial temperature extreme point, and judging the speed extreme points subsequent to the initial speed extreme point and the temperature extreme points subsequent to the initial temperature extreme point;

[0024] Step 1.23: If the absolute value of the difference between the determined speed extreme point and / or temperature extreme point and the initial speed extreme point and / or initial temperature extreme point is greater than the corresponding deletion threshold, then record the speed extreme point and / or temperature extreme point and use it as the initial speed extreme point and / or initial temperature extreme point for determining subsequent speed extreme points and / or temperature extreme points.

[0025] Step 1.24, if the absolute value of the difference between the judged speed extreme point and / or temperature extreme point and the initial speed extreme point and / or initial temperature extreme point is less than the corresponding deletion threshold, the judged speed extreme point and / or temperature extreme point is discarded, and the speed extreme points and / or temperature extreme points subsequent to the discarded speed extreme point and / or temperature extreme point are continued to be judged.

[0026] A further improvement of the present invention is that the specific operation of matching the peak-valley values ​​of the engine's measured speed spectrum with the peak-valley values ​​of the engine's measured temperature spectrum in step 2 includes: comparing the number of speed extreme points corresponding to the peak-valley values ​​of the engine's measured speed spectrum after filtering using the rain flow filtering algorithm with the number of temperature extreme points of the peak-valley values ​​of the engine's measured temperature spectrum, and searching for and matching the peak-valley values ​​of the engine's measured speed spectrum or the peak-valley values ​​of the engine's measured temperature spectrum with a smaller number of points using the peak-valley values ​​of the engine's measured speed spectrum or the peak-valley values ​​of the engine's measured temperature spectrum with a larger number of points.

[0027] A further improvement of the present invention is that the specific operations of step 3 include:

[0028] Step 3.1: Identify and count the rising and falling cycles in the speed cycle within the engine's measured speed spectrum;

[0029] Step 3.2: Perform dimensionality reduction on the measured engine speed spectrum, including converting the speed cycle into a stress cycle and performing dimensionality reduction.

[0030] Step 3.3: Based on the dimensionality reduction results, perform cluster analysis on the rising and falling cycles, and determine the frequency of each load level based on the cluster analysis results.

[0031] A further improvement of the present invention is that the specific operations of step 4 include:

[0032] Identify the speed holding section of the engine's measured speed spectrum. The identification operation includes setting a threshold and searching for the points before and after each speed extreme point based on the threshold.

[0033] Determine the speed values ​​in the engine's measured speed spectrum from the speed extreme point forward, find a point n where the absolute value of the speed difference between point n and the extreme point is greater than a threshold, and record the time of point n+1;

[0034] From the extreme speed point, judge the speed value in the engine's measured speed spectrum backward, find a point m, where the absolute value of the speed difference between point m and the extreme speed point is greater than the threshold, and record the time of point m-1;

[0035] Calculate the time difference between point m-1 and point n+1. If it is greater than the set time N, it is considered that the material will suffer creep damage at this time, and record it as the speed holding section. Record the speed holding size and speed holding time.

[0036] A further improvement of the present invention is that the specific operation of compiling the speed program block spectrum in step 5 includes: setting a 0-maximum-0 cycle according to the speed spectrum number used for data analysis, and according to the load level in step 3 and the total speed holding time under each load level in step 4, with the 0-maximum-0 cycle as the boundary, inserting a load increase cycle before 0-maximum-0, and inserting a load decrease cycle after 0-maximum-0 to complete the speed program block spectrum compilation work.

[0037] A further improvement of the present invention is that the specific operations of step 6 include: determining the temperature cycle corresponding to each speed cycle based on the correlation between speed and temperature, and determining the stress distribution in the hot end disk of the turbine at various ambient temperatures through finite element analysis; based on the magnitude of the thermal stress at the dangerous point in the hot end disk and according to the Miner linear damage accumulation principle, the thermal cycle is equivalent to a speed cycle, and the temperature in each speed cycle is replaced by a constant temperature, and a hot end disk life test spectrum is compiled based on the speed program block spectrum.

[0038] The temperature spectrum and speed spectrum, which have a greater impact on the life of the engine hot end disk, were selected as spectrum compilation parameters. The correlation between temperature and speed was obtained. Based on the clustering results of the speed spectrum and the correlation between temperature and speed, a multi-parameter life test spectrum of the hot end disk including temperature and speed was compiled, which is beneficial to the life prediction of the engine hot end disk. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of a method for compiling a hot end disc life test spectrum in an embodiment of the present invention;

[0040] Figure 2a A histogram of the rotational speed peak value obtained by counting the rotational speed spectrum bins in an embodiment of the present invention;

[0041] Figure 2b A histogram of rotation speed valley values ​​obtained by counting the rotation speed spectrum bins in an embodiment of the present invention;

[0042] Figure 2c A temperature peak histogram obtained by counting the temperature spectrum bins in an embodiment of the present invention;

[0043] Figure 2d A temperature valley histogram obtained by counting the temperature spectrum bins in an embodiment of the present invention;

[0044] Figure 3 This is a graph showing how the maximum number of boxes at the peak and valley values ​​of the rotational speed changes with the deletion threshold in an embodiment of the present invention;

[0045] Figure 4 Graph showing the maximum number of boxes in the temperature spectrum peak and valley values ​​changing with the deletion threshold in an embodiment of the present invention;

[0046] Figure 5 This is a graph showing the correlation between temperature and peak speed in an embodiment of the present invention;

[0047] Figure 6 Graph showing the correlation between temperature and rotation speed valley value in an embodiment of the present invention;

[0048] Figure 7 Schematic diagram of load raising and lowering cycles in an embodiment of the present invention;

[0049] Figure 8 A schematic diagram of the classification of lateral load levels during a load-raising cycle in an embodiment of the present invention;

[0050] Figures 9a to 9f Schematic diagram of load increase cycle clustering results in an embodiment of the present invention;

[0051] Figure 10 Schematic diagram of the classification of lateral load levels in a load drop cycle in an embodiment of the present invention;

[0052] Figures 11a to 11fThis is a load drop cycle clustering result diagram in an embodiment of the present invention;

[0053] Figure 12 Schematic diagram of load spectrum dimensionality reduction considering stress sensitivity in an embodiment of the present invention;

[0054] Figure 13 RIAE diagram of the measured spectrum in the embodiment of the present invention;

[0055] Figure 14 is the frequency of each lateral load level in the load rising cycle in the embodiment of the present invention;

[0056] Figure 15 is the frequency of each lateral load level in the load drop cycle in the embodiment of the present invention;

[0057] Figure 16 This is the speed block spectrum in the embodiment of the present invention;

[0058] Figure 17 This is a test spectrum of the hot end disk life considering the temperature effect in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0060] This embodiment provides a method for compiling a hot end disk life test spectrum based on a measured spectrum. The hot end disk life test spectrum is compiled using the temperature and speed data from 37 measured spectra of a certain type of engine. The engine measured spectrum includes the engine measured temperature spectrum and speed spectrum, such as Figure 1 As shown, the following steps are included:

[0061] Step 1: Convert the engine measured temperature spectrum into a percentage, count the peak and valley values ​​of the engine measured speed spectrum and the engine measured temperature spectrum in bins, perform small load deletion with different deletion thresholds, and analyze the changes of the bin counting results with the deletion threshold to determine the optimal deletion threshold of the engine measured speed spectrum and the engine measured temperature spectrum. Perform small cycle deletion on the engine measured speed spectrum and the engine measured temperature spectrum according to the optimal deletion threshold of the engine measured speed spectrum and the engine measured temperature spectrum. Use the rain flow filtering algorithm to filter and count the peak and valley values ​​of the engine measured speed spectrum and the engine measured temperature spectrum after small cycle deletion. The statistical results include the peak and valley values ​​of the engine measured speed spectrum and the engine measured temperature spectrum and their corresponding time.

[0062] The measured engine speed spectrum data is initially in percentage form, so it doesn't need to be percented. When converting the measured engine temperature spectrum data to percentages, the analyzed engine temperature spectrum is statistically analyzed to determine the maximum temperature. The temperature data is then converted to a ratio relative to the maximum temperature value, completing the percentile conversion.

[0063] 1) In this embodiment, the bin width is 1, and the peak and valley values ​​of the engine speed spectrum and the engine temperature spectrum are counted in bins. The statistical speed and temperature distribution is as follows: Figures 2a to 2d As shown in the figure, the results of the bin counting show that the extreme values ​​of the speed peak data mostly appear in the 91% to 92% bin segment, the extreme values ​​of the speed valley data mostly appear in the 87% to 88% bin segment, and the extreme values ​​of the temperature peak data mostly appear in the 79% to 80% bin segment, and the extreme values ​​of the temperature valley data mostly appear in the 76% to 77% bin segment. Therefore, the statistical data of these bin segments are analyzed as the deletion threshold changes. Figure 3 , Figure 4 As shown in the figure, it can be seen that when the deletion threshold is greater than 1%, the statistical values ​​of the rotation speed and temperature under the bin counting tend to be stable, so the deletion threshold is set to 1% of the maximum value.

[0064] The specific operations of small loop deletion include:

[0065] a) Extracting and statistically analyzing peak and valley data of the engine's measured speed spectrum and temperature spectrum;

[0066] b) Using the initial extreme point as the starting point, judge the extreme points that appear later than the initial extreme point;

[0067] c) If the absolute value of the difference between the extreme point being judged and the initial point is greater than the deletion threshold, the extreme point is recorded and used as the initial point to judge subsequent extreme points;

[0068] d) If the absolute value of the difference between the determined extreme point and the initial point is less than the deletion threshold, the point is discarded and the determination of the subsequent extreme points continues.

[0069] Step 2: Match the peak-valley values ​​of the measured engine speed spectrum with the peak-valley values ​​of the measured engine temperature spectrum. Least squares fitting is performed based on the matched peak-valley values ​​of the measured engine speed spectrum and the measured engine temperature spectrum to obtain a correlation between speed and temperature. Specifically, the number of speed extreme points corresponding to the peak-valley values ​​of the measured engine speed spectrum obtained in Step 1 is compared with the number of temperature extreme points of the peak-valley values ​​of the measured engine temperature spectrum. The peak-valley values ​​of the measured engine speed spectrum or the measured temperature spectrum with fewer points are used to search for and match the peak-valley values ​​of the measured engine speed spectrum or the measured temperature spectrum with more points. In this embodiment, it is assumed that after the small loop deletion operation, the number of extreme points in the measured engine speed spectrum is less than the number of extreme points in the temperature spectrum. Speed ​​and temperature data matching is performed using the following algorithm: Starting with the first temperature extreme point, it is compared with the current speed extreme point. If the temperature extreme point time is less than the speed extreme point time, the relationship between the next temperature extreme time and the speed extreme time is compared.

[0070] If the current engine temperature spectrum has an extreme point A2 at time A t2 Greater than the speed extreme point B time B t , and the time of the extreme point in the previous engine temperature spectrum is less than the time A1 of the speed extreme point t1 , then compare A t2 -B t With B t -A t1 If A t2 -B t Greater than B t -A t1 , then record point A2 because it is closer to point B. t2 -B t Less than B t -A t1 , then record point A1.

[0071] Convert the percentage temperature into actual temperature, and plot the relationship between temperature and speed with temperature as the horizontal axis and speed as the vertical axis. It is found that the relationship between temperature and speed is basically a linear relationship, and the least squares method is used to fit it. The fitting results are as follows: Figure 5 and Figure 6 shown.

[0072] Step 3: Perform cluster analysis on the rising and falling segments of the speed cycle in the measured engine speed spectrum and determine the load level.

[0073] The main cycle and sub-cycle in each engine speed spectrum are divided. Taking the 30th engine speed spectrum as an example, the specific division method is as follows: Figure 7Through the above operations, all secondary cycles are divided into 780 load-increasing sub-cycles and 835 load-decreasing sub-cycles;

[0074] The cluster analysis process includes:

[0075] First, a cluster analysis was performed on the 780 load-raising cycles and they were divided into 12 levels, such as Figure 8 shown.

[0076] The clustering results of all load-raising cycles are as follows: Figure 9a As shown, each cluster is represented by a different color, where the red mark is the cluster center of each cluster. Calculate the boundary density threshold of each cluster and determine the noise result as follows Figure 9b As shown, the core sample points are as follows Figure 9c shown.

[0077] Since the number of core sample points of some clusters is greater than 40 (the number of main loops), it is necessary to merge the basic clusters to reduce the number of clusters and ensure the minimum number of samples in each cluster. The merging results are as follows: Figures 9c to 9f After merging, there are 6 clusters in total, that is, there are 6 lateral load levels in the load increase cycle.

[0078] The analysis process of the load drop cycle is basically similar. The critical load level division diagram is shown in the following figure: Figure 10 As shown, there are 12 basic clusters in total.

[0079] The clustering results are shown in Figure 11. After the basic clusters are merged, there are three clusters, that is, there are six lateral load levels in the load drop cycle.

[0080] Load spectrum dimensionality reduction:

[0081] The loads in the load-raising and load-lowering cycles are all two-dimensional loads. To facilitate the subsequent determination of the load frequency, it is necessary to reduce the dimension to a one-dimensional load. Based on the load-bearing characteristics of the engine's rotating parts, assuming that the centrifugal stress is generally proportional to the square of the engine's speed, the relationship between the stress and speed of the engine's test part is assumed to be:

[0082]

[0083] Where σ mv is the prestress of the wheel structure in the non-working state, which is assumed to be 0 in this paper; n max 、n min are the maximum and minimum speeds of the engine in a certain cycle, σ max , σ min are the maximum and minimum stresses at the corresponding speeds, a is the coefficient of relationship between speed and stress, and n is the speed value.

[0084] Under the analyzed speed cycle, the stress mean σ of the test part m , stress amplitude σ a for:

[0085]

[0086] pass Figure 12 The load spectrum dimension reduction is performed using the method of

[0087] Load frequency determination:

[0088] Through genetic algorithm, Figure 13 The RIAE equation in the optimization solution is as follows: Figure 14 、 Figure 15 shown.

[0089] Step 4: Identify the load-holding segment in the measured engine speed spectrum, and calculate the duration and load size information of the load-holding segment.

[0090] Based on creep damage theory, it is believed that when a load is maintained for more than 100 seconds, the creep damage rate will reach a stable value. Therefore, the speeds and holding times in the measured load spectrum are identified. When the holding time exceeds 100 seconds, it is identified as a creep segment, and the speed level and holding time of this segment are recorded.

[0091] Step 5: Prepare a speed block spectrum based on the load level determined according to the load cycle in step 3 and the holding load size and duration determined in step 4.

[0092] Take the 35 main cycles as the highest load level, and then insert the hysteresis loop positions of each lateral load level in the load increase cycle into the front of the main cycle in the order of the longitudinal load level from small to large. Then, according to the hysteresis loop positions of each lateral load level in the load decrease cycle, insert the hysteresis loop positions into the back of the main cycle in the order of the longitudinal load level from small to large. The speed program block spectrum is obtained as follows: Figure 16 shown.

[0093] Step 6: Based on the effect of temperature on the life damage of the hot end disk, the correlation between speed and temperature, and the speed program block spectrum compiled in step 5, compile a hot end disk life test spectrum based on the measured spectrum.

[0094] According to the correlation between speed and temperature, the temperature cycle corresponding to each speed cycle is determined, and the stress distribution in the hot end disk of the turbine at various ambient temperatures is determined by finite element method. According to the thermal stress size at the dangerous point in the hot end disk and Miner's linear damage accumulation principle, the thermal cycle is equivalent to the speed cycle, and the temperature in each speed cycle is replaced by a constant temperature, such as Figure 17 As shown in the figure, the hot end disc life test spectrum is compiled based on the speed program block spectrum.

[0095] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined similarly as herein, will not be interpreted in an idealized or overly formal sense.

[0096] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for compiling a hot end disk life test spectrum based on a measured spectrum, characterized by: The steps include: Step 1: Extracting the temperature extreme value and speed extreme value data based on the engine's measured temperature spectrum and speed spectrum, that is, obtaining the peak-valley values ​​of the engine's measured speed spectrum and the peak-valley values ​​of the engine's measured temperature spectrum; Step 2: Match the peak-valley values ​​of the engine's measured speed spectrum with the peak-valley values ​​of the engine's measured temperature spectrum, and use the least squares method to fit the matched peak-valley values ​​of the engine's measured speed spectrum and the engine's measured temperature spectrum to obtain the correlation between the speed and temperature; Step 3: Perform cluster analysis on the rising and falling sections of the speed cycle in the engine's measured speed spectrum to determine the load level in the compiled life test spectrum; Step 4: Identify the load-holding segment in the engine's measured speed spectrum, and calculate the duration and load information of the load-holding segment; Step 5: Prepare a speed block spectrum based on the load level determined according to the load cycle in step 3 and the holding load size and duration determined in step 4; Step 6: Based on the effect of temperature on the life damage of the hot end disk, the correlation between speed and temperature, and the speed program block spectrum compiled in step 5, compile a hot end disk life test spectrum based on the measured spectrum.

2. The method for compiling a hot end disk life test spectrum based on a measured spectrum according to claim 1, characterized in that: In step 1, the extraction of temperature extreme value and speed extreme value data based on the engine's measured temperature spectrum and speed spectrum is completed, specifically including: Step 1.1: Determine the speed and temperature deletion thresholds based on the damage and the trend of the distribution of the measured engine speed spectrum and the measured engine temperature spectrum as the deletion threshold changes; Step 1.2: Perform a small cycle deletion on the engine speed spectrum and the engine temperature spectrum using the deletion threshold determined in step 1.1; Step 1.3: Use the rain flow counting algorithm to perform cycle counting on the engine speed spectrum and the engine temperature spectrum, and determine the peak and valley point data of the engine speed spectrum and the engine temperature spectrum based on the cycle counting results.

3. The method for compiling a hot end disk life test spectrum based on a measured spectrum according to claim 2, characterized in that: The step 1.1 specifically includes the following operations: Step 1.11, converting the engine temperature spectrum and the engine speed spectrum into percentage values; Step 1.12, binning and counting the peak and valley values ​​of the engine speed spectrum and the engine temperature spectrum, which are percentage values; Step 1.13: Based on the bin counting results, analyze the statistical data of the bin segments with high bin counts as the deletion threshold changes to determine the optimal deletion thresholds for the rotation speed and temperature.

4. The method for compiling a hot end disk life test spectrum based on a measured spectrum according to claim 3, characterized in that: The specific operations of step 1.2 include: Step 1.21, extracting peak and valley values ​​from the measured engine speed spectrum and the measured engine temperature spectrum to obtain data including corresponding speed extreme value points and temperature extreme value points, wherein the corresponding speed extreme value points include speed peak extreme value points and speed valley extreme value points, and the corresponding temperature extreme value points include temperature peak extreme value points and temperature valley extreme value points; Step 1.22, determining the initial speed extreme point and the initial temperature extreme point, and judging the speed extreme point subsequent to the initial speed extreme point and the temperature extreme point subsequent to the initial temperature extreme point; Step 1.23: If the absolute value of the difference between the determined speed extreme point and / or temperature extreme point and the initial speed extreme point and / or initial temperature extreme point is greater than the corresponding deletion threshold, then record the speed extreme point and / or temperature extreme point and use it as the initial speed extreme point and / or initial temperature extreme point for determining subsequent speed extreme points and / or temperature extreme points. Step 1.24, if the absolute value of the difference between the judged speed extreme point and / or temperature extreme point and the initial speed extreme point and / or initial temperature extreme point is less than the corresponding deletion threshold, the judged speed extreme point and / or temperature extreme point is discarded, and the speed extreme points and / or temperature extreme points subsequent to the discarded speed extreme point and / or temperature extreme point are continued to be judged.

5. The method for compiling a hot end disk life test spectrum based on a measured spectrum according to claim 2, characterized in that: The specific operation of matching the peak-valley values ​​of the engine's measured speed spectrum with the peak-valley values ​​of the engine's measured temperature spectrum in step 2 includes: comparing the number of speed extreme points corresponding to the peak-valley values ​​of the engine's measured speed spectrum after filtering using the rain flow filtering algorithm with the number of temperature extreme points of the peak-valley values ​​of the engine's measured temperature spectrum, and searching for and matching the peak-valley values ​​of the engine's measured speed spectrum or the peak-valley values ​​of the engine's measured temperature spectrum with a smaller number of points using the peak-valley values ​​of the engine's measured speed spectrum or the peak-valley values ​​of the engine's measured temperature spectrum with a larger number of points.

6. The method for compiling a hot end disk life test spectrum based on a measured spectrum according to claim 1, characterized in that: The specific operations of step 3 include: Step 3.1: Identify and count the rising and falling cycles in the speed cycle within the engine's measured speed spectrum; Step 3.2: Perform dimensionality reduction on the measured engine speed spectrum, including converting the speed cycle into a stress cycle and performing dimensionality reduction. Step 3.3: Based on the dimensionality reduction results, perform cluster analysis on the rising and falling cycles, and determine the frequency of each load level based on the cluster analysis results.

7. The method for compiling a hot end disk life test spectrum based on a measured spectrum according to claim 1, characterized in that: The specific operations of step 4 include: Identify the speed holding section of the engine's measured speed spectrum. The identification operation includes setting a threshold and searching for the points before and after each speed extreme point based on the threshold. Determine the speed values ​​in the engine's measured speed spectrum from the speed extreme point forward, find a point n where the absolute difference between the speed value at point n and the speed extreme point is greater than a threshold, and record the time at point n+1; From the extreme speed point, judge the speed value in the engine's measured speed spectrum backward, find a point m, where the absolute value of the speed difference between point m and the extreme speed point is greater than the threshold, and record the time of point m-1; Calculate the time difference between point m-1 and point n+1. If it is greater than the set time N, it is considered that the material will suffer creep damage at this time, and record it as the speed holding section. Record the speed holding size and speed holding time.

8. The method for compiling a hot end disk life test spectrum based on a measured spectrum according to claim 1, characterized in that: The specific operations of compiling the speed program block spectrum in step 5 include: Set the 0-max-0 cycle according to the speed spectrum used for data analysis. Based on the load level in step 3 and the total speed holding time under each load level in step 4, use the 0-max-0 cycle as the boundary, insert a load increase cycle before 0-max-0, and insert a load decrease cycle after 0-max-0 to complete the speed program block spectrum compilation.

9. The method for compiling a hot end disk life test spectrum based on a measured spectrum according to claim 1, characterized in that: The specific operations of step 6 include: determining the temperature cycle corresponding to each speed cycle based on the correlation between speed and temperature, and determining the stress distribution in the hot end disk of the turbine at various ambient temperatures through finite element analysis. According to the magnitude of the thermal stress at the dangerous point in the hot end disk and the Miner linear damage accumulation principle, the thermal cycle is equivalent to a speed cycle, and the temperature in each speed cycle is replaced by a constant temperature. The hot end disk life test spectrum is compiled based on the speed program block spectrum.

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