Aero-engine stability margin analysis method based on performance index network

Abstract

The invention discloses an aero-engine stability margin analysis method based on a performance index network, and the method comprises the steps: obtaining the test run data of an aero-engine which is qualified in test run, and screening out a performance index for constructing an aero-engine performance index network; constructing an aero-engine simulation operation model by using the performance index network, constructing an aero-engine stability margin analysis model corresponding to the selected operation stage, solving simulation operation parameters in the model, and calculating a stability margin value corresponding to each operation stage; and determining a stability margin qualified curve of each operation stage by using different test run data, wherein the stability margin qualified curve is used for evaluating the stability of a certain specified operation stage of the aero-engine to be subjected to stability margin evaluation. According to the method, objective and global analysis can be carried out on the aero-engine system, interpretability is achieved, meanwhile, the change rule of the stability margin of each state of the aero-engine can be dynamically described, and a new guiding thought is provided for aero-engine design.

Description

Technical Field

[0001] The invention relates to the technical field of aircraft engine stability margin analysis, and in particular to an aircraft engine stability margin analysis method based on a performance index network. Background Art

[0002] At present, as the highest-end product in the field of equipment manufacturing, aircraft engines represent a country's scientific and technological level and comprehensive national strength. They have always been regarded as core technologies that affect national air transportation, national defense security and maintain national strategic advantages. Aircraft engines have complex components, diverse operating conditions and complex working environments, and their stability margin analysis faces huge challenges. When the stability margin of aircraft engines decreases or the operation is unstable, it may cause problems such as engine rotational stall, blade flutter and coupled vibration, causing huge economic losses, and will seriously threaten the safety of personnel and even directly endanger national security. Accurate stability margin analysis methods can reflect the dynamic stability margin change law of equipment, minimize downtime caused by performance degradation, facilitate active maintenance, and prevent huge losses caused by accidents. Therefore, conducting aircraft engine stability margin analysis is the core basic work of aircraft engine health management, an important guarantee for the safe and efficient operation of aircraft engines, and can further improve the intelligent operation level of aircraft engines, highlight core competitiveness and optimize resource allocation.

[0003] In practical engineering applications, the stability margin analysis of aircraft engines has been widely carried out. Traditional stability margin analysis methods usually analyze the stability margin of a single indicator, such as combustion chamber stability margin, aerodynamic stability margin or control system stability margin. The new stability margin analysis method is mainly based on artificial intelligence technologies such as machine learning and deep learning, and relies on big data to perform stability margin analysis. However, the above analysis methods all have obvious defects: the traditional analysis method does not show the stable state of aircraft engine operation affected by many factors, does not consider the correlation between multiple performance indicators, and it is difficult to obtain the overall stability margin of the aircraft engine; the new analysis method is still in a relatively shallow stage, and it is difficult to use a scientific method to explain the change mechanism of the stability margin of the aircraft engine. Therefore, in view of the above defects, it is necessary to study how to improve the stability margin analysis ability of aircraft engines in practical engineering.

[0004] The existing technologies for aircraft engine stability margin analysis can be roughly divided into two categories: first, traditional system stability margin analysis technology and its improved technology; second, artificial intelligence-based technology.

[0005] For the first type of solution, for example, the patent application with publication number CN118780194A: A method for evaluating the stability margin of an aviation gas turbine engine, which calculates the engine stability margin based on the engine blade rotor and combined with the compressor surge data. However, the shortcomings of this type of solution are that the independence of indicators will affect the accuracy of the calculation, and the evaluation results are mainly obtained by linear operation of indicators, which cannot reflect the qualitative changes and emergent laws of the system caused by the nonlinear relationship and fusion of indicators, and it is difficult to accurately analyze the stability margin of complex systems.

[0006] For the second type of solution, for example, the patent application with publication number CN117252109A: a method and system for analyzing the stability of an aircraft engine based on data processing, which uses a neural network model to predict the stability margin of an aircraft engine; however, this type of artificial intelligence-based technology has a black box characteristic, which means that it is impossible to clearly determine the result generation mechanism of the model, and it is difficult to scientifically explain the mechanism of changes in the stability margin of an aircraft engine and its internal relationship. Summary of the invention

[0007] The purpose of the present invention is to provide an aero-engine stability margin analysis method based on a performance index network, so as to solve the problems existing in the existing aero-engine stability margin analysis method.

[0008] In order to achieve the above tasks, the present invention adopts the following technical solutions:

[0009] A method for analyzing the stability margin of an aero-engine based on a performance index network, comprising:

[0010] For aircraft engines that have passed the test run, obtain their test run data and select the operation phase for stability margin analysis, and pre-process the performance indicators included in the selected operation phase;

[0011] For the preprocessed performance indicators, an aircraft engine performance indicator network corresponding to the selected operation stage is constructed based on complex network theory; the preprocessed performance indicators are used as nodes, and the interaction relationships between different nodes are constructed as edges between nodes based on the Pearson correlation coefficient algorithm;

[0012] Based on differential equations, the aircraft engine performance index network is used to construct an aircraft engine simulation operation model, and based on the co-average degree of nodes in the aircraft engine performance index network, an aircraft engine stability margin analysis model corresponding to the selected operation stage is constructed;

[0013] Using the aircraft engine performance index network and combining the operating data of the performance index, solving the simulation operation parameters in the aircraft engine simulation operation model;

[0014] Based on the solved simulation operation parameters, determine the stability margin value of the stability margin analysis model of the aircraft engine in the selected operation stage;

[0015] The qualified test data of multiple aircraft engine tests are used to determine the qualified stability margin curve of each selected operation stage; for a specified operation stage of the aircraft engine to be evaluated for stability margin, the test data of the operation stage is obtained and the stability margin value is calculated; based on the relationship between the stability margin value and the corresponding qualified stability margin curve, it is determined whether the specified operation stage of the aircraft engine to be evaluated for stability margin is stable.

[0016] Furthermore, the preprocessing of the performance indicators included in the selected operation stage includes:

[0017] For the selected operation phase, eliminate the performance indicators that are not involved in the normal operation of the aircraft engine, including performance indicators related to protective measures.

[0018] Furthermore, the constructing of the interaction relationship between different nodes as the edge between the nodes based on the Pearson correlation coefficient algorithm includes:

[0019] Note that there are N performance indicators after preprocessing, and the number of sampling moments of the performance indicators in this operation phase is n. For each pair of performance indicators (x i ,x j ), the Pearson correlation coefficient between them is expressed as follows:

[0020]

[0021] In the above formula, r ij Represents the performance index x i and performance index x j The Pearson correlation coefficient between is an N×N correlation coefficient matrix; ik and x jk Represents the performance index x i and performance index x j The corresponding operating data at sampling time k, and Represents the performance index x i and performance index x j Average running data at all sampling moments;

[0022]

[0023] In the above formula, A ij Represents the performance index x i and performance index x j The edge between ij is the correlation coefficient matrix rij The significance level matrix can reflect the confidence level of the performance index correlation coefficient calculation at the corresponding position; 0 is the constructed threshold for the significance level.

[0024] Furthermore, the aircraft engine simulation operation model is constructed based on the differential equation and using the aircraft engine performance index network, which is expressed as:

[0025]

[0026] Where t is the time parameter, x i 、x j Respectively represent the i-th and j-th performance indicators in the aircraft engine performance indicator network, A ij Represents the performance index x in the aircraft engine performance index network i and performance index x j The edges between them, F and g are simulation operation parameters, which represent the physical characteristics and dynamic behaviors of the aircraft engine under the thrust and IGV angle corresponding to the test parameters.

[0027] Furthermore, the calculation process of the average degree and co-average degree of the node is:

[0028]

[0029] Where d i Represents the node x i The number of connected edges is called the degree of the node;

[0030]

[0031] Where d represents all nodes in the aircraft engine performance index network connected to node x i The average value of connected edges is called average degree;

[0032]

[0033] Where d nn It is the average degree of the neighboring nodes of each node in the aircraft engine performance index network, which is called the co-average degree.

[0034] Furthermore, the aeroengine stability margin analysis model corresponding to the selected operation phase is constructed based on the co-average degree of the nodes in the aeroengine performance index network, which is expressed as:

[0035]

[0036] In the above formula, d nnIt is the co-average degree of each node in the aircraft engine performance index network. Re(λ) represents the stability margin value of the aircraft engine performance index network at the selected operation stage. When Re(λ)≥0, the aircraft engine is unstable at this operation stage; when Re(λ)<0, the aircraft engine is stable at this operation stage.

[0037] Furthermore, the method of using the aircraft engine performance index network and combining the operating data of the performance index to solve the simulated operating parameters in the aircraft engine simulated operating model includes:

[0038]

[0039] in, and A ij + and A ij - The average degree of is the performance index x i The mathematical expectation of ij + is the promotion matrix of the aircraft engine performance index network. ij A ij - is the inhibition matrix of the performance indicator network, by extracting A in the performance indicator network ij A ij =A ij + +A ij - .

[0040] Furthermore, the method further comprises:

[0041] If the result of the selected operating phase of the aircraft engine to be evaluated for stability margin is that the operation is stable, and the calculated stability margin value falls on the lowest point of the stability margin qualified curve, it indicates that the selected operating state is currently the optimal stable state; otherwise, the working parameters of the aircraft engine to be evaluated for stability margin are adjusted and the above stability margin analysis is performed again, so that the stability margin value is located at or as close as possible to the lowest point of the stability margin qualified curve, and the adjusted working parameters are saved for the subsequent working process of the aircraft engine.

[0042] An aircraft engine stability margin analysis device comprises a processor, a memory and a computer program stored in the memory; when the processor is executed by a computer, the aircraft engine stability margin analysis method based on a performance index network is implemented.

[0043] A computer-readable storage medium stores a computer program; when the computer program is executed by a processor, the aero-engine stability margin analysis method based on a performance indicator network is implemented.

[0044] Compared with the prior art, the present invention has the following technical features:

[0045] 1. The present invention is based on test data and complex network methods and can objectively and globally analyze highly integrated, internally coupled and nonlinear aircraft engine systems based on real aircraft engine test data.

[0046] 2. The aircraft engine performance index network and aircraft engine stability margin analysis model constructed by the present invention can accurately analyze the stability margin of the system in various states, and can explain the relationship between the performance index network and the stability margin, and are interpretable.

[0047] 3. The indicators of the aircraft engine stability margin analysis method proposed in the present invention can dynamically characterize the changing law of the stability margin of each state of the aircraft engine, obtain the operating stage of the aircraft engine under the optimal stability margin, obtain the system stability margin improvement plan, and provide new guiding ideas for aircraft engine design. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the process of the present invention;

[0049] Figure 2 This is a schematic diagram of an aircraft engine performance index network in an embodiment of the present invention;

[0050] Figure 3 Schematic diagram of an aircraft engine stability margin analysis model in an embodiment of the present invention;

[0051] Figure 4 The stability margin analysis results of the aircraft engine in each state in the embodiment of the present invention;

[0052] Figure 5 This is a solution for improving the stability margin of an aircraft engine in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] Based on the test data of aircraft engines, the present invention combines the advantages of complex network theory and proposes an aircraft engine stability margin analysis method based on performance indicator network, which can accurately and dynamically characterize the comprehensive stability margin of aircraft engines, obtain the stability margin of the corresponding state of aircraft engines, and use it as the judgment standard for aircraft engine health management.

[0054] See attached Figure 1 The present invention provides an aero-engine stability margin analysis method based on a performance index network, comprising the following steps:

[0055] Step 1: For an aircraft engine that has passed the test run, its test run data is obtained and an operation phase for stability margin analysis is selected, and the performance indicators included in the selected operation phase are preprocessed.

[0056] For aircraft engines that have passed the test run, test run data is obtained; during the test run, the same thrust and IGV angle should be maintained in the same operating phase. The test run data is collected during the complete flight process of the aircraft engine, and the flight process includes multiple operating phases, including start-up, acceleration, ascent, and stable operation; the test run data in each operating phase includes multiple performance indicators, such as low-pressure rotor speed, lubricating oil temperature, etc.

[0057] Among all the operation phases, select the operation phase that needs to be analyzed for stability margin; for example, the operation phases can be sorted according to the maintenance time of all the operation phases, and the one or the first several operation phases with the longest maintenance time can be selected for subsequent analysis; Figure 4 As shown, in one embodiment of the present invention, six operation stages are selected for analysis.

[0058] For the selected operation phase, the performance indicators that are not involved in the normal operation of the aircraft engine are eliminated, such as the anti-surge voltage meter value and lubricating oil pressure and other performance indicators related to protective measures. The remaining performance indicators are used to subsequently construct the aircraft engine performance indicator network.

[0059] Step 2, for the preprocessed performance indicators, construct an aircraft engine performance indicator network corresponding to the selected operation stage based on complex network theory; wherein the preprocessed performance indicators are used as nodes, and the interaction relationships between different nodes are constructed as edges between nodes based on the Pearson correlation coefficient algorithm.

[0060] Specifically, the Pearson correlation coefficient is used to measure the linear correlation between two variables. For the preprocessed performance indicators, the interaction relationship between different performance indicators can be calculated.

[0061] Assuming that there are N preprocessed performance indicators (i.e., nodes) in the selected operation phase, and the number of sampling moments of the performance indicators in the operation phase is n, an n×N performance indicator matrix can be formed; for each pair of performance indicators (x i ,x j ), the Pearson correlation coefficient between them can be calculated as follows:

[0062]

[0063] In the above formula, r ij Represents the performance index x i and performance index xj The Pearson correlation coefficient between is an N×N correlation coefficient matrix; ik and x jk Represents the performance index x i and performance index x j The corresponding operating data at sampling time k, and Represents the performance index x i and performance index x j Average running data over all sampling times.

[0064]

[0065] In the above formula, A ij Represents the performance index x i and performance index x j The edge between ij is the correlation coefficient matrix r ij The significance level matrix can reflect the confidence level of the performance index correlation coefficient calculation at the corresponding position; 0 is the construction threshold of the significance level, and it is generally believed that p 0 =0.05; thus, the aircraft engine performance index network is constructed, that is, when the significance level matrix is ​​less than 0.05, the correlation coefficient is considered to be credible, and the interaction relationship between the performance indicators is r ij , which is used as the performance index x i and performance index x j The edges between them construct the aircraft engine performance index network, see Figure 2 The example given.

[0066] Step 3, based on differential equations, using the aircraft engine performance index network to construct an aircraft engine simulation operation model, and based on the co-average degree of nodes in the aircraft engine performance index network, construct an aircraft engine stability margin analysis model corresponding to the selected operation stage.

[0067] The interaction relationship between performance indicators is common in the aircraft engine performance indicator network. Differential equations are used to simulate the dynamic changes between performance indicators. Based on the aircraft engine performance indicator network, an aircraft engine simulation operation model is constructed as follows:

[0068]

[0069] Where t is the time parameter, x i 、x j Respectively represent the i-th and j-th performance indicators (nodes) in the aircraft engine performance indicator network, A ij Represents the performance index x in the aircraft engine performance index networki and performance index x j The edges between them, F and g are simulation operation parameters, which represent the physical characteristics and dynamic behaviors of the aircraft engine under the thrust and IGV angle corresponding to the test parameters, including but not limited to the damping characteristics, inertial response and other related characteristics of the system, which are solved through subsequent steps.

[0070] The topological properties of the aircraft engine performance index network are used to characterize the aircraft engine stability margin index, which is expressed as follows:

[0071]

[0072] Where d i Represents the node x i The number of connected edges is called the degree of the node.

[0073]

[0074] Where d represents all nodes in the aircraft engine performance index network connected to node x i The average value of connected edges is called average degree.

[0075]

[0076] Where d nn It is the average degree of the neighboring nodes of each node in the aircraft engine performance index network, which is called the co-average degree.

[0077] According to the parametric stability analysis method of complex systems, an aircraft engine stability margin analysis model is constructed based on the aircraft engine simulation operation model, which is specifically expressed as follows:

[0078]

[0079] In the above formula, Re(λ) represents the stability margin value under the aircraft engine performance index network of the selected operation stage. When Re(λ)≥0, the aircraft engine is unstable at this operation stage; when Re(λ)<0, the aircraft engine is stable at this operation stage. The larger the absolute value of Re(λ), the stronger the stability margin of the aircraft engine at this operation stage and the stronger the anti-interference ability.

[0080] Step 4, using the aircraft engine performance index network and combining the operating data of the performance index, solving the simulation operation parameters in the aircraft engine simulation operation model.

[0081] Calculate the mathematical expectation of each performance index (node) in the aircraft engine performance index network to obtain the expected state of the aircraft engine at a certain operation stage, so as to determine the simulation operation parameters F and g at the selected operation stage:

[0082]

[0083] in, and A ij + and A ij - The average degree of is the performance index x i The mathematical expectation of ij + is the promotion matrix of the aircraft engine performance index network. ij A ij - is the inhibition matrix of the performance indicator network, by extracting A in the performance indicator network ij A ij =A ij + +A ij - ; Normally, g=1 can be set.

[0084] Step 5: Determine the stability margin value of the stability margin analysis model of the aircraft engine in the selected operation stage based on the solved simulation operation parameters.

[0085] According to the above formula (6) and formula (8), the average degree d of the node in the selected operation stage can be obtained nn As well as the specific values ​​of the simulated operating parameters F, g and , these values ​​are substituted into the aircraft engine stability margin analysis model of formula (7) to obtain the stability margin value in the corresponding operating stage.

[0086] For example, the simulated operating parameter F of an aircraft engine at a certain operating stage is C 1 , whose performance index network d nn The value is C 2 , and C 2 >C 1 , if g = 1, then the stability margin of the aircraft engine in this state is

[0087]

[0088] Step 6, using the test data of the aircraft engine that has passed multiple test runs, determine the stability margin pass curve for each selected operation stage;

[0089] For a specified operating phase of an aircraft engine for which stability margin assessment is to be performed, the test data of the operating phase is obtained and an aircraft engine performance index network is constructed according to the same method as described above, and corresponding simulation operating parameters are calculated. The stability margin value is calculated based on the simulation operating parameters; based on the relationship between the stability margin value and the corresponding stability margin qualified curve, it is determined whether the specified operating phase of the aircraft engine for which stability margin assessment is to be performed is operating stably.

[0090] For the test data of aircraft engines that have passed multiple test runs, for each selected operating stage, the corresponding stability margin value can be obtained through steps 1 to 5; curve fitting is performed on all stability margin values ​​in each operating stage to obtain the stability margin qualified curve corresponding to the operating stage.

[0091] For example, in a certain embodiment, 10 groups of qualified test data are selected, and six operating stages of the aircraft engine test are selected for analysis; taking the first operating stage as an example, 10 stability margin values ​​corresponding to the first operating stage can be obtained through 10 groups of test data, and after curve fitting, the qualified stability margin curve of the first operating stage is obtained; the same method can be used to obtain the qualified stability margin curves of the remaining 5 operating stages.

[0092] For an aircraft engine to be evaluated, after obtaining its test data, select the six corresponding operation stages to be evaluated respectively; still taking the first operation stage as an example, after obtaining the test data of the operation stage, construct the aircraft engine performance index network of the first operation stage according to the preprocessing of step 1 and the process of step 2, and calculate the stability margin value Re(λ) of the first operation stage according to the same method of steps 3 to 5; determine whether the stability margin value Re(λ) falls on the stability margin qualified curve of the first operation stage, if it is on the stability margin qualified curve, it indicates that the first operation stage of the aircraft engine to be evaluated is stable; if it is not on the stability margin qualified curve, or the stability margin value Re(λ)≥0, then the first operation stage is unstable. The same method can be used to analyze the stability of the remaining operation stages of the aircraft engine.

[0093] By establishing the stability margin qualified curves of the above-mentioned operating stages, carefully observing and analyzing the range of the stability margin values ​​in different operating stages, we can gain an in-depth understanding of the stability level of the aircraft engine in the operating stage and further identify possible optimization space; the stability margin qualified curve intuitively shows the relationship between the stability margin value and the simulated operating parameters, thereby obtaining the optimal stability margin suitable for the aircraft engine (i.e., the lowest point of the stability margin qualified curve) and the corresponding simulated operating parameters; in addition, the synergy between the components of the aircraft engine can be optimized by enhancing the correlation between the components, thereby reducing the dynamic instability factors of the system, improving the stability margin values ​​of the aircraft engine in all working stages, and enhancing its adaptability under extreme or adverse conditions.

[0094] On the basis of the above technical solution, the method may further include:

[0095] Step 7: If the result of the selected operation phase of the aircraft engine to be evaluated for stability margin is stable operation, and the calculated stability margin value falls on the lowest point of the stability margin qualified curve, it indicates that the selected operation state is currently the best stable state; otherwise, the operating parameters of the aircraft engine to be evaluated for stability margin are adjusted and the above stability margin analysis is performed again, so that the stability margin value is at or as close as possible to the lowest point of the stability margin qualified curve, and the adjusted operating parameters are saved for subsequent operation of the aircraft engine; the same method can be used to adjust the operating parameters of each selected operation phase, so that each operation phase can reach or approach the best stable state as much as possible.

[0096] One embodiment of the present invention uses a certain aircraft engine manufacturer as the research object, and collects test data of two engines at 12 different operating stages under preset test parameters. The aircraft engine test data records dozens of performance indicators such as thrust, air pressure and temperature, fuel pressure and temperature, air flow, fuel flow, engine speed, control valve opening, etc. during the aircraft engine test. The data is high-dimensional, heterogeneous, multivariate and multimodal, and the performance indicators are highly correlated.

[0097] By screening the performance indicators of aircraft engine test data at 12 different operating stages, a total of 30 performance indicators are extracted for each aircraft engine at each operating stage, as follows:

[0098] Low-pressure rotor speed (N1), high-pressure rotor speed (N2), turbine after-gas temperature (N3), lubricating oil temperature (N4), main oil circuit pressure (N5), auxiliary oil circuit pressure (N6), high-pressure compressor and low-pressure compressor outlet air pressure ratio (N7), high-pressure compressor inlet adjustable guide vane angle (N8), cam box position (N9), fuel low flow sensor (N10), engine thrust value (N11), intake casing vibration value (N12), fuel nozzle vibration value (N13), high-pressure turbine bearing vibration value (N14), low-pressure compressor outlet pressure (N15), high-pressure compressor outlet pressure (N16), low-pressure compressor outlet temperature (N17) , high-pressure compressor outlet left side temperature (N18), high-pressure compressor outlet right side temperature (N19), test chamber front static pressure (N20), high-pressure compressor and low-pressure turbine outlet air pressure ratio (N21), fuel adjustment voltage meter (N22), catalytic igniter fuel supply pressure (N23), low-pressure fuel pump after-pump pressure (N24), high-pressure fuel pump after-pump pressure (N25), low-pressure compressor outlet air pressure entering the pressure ratio regulator (N26), high-pressure compressor outlet air pressure entering the pressure ratio regulator (N27), low-pressure turbine outlet air pressure entering the pressure ratio regulator (N28), exhaust mixer static pressure (N29), low-pressure fuel pump pump front pressure (N30).

[0099] In this embodiment, the performance index network constructed by the performance indexes selected by one of the aircraft engines that has passed the test run at a certain operation stage is as follows: Figure 2 shown.

[0100] In order to evaluate the stability margin analysis model constructed by the present invention, this embodiment uses the performance index network A obtained by the relationship between the performance indicators at a given time t. ij , through the stability margin analysis model of the aircraft engine, the stability margin analytical value is obtained, such as Figure 3 As shown; the verification shows that the difference between the actual value of the stability margin of the aircraft engine in each operating stage and the stability margin value calculated by the stability margin analysis model is very small, and the average relative error is less than 1%, indicating that the stability margin analysis model constructed by this scheme using real performance index data can accurately characterize the status of each performance index and the overall state of the aircraft engine, and the stability margin analysis model is very suitable for the analysis of the aircraft engine test process.

[0101] like Figure 4 As shown, the aero-engine stability margin analysis and solution results corresponding to various simulated operating parameters of the aero-engine in different states in this embodiment are displayed, indicating the effectiveness of the analysis method of the present invention.

[0102] like Figure 5As shown, an improvement scheme for increasing the stability margin of all states of an aircraft engine by enhancing the correlation between aircraft engine components is presented.

[0103] The embodiments show that the aircraft engine stability margin analysis model constructed by the present invention has an average relative error of less than 1%, which means that the method of the present invention can accurately describe the state of the system at each stage, and the form of differential equations can explain the relationship between the performance index and the system state change pattern, which is interpretable; the aircraft engine stability margin analysis method proposed by the present invention can dynamically calculate the stability margin of each state of the aircraft engine, and can find the optimal stability margin of the aircraft engine in this state, providing guidance for production, which has obvious advantages.

[0104] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for analyzing the stability margin of an aircraft engine based on a performance index network, characterized in that: include: For aircraft engines that have passed the test run, obtain their test run data and select the operation phase for stability margin analysis, and pre-process the performance indicators included in the selected operation phase; For the preprocessed performance indicators, the aircraft engine performance indicator network corresponding to the selected operation stage is constructed based on complex network theory; The preprocessed performance indicators are used as nodes, and the interaction relationships between different nodes are constructed based on the Pearson correlation coefficient algorithm as the edges between nodes; Based on differential equations, the aircraft engine performance index network is used to construct an aircraft engine simulation operation model, and based on the co-average degree of nodes in the aircraft engine performance index network, an aircraft engine stability margin analysis model corresponding to the selected operation stage is constructed; Using the aircraft engine performance index network and combining the operating data of the performance index, solving the simulation operation parameters in the aircraft engine simulation operation model; Based on the solved simulation operation parameters, determine the stability margin value of the stability margin analysis model of the aircraft engine in the selected operation stage; Using the test data of aircraft engines that have passed multiple test runs, determine the stability margin pass curve for each selected operation phase; For a specified operation phase of the aircraft engine for which stability margin assessment is to be performed, obtaining test data of the operation phase and calculating the stability margin value; According to the relationship between the stability margin value and the corresponding stability margin qualified curve, it is determined whether the aircraft engine to be evaluated for stability margin is running stably in the designated operation phase.

2. The method for analyzing the stability margin of an aircraft engine based on a performance index network according to claim 1, characterized in that: The preprocessing of the performance indicators included in the selected operation stage includes: For the selected operation phase, eliminate the performance indicators that are not involved in the normal operation of the aircraft engine, including performance indicators related to protective measures.

3. The method for analyzing the stability margin of an aircraft engine based on a performance index network according to claim 1, characterized in that: The method of constructing the interaction relationship between different nodes as the edge between the nodes based on the Pearson correlation coefficient algorithm includes: Note that there are N performance indicators after preprocessing, and the number of sampling moments of the performance indicators in this operation phase is n. For each pair of performance indicators (x i ,x j ), the Pearson correlation coefficient between them is expressed as follows: In the above formula, r ij Represents the performance index x i and performance index x j The Pearson correlation coefficient between is an N×N correlation coefficient matrix; ik and x jk Represents the performance index x i and performance index x j The corresponding operating data at sampling time k, and Represents the performance index x i and performance index x j Average running data at all sampling moments; In the above formula, A ij Represents the performance index x i and performance index x j The edge between ij is the correlation coefficient matrix r ij The significance level matrix can reflect the confidence level of the performance index correlation coefficient calculation at the corresponding position; p0 is the construction threshold of the significance level.

4. The method for analyzing the stability margin of an aircraft engine based on a performance index network according to claim 3, characterized in that: The aero-engine simulation operation model is constructed based on the differential equation and using the aero-engine performance index network, which is expressed as: Where t is the time parameter, x i 、x j Respectively represent the i-th and j-th performance indicators in the aircraft engine performance indicator network, A ij Represents the performance index x in the aircraft engine performance index network i and performance index x j The edges between them, F and g are simulation operation parameters, which represent the physical characteristics and dynamic behaviors of the aircraft engine under the thrust and IGV angle corresponding to the test parameters.

5. The method for analyzing the stability margin of an aircraft engine based on a performance index network according to claim 3, characterized in that: The calculation process of the average degree and co-average degree of the node is: Where d i Represents the node x i The number of connected edges is called the degree of the node; In the formula Represents all nodes x in the aircraft engine performance index network i The average value of connected edges is called average degree; Where d nn It is the average degree of the neighboring nodes of each node in the aircraft engine performance index network, which is called the co-average degree.

6. The method for analyzing the stability margin of an aircraft engine based on a performance index network according to claim 4, characterized in that: The aero-engine stability margin analysis model corresponding to the selected operation stage is constructed based on the co-average degree of the nodes in the aero-engine performance index network, which is expressed as: In the above formula, d nn It is the co-average degree of each node in the aircraft engine performance index network. Re(λ) represents the stability margin value of the aircraft engine performance index network at the selected operation stage. When Re(λ)≥0, the aircraft engine is unstable at this operation stage; when Re(λ)<0, the aircraft engine is stable at this operation stage.

7. The method for analyzing the stability margin of an aircraft engine based on a performance index network according to claim 6, characterized in that: The method of using the aircraft engine performance index network and combining the operating data of the performance index to solve the simulated operating parameters in the aircraft engine simulated operating model includes: in, and A ij + and A ij - The average degree of is the performance index x i The mathematical expectation of ij + is the promotion matrix of the aircraft engine performance index network. ij A ij - is the inhibition matrix of the performance indicator network, by extracting A in the performance indicator network ij A ij =A ij + +A ij - .

8. The method for analyzing the stability margin of an aircraft engine based on a performance index network according to claim 1, characterized in that: The method further comprises: If the result of the selected operating phase of the aircraft engine to be evaluated for stability margin is that the operation is stable, and the calculated stability margin value falls on the lowest point of the stability margin qualified curve, it indicates that the selected operating state is currently the optimal stable state; otherwise, the working parameters of the aircraft engine to be evaluated for stability margin are adjusted and the above stability margin analysis is performed again, so that the stability margin value is located at or as close as possible to the lowest point of the stability margin qualified curve, and the adjusted working parameters are saved for the subsequent working process of the aircraft engine.

9. An aircraft engine stability margin analysis device, comprising a processor, a memory, and a computer program stored in the memory; characterized in that: When the processor is executed by a computer, the aircraft engine stability margin analysis method based on a performance indicator network according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, wherein a computer program is stored in the medium; characterized in that: When the computer program is executed by a processor, the aircraft engine stability margin analysis method based on a performance indicator network according to any one of claims 1 to 8 is implemented.

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