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

Abstract

The application discloses an aero-engine stability margin analysis method based on a performance index network, and comprises the following steps: obtaining test data of a qualified aero-engine in a test run and screening performance indexes used for constructing an aero-engine performance index network; constructing an aero-engine simulation running model by using the performance index network, and constructing an aero-engine stability margin analysis model corresponding to a selected running stage; solving simulation running parameters in the model, and calculating a stability margin value corresponding to each running stage; and determining a stability margin qualified curve of each running stage by using different test data, so as to evaluate the stability of a specified running stage of an aero-engine to be evaluated. The method can objectively and globally analyze an aero-engine system, is interpretable, can dynamically depict variation rules of stability margins of various states of the aero-engine, and provides a new guiding thought for aero-engine design.

Description

Technical Field

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

[0002] Currently, aircraft engines, as the highest-end products in the equipment manufacturing sector, represent a country's technological prowess and overall national strength. They have long been considered a core technology that impacts national air transport, national defense security, and maintaining national strategic advantages. Due to their complex components, diverse operating conditions, and complex operating environments, stability margin analysis presents significant challenges. A decrease in stability margin or unstable operation can lead to problems such as rotating stall, blade flutter, and coupled vibration, resulting in significant economic losses, serious threats to personnel safety, and even direct threats to national security. Accurate stability margin analysis methods can reflect the dynamic stability margin variations of equipment, minimize downtime caused by performance degradation, facilitate proactive maintenance, and prevent significant losses from accidents. Therefore, conducting aircraft engine stability margin analysis is a core and fundamental task in aircraft engine health management and a crucial guarantee for safe and efficient aircraft engine operation. It can also further enhance the intelligent operation of aircraft engines, highlight core competitiveness, and optimize resource allocation.

[0003] Stability margin analysis of aircraft engines has been widely performed in practical engineering applications. Traditional stability margin analysis methods typically analyze the stability margin of a single indicator, such as the combustion chamber stability margin, aerodynamic stability margin, or control system stability margin. Newer stability margin analysis methods, primarily based on artificial intelligence technologies such as machine learning and deep learning, rely on big data to perform stability margin analysis. However, these analysis methods all have significant drawbacks: Traditional analysis methods fail to account for the multifaceted stability state of aircraft engines, which is affected by various factors, and fail to consider the correlations between various performance indicators, making it difficult to determine the overall stability margin of an aircraft engine. Newer analysis methods are still at a relatively early stage, and they struggle to scientifically explain the mechanisms underlying the variation of aircraft engine stability margins. Therefore, to address these shortcomings, research is necessary to improve the stability margin analysis capabilities of aircraft engines in practical engineering applications.

[0004] 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, patent application CN118780194A, titled "A Method for Assessing the Stability Margin of an Aviation Gas Turbine Engine," calculates the engine stability margin based on the engine blade rotor and combined with compressor surge data. However, this approach suffers from the fact that the independence of indicators affects calculation accuracy, and the evaluation results are primarily derived through linear operations on the indicators, failing to reflect the qualitative changes and emergent patterns of the system arising from the nonlinear relationships and integration of the indicators. This makes it 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 data processing-based aircraft engine stability analysis method and system, which uses a neural network model to predict the aircraft engine's stability margin; however, this type of artificial intelligence-based technology has black box characteristics, which means that it is impossible to clearly determine the mechanism for generating the model's results, and it is difficult to scientifically explain the mechanism of changes in the aircraft engine's stability margin 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 includes:

[0010] For aircraft engines that have passed the test run, the test run data is obtained and the operating phase for stability margin analysis is selected. The performance indicators included in the selected operating phase are pre-processed.

[0011] Based on the preprocessed performance indicators, an aircraft engine performance indicator network corresponding to the selected operating 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 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 phase is constructed;

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

[0014] Based on the obtained simulated operating parameters, the stability margin value of the stability margin analysis model of the aircraft engine in the selected operating stage is determined;

[0015] The method uses test data from multiple qualified aircraft engine tests to determine a stability margin qualified curve for each selected operating phase. For a specified operating phase of an aircraft engine for which a stability margin assessment is to be performed, the test data for that operating phase is obtained and a stability margin value is calculated. Based on the relationship between the stability margin value and the corresponding stability margin qualified curve, it is determined whether the aircraft engine for which a stability margin assessment is to be performed operates stably in the specified operating phase.

[0016] Furthermore, the pre-processing of the performance indicators included in the selected operation phase 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 interaction relationships between different nodes as edges between 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; p0 is the construction threshold of the significance level.

[0024] Furthermore, the aircraft engine performance index network is used to construct an aircraft engine simulation operation model based on the differential equation, 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 Between the edges, 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, based on the co-average degree of the nodes in the aircraft engine performance index network, an aircraft engine stability margin analysis model corresponding to the selected operation phase is constructed, which is expressed as:

[0035]

[0036] In the above formula, d nnis 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 in the selected operating phase. When Re(λ) ≥ 0, the aircraft engine is unstable in this operating phase; when Re(λ) < 0, the aircraft engine is stable in this operating phase.

[0037] Furthermore, the method of using the aircraft engine performance index network and combining the performance index operation data to solve the simulation operation parameters in the aircraft engine simulation operation 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 A ij + The promotion matrix of the aircraft engine performance index network is extracted by A ij A ij - is the inhibition matrix of the performance index network, by extracting A ij A ij =A ij + +A ij - .

[0040] Furthermore, the method further comprises:

[0041] If the selected operating phase of the aircraft engine for which the stability margin assessment is to be performed is determined to be stable, and the calculated stability margin value falls on the lowest point of the stability margin qualification curve, then the selected operating state is currently the optimal stable state. Otherwise, the operating parameters of the aircraft engine for which the stability margin assessment is to be performed are adjusted, and the stability margin analysis is repeated so that the stability margin value is at or as close as possible to the lowest point of the stability margin qualification curve. The adjusted operating parameters are then saved for use in subsequent operating processes 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 aviation engine performance index network and aviation 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 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 results of the aircraft engine stability margin analysis in various states according to the embodiment of the present invention are as follows;

[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. It 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 this 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, obtain its test run data, select an operating phase for stability margin analysis, and pre-process the performance indicators included in the selected operating phase.

[0056] For aircraft engines that pass the test run, test data is collected. During the test run, the same thrust and IGV angle should be maintained within the same operating phase. The test data is collected during the aircraft engine's complete flight process, which includes multiple operating phases, including startup, acceleration, ascent, and steady operation. The test data for each operating phase includes multiple performance indicators, such as low-pressure rotor speed and lubricating oil temperature.

[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 operating stages are selected for analysis.

[0058] For the selected operation phase, 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: Based on the complex network theory, an aircraft engine performance indicator network corresponding to the selected operating stage is constructed for the preprocessed performance indicators. The preprocessed performance indicators are used as nodes, and the interaction relationships between different nodes are constructed as edges 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 pre-processed performance indicators, the interaction relationship between different performance indicators can be calculated.

[0061] Assuming that there are N pre-processed 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 moments.

[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; p0 is the construction threshold of the significance level, which is generally considered to be p0 = 0.05; thus, the aircraft engine performance index network is constructed, that is, if 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 the differential equation, the aircraft engine performance index network is used to construct an aircraft engine simulation operation model, and based on the co-average degree of the nodes in the aircraft engine performance index network, an aircraft engine stability margin analysis model corresponding to the selected operation stage is constructed.

[0067] The interaction 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 network i and performance index xj 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 will be solved in 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 a 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. The specific expression is as follows:

[0078]

[0079] In the above formula, Re(λ) represents the stability margin value of the aircraft engine performance index network in the selected operating phase. When Re(λ) ≥ 0, the aircraft engine is unstable in this operating phase. When Re(λ) < 0, the aircraft engine is stable in this operating phase. The larger the absolute value of Re(λ), the stronger the aircraft engine's stability margin and anti-interference capability in this operating phase.

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

[0081] Calculate the mathematical expectation of each performance indicator (node) in the aircraft engine performance indicator network to obtain the expected state of the aircraft engine at a certain operating stage, thereby determining the simulation operating parameters F and g at the selected operating stage:

[0082]

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

[0084] Step 5: Based on the obtained simulated operating parameters, a stability margin value of the stability margin analysis model of the aircraft engine in the selected operating stage is determined.

[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 brought 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 C1, and the d of its performance index network is nn The value is C2, and C2>C1. If g=1, 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 phase;

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

[0090] For the test data of aircraft engines that have passed multiple test runs, the corresponding stability margin value can be obtained for each selected operating stage 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 one embodiment, 10 sets 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 the 10 sets of test data. After curve fitting, a qualified stability margin curve for the first operating stage is obtained. The same method can be used to obtain the qualified stability margin curves for the remaining five operating stages.

[0092] For an aircraft engine to be evaluated, after obtaining its test data, the stability margin assessment is performed for each of the six corresponding operating stages. Still taking the first operating stage as an example, after obtaining the test data for this stage, the aircraft engine performance indicator network for the first operating stage is constructed according to the preprocessing process in steps 1 and 2. The stability margin value Re(λ) for the first operating stage is calculated using the same method in steps 3 to 5. A determination is then made as to whether the stability margin value Re(λ) falls on the stability margin qualification curve for the first operating stage. If so, the aircraft engine is operating stably in the first operating stage. If not, or if the stability margin value Re(λ) ≥ 0, the aircraft engine is operating unstably in the first operating stage. The same method can be used to analyze the stability of the aircraft engine in the remaining operating stages.

[0093] By establishing the stability margin qualification curves for each of the above-mentioned operating stages, we carefully observe and analyze the range of stability margin values ​​in different operating stages, gain a deeper understanding of the stability level of the aircraft engine in each operating stage, and further identify possible optimization space. The stability margin qualification 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 qualification curve) and the corresponding simulated operating parameters. In addition, by enhancing the correlation between the various components of the aircraft engine, the synergy between the components can be optimized, thereby reducing the dynamic instability factors of the system, improving the stability margin value of the aircraft engine in all operating stages, and enhancing its adaptability under extreme or adverse operating conditions.

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

[0095] In step 7, if the selected operating phase of the aircraft engine for which the stability margin assessment is to be performed is determined to be stable, and the calculated stability margin value falls at the lowest point of the stability margin qualification curve, then the selected operating state is currently the optimal stable state. Otherwise, the operating parameters of the aircraft engine for which the stability margin assessment is to be performed are adjusted, and the stability margin analysis is repeated, so that the stability margin value is at or as close as possible to the lowest point of the stability margin qualification curve. The adjusted operating parameters are stored and used in subsequent operating processes of the aircraft engine. The operating parameters of each selected operating phase can be adjusted using the same method to ensure that each operating phase reaches or approaches the optimal stable state as much as possible.

[0096] One embodiment of the present invention surveyed an aircraft engine manufacturer and collected test data from two engines under preset test parameters during 12 different operating phases. The test data recorded dozens of performance indicators during the test, including thrust, air pressure and temperature, fuel pressure and temperature, air flow, fuel flow, engine speed, and control valve opening. This data was highly dimensional, heterogeneous, multivariate, and multimodal, with strong correlations between performance indicators.

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

[0098] Low-pressure rotor speed (N1), high-pressure rotor speed (N2), turbine exhaust gas temperature (N3), lubricating oil temperature (N4), main oil line pressure (N5), auxiliary oil line pressure (N6), ratio of high-pressure compressor and low-pressure compressor outlet air pressure (N7), angle of high-pressure compressor inlet adjustable guide vane (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 control voltmeter (N22), catalytic igniter fuel supply pressure (N23), low-pressure fuel pump outlet pressure (N24), high-pressure fuel pump outlet 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 outlet pressure (N30).

[0099] In this embodiment, the performance index network constructed by selecting the performance indexes of a qualified aircraft engine 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 mutual relationship of the performance indicators at a given time t. ij , the stability margin analytical value is obtained through the stability margin analysis model of the aircraft engine, such as Figure 3 As shown in the figure, 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%. This shows 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 status of the aircraft engine, and this stability margin analysis model is very suitable for the analysis of the aircraft engine test process.

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

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

[0103] The examples 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 performance indicators and system state change patterns, and 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 various embodiments of the present application, and should all be included in the scope of protection 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, the test run data is obtained and the operating phase for stability margin analysis is selected. The performance indicators included in the selected operating phase are pre-processed. For the pre-processed performance indicators, an aircraft engine performance indicator network corresponding to the selected operation stage is constructed based on complex network theory; The pre-processed performance indicators are used as nodes, and the interaction relationships between different nodes are constructed based on the Pearson correlation coefficient algorithm as edges between nodes; Based on the differential equation, the aircraft engine performance index network is used to construct an aircraft engine simulation operation model, which can be expressed as: ； In the formula t is the time parameter, and the performance indicators after preprocessing are N indivual, 、 Represents the first i 、 j performance indicators, Represents the performance index in the aircraft engine performance index network and performance indicators Between the edges, F and g are the simulated operating parameters, which are expressed as the physical characteristics and dynamic behavior of the aircraft engine under the thrust and IGV angle corresponding to the test parameters; 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 and expressed as: ； In the above formula, is the co-average degree of each node in the aircraft engine performance index network, represents the stability margin value under the aircraft engine performance index network at the selected operation stage, When , the aircraft engine is unstable in this operation stage; When , the aircraft engine is stable in this operation stage; Utilizing the aircraft engine performance index network and combining the performance index operation data, solving the simulation operation parameters in the aircraft engine simulation operation model; Based on the obtained simulated operating parameters, the stability margin value of the stability margin analysis model of the aircraft engine in the selected operating stage is determined; Using the test data of multiple qualified aircraft engine tests, determine the stability margin qualified curve for each selected operating phase; For a specific operating phase of an aircraft engine for which stability margin assessment is to be performed, obtaining test data for the operating phase and calculating the stability margin value; According to the relationship between the stability margin value and the corresponding stability margin qualification curve, it is determined whether the aircraft engine to be subjected to stability margin evaluation is operating stably in a specified operating 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 phase 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, wherein: The method of constructing the interaction relationship between different nodes as the edge between the nodes based on the Pearson correlation coefficient algorithm includes: The performance indicators after preprocessing are N The number of sampling moments of performance indicators in this operation phase is n , then for each pair of performance indicators , the Pearson correlation coefficient between them is expressed as follows: ； In the above formula, Indicates performance indicators and performance indicators The Pearson correlation coefficient between The correlation coefficient matrix of and Represents performance indicators and performance indicators At the sampling time k The corresponding running data, and Represents performance indicators and performance indicators Average running data at all sampling moments; ； In the above formula, Indicates performance indicators and performance indicators Between the edges, is the correlation coefficient matrix The significance level matrix can reflect the confidence level of the performance index correlation coefficient calculation at the corresponding position; is the constructed threshold for 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 calculation process of the average degree and co-average degree of the node is: ； In the formula Representation and Node The number of connected edges is called the degree of the node; ； In the formula Represents all nodes in the aircraft engine performance index network The average value of connected edges is called average degree; ； In the formula 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.

5. The method for analyzing the stability margin of an aircraft engine based on a performance index network according to claim 1, wherein: The method of using the aircraft engine performance index network and combining the performance index operation data to solve the simulation operation parameters in the aircraft engine simulation operation model includes: ； in, and for and The average degree of Performance indicators The mathematical expectation of The promotion matrix of the aircraft engine performance index network is extracted by The positive value of is obtained; is the inhibition matrix of the performance index network, by extracting The negative value of is obtained; .

6. 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 selected operating phase of the aircraft engine for which the stability margin assessment is to be performed is determined to be stable, and the calculated stability margin value falls on the lowest point of the stability margin qualification curve, then the selected operating state is currently the optimal stable state. Otherwise, the operating parameters of the aircraft engine for which the stability margin assessment is to be performed are adjusted, and the stability margin analysis is repeated so that the stability margin value is at or as close as possible to the lowest point of the stability margin qualification curve. The adjusted operating parameters are then saved for use in subsequent operating processes of the aircraft engine.

7. 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 6 is implemented.

8. A computer-readable storage medium storing a computer program; wherein: 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 6 is implemented.

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