Method and system for determining compilation precision requirement of test-run spectrum of acceleration task of aero-engine

Abstract

The invention provides an aero-engine acceleration task test-run spectrum compilation precision demand determination method and system. The method comprises the steps of obtaining a first overhaul period and an overhaul interval in the total life of an aero-engine; acquiring an external field use load spectrum of the aero-engine to determine load dispersibility; constructing an engine group service mission profile virtual sample according to the load dispersity; determining damage accumulation dispersity under the service load of the engine group according to the virtual sample of the service mission profile of the engine group; a fault-free data small sample reliability analysis method is adopted to analyze the damage accumulation dispersity under the service load of the engine group, and the engine group service reliability of the test run data in each rebuilding period is obtained; and determining the compilation precision of the acceleration task test run spectrum of the aero-engine in each rebuilding period according to the service reliability of the engine group. According to the method, the compilation precision requirements of the test run spectrum of the acceleration task can be analyzed in different life periods.

Description

Technical Field

[0001] The present invention belongs to the technical field of aircraft engine structure life assessment, and in particular relates to a method and system for determining accuracy requirements for compiling an aircraft engine acceleration mission test spectrum. Background Art

[0002] There are discrepancies between the comprehensive mission spectrum of long-term engine testing and the spectrum used in actual operation. Analysis of actual engine maintenance and use shows that a large number of engines continue to function normally beyond their prescribed overhaul period, resulting in a waste of engine life. With the advancement of engine technology, the total life and overhaul period of engines are increasing, making full-life long-term testing a significant expense and time-consuming process. To overcome the shortcomings of long-term engine testing, an accelerated mission test spectrum was compiled based on the damage equivalence principle and the comprehensive mission spectrum of long-term engine testing. Experimental research on engines using accelerated mission testing was conducted to determine the total life, first overhaul period, and overhaul interval of the engine. Different test purposes require different spectrum compilation accuracy requirements. Compilation accuracy refers to the difference between the damage caused to the engine by the test spectrum and the damage caused by the measured spectrum. The smaller the error, the higher the compilation accuracy, while the larger the error, the lower the compilation accuracy.

[0003] Determining the accuracy of accelerated mission test run profiles is crucial for engine performance evaluation during the design and testing phases. High-precision test run profiles accurately simulate the various operating conditions and loads encountered in actual flight, effectively verifying the engine's structural strength, system reliability, and safety. This not only helps identify and resolve potential design flaws in advance, reducing flight test risks, but also optimizes engine performance and enhances its stability and durability in actual operation. Furthermore, accurate test run profile compilation provides reliable data support for subsequent engine tests, shortening R&D cycles and reducing R&D costs.

[0004] During aircraft engine development, life testing plays a key role in determining engine lifespan and ensuring operational reliability. Currently, the impact of life test spectrum compilation accuracy on engine safety remains unclear. Quantitative analysis methods for engine life test spectrum compilation accuracy requirements are lacking, hindering the evaluation of complete aircraft engine life testing. Therefore, a method for determining the accuracy requirements for aircraft engine accelerated mission test spectrum compilation is urgently needed. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method and system for determining the accuracy requirements for compiling an aero-engine acceleration mission test spectrum.

[0006] In a first aspect, the present invention provides a method for determining the accuracy requirements for compiling an aircraft engine acceleration mission test spectrum, comprising:

[0007] Obtain the first overhaul period and overhaul interval within the total life of the aircraft engine;

[0008] Obtain the load spectrum of aircraft engines in field use to determine load dispersion;

[0009] Construct a virtual sample of the engine fleet's service mission profile based on load dispersion;

[0010] The damage accumulation dispersion of the engine group under service load is determined based on the virtual sample of the engine group service mission profile;

[0011] The small sample reliability analysis method of fault-free data is used to analyze the dispersion of damage accumulation under service load of the engine fleet, and the service reliability of the engine fleet based on the test data within each overhaul period is obtained.

[0012] The accuracy of the accelerated mission test spectrum compilation of aircraft engines during each overhaul period is determined based on the service reliability of the engine fleet.

[0013] Optionally, obtaining an aircraft engine field load spectrum to determine load dispersion includes:

[0014] Obtain the load spectrum of aircraft engines in field use to calculate the load characteristics of the engine fleet's service mission profile;

[0015] Rainflow filtering and load segment classification are used to extract the cyclic peak-valley rainflow count matrix of fatigue load and the holding speed and holding time of creep load from the load characteristics.

[0016] Normal distribution, Weibull distribution or extreme value distribution is used to fit the cyclic peak-valley rainflow count matrix of fatigue load and the dispersion of holding speed and holding time of creep load.

[0017] Optionally, determining the damage accumulation dispersion under the service load of the engine group based on the virtual sample of the service mission profile of the engine group includes:

[0018] During each overhaul period, N-1 samples are randomly selected from the virtual sample. Based on the rainflow counting matrix and linear damage accumulation theory, the fatigue damage, creep damage, and total damage of each sample are calculated. Among the N aircraft engines, one is used for endurance testing and N-1 are used for field use, generating N virtual mission profile samples.

[0019] The cumulative damage distribution of the first M mission sections was statistically analyzed, the upper and lower limits of damage and the concentration characteristics were analyzed, and the damage changes under the service load of the engine group were obtained.

[0020] Optionally, determining the compilation accuracy of the accelerated mission test spectrum of the aircraft engine in each overhaul period based on the service reliability of the engine fleet includes:

[0021] Obtain the impact curve of damage accumulation on the service reliability of the engine group under the service load of the engine group, so as to quantify the mapping relationship between the compilation accuracy of the acceleration mission test spectrum and the service reliability of the engine group;

[0022] A multi-factor response surface model was constructed with the compilation accuracy of the accelerated mission test spectrum as the independent variable and the service reliability of the engine fleet as the dependent variable.

[0023] A multivariate response surface model was used for regression analysis to determine the optimal notation accuracy threshold within each revision period.

[0024] In a second aspect, the present invention provides a system for determining accuracy requirements for compiling an aircraft engine acceleration mission test spectrum, comprising:

[0025] A first acquisition module is used to obtain the first overhaul period and overhaul interval within the total life of the aircraft engine;

[0026] The second acquisition module is used to obtain the load spectrum of the aircraft engine in field use to determine the load dispersion;

[0027] A construction module for constructing virtual samples of engine fleet service mission profiles based on load dispersion;

[0028] The first determination module is used to determine the damage accumulation dispersion under the service load of the engine group based on the virtual sample of the service mission profile of the engine group;

[0029] The dispersion analysis module is used to analyze the dispersion of damage accumulation under the service load of the engine fleet using the small sample reliability analysis method of fault-free data, and obtain the service reliability of the engine fleet based on the test data within each overhaul period;

[0030] The second determination module is used to determine the compilation accuracy of the accelerated mission test spectrum of the aircraft engine in each overhaul period based on the service reliability of the engine fleet.

[0031] Optionally, the second acquisition module includes:

[0032] The first acquisition unit is used to obtain the aircraft engine field load spectrum to calculate the load characteristics of the engine fleet service mission profile;

[0033] An extraction unit is used to extract the cyclic peak-valley value rainflow count matrix of fatigue load and the holding speed and holding time of creep load from the load characteristics by using rainflow filtering and load segment classification;

[0034] The fitting unit is used to fit the cyclic peak-valley rainflow count matrix of fatigue load and the dispersion of holding speed and holding time of creep load using normal distribution, Weibull distribution or extreme value distribution.

[0035] Optionally, the first determining module includes:

[0036] The damage calculation unit is used to randomly select N-1 samples from the virtual sample during each overhaul period and calculate the fatigue damage, creep damage, and total damage of each sample based on the rainflow counting matrix and linear damage accumulation theory. Among the N aircraft engines, one is used for endurance testing and N-1 are used for field use, generating N virtual mission profile samples.

[0037] The statistical unit is used to count the cumulative damage distribution of the first M mission sections, analyze the upper and lower limits of damage and concentration characteristics, and obtain the damage changes under the service load of the engine group.

[0038] Optionally, the second determining module includes:

[0039] The second acquisition unit is used to obtain the impact curve of damage accumulation on the service reliability of the engine group under the service load of the engine group, so as to quantify the mapping relationship between the compilation accuracy of the acceleration mission test spectrum and the service reliability of the engine group;

[0040] A construction unit is used to construct a multi-factor response surface model with the compilation accuracy of the accelerated mission test spectrum as the independent variable and the service reliability of the engine fleet as the dependent variable;

[0041] Determine the unit for regression analysis of multivariate response surface models to determine the optimal notation accuracy threshold within each revision period.

[0042] In a third aspect, the present invention provides a computer device comprising a processor and a memory; wherein, when the processor executes the computer program stored in the memory, it implements the steps of the method for determining the accuracy requirements for compiling an aircraft engine acceleration mission test spectrum as described in the first aspect.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, the steps of the method for determining the accuracy requirements for compiling an aircraft engine acceleration mission test spectrum described in the first aspect are implemented.

[0044] The present invention provides a method and system for determining the accuracy requirements for compiling accelerated mission test run spectra for aircraft engines. The method constructs a virtual sample of a fleet's service mission profile based on the dispersion of the load spectrum, establishes a method for characterizing the dispersion of damage accumulation under fleet service loads, establishes a method for evaluating the fleet's service reliability using a small sample of life test data without faults, analyzes the effects of different test run spectra compilation accuracies on the service reliability of the engine fleet, and establishes a method for analyzing the accuracy requirements for life test run spectra compilation at different life test stages. The method, established by the present invention, quantitatively analyzes the impact of life test run spectra compilation accuracy on aircraft engine safety and can analyze the accuracy requirements for accelerated mission test run spectra compilation at different life test stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 A flowchart of a method for determining accuracy requirements for compiling an aircraft engine acceleration mission test spectrum provided by an embodiment of the present invention;

[0047] Figure 2 A cross-sectional view of a flight mission of a civil turboshaft engine provided by an embodiment of the present invention;

[0048] Figure 3 A task profile diagram of the regularization process provided by an embodiment of the present invention;

[0049] Figure 4 A load segment classification diagram provided by an embodiment of the present invention;

[0050] Figure 5 The upper and lower limit diagrams of the fatigue damage accumulation law provided by the embodiment of the present invention;

[0051] Figure 6 A diagram showing the cumulative fatigue damage distribution of the first 1000 sections provided in an embodiment of the present invention;

[0052] Figure 7 This is a diagram showing the cumulative fatigue damage distribution of the first 2000 sections provided by the embodiment of the present invention;

[0053] Figure 8 This is a diagram showing the cumulative fatigue damage distribution of the first 3000 sections provided by the embodiment of the present invention;

[0054] Figure 9 This is a diagram showing the cumulative fatigue damage distribution of the first 4000 sections provided by the embodiment of the present invention;

[0055] Figure 10 The upper and lower limit diagrams of the creep damage accumulation law provided by the embodiment of the present invention;

[0056] Figure 11 Cumulative creep damage distribution diagram of the first 1000 sections provided by the embodiment of the present invention;

[0057] Figure 12 Cumulative creep damage distribution diagram of the first 2000 sections provided by the embodiment of the present invention;

[0058] Figure 13Cumulative creep damage distribution diagram of the first 3000 sections provided by the embodiment of the present invention;

[0059] Figure 14 Cumulative distribution diagram of creep damage of the first 4000 sections provided by the embodiment of the present invention;

[0060] Figure 15 A diagram showing the impact of the total cumulative damage of the mission profile on reliability provided by an embodiment of the present invention;

[0061] Figure 16 A graph showing the relationship between the service reliability and the coding accuracy of the fleet during the first overhaul period provided by an embodiment of the present invention;

[0062] Figure 17 A graph showing the relationship between the fleet service reliability and the coding error during the second overhaul interval provided by an embodiment of the present invention;

[0063] Figure 18 A graph showing the relationship between the service reliability of a fleet during the third overhaul interval and the coding error provided by an embodiment of the present invention;

[0064] Figure 19 A graph showing the relationship between fleet service reliability and coding error during the fourth overhaul interval provided by an embodiment of the present invention;

[0065] Figure 20 A schematic diagram of the structure of a system for determining the accuracy requirements for compiling an aero-engine acceleration mission test spectrum provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0067] Example 1

[0068] like Figure 1 As shown, an embodiment of the present invention provides a method for determining the accuracy requirement for compiling an aircraft engine acceleration mission test spectrum, comprising:

[0069] Step 101: Obtain the first overhaul period and overhaul interval within the total life of the aircraft engine.

[0070] Aviation engine tests are usually carried out in the following order: first, material tests of relevant components are carried out to determine the basic mechanical properties of the materials; second, simulated component tests are carried out to obtain the life of the components under complex load conditions; again, low-cycle fatigue tests are carried out on key components such as the wheel disc to obtain the low-cycle fatigue life of key components; then, endurance tests are carried out on key components such as the wheel disc to obtain the endurance life of key components; finally, low-cycle fatigue tests are carried out on the entire engine to obtain the low-cycle fatigue life of the entire engine, and finally, full-life tests are carried out on the entire engine.

[0071] To overcome the shortcomings of long-term engine life testing, an accelerated mission test spectrum was compiled based on the damage equivalence principle and the comprehensive mission spectrum of long-term engine testing. This accelerated mission test method was used to conduct experimental research on the engine to determine the engine's total life, first overhaul period, and overhaul interval. Different test objectives require different spectrum compilation accuracy requirements. Compilation accuracy specifically refers to the difference between the damage caused by the test spectrum and the damage caused by the measured spectrum. The smaller the difference, the higher the compilation accuracy; the larger the difference, the lower the compilation accuracy.

[0072] Step 102: Obtain an aircraft engine field load spectrum to determine load dispersion.

[0073] This example performed a cluster analysis on the mission profiles of a certain type of civilian turboshaft engine, classifying them into five categories. Based on the variations in profile parameters and prior knowledge, these five profile types were determined to be cruise, low-medium maneuverability, cruise maneuverability, medium-high maneuverability, and high maneuverability. Fifty profiles were selected for cluster analysis, resulting in five typical mission profiles and their characteristics, as shown in Table 1.

[0074] Table 1 Summary of characteristics of five typical profiles

[0075] Section Type quantity Cross-sectional characteristics Damage estimation Cruise 10 The fluctuation is very small, mainly in a long-term cruising state Smaller Medium and low mobility 7 The fluctuation is large, but the total number of cycles in a single start is small generally Cruise maneuvering class 12 It also has a long period of hold and several large fluctuations Larger Medium and high mobility 10 The fluctuation is large, but the total number of cycles in a single start is large Larger High mobility 11 The fluctuation is very large, and the total number of cycles for a single start is very high great

[0076] The mission mix of the five profiles is shown in Table 2. The mission mix is ​​usually expressed in the number of starts per thousand hours.

[0077] Table 2 Mixed frequency table of five types of profile tasks

[0078] Section Type Number of starts per thousand hours / times Cruise 77.97 Medium and low mobility 54.58 Cruise maneuvering class 93.56 Medium and high mobility 77.97 High mobility 85.76

[0079] A mission profile of a turboshaft engine is as follows Figure 2 As shown, it includes sub-cycles with different peak and valley values, load-holding sections with different speed levels, and different speed change rates.

[0080] In order to extract the load characteristics such as low cycle fatigue, creep and thermal shock, the mission profile needs to be regularized. The processing steps are as follows:

[0081] (1) Perform rain flow filtering on the load spectrum mission profile, delete smaller loads, and obtain peak and valley values, such as Figure 3 Indicated by asterisk.

[0082] (2) Based on the peak-valley data, the data between adjacent peak-valley values ​​are identified, and the load-holding section is identified according to the load fluctuation. The peak-valley values ​​and the load-holding section are combined to determine the inflection points in the mission profile, and the inflection points are connected in sequence to obtain a regularized mission profile, such as Figure 3 shown.

[0083] (3) According to Figure 4 The load segment classification method shown classifies the load spectrum segments into six typical load segments: valley type load holding segment, peak type load holding segment, rising edge load holding segment, falling edge load holding segment, and rising edge and falling edge, and enters the information of each typical load spectrum segment in the mission profile into the database.

[0084] (4) Count the rain flow on the mission profile, obtain the cyclic peak-valley rain flow count matrix, and enter it into the database.

[0085] The study focused on the turbine disk, a key component in engine lifespan. The main load factors affecting its lifespan are fatigue and creep. To characterize the flight mission profile and include load information, the relevant load characteristics of fatigue and creep loads were recorded and characterized. The specific characterization method is as follows:

[0086] (1) Fatigue load

[0087] The fatigue load borne by the engine comes from the speed change cycle. For civil turboshaft engines, the maximum speed varies due to the difference in takeoff weight for each mission. Therefore, it is different from the statistical analysis of the load spectrum of fixed-wing aircraft. The maximum speed of the profile is also a parameter that needs to be statistically analyzed.

[0088] The sub-cycles in the mission profile are counted using the rainflow counting method. Based on the speed cycle distribution law of civil turboshaft engines, different load cycle peak and valley value levels are reasonably selected, among which:

[0089] The cyclic valley load levels are selected as: [0,50,60,70,80,82.5,85,87.5,90,92,94]

[0090] The cyclic peak load levels are selected as: [60,70,80,85,87.5,90,92,94,96,98,101]

[0091] The rain flow count matrix of the cyclic peak and valley values ​​of a certain flight mission profile is shown in Table 3.

[0092] Table 3 Matrix table of rain flow counts of peak and valley values ​​in a flight mission profile cycle

[0093]

[0094] In summary, the description parameters of fatigue load characteristics are selected as: maximum speed and cyclic peak-valley rainflow count matrix.

[0095] (2) Creep load

[0096] The creep load in the engine load spectrum comes from the stage where the engine speed remains constant. Figure 4 The load segment classification defined includes four types of load segments involving creep loads: valley type load holding segment, peak type load holding segment, rising edge load holding segment and falling edge load holding segment.

[0097] Factors influencing the accumulation of creep damage in materials and structures include creep stress, temperature, and hold time. In aircraft engines, speed and temperature are strongly correlated, so speed and temperature can be used to statistically analyze only speed characteristics. In summary, for creep loads, the three parameters that can be extracted include creep load segment type, hold speed, and hold time.

[0098] In order to characterize the dispersion of characteristic loads, five distribution functions, including normal distribution, lognormal distribution, two-parameter Weibull distribution, three-parameter Weibull distribution and extreme value distribution, are used to fit the distribution of each parameter.

[0099] (1) Fatigue load dispersion

[0100] Taking the maximum speed as an example, it is found through comparison that the maximum speed distribution error fitted by extreme value distribution is the smallest, with position parameter μ=92.856, shape parameter σ=1.523, and distribution fitting residual of 0.0052.

[0101] For the cyclic peak-valley rainflow count matrix, according to the matrix characteristics given in Table 3, some elements of the matrix may be 0. Therefore, in the process of characterizing the dispersion of the cyclic peak-valley rainflow count matrix, it is necessary to first analyze the probability that each element of the matrix is ​​not 0. Then, the data that is not 0 in the elements at the same position are extracted and distribution fitting is performed on them. The distribution fitting results are shown in Table 4.

[0102] Table 4 Rainflow count matrix element distribution fitting table

[0103]

[0104] Based on the distribution fitting of each element of the cyclic peak-valley rainflow count matrix given in Table 4, probabilistic modeling can be performed to obtain a fatigue load dispersion characterization model for civil turboshaft engines.

[0105] (2) Creep load dispersion

[0106] The creep load in the mission profile is described by rotational speed and holding time. To facilitate characterization, the creep load is graded, and the holding time within each grade is extracted and characterized by dispersion using different distribution functions. At the same time, the probability of occurrence of valley-type holding segments, peak-type holding segments, rising edge holding segments, and falling edge holding segments in different creep load grades is statistically analyzed.

[0107] The creep load levels used in the analysis are:

[0108] [60,70,75,80,82,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101]

[0109] After distribution fitting, the distribution of creep load duration at each speed level is obtained as shown in Table 5.

[0110] Table 5 Load duration distribution fitting results for each speed level

[0111]

[0112] According to the duration of the load at each speed level given in Table 5, combined with the frequency of occurrence of a certain load level in a single section and the probability of different types of load-holding sections, a random characterization model of the creep load of a civil turboshaft engine can be obtained.

[0113] Step 103: construct a virtual sample of the engine fleet service mission profile based on the load dispersion.

[0114] Since the load spectrum of the fleet is dispersed when performing different flight missions, in order to simulate the structural damage evolution law of the fleet under service conditions, a digital virtual mission profile sample of the fleet's service load is constructed based on the completed civilian turboshaft engine mission profile load dispersion characterization results.

[0115] Step 104 : determining the damage accumulation dispersion under the service load of the engine group based on the virtual sample of the service mission profile of the engine group.

[0116] According to the engine Group C inspection requirements outlined in GJB242A-2018, one engine from the production batch should undergo endurance testing according to a sampling plan. Assuming a batch of 100 engines, one engine undergoes endurance testing, while 99 engines are used in the field. This generates a total of 100 virtual samples of engine mission profiles. During damage calculation, 99 virtual samples of mission profiles are randomly selected within each lifecycle based on the engine's usage characteristics.

[0117] Taking the turbine disk as the research object, the evolution laws of fatigue load damage accumulation and creep load damage accumulation are mainly considered.

[0118] (1) Fatigue damage accumulation evolution law

[0119] Fatigue damage comes from stress fluctuations caused by speed changes. To calculate fatigue damage, first calculate the fatigue damage caused by unit load (1 cycle) of each element in the rainflow counting peak-valley value matrix based on the peak-valley value array used by rainflow counting, and construct the fatigue damage matrix per unit load of the rainflow counting peak-valley value matrix

[0120] Based on the speed and load peak-valley rainflow counting patterns given in Table 4, the speed rainflow count matrix for 100 engines, totaling 4000 sections, was resampled. Random sampling was performed based on the distribution type of each element in the rainflow count peak-valley matrix given in column 5 of Table 4 and the distribution parameters of each distribution type given in columns 6 through 8 to obtain a random number matrix.

[0121] The fourth column in Table 4 shows the probability that the element in the matrix is ​​not 0. To account for the above influence, MATLAB is used to randomly sample an average distribution array, and then the array is compared with the corresponding elements in the fourth column of Table 4. When the random array element is greater than the element in the fourth column of Table 4, the cycle number of the element is 0. In this way, the rain flow count peak and valley value matrix of the virtual samples of each mission profile of the civil turboshaft engine can be obtained.

[0122] Using the linear damage accumulation theory, the unit load fatigue damage matrix of each section is and the rainflow count peak-valley matrix By multiplying and summing the corresponding elements, we can get the total fatigue damage in a virtual sample of the mission profile. By accumulating the damage of each section of each engine, we can get the fatigue damage accumulation law of the virtual sample of the mission profile of the civil turboshaft engine, such as Figure 5 As shown in Figure 2, it can be seen that there is a significant difference between the upper and lower limits of fatigue damage for the same mission profile. Figure 6 、 Figure 7 、 Figure 8 and Figure 9 As shown in Figure 2, the total damage distribution of the first 1000 sections, the first 2000 sections, the first 3000 sections, and the first 4000 sections is shown. Figures 6 to 9 It can be seen that the section fatigue damage accumulation has significant dispersion under different cumulative cycle numbers, and the distribution characteristics are different. Common distributions such as normal distribution and Weibull distribution cannot accurately describe the distribution characteristics of section fatigue damage accumulation.

[0123] (2) Creep damage accumulation evolution law

[0124] Creep loads originate from structural and material deformations caused by the engine's speed hold-up phase. Table 5 shows the fitting results for the creep load duration distribution. As can be seen from the table, the dispersion of creep loads requires two characteristic dispersions. First, based on the probability of a profile containing a hold-up phase, given in the fifth column of Table 5, the number of profiles containing each level of hold-up phase is determined. Then, based on the distribution of the number of hold-up phases within each profile, random sampling is performed based on the distribution type and parameters to determine the number of each level of hold-up phase within each mission profile. Finally, random sampling is performed based on the total number of hold-up phases combined with the distribution characteristics of each hold-up phase duration. The durations of each hold-up phase are then assigned sequentially to virtual samples of each mission profile to obtain the creep dispersion characteristics of each virtual sample.

[0125] The mission profile virtual sample contains a large amount of random load-keeping mission segment data, and the load order information is missing during the sampling process. If the nonlinear creep damage accumulation law is used, the calculation process will be extremely complicated. Therefore, the LM equation is used to calculate the creep damage, and the linear damage accumulation theory is used to accumulate the creep damage.

[0126] By accumulating the creep damage of each section of each engine, the creep damage accumulation law of the virtual sample of the mission section of the civil turboshaft engine can be obtained, such as Figure 5 As shown in Figure 2, it can be seen that there is a significant difference between the upper and lower limits of creep damage for the same mission profile. Figure 11 、 Figure 12 、 Figure 13 and Figure 14 As shown in Figure 2, the total damage distribution of the first 1000 sections, the first 2000 sections, the first 3000 sections, and the first 4000 sections is shown. Figures 11 to 14 It can be seen that the creep damage accumulation of the profile under different cumulative cycle numbers has a significant dispersion, with obvious concentration characteristics near the mean, and the overall distribution characteristics are approximately normal distribution.

[0127] (3) Cumulative evolution of total damage in the section

[0128] To calculate the total damage of the section, the fatigue damage and creep damage of each section are linearly accumulated to obtain the cumulative evolution of the total section damage. The upper and lower limits of the total section damage accumulation are as follows: Figure 10 As shown in the figure, it can be seen that there is a significant difference between the upper and lower limits of total damage for the same mission profile.

[0129] like Figure 11 、 Figure 12 、 Figure 13 and Figure 14As shown in Figure 2, the total damage distribution of the first 1000 sections, the first 2000 sections, the first 3000 sections, and the first 4000 sections is shown. Figures 11 to 14 As can be seen from the figure, the creep damage accumulation of the sections under different cumulative cycle numbers has a significant dispersion. Among them, the total cumulative damage distribution of the first 1000 sections is less concentrated, showing a certain random distribution feature. The total damage accumulation of subsequent sections has a clear concentration feature near the mean, and the overall distribution characteristics are close to normal distribution.

[0130] Step 105 , using a small sample reliability analysis method of fault-free data to analyze the damage accumulation dispersion under the service load of the engine group, and obtain the service reliability of the engine group based on the test data within each overhaul period.

[0131] In this step, the zero-failure data reliability evaluation method based on the E-Bayes method is used as the reliability evaluation method for engine life test data.

[0132] Assume that the distribution function of the life span T of a certain component of an aircraft engine is F(t,θ), θ∈Θ, Θ is the parameter space, and the truncation time is t1, t2,…, t k (0 <t1<t2<…<t k ), at t i A total of ni samples are tested at (i=1,2,…,k). Assuming that none of the samples involved in the test fail, it can be assumed that the life of the samples used is greater than the truncation time t i , then the timed censored test data can be said to be zero-failure data, recorded as (t i ,n i ).

[0133] Through analysis, the following test information can be obtained:

[0134] 1) The component life T obeys the distribution function F(t,θ), denoted as F(t);

[0135] 2) The failure probability of the component at time ti is denoted as p i =P(T≤t i ), then p0 <p1<p2<…<p k ;

[0136] 3) When t = 0, the failure probability of the component p0 = P(T≤0) = 0;

[0137] 4) Record i =n i +n i+1 +…+n k Indicates that at t i Always have S i samples have not failed, that is, there are S iThe life of the sample is greater than t i .

[0138] Therefore, the key to this problem is how to scientifically evaluate the reliability index of components by using the non-failure data (t i , n i ) information in the above censored life test.

[0139] (1) Failure probability estimation

[0140] Before determining the E-Bayes estimate of the failure probability p i , its prior distribution must be determined first.

[0141] According to engineering experience, since each sample is independently tested and the test results have only two possible outcomes: normal and failure, it can be inferred that the sample population follows a binomial distribution. At this time, the Beta distribution can be selected as the conjugate prior distribution of the failure probability p i . If the prior distribution of p i is the Beta distribution (the distribution parameters are a and b), denoted as B(a, b), its probability density function is:[[]]

[0142]

[0143] In the formula, 0 < p i < 1,

[0144] Since π(p i / a, b) must be a decreasing function of p i to be used as the prior distribution density function of p i , its derivative is obtained as:[[]]

[0145]

[0146] When 0 < a ≤ 1, b > 1, dπ(p i / a, b) / dp i < 0, at this time π(p i / a, b) is a decreasing function of p i .

[0147] The unknown parameters contained in the prior distribution are called hyperparameters. How to use prior information to determine hyperparameters is a problem that must be studied when using the Bayes method. According to prior information, it can be assumed that the hyperparameters in the prior distribution of p i follow a uniform distribution. The following gives the definition and estimation of the E-Bayes estimate of p i in the cases of two hyperparameters and one hyperparameter respectively.

[0148] 1) The case where both hyperparameters are uniformly distributed

[0149] The prior distributions of the hyperparameters a and b follow the uniform distributions U(0, 1) and U(1, c), where c is a constant greater than 1 (usually given by experts combining historical data and experience).

[0150] In this embodiment, Definition 1 is set as: It is said that is the E - Bayes estimate of p i , where is the Bayes estimate of p i , D = {(a, b): 0 < a < 1, 1 < b < c}, and p(a, b) is the density function of a and b in the region D.

[0151] It can be seen from Definition 1 above that the E - Bayes estimate i of p

[0152]

[0153] is the mathematical expectation of the Bayes estimate of p i with respect to the hyperparameters a and b.

[0154] For a certain component, k times of time - truncated tests are carried out, and the result is that none of the samples used fails. The obtained non - failure data is (t i , n i ), denoted as

[0155]

[0156] If the prior density function π(p i / a, b) of p i is given by Equation (1), under the squared - loss, the E - Bayes estimate of p i is [[ID= forty - five]]

[0157]

[0158] In the formula,

[0159]

[0160] q2(s i , c) = (s i +1 + c)[ln(s i +1 + c)-1]-(s i +c)[ln(s i +c)-1] (8)

[0161] 2) The case where one hyperparameter follows a uniform distribution

[0162] When the hyperparameter a = 1, the prior density function of p i is:[[]]

[0163] π(pi / b) = b(1 - p i ) b-1 (9)

[0164] where 0 < p i < 1; the prior distribution of the hyperparameter b follows a uniform distribution U(1, c), where c is a constant greater than 1.

[0165] When the prior distribution is a Beta distribution and a = 1, the larger b is, the thinner the tail of its distribution density function, and the worse the robustness of the Bayes estimation model. Therefore, the value of c should not be too large.

[0166] In this embodiment, Definition 2 is set: It is called the E - Bayes estimate of p i where is the Bayes estimate of p i , D' = {b: 1 < b < c}, and π(b) is the density function of b in the region D'.

[0167] It can be seen from the above Definition 2 that the E - Bayes estimate of p i is

[0168]

[0169] The component is subjected to k times of time - truncated life tests, and the result is that none of the samples used fails, obtaining the data (t i , n i ), denoted as

[0170]

[0171] If the prior distribution function π(p i / b) is given by Equation (9), under the squared - loss, the E - Bayes estimate of p i is i is

[0172]

[0173] (2) Reliability index estimation

[0174] Assume that the life T of the component follows a Weibull distribution, and its distribution function is expressed as:

[0175]

[0176] where m represents the shape parameter; η represents the scale parameter (also known as the characteristic life). In engineering practice, generally, it is known that the shape parameter of the Weibull distribution is between 1 and 10.

[0177] The reliability function of the component at time t is expressed as:

[0178]

[0179] According to the least squares estimation method, the weighted least squares estimation of Weibull distribution parameters m and η is proposed as follows:

[0180]

[0181] Wherein, the intermediate variables are as follows:

[0182]

[0183] Among them, w i is the weight value. There are two main weighting methods: one is simple test time weighting; the other is a composite weighting that combines the test time and the corresponding sample size. This embodiment adopts the second weighting method, that is, weighting according to each test time and the corresponding number of test samples, expressed as:

[0184]

[0185] According to the above expression, the point estimate of the average life θ of the component is

[0186]

[0187] The point estimate of the component reliability R(t) at time t is:

[0188]

[0189] When the component life follows the Weibull distribution and the shape parameter is known, let l=t m , then the Weibull distribution is rewritten as an exponential distribution, that is, the distribution function is

[0190]

[0191] The Weibull distribution is thus converted to an exponential distribution, providing a one-sided lower confidence limit for the reliability. As shown in the above analysis, the Weibull distribution parameters can be calculated using the weighted least squares method. Based on this, this embodiment provides two-sided confidence intervals for the reliability indicators (mean life and reliability) of components that follow a Weibull distribution.

[0192] The components whose life follows the Weibull distribution are tested k times with timed tailing. None of the samples fail. The data obtained is (t i ,n i )(i=1,2,…,k), if the shape parameter m is known, then:

[0193] The confidence interval of the mean lifespan θ with a confidence level of 1-α is

[0194]

[0195] The confidence interval of the reliability R(t) with a confidence level of 1-α is

[0196]

[0197] The length of the two-sided confidence interval of reliability is expressed as The average length of the reliability two-sided confidence interval is represented by △, then

[0198]

[0199] Based on the damage accumulation of 100 engine mission profile virtual samples, the fatigue, creep and total damage of each profile are averaged to obtain the average fatigue damage of the profile. 3.69×10 -5 , average creep damage of the section 2.13×10 -4 The average total damage of the section is 2.50×10 -4 Assuming the total allowable structural damage is 1, the design life can be considered to be about 4,000 takeoffs and landings. It is assumed that the entire engine life is divided into four overhaul periods, and the interval between each overhaul period is 1,000 takeoffs and landings.

[0200] During the whole-unit engine service life assessment process, since whole-unit engine life testing typically involves randomly selecting one or two units from a batch and is typically a timed truncation test, it is a typical reliability assessment problem with extremely small sample zero-failure data. Therefore, expanding the reliability assessment sample is particularly important. Here, damage data from the fleet's field service is used as a foundation. From this, a sample can be constructed from damage data over the fleet's entire lifespan. The resulting structural damage sample data from whole-unit life testing and field service is shown in Table 6.

[0201] Table 6 Whole machine life test and field use structure damage sample table

[0202]

[0203]

[0204] After calculation, the influence of the total cumulative damage of the mission profile on reliability is as follows: Figure 15 As shown, from Figure 15 It can be seen that with the gradual accumulation of profile damage, the service reliability of the fleet evaluated through the whole-machine life test and field use data gradually decreases.

[0205] Step 106 , determining the compilation accuracy of the accelerated mission test spectrum for the aircraft engine during each overhaul period based on the service reliability of the engine fleet.

[0206] In this embodiment, the spectrum compilation accuracy is defined as the error between the damage caused by the whole-machine life test spectrum and the average damage of the fleet. This error is divided into negative and positive errors. A negative error indicates that the damage caused by the whole-machine life test spectrum is smaller than the average damage caused by the fleet's field service. Using a whole-machine life test spectrum with a negative error to pass the whole-machine life test will result in an evaluation result that is biased towards danger. A positive error indicates that the damage caused by the whole-machine life test spectrum is greater than the average damage caused by field service. Using a whole-machine life test spectrum with a positive error to conduct and pass the whole-machine life test will result in an evaluation result that is biased towards safety.

[0207] Assuming that the structural cumulative damage is 0.25, 0.5, 0.75 and 1 respectively (corresponding to the average structural cumulative damage after different life periods), the above method can be used to calculate the influence of the whole-machine life test carried out and passed in different life periods using different precision on the service reliability of the fleet, as shown in the following example: Figure 16 、 Figure 17 、 Figure 18 and Figure 19 As shown. Figures 16 to 19 As can be seen from the figure, the accuracy of the life test spectrum during the first overhaul period has little impact on the fleet's service reliability. During the remaining three lifespans, as the accuracy of the life test spectrum transitions from negative to positive error, the life test spectrum gradually increases in weight. After the fleet passes the life test using this spectrum, the fleet's service reliability increases. During the first overhaul period, when the engine has a significant amount of remaining life, selecting life test spectrums with varying accuracy has little impact on the fleet's service reliability. During the subsequent overhaul intervals, as structural damage accumulates, the fleet's service reliability gradually decreases. To improve the fleet's service reliability, the life test spectrum needs to be weighted, and a life test spectrum with positive error should be used.

[0208] In summary, this embodiment provides a method for determining the accuracy requirements for the compilation of accelerated mission test spectra for aircraft engines. It analyzes the process of life test for civil turboshaft engines based on the typical characteristics of aircraft engine life test work; constructs a virtual sample of the service mission profile of the fleet based on the dispersion of the load spectrum, and establishes a method for characterizing the dispersion of damage accumulation under the service load of the fleet; establishes a fleet service reliability evaluation method for life test data of a small sample of fault-free data based on the weighted E-Bayes method; analyzes the influence of different test spectra compilation accuracies on the service reliability of the engine fleet, and establishes a method for analyzing the accuracy requirements for life test spectra compilation at different life test stages through the response surface analysis method. The quantitative analysis method for the impact of the accuracy of life test spectra compilation on the safety of aircraft engines established in this embodiment can analyze the accuracy requirements for life test spectra compilation during different life periods.

[0209] Example 2

[0210] Based on the same inventive concept as Example 1, this embodiment also provides a system for determining the accuracy requirements for compiling an aircraft engine acceleration mission test spectrum. Since the principle of solving the problem by this system is similar to the aforementioned method for determining the accuracy requirements for compiling an aircraft engine acceleration mission test spectrum, the implementation of this system can refer to the implementation of the method for determining the accuracy requirements for compiling an aircraft engine acceleration mission test spectrum.

[0211] like Figure 20 As shown in the figure, the system for determining the accuracy requirements of the compilation of the aircraft engine acceleration mission test spectrum includes:

[0212] The first acquisition module 10 is used to obtain the first overhaul period and the overhaul interval within the total life of the aircraft engine.

[0213] The second acquisition module 20 is used to obtain the load spectrum of the aircraft engine in field use to determine the load dispersion.

[0214] The construction module 30 is used to construct a virtual sample of the service mission profile of the engine fleet according to the load dispersion.

[0215] The first determination module 40 is configured to determine the damage accumulation dispersion under the service load of the engine group according to the virtual sample of the service mission profile of the engine group.

[0216] The dispersion analysis module 50 is used to analyze the damage accumulation dispersion under the service load of the engine group using a small sample reliability analysis method of fault-free data, and obtain the service reliability of the engine group based on the test data within each overhaul period.

[0217] The second determination module 60 is used to determine the compilation accuracy of the accelerated mission test spectrum of the aircraft engine in each overhaul period according to the service reliability of the engine fleet.

[0218] Exemplarily, the second acquisition module includes:

[0219] The first acquisition unit is used to obtain the load spectrum of the aircraft engine in field use to calculate the load characteristics of the engine fleet service mission profile.

[0220] The extraction unit is used to extract the cyclic peak and valley value rain flow count matrix of fatigue load and the holding speed and holding time of creep load from the load characteristics by using rain flow filtering and load segment classification.

[0221] The fitting unit is used to fit the cyclic peak-valley rainflow count matrix of fatigue load and the dispersion of holding speed and holding time of creep load using normal distribution, Weibull distribution or extreme value distribution.

[0222] Exemplarily, the first determining module includes:

[0223] The damage calculation unit is used to randomly select N-1 samples from the virtual samples during each overhaul period, and calculate the fatigue damage, creep damage and total damage of each sample based on the rainflow counting matrix and linear damage accumulation theory. Among them, one of the N aircraft engines is used for endurance testing, and N-1 are used for field use, generating N mission profile virtual samples.

[0224] The statistical unit is used to count the cumulative damage distribution of the first M mission sections, analyze the upper and lower limits of damage and concentration characteristics, and obtain the damage changes under the service load of the engine group.

[0225] Exemplarily, the second determining module includes:

[0226] The second acquisition unit is used to obtain the influence curve of damage accumulation under the service load of the engine group on the service reliability of the engine group, so as to quantify the mapping relationship between the compilation accuracy of the acceleration mission test spectrum and the service reliability of the engine group.

[0227] A construction unit is used to construct a multi-factor response surface model with the compilation accuracy of the accelerated mission test spectrum as the independent variable and the service reliability of the engine group as the dependent variable.

[0228] Determine the unit for regression analysis of multivariate response surface models to determine the optimal notation accuracy threshold within each revision period.

[0229] For more specific working processes of the above modules, please refer to the corresponding content disclosed in Example 1, which will not be repeated here.

[0230] Example 3

[0231] This embodiment provides a computer device, including a processor and a memory; wherein, when the processor executes the computer program stored in the memory, it implements the steps of the method for compiling accuracy requirements based on the aircraft engine acceleration mission test spectrum described in Example 1.

[0232] For more specific details about the above method, please refer to the corresponding content disclosed in Example 1, which will not be repeated here.

[0233] Example 4

[0234] This embodiment provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, the steps of the method for compiling the accuracy requirement of the aircraft engine acceleration mission test spectrum described in Example 1 are implemented.

[0235] For more specific details about the above method, please refer to the corresponding content disclosed in Example 1, which will not be repeated here.

[0236] Example 5

[0237] This embodiment provides a computer program product, including computer executable instructions or a computer program. When the computer executable instructions or the computer program are executed by a processor, the steps of the method for compiling the accuracy requirement of the aircraft engine acceleration mission test spectrum described in Example 1 are implemented.

[0238] For more specific details about the above method, please refer to the corresponding content disclosed in Example 1, which will not be repeated here.

[0239] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar parts between the various embodiments will be sufficient. The systems, devices, storage media, and computer program products disclosed in the embodiments correspond to the methods disclosed in the embodiments, so their descriptions are relatively simplified. For relevant details, refer to the method descriptions.

[0240] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus a necessary general-purpose hardware platform. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion 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 storage medium such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention or certain portions of the embodiments.

[0241] In some embodiments, computer-executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0242] As an example, computer-executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinating files (e.g., files storing one or more modules, subroutines, or code portions).

[0243] By way of example, computer-executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed across multiple sites and interconnected by a communication network.

[0244] The present invention has been described in detail above with reference to specific embodiments and exemplary examples. However, these descriptions should not be construed as limiting the present invention. Those skilled in the art will appreciate that various equivalent substitutions, modifications, or improvements may be made to the technical solutions and implementations of the present invention without departing from the spirit and scope of the present invention, all of which fall within the scope of the present invention. The scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for determining the accuracy requirements for compiling an aircraft engine acceleration mission test spectrum, characterized in that: include: Obtain the first overhaul period and overhaul interval within the total life of the aircraft engine; Obtain the load spectrum of aircraft engines in field use to determine load dispersion; Construct a virtual sample of the engine fleet's service mission profile based on load dispersion; The damage accumulation dispersion of the engine group under service load is determined based on the virtual sample of the engine group service mission profile; The small sample reliability analysis method of fault-free data is used to analyze the dispersion of damage accumulation under service load of the engine fleet, and the service reliability of the engine fleet based on the test data within each overhaul period is obtained. The accuracy of the accelerated mission test spectrum compilation of aircraft engines during each overhaul period is determined based on the service reliability of the engine fleet.

2. The method for determining the accuracy requirement for compiling an aircraft engine acceleration mission test spectrum according to claim 1, characterized in that: The method of obtaining the aircraft engine field load spectrum to determine load dispersion includes: Obtain the load spectrum of aircraft engines in field use to calculate the load characteristics of the engine fleet's service mission profile; Rainflow filtering and load segment classification are used to extract the cyclic peak-valley rainflow count matrix of fatigue load and the holding speed and holding time of creep load from the load characteristics. Normal distribution, Weibull distribution or extreme value distribution is used to fit the cyclic peak-valley rainflow count matrix of fatigue load and the dispersion of holding speed and holding time of creep load.

3. The method for determining the accuracy requirement for compiling an aircraft engine acceleration mission test spectrum according to claim 1 is characterized in that: Determining the damage accumulation dispersion of the engine group under the service load based on the virtual sample of the engine group service mission profile includes: During each overhaul period, N-1 samples are randomly selected from the virtual sample. Based on the rainflow counting matrix and linear damage accumulation theory, the fatigue damage, creep damage, and total damage of each sample are calculated. Among the N aircraft engines, one is used for endurance testing and N-1 are used for field use, generating N virtual mission profile samples. The cumulative damage distribution of the first M mission sections was statistically analyzed, the upper and lower limits of damage and the concentration characteristics were analyzed, and the damage changes under the service load of the engine group were obtained.

4. The method for determining the accuracy requirement for compiling an aircraft engine acceleration mission test spectrum according to claim 1, characterized in that: Determining the accuracy of the accelerated mission test spectrum compilation for an aircraft engine during each overhaul period based on the service reliability of the engine fleet includes: Obtain the impact curve of damage accumulation on the service reliability of the engine group under the service load of the engine group, so as to quantify the mapping relationship between the compilation accuracy of the acceleration mission test spectrum and the service reliability of the engine group; A multi-factor response surface model was constructed with the compilation accuracy of the accelerated mission test spectrum as the independent variable and the service reliability of the engine fleet as the dependent variable. A multivariate response surface model was used for regression analysis to determine the optimal notation accuracy threshold within each revision period.

5. A system for determining the accuracy requirements of aircraft engine acceleration mission test spectrum compilation, characterized in that: include: A first acquisition module is used to obtain the first overhaul period and overhaul interval within the total life of the aircraft engine; The second acquisition module is used to obtain the load spectrum of the aircraft engine in field use to determine the load dispersion; A construction module for constructing virtual samples of engine fleet service mission profiles based on load dispersion; The first determination module is used to determine the damage accumulation dispersion under the service load of the engine group based on the virtual sample of the service mission profile of the engine group; The dispersion analysis module is used to analyze the dispersion of damage accumulation under the service load of the engine fleet using the small sample reliability analysis method of fault-free data, and obtain the service reliability of the engine fleet based on the test data within each overhaul period; The second determination module is used to determine the compilation accuracy of the accelerated mission test spectrum of the aircraft engine in each overhaul period based on the service reliability of the engine fleet.

6. The system for determining the accuracy requirement of aircraft engine acceleration mission test spectrum compilation according to claim 5, characterized in that: The second acquisition module includes: The first acquisition unit is used to obtain the aircraft engine field load spectrum to calculate the load characteristics of the engine fleet service mission profile; An extraction unit is used to extract the cyclic peak-valley value rainflow count matrix of fatigue load and the holding speed and holding time of creep load from the load characteristics by using rainflow filtering and load segment classification; The fitting unit is used to fit the cyclic peak-valley rainflow count matrix of fatigue load and the dispersion of holding speed and holding time of creep load using normal distribution, Weibull distribution or extreme value distribution.

7. The system for determining the accuracy requirements for compiling an aircraft engine acceleration mission test spectrum according to claim 5 is characterized in that: The first determining module includes: The damage calculation unit is used to randomly select N-1 samples from the virtual sample during each overhaul period and calculate the fatigue damage, creep damage, and total damage of each sample based on the rainflow counting matrix and linear damage accumulation theory. Among the N aircraft engines, one is used for endurance testing and N-1 are used for field use, generating N virtual mission profile samples. The statistical unit is used to count the cumulative damage distribution of the first M mission sections, analyze the upper and lower limits of damage and concentration characteristics, and obtain the damage changes under the service load of the engine group.

8. The system for determining the accuracy requirement of aircraft engine acceleration mission test spectrum compilation according to claim 5, characterized in that: The second determining module includes: The second acquisition unit is used to obtain the impact curve of damage accumulation on the service reliability of the engine group under the service load of the engine group, so as to quantify the mapping relationship between the compilation accuracy of the acceleration mission test spectrum and the service reliability of the engine group; A construction unit is used to construct a multi-factor response surface model with the compilation accuracy of the accelerated mission test spectrum as the independent variable and the service reliability of the engine fleet as the dependent variable; Determine the unit for regression analysis of multivariate response surface models to determine the optimal notation accuracy threshold within each revision period.

9. A computer device, characterized in that: It comprises a processor and a memory; wherein, when the processor executes the computer program stored in the memory, it implements the steps of the method for determining the accuracy requirements for compiling an aircraft engine acceleration mission test spectrum as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that Used to store computer programs; when the computer programs are executed by the processor, the steps of the method for determining the accuracy requirements for compiling an aircraft engine acceleration mission test spectrum as described in any one of claims 1 to 4 are implemented.

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