Aero-engine program block spectrum load grade determination method and system, computer equipment, storage medium and program product

Abstract

The application provides an aero-engine program block spectrum load grade determination method, a system, computer equipment, a storage medium and a program product, wherein the method comprises: performing rainflow counting on each flight profile of an aero-engine to obtain the mean value and amplitude value of a plurality of load cycles, so as to obtain the mean amplitude value pair of each load cycle; taking the load history time corresponding to the maximum load in a target flight profile as a time threshold, and determining the secondary cycle in the target flight profile as a load rising cycle or a load descending cycle; performing clustering analysis on each load rising cycle and each load descending cycle respectively, and determining the cluster centers by taking the mean amplitude value pair of the load cycle as an analysis variable; and taking the load grade corresponding to each cluster center and the load grade corresponding to the mean amplitude value pair of each main cycle as the program block spectrum load grade of the target aero-engine. The application considers the difference of actual load spectrum and embodies the inherent properties of real service load, thereby improving the aero-engine life examination precision.

Description

Technical Field

[0001] This invention belongs to the field of aero-engine load spectrum compilation technology, and particularly relates to a method for determining the load level of aero-engine program block spectrum, system computer equipment, storage medium and program product. Background Technology

[0002] The life test program block spectrum of aero-engines is an important basis for carrying out durability tests and life-determining and life-extending tests of engine components. However, a key problem in compiling an equivalent program block spectrum based on field measured spectrum is handling the numerous types of load cycles.

[0003] The field load spectrum of aero-engines is extremely complex. Considering both economic efficiency and the requirements of testing equipment, this complex load spectrum is difficult to apply to the life assessment of engine components on relevant testing equipment. Therefore, it is necessary to simplify and transform the field load spectrum according to the loading form. Currently, the main simplification methods are constant amplitude fatigue load spectrum and program block spectrum. Constant amplitude fatigue testing uses a constant amplitude as the loading load to study the life of components under specific operating conditions, but it is too simplistic, and the tested life differs significantly from the actual working life. Program block spectrum, on the other hand, uses a program block composed of multi-level amplitudes to replace the actual load. It can include more load factors consistent with the actual load spectrum, thereby improving the test accuracy. Furthermore, the requirements for testing equipment are significantly lower than those for the actual load spectrum, offering good economy and strong practical operability.

[0004] Currently, both domestically and internationally, the number of levels for program block spectra includes 4, 8, 16, and 32 levels, but the 8-level load division is the most common. There are two commonly used methods for dividing the step intervals of an 8-level program load spectrum: the equal interval method and the non-equal interval method. The equal interval method divides the load range into equal intervals, while the non-equal interval method, also known as the ratio coefficient method (1, 0.95, 0.85, 0.725, 0.575, 0.425, 0.275, 0.125), where the size of each step level is obtained by multiplying the maximum load value by the ratio coefficient.

[0005] However, the determination of the program block spectrum depends to some extent on the type of test equipment and the type of load data, especially the cyclic characteristics of the actual load spectrum. If the differences in the actual load spectrum are ignored and a specific level is adhered to excessively, the inherent properties of real service loads cannot be reflected, thus losing the essential meaning of simulating the real load history, reducing the accuracy of life assessment, and making it difficult to achieve the purpose of life assessment of aero-engine components on the test equipment. Summary of the Invention

[0006] This invention provides a method for determining the load level of aero-engine program block spectrum, a system computer device, a storage medium, and a program product. It takes into account the differences in the actual load spectrum and reflects the inherent attributes of the actual service load, thereby improving the accuracy of aero-engine life assessment and achieving the purpose of life assessment of aero-engine components on a test instrument.

[0007] In a first aspect, the present invention provides a method for determining the program block spectrum load level of an aero-engine, comprising:

[0008] Obtain multiple measured flight profiles of the target aero-engine;

[0009] Rainflow counts were performed on each flight profile to obtain the mean and amplitude of multiple load cycles, which were used as the mean amplitude pair for each load cycle.

[0010] Using the load history time corresponding to the maximum load in the target flight profile as the time threshold, the secondary loops in the target flight profile before the time threshold are determined as load increase loops, and the secondary loops after the time threshold are determined as load decrease loops; among them, the load loop corresponding to the maximum load in the target flight profile is the main loop, and the remaining load loops are secondary loops.

[0011] Cluster analysis was performed on each load ascent cycle and each load descent cycle in all flight profiles, and the average amplitude of the load cycles was used as the analysis variable to determine each cluster center; where each cluster center corresponds to one load level.

[0012] The average amplitude value of each main loop is used as the load level corresponding to the load level of each cluster center as the program block spectrum load level of the target aero-engine.

[0013] Optionally, cluster analysis is performed on each load ascent cycle and each load descent cycle in all flight profiles, using the load cycle mean amplitude pairs as analysis variables to determine each cluster center; wherein, one cluster center corresponds to one load level, including:

[0014] The Euclidean distance d between any two pairs of mean amplitude values ​​in all load rise cycles is calculated using the following formula. ij :

[0015]

[0016] Where, m i m is the mean of load cycle i across all load increase cycles. j a is the mean of load cycle j across all load increase cycles; i a is the magnitude of load cycle i in all load increase cycles; j The magnitude of load cycle j in all load increase cycles;

[0017] The local density ρ of the mean amplitude pair of load cycle i is calculated using the following formula. i :

[0018]

[0019] Where k1 is the local density parameter of the mean amplitude pair of load cycle i, used to represent the k1 nearest mean amplitude pairs around the mean amplitude pair of load cycle i; n' is the total number of load rise cycles in all flight profiles.

[0020] Sort all local densities in descending order for all load increase cycles to obtain the first index of the descending order;

[0021] The density nearest neighbor distance δ of the mean amplitude pairs of load cycle i in all load rise cycles is calculated using the following formula. i :

[0022]

[0023] Where, q i q represents the first index corresponding to load cycle i in descending order; j This is the first index corresponding to load cycle j after sorting in descending order; For load cycle q i With load cycle q j The Euclidean distance between the mean amplitude pairs;

[0024] Local density ρ i nearest neighbor distance δ of density i The product of and is used as the decision variable γ for load cycle i. i ;

[0025] In all load increase cycles, the load values ​​corresponding to decision variables that are greater than the critical value of the first decision variable jump change are used as cluster centers.

[0026] Optionally, cluster analysis is performed on each load ascent cycle and each load descent cycle in all flight profiles, using the load cycle mean amplitude pairs as analysis variables to determine each cluster center; wherein, one cluster center corresponds to one load level, including:

[0027] The Euclidean distance d between any two pairs of mean amplitude values ​​in all load d d cycles is calculated using the following formula. mn :

[0028]

[0029] Where, m m The mean of load cycles m across all load descent cycles; mn a is the mean of load cycles n across all load descent cycles; m a is the amplitude of load cycle m in all load descent cycles; n The magnitude of load cycle n in all load descent cycles;

[0030] The local density ρ of the mean amplitude pair of load cycles m is calculated using the following formula. m :

[0031]

[0032] Where k2 is the local density parameter of the mean amplitude pairs of load cycle m, used to represent the k2 nearest mean amplitude pairs around the mean amplitude pairs of load cycle m; n” is the total number of load descent cycles in all flight profiles;

[0033] Sort all local densities in descending order across all load descent cycles to obtain the second index after descending order;

[0034] The density nearest neighbor distance δ of the mean amplitude pairs of load cycle m in all load descent cycles is calculated using the following formula. m :

[0035]

[0036] Where, q m The second subscript corresponding to load cycle m after sorting in descending order; q n This is the second subscript corresponding to load cycle n after sorting in descending order; For load cycle q m With load cycle q n The Euclidean distance between the mean amplitude pairs;

[0037] Local density ρ m nearest neighbor distance δ of density m The product of and is used as the decision variable γ for the load cycle m. m ;

[0038] In all load descent cycles, the load values ​​corresponding to decision variables that are greater than the critical value for the jump change of the second decision variable are used as cluster centers.

[0039] Secondly, the present invention provides a system for determining the program block spectrum load level of an aero-engine, comprising:

[0040] The acquisition module is used to acquire multiple measured flight profiles of the target aero-engine;

[0041] The rainflow counting module is used to count rainflows for each flight profile to obtain the mean and amplitude of multiple load cycles, which are used as the mean amplitude pairs for each load cycle.

[0042] The first determining module is used to determine the secondary loops in the target flight profile before the time threshold as load increase loops and the secondary loops after the time threshold as load decrease loops, using the load history time corresponding to the maximum load in the target flight profile as the time threshold. Among them, the load loop corresponding to the maximum load in the target flight profile is the main loop, and the remaining load loops are secondary loops.

[0043] The clustering module is used to perform cluster analysis on each load ascent cycle and each load descent cycle in all flight profiles, using the load cycle mean amplitude pairs as the analysis variables to determine each cluster center; where each cluster center corresponds to one load level;

[0044] The second determination module is used to take the average amplitude value of each main loop as the load level corresponding to the load level of each cluster center as the program block spectrum load level of the target aero-engine.

[0045] Optionally, the clustering module includes:

[0046] The first calculation unit is used to calculate the Euclidean distance d between the average amplitude pairs of any two load cycles in all load rise cycles according to the following formula. ij :

[0047]

[0048] Where, m i m is the mean of load cycle i across all load increase cycles. j a is the mean of load cycle j across all load increase cycles; i a is the magnitude of load cycle i in all load increase cycles; j The magnitude of load cycle j in all load increase cycles;

[0049] The second calculation unit is used to calculate the local density ρ of the mean amplitude pair of load cycle i according to the following formula. i :

[0050]

[0051] Where k1 is the local density parameter of the mean amplitude pair of load cycle i, used to represent the k1 nearest mean amplitude pairs around the mean amplitude pair of load cycle i; n' is the total number of load rise cycles in all flight profiles.

[0052] The first descending sorting unit is used to sort all local densities in descending order in all load increase cycles to obtain the first subscript after descending sorting.

[0053] The third calculation unit is used to calculate the density nearest neighbor distance δ of the average amplitude pairs of load cycle i in all load rise cycles according to the following formula. i :

[0054]

[0055] Where, q i q represents the first index corresponding to load cycle i in descending order; j This is the first index corresponding to load cycle j after sorting in descending order; For load cycle q i With load cycle q j The Euclidean distance between the mean amplitude pairs;

[0056] The first defining unit is used to determine the local density ρ. i nearest neighbor distance δ of density i The product of and is used as the decision variable γ for load cycle i. i ;

[0057] The second determining unit is used to select the load values ​​corresponding to decision variables that are greater than the critical value of the first decision variable jump change in all load increase cycles as cluster centers.

[0058] Optionally, the clustering module includes:

[0059] The fourth calculation unit is used to calculate the Euclidean distance d between the average amplitude pairs of any two load cycles in all load d d cycles according to the following formula. mn :

[0060]

[0061] Where, m m The mean of load cycles m across all load descent cycles; m n a is the mean of load cycles n across all load descent cycles; m a is the amplitude of load cycle m in all load descent cycles; n The magnitude of load cycle n in all load descent cycles;

[0062] The fifth calculation unit is used to calculate the local density ρ of the mean amplitude pair of load cycles m according to the following formula. m :

[0063]

[0064] Where k2 is the local density parameter of the mean amplitude pairs of load cycle m, used to represent the k2 nearest mean amplitude pairs around the mean amplitude pairs of load cycle m; n” is the total number of load descent cycles in all flight profiles;

[0065] The second descending sorting unit is used to sort all local densities in descending order in all load descent cycles to obtain the second subscript after descending sorting.

[0066] The sixth calculation unit is used to calculate the density nearest neighbor distance δ of the average amplitude pairs of load cycles m in all load descent cycles according to the following formula. m :

[0067]

[0068] Where, q m The second subscript corresponding to load cycle m after sorting in descending order; q n This is the second subscript corresponding to load cycle n after sorting in descending order; For load cycle q m With load cycle q n The Euclidean distance between the mean amplitude pairs;

[0069] The third determining unit is used to determine the local density ρ m nearest neighbor distance δ of density m The product of and is used as the decision variable γ for the load cycle m. m ;

[0070] The fourth determining unit is to use the load value corresponding to the decision variable that is greater than the critical value of the second decision variable jump change in all load descent cycles as the cluster center.

[0071] Thirdly, the present invention provides a computer device including a processor and a memory; wherein, when the processor executes a computer program stored in the memory, it implements the steps of the method for determining the program block spectrum load level of an aero-engine as described in the first aspect.

[0072] Fourthly, the present invention provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, it implements the steps of the method for determining the spectrum load level of the aero-engine program block as described in the first aspect.

[0073] Fifthly, the present invention provides a computer program product, including computer-executable instructions or a computer program, wherein when the computer-executable instructions or the computer program are executed by a processor, the steps of the method for determining the program block spectrum load level of an aero-engine as described in the first aspect are implemented.

[0074] This invention provides a method for determining the load level of an aero-engine program block spectrum, a system computer device, a storage medium, and a program product. The method considers the inherent characteristics of the aero-engine load spectrum, separating the main operating cycle from other cycles, thus preserving the characteristic that the takeoff and landing cycle is the primary source of low-cycle fatigue damage. Secondary cycles are divided into load-increasing cycles and load-decreasing cycles based on the maximum load point, conforming to the fatigue characteristics where secondary cycle loads delay fatigue damage before overload and accelerate fatigue damage after overload. Compared to adhering to a fixed eight-level load, this method considers fatigue characteristics more comprehensively. Clustering methods are used to determine the load level of secondary cycles, identifying cycles with frequent load occurrences as load levels. This fully utilizes the statistical characteristics of the measured load spectrum of the aero-engine, better reflecting the actual engineering characteristics of the load spectrum. This provides a more theoretically grounded method for determining load levels in the program block spectrum compilation of aero-engine components, improving the accuracy of component life testing and further providing a basis for aero-engine life determination. Attached Figure Description

[0075] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0076] Figure 1 A flowchart illustrating a method for determining the program block spectrum load level of an aero-engine, provided in an embodiment of the present invention;

[0077] Figure 2 This is a schematic diagram of the flight profile of an aero-engine provided in an embodiment of the present invention;

[0078] Figure 3 This is a histogram of rainflow counts for all flight profiles provided in the embodiments of the present invention;

[0079] Figure 4 This is a schematic diagram illustrating the division of the main loop and secondary loop in an embodiment of the present invention;

[0080] Figure 5 This is a schematic diagram illustrating the division of load increase cycle and load decrease cycle according to an embodiment of the present invention;

[0081] Figure 6 The load rise cyclic rainflow histogram provided in the embodiments of the present invention;

[0082] Figure 7 The load-decreasing cyclic rainflow histogram provided in this embodiment of the invention;

[0083] Figure 8This is a schematic diagram of cluster center decision-making provided in an embodiment of the present invention;

[0084] Figure 9 This is a diagram showing the classification results of the measured load spectrum of an aero-engine provided in an embodiment of the present invention;

[0085] Figure 10 This is an example diagram illustrating the classification of measured load spectrum levels for an aero-engine, provided in an embodiment of the present invention.

[0086] Figure 11 This is a schematic diagram of the three-stage load increase cycle provided in an embodiment of the present invention;

[0087] Figure 12 This is a schematic diagram of the load division of a two-stage load descent cycle provided in an embodiment of the present invention;

[0088] Figure 13 This is a schematic diagram of the structure of an aero-engine program block spectrum load level determination system provided in an embodiment of the present invention. Detailed Implementation

[0089] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0090] To address the issue that existing methods for determining the number of load levels in the program block spectrum do not consider the actual cyclic characteristics of the load spectrum, this invention, based on measured cyclic characteristics of the load spectrum and according to the load characteristics of aero-engines, divides the load spectrum into primary and secondary cycles. The primary cycle is treated as a separate load level, while the secondary cycles employ clustering to determine representative load cycles as each load level. This approach maximizes the simulation of the aero-engine's external flight loads, providing a foundation for the development of life assessment spectra for key aero-engine components, thereby improving the accuracy of life assessment and providing crucial data for determining and extending the lifespan of aero-engine components.

[0091] Example 1

[0092] like Figure 1 As shown, this embodiment of the invention provides a method for determining the program block spectrum load level of an aero-engine, including:

[0093] Step 101: Obtain multiple flight profiles of the target aero-engine obtained from actual measurements.

[0094] This step uses 40 measured flight profiles of a certain type of aero-engine, after filtering and small load removal processing, as an example. These include 14 high-maneuverability flight profiles, 16 low-maneuverability flight profiles, and 10 cruise flight profiles. Figure 2 As shown.

[0095] Step 102: Count the rainflow for each flight profile to obtain the mean and amplitude of multiple load cycles, which will be used as the mean amplitude pair for each load cycle.

[0096] In this step, rainflow counting was performed on 40 flight profiles, resulting in a total of 1655 load cycles. Figure 3 As shown, the rainflow matrix histogram is used for intuitive visualization.

[0097] Step 103: Using the load history time corresponding to the maximum load in the target flight profile as the time threshold, the secondary loops in the target flight profile before the time threshold are determined as load increase loops, and the secondary loops after the time threshold are determined as load decrease loops; wherein, the load loop corresponding to the maximum load in the target flight profile is the main loop, and the remaining load loops are secondary loops.

[0098] In this step, the 0-max-0 cycle of each flight profile is taken as the main cycle. For the same aero-engine, the maximum working load in different flight cycles is determined, so the average amplitude pair of the main cycle can be determined as (max / 2, max / 2); max represents the maximum load in the aero-engine flight profile; the number of main cycles is consistent with the number of flight profiles. For example... Figure 4 As shown, except for the 0-max-0 cycle, all remaining load cycles are considered as secondary cycles.

[0099] The load history time t corresponding to the maximum load of 100 max As a dividing line, t max The previous load history was determined to be a load rise cycle, and t was also determined. max The subsequent load history was determined to be a load descent cycle. Taking the 40th flight profile as an example, the specific division method is as follows: Figure 5 As shown. Through the above operations, all secondary cycles are divided into 780 load increase cycles, and the rainflow matrix histogram is shown below. Figure 6 As shown, the rainflow matrix histogram for 835 load descent cycles is as follows. Figure 7 As shown.

[0100] Step 104: Perform cluster analysis on each load ascent cycle and each load descent cycle in all flight profiles, and determine each cluster center using the load cycle mean amplitude pairs as analysis variables; where each cluster center corresponds to a load level.

[0101] In this step, for example, the Euclidean distance d between any two pairs of mean amplitude values ​​in all load rise cycles is calculated according to the following formula. ij :

[0102]

[0103] Where, m i m is the mean of load cycle i across all load increase cycles. j a is the mean of load cycle j across all load increase cycles; i a is the magnitude of load cycle i in all load increase cycles; j Let be the amplitude of load cycle j in all load increase cycles.

[0104] The local density ρ of the mean amplitude pair of load cycle i is calculated using the following formula. i :

[0105]

[0106] Where k1 is the local density parameter of the mean amplitude pair of load cycle i, used to represent the k1 nearest mean amplitude pairs around the mean amplitude pair of load cycle i; n' is the total number of load rise cycles in all flight profiles.

[0107] Sort all local densities in descending order for all load increase cycles to obtain the first index after descending order.

[0108] The density nearest neighbor distance δ of the mean amplitude pairs of load cycle i in all load rise cycles is calculated using the following formula. i :

[0109]

[0110] Where, q i q represents the first index corresponding to load cycle i in descending order; j This is the first index corresponding to load cycle j after sorting in descending order; For load cycle q i With load cycle q j The Euclidean distance between the average amplitude and the mean amplitude.

[0111] Local density ρ i nearest neighbor distance δ of density i The product of and is used as the decision variable γ for load cycle i. i .

[0112] like Figure 8 As shown, in all load increase cycles, the load values ​​corresponding to decision variables that are greater than the critical value of the first decision variable jump change are used as cluster centers.

[0113] The Euclidean distance d between any two pairs of mean amplitude values ​​in all load d d cycles is calculated using the following formula. mn :

[0114]

[0115] Where, m m The mean of load cycles m across all load descent cycles; m n a is the mean of load cycles n across all load descent cycles; m a is the amplitude of load cycle m in all load descent cycles; n This represents the magnitude of load cycle n across all load descent cycles.

[0116] The local density ρ of the mean amplitude pair of load cycles m is calculated using the following formula. m :

[0117]

[0118] Where k2 is the local density parameter of the mean amplitude pairs of load cycle m, used to represent the k2 nearest mean amplitude pairs around the mean amplitude pairs of load cycle m; n” is the total number of load descent cycles in all flight profiles.

[0119] Sort all local densities in descending order across all load descent cycles to obtain the second index after descending order.

[0120] The density nearest neighbor distance δ of the mean amplitude pairs of load cycle m in all load descent cycles is calculated using the following formula. m :

[0121]

[0122] Where, q m The second subscript corresponding to load cycle m after sorting in descending order; q n This is the second subscript corresponding to load cycle n after sorting in descending order; For load cycle q m With load cycle q n The Euclidean distance between the average amplitude and the mean amplitude.

[0123] Local density ρ m nearest neighbor distance δ of density m The product of and is used as the decision variable γ for the load cycle m. m .

[0124] In all load descent cycles, the load values ​​corresponding to decision variables that are greater than the critical value for the jump change of the second decision variable are used as cluster centers.

[0125] To make the scheme of this step clearer, this embodiment of the invention uses 780 rising load cycles as an example for cluster analysis:

[0126] First, based on the calculated Euclidean distance d ij Construct the n'-dimensional first symmetric distance matrix D1:

[0127]

[0128] Based on the first symmetric distance matrix D1, calculate the reciprocal of the k-nearest neighbor mean of each average amplitude pair in all load rise cycles to obtain the local density vector of all load rise cycles:

[0129]

[0130] Local density vector Sort in descending order to obtain the first index vector after descending order:

[0131]

[0132] in,

[0133] Then, calculate the density nearest neighbor distance vector for each load rise cycle:

[0134]

[0135] Based on local density ρ i nearest neighbor distance δ of density i Calculate the decision variable vector:

[0136]

[0137] Finally, for the decision variable vector Sort the variables in descending order and plot the trend of change of the decision variables. Use the critical value γ' = 0.1209, which is the threshold for observing jump changes in the decision variables, as the discrimination threshold, and take the loadings corresponding to all decision variables greater than 0.1209 as the cluster centers.

[0138] Step 105: The average amplitude value of each main loop is used as the load level corresponding to the load level of each cluster center as the program block spectrum load level of the target aero-engine.

[0139] In this step, the load cycle mean amplitude pairs corresponding to all cluster centers in the load rise cycle are used as the load levels of the measured load spectrum of the aero-engine, which are divided into t levels, namely U1, U2, ..., U t The load cycle mean amplitude pairs corresponding to all cluster centers in the load descent cycle are used as the load levels of the measured load spectrum of the aero-engine, which are divided into p levels, namely H1, H2, ..., Hp Summarize the results of the main cycle and secondary cycle classification of the measured load spectrum of aero-engines, such as... Figure 9 As shown.

[0140] According to the plan in step 104, such as Figure 10 and Figure 11 As shown, the load levels in the ascent cycle of the measured load spectrum of an aero-engine are divided into three levels, as follows: Figure 10 and Figure 12 As shown, the load levels in the descent cycle are divided into two levels.

[0141] In summary, the method for determining the load level of the aero-engine program block spectrum provided in this embodiment takes into account the inherent characteristics of the aero-engine load spectrum, separates the main cycle of the aero-engine from other cycles, and retains the working characteristic that the take-off and landing cycle is the main source of low-cycle fatigue damage. It divides the secondary cycles into load-increasing cycles and load-decreasing cycles according to the maximum load point, which conforms to the fatigue characteristics that the secondary cycle load delays fatigue damage before overload and accelerates fatigue damage after overload. Compared with adhering to a fixed eight-level load, it considers fatigue characteristics more comprehensively. It uses a clustering method to determine the load level of the secondary cycles, identifying the cycles with the most frequent loads as load levels. This fully utilizes the statistical characteristics of the measured load spectrum of the aero-engine, and is more consistent with the actual engineering characteristics of the load spectrum. This provides a more theoretically grounded method for determining the load level for the program block spectrum of aero-engine components, which is beneficial for improving the accuracy of component life testing and further provides a basis for aero-engine life determination.

[0142] Example 2

[0143] Based on the same inventive concept as Embodiment 1, this embodiment also provides an aero-engine program block spectrum load level determination system. Since the principle of this system in solving the problem is similar to the aforementioned aero-engine program block spectrum load level determination method, the implementation of this system can refer to the implementation of the aero-engine program block spectrum load level determination method.

[0144] like Figure 13 As shown, the aero-engine program block spectrum load level determination system includes:

[0145] The acquisition module 10 is used to acquire multiple flight profiles of the target aero-engine as measured in actual tests.

[0146] Rainflow counting module 20 is used to count rainflow for each flight profile to obtain the mean and amplitude of multiple load cycles, which are used as the mean amplitude pair for each load cycle.

[0147] The first determining module 30 is used to determine the secondary loops in the target flight profile before the time threshold as load increase loops and the secondary loops after the time threshold as load decrease loops, using the load history time corresponding to the maximum load in the target flight profile as the time threshold. The load loop corresponding to the maximum load in the target flight profile is the main loop, and the remaining load loops are secondary loops.

[0148] Clustering module 40 is used to perform cluster analysis on each load ascent cycle and each load descent cycle in all flight profiles, and to determine each cluster center using the load cycle mean amplitude pairs as analysis variables; wherein, one cluster center corresponds to one load level.

[0149] The second determining module 50 is used to take the average amplitude value of each main loop as the load level corresponding to the load level of each cluster center as the program block spectrum load level of the target aero-engine.

[0150] For example, the clustering module includes:

[0151] The first calculation unit is used to calculate the Euclidean distance d between the average amplitude pairs of any two load cycles in all load rise cycles according to the following formula. ij :

[0152]

[0153] Where, m i m is the mean of load cycle i across all load increase cycles. j a is the mean of load cycle j across all load increase cycles; i a is the magnitude of load cycle i in all load increase cycles; j Let be the amplitude of load cycle j in all load increase cycles.

[0154] The second calculation unit is used to calculate the local density ρ of the mean amplitude pair of load cycle i according to the following formula. i :

[0155]

[0156] Where k1 is the local density parameter of the mean amplitude pair of load cycle i, used to represent the k1 nearest mean amplitude pairs around the mean amplitude pair of load cycle i; n' is the total number of load rise cycles in all flight profiles.

[0157] The first descending sorting unit is used to sort all local densities in descending order in all load increase cycles to obtain the first index after descending sorting.

[0158] The third calculation unit is used to calculate the density nearest neighbor distance δ of the average amplitude pairs of load cycle i in all load rise cycles according to the following formula. i :

[0159]

[0160] Where, q i q represents the first index corresponding to load cycle i in descending order; j This is the first index corresponding to load cycle j after sorting in descending order; For load cycle q i With load cycle q j The Euclidean distance between the average amplitude and the mean amplitude.

[0161] The first defining unit is used to determine the local density ρ. i nearest neighbor distance δ of density i The product of and is used as the decision variable γ for load cycle i. i .

[0162] The second determining unit is used to select the load values ​​corresponding to decision variables that are greater than the critical value of the first decision variable jump change in all load increase cycles as cluster centers.

[0163] For example, the clustering module includes:

[0164] The fourth calculation unit is used to calculate the Euclidean distance d between the average amplitude pairs of any two load cycles in all load d d cycles according to the following formula. mn :

[0165]

[0166] Where, m m The mean of load cycles m across all load descent cycles; m n a is the mean of load cycles n across all load descent cycles; m a is the amplitude of load cycle m in all load descent cycles; n This represents the magnitude of load cycle n across all load descent cycles.

[0167] The fifth calculation unit is used to calculate the local density ρ of the mean amplitude pair of load cycles m according to the following formula. m :

[0168]

[0169] Where k2 is the local density parameter of the mean amplitude pairs of load cycle m, used to represent the k2 nearest mean amplitude pairs around the mean amplitude pairs of load cycle m; n” is the total number of load descent cycles in all flight profiles.

[0170] The second descending sorting unit is used to sort all local densities in descending order across all load descent cycles, resulting in the second subscript after descending sorting.

[0171] The sixth calculation unit is used to calculate the density nearest neighbor distance δ of the average amplitude pairs of load cycles m in all load descent cycles according to the following formula. m :

[0172]

[0173] Where, q m The second subscript corresponding to load cycle m after sorting in descending order; q n This is the second subscript corresponding to load cycle n after sorting in descending order; For load cycle q m With load cycle q n The Euclidean distance between the average amplitude and the mean amplitude.

[0174] The third determining unit is used to determine the local density ρ m nearest neighbor distance δ of density m The product of and is used as the decision variable γ for the load cycle m. m .

[0175] The fourth determining unit is to use the load value corresponding to the decision variable that is greater than the critical value of the second decision variable jump change in all load descent cycles as the cluster center.

[0176] For more detailed information on the working process of each of the above modules, please refer to the relevant content disclosed in Example 1, which will not be repeated here.

[0177] Example 3

[0178] This embodiment provides a computer device, including a processor and a memory; wherein, when the processor executes a computer program stored in the memory, it implements the steps of the method for determining the program block spectrum load level of an aero-engine as described in Embodiment 1.

[0179] For a more detailed explanation of the above method, please refer to the relevant content disclosed in Example 1, which will not be repeated here.

[0180] Example 4

[0181] This embodiment provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, it implements the steps of the method for determining the spectrum load level of the aero-engine program block as described in Embodiment 1.

[0182] For a more detailed explanation of the above method, please refer to the relevant content disclosed in Example 1, which will not be repeated here.

[0183] Example 5

[0184] 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, they implement the steps of the method for determining the program block spectrum load level of an aero-engine as described in Embodiment 1.

[0185] For a more detailed explanation of the above method, please refer to the relevant content disclosed in Example 1, which will not be repeated here.

[0186] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems, devices, storage media, and computer program products disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

[0187] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute 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, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0188] In some embodiments, computer-executable instructions may take the form of programs, software, software modules, scripts, 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 stand-alone programs or as modules, components, subroutines, or other units suitable for use in a computing environment.

[0189] As an example, computer-executable instructions may, but do not necessarily, correspond to files in a file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple co-located files (e.g., files that store one or more modules, subroutines, or code sections).

[0190] As an example, computer-executable instructions can be deployed to execute on a single electronic device, or on multiple electronic devices located at one location, or on multiple electronic devices distributed across multiple locations and interconnected via a communication network.

[0191] 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 understand that various equivalent substitutions, modifications, or improvements can be made to the technical solutions and embodiments of the present invention without departing from the spirit and scope of the invention, and all such modifications and improvements fall within the scope of the present invention. The scope of protection of the present invention is defined by the appended claims.

Claims

1. A method for determining the load level of aero-engine program block spectrum, characterized in that, include: Obtain multiple measured flight profiles of the target aero-engine; Rainflow counts were performed on each flight profile to obtain the mean and amplitude of multiple load cycles, which were used as the mean amplitude pair for each load cycle. Using the load history time corresponding to the maximum load in the target flight profile as the time threshold, the secondary loops in the target flight profile before the time threshold are determined as load increase loops, and the secondary loops after the time threshold are determined as load decrease loops; among them, the load loop corresponding to the maximum load in the target flight profile is the main loop, and the remaining load loops are secondary loops. Cluster analysis was performed on each load ascent cycle and each load descent cycle in all flight profiles, and the average amplitude pairs of load cycles were used as the analysis variables to determine the cluster centers; where each cluster center corresponds to one load level. The average amplitude value of each main loop is used as the load level corresponding to the load level of each cluster center as the program block spectrum load level of the target aero-engine.

2. The method for determining the load level of an aero-engine program block spectrum according to claim 1, characterized in that, Cluster analysis was performed on each load ascent cycle and each load descent cycle in all flight profiles, with the load cycle mean amplitude pairs as the analysis variables to determine each cluster center; wherein, each cluster center corresponds to one load level, including: The Euclidean distance d between any two pairs of mean amplitude values ​​in all load rise cycles is calculated using the following formula. ij : ； Where, m i m is the mean of load cycle i across all load increase cycles. j a is the mean of load cycle j across all load increase cycles; i a is the magnitude of load cycle i in all load increase cycles; j The magnitude of load cycle j in all load increase cycles; The local density ρ of the mean amplitude pair of load cycle i is calculated using the following formula. i : ； Where k1 is the local density parameter of the mean amplitude pair of load cycle i, used to represent the k1 nearest mean amplitude pairs around the mean amplitude pair of load cycle i; n' is the total number of load rise cycles in all flight profiles. Sort all local densities in descending order for all load increase cycles to obtain the first index of the descending order; The density nearest neighbor distance δ of the mean amplitude pairs of load cycle i in all load rise cycles is calculated using the following formula. i : ； Where, q i q represents the first index corresponding to load cycle i after sorting in descending order; j This is the first index corresponding to load cycle j after sorting in descending order; For load cycle q i With load cycle q j The Euclidean distance between the mean amplitude pairs; Local density ρ i nearest neighbor distance δ of density i The product of and is used as the decision variable γ for load cycle i. i ; In all load increase cycles, the load values ​​corresponding to decision variables that are greater than the critical value of the first decision variable jump change are used as cluster centers.

3. The method for determining the load level of an aero-engine program block spectrum according to claim 1, characterized in that, Cluster analysis was performed on each load ascent cycle and each load descent cycle in all flight profiles, with the load cycle mean amplitude pairs as the analysis variables to determine each cluster center; wherein, each cluster center corresponds to one load level, including: The Euclidean distance d between any two pairs of mean amplitude values ​​in all load d d cycles is calculated using the following formula. mn : ； Where, m m The mean of load cycles m across all load descent cycles; m n a is the mean of load cycles n across all load descent cycles; m a is the amplitude of load cycle m in all load descent cycles; n The magnitude of load cycle n in all load descent cycles; The local density ρ of the mean amplitude pair of load cycles m is calculated using the following formula. m : ； Where k2 is the local density parameter of the mean amplitude pairs of load cycle m, used to represent the k2 nearest mean amplitude pairs around the mean amplitude pairs of load cycle m; n'' is the total number of load descent cycles in all flight profiles; Sort all local densities in descending order across all load descent cycles to obtain the second index after descending order; The density nearest neighbor distance δ of the mean amplitude pairs of load cycle m in all load descent cycles is calculated using the following formula. m : ； Where, q m The second subscript corresponding to load cycle m after sorting in descending order; q n This is the second subscript corresponding to load cycle n after sorting in descending order; For load cycle q m With load cycle q n The Euclidean distance between the mean amplitude pairs; Local density ρ m nearest neighbor distance δ of density m The product of and is used as the decision variable γ of the load cycle m. m ; In all load descent cycles, the load values ​​corresponding to decision variables that are greater than the critical value for the jump change of the second decision variable are used as cluster centers.

4. A system for determining the program block spectrum load level of an aero-engine, characterized in that, include: The acquisition module is used to acquire multiple measured flight profiles of the target aero-engine; The rainflow counting module is used to count rainflows for each flight profile to obtain the mean and amplitude of multiple load cycles, which are used as the mean amplitude pairs for each load cycle. The first determining module is used to determine the secondary loops in the target flight profile before the time threshold as load increase loops and the secondary loops after the time threshold as load decrease loops, using the load history time corresponding to the maximum load in the target flight profile as the time threshold. Among them, the load loop corresponding to the maximum load in the target flight profile is the main loop, and the remaining load loops are secondary loops. The clustering module is used to perform cluster analysis on each load ascent cycle and each load descent cycle in all flight profiles, using the load cycle mean amplitude pairs as the analysis variables to determine each cluster center; where each cluster center corresponds to one load level; The second determination module is used to take the average amplitude value of each main loop as the load level corresponding to the load level of each cluster center as the program block spectrum load level of the target aero-engine.

5. The aero-engine program block spectrum load level determination system according to claim 4, characterized in that, The clustering module includes: The first calculation unit is used to calculate the Euclidean distance d between the average amplitude pairs of any two load cycles in all load rise cycles according to the following formula. ij : ； Where, m i m is the mean of load cycle i across all load increase cycles. j a is the mean of load cycle j across all load increase cycles; i a is the magnitude of load cycle i in all load increase cycles; j The magnitude of load cycle j in all load increase cycles; The second calculation unit is used to calculate the local density ρ of the mean amplitude pair of load cycle i according to the following formula. i : ； Where k1 is the local density parameter of the mean amplitude pair of load cycle i, used to represent the k1 nearest mean amplitude pairs around the mean amplitude pair of load cycle i; n' is the total number of load rise cycles in all flight profiles. The first descending sorting unit is used to sort all local densities in descending order in all load increase cycles to obtain the first subscript after descending sorting. The third calculation unit is used to calculate the density nearest neighbor distance δ of the average amplitude pairs of load cycle i in all load rise cycles according to the following formula. i : ； Where, q i q represents the first index corresponding to load cycle i after sorting in descending order; j This is the first index corresponding to load cycle j after sorting in descending order; For load cycle q i With load cycle q j The Euclidean distance between the mean amplitude pairs; The first defining unit is used to determine the local density ρ. i nearest neighbor distance δ of density i The product of and is used as the decision variable γ for load cycle i. i ; The second determining unit is used to select the load values ​​corresponding to decision variables that are greater than the critical value of the first decision variable jump change in all load increase cycles as cluster centers.

6. The aero-engine program block spectrum load level determination system according to claim 4, characterized in that, The clustering module includes: The fourth calculation unit is used to calculate the Euclidean distance d between any two pairs of mean amplitude values ​​in all load d-decay cycles according to the following formula. mn : ； Where, m m The mean of load cycles m across all load descent cycles; m n a is the mean of load cycles n across all load descent cycles; m a is the amplitude of load cycle m in all load descent cycles; n The magnitude of load cycle n in all load descent cycles; The fifth calculation unit is used to calculate the local density ρ of the mean amplitude pair of load cycles m according to the following formula. m : ； Where k2 is the local density parameter of the mean amplitude pairs of load cycle m, used to represent the k2 nearest mean amplitude pairs around the mean amplitude pairs of load cycle m; n'' is the total number of load descent cycles in all flight profiles; The second descending sorting unit is used to sort all local densities in descending order in all load descent cycles to obtain the second subscript after descending sorting. The sixth calculation unit is used to calculate the density nearest neighbor distance δ of the average amplitude pairs of load cycles m in all load descent cycles according to the following formula. m : ； Where, q m The second subscript corresponding to load cycle m after sorting in descending order; q n This is the second subscript corresponding to load cycle n after sorting in descending order; For load cycle q m With load cycle q n The Euclidean distance between the mean amplitude pairs; The third determining unit is used to determine the local density ρ m nearest neighbor distance δ of density m The product of and is used as the decision variable γ of the load cycle m. m ; The fourth determining unit is to use the load value corresponding to the decision variable that is greater than the critical value of the second decision variable jump change in all load descent cycles as the cluster center.

7. A computer device, characterized in that, It includes 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 program block spectrum load level of an aero-engine as described in any one of claims 1-3.

8. A computer-readable storage medium, characterized in that, Used to store computer programs; when the computer programs are executed by a processor, they implement the steps of the method for determining the spectrum load level of the aero-engine program block as described in any one of claims 1-3.

9. A computer program product, characterized in that, It includes computer-executable instructions or computer programs, which, when executed by a processor, implement the steps of the method for determining the program block spectrum load level of an aero-engine as described in any one of claims 1-3.

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