Testability simulation method based on function performance model of airborne system

Abstract

The application relates to a testability simulation method based on an aviation airborne system function performance model, which comprises the following steps: S1, acquiring aviation airborne system function performance model information, and performing testability simulation design; S2, using a state space equation method to establish the function performance model of the aviation airborne system; S3, designing the testability diagnosis model of the aviation airborne system according to the state updating space and the sensing measurement space of the aviation airborne system; S4, analyzing the fault mode of the aviation airborne system according to a test fault simulation list, and updating a fault matrix; S5, completing simulation of the testability diagnosis model of the aviation airborne system, and recording and analyzing the results. The application is characterized by testability design characteristics of the aviation airborne system, a digital form representation is proposed through a fault discrimination space, static function logic qualitative analysis is changed into dynamic performance behavior quantitative verification, and the problem that current system testability simulation cannot be carried out based on performance behavior and combined faults and associated faults is solved.

Description

Technical Field

[0001] This invention belongs to the field of testing technology, and specifically relates to a test simulation method based on the functional performance model of an airborne system. Background Technology

[0002] Testability refers to a design characteristic that enables a system to determine its operational status in a timely and accurate manner and isolate its internal faults. Due to the expansion of functional coverage and the continuous increase in performance requirements, the field failure rate of complex airborne systems in service remains high. As an important means of supporting maintenance, replacement, and logistical support, testability design is beginning to occupy an increasingly important position in the development system of airborne systems.

[0003] Since the 1980s, domestic and international research institutions have conducted extensive research on testability modeling and simulation techniques, resulting in many effective modeling methods. Representative methods include correlation modeling, structured modeling, and multi-signal flow modeling. Correlation modeling logically defines the link between faults and tests; structured modeling, relying on the topology of the system object, directly and clearly provides fault propagation paths between system components; multi-signal flow modeling integrates the advantages of correlation and structured modeling methods, introducing multiple signal information flows into the physical flow of the topology, organically combining faults, tests, and signals, effectively supporting the design and development of fault diagnosis strategies for complex systems. While these methods differ significantly in their presentation, their core objective is to find the mapping relationship between fault phenomena within the system object and the product's testability. This belongs to the knowledge-inductive expert system modeling method, more suitable for forward design in the early stages of system product development. It lacks behavioral representations that synergize with the object's performance characteristics, and the model's correctness verification relies on the subjective evaluation of modelers and reviewers, making it difficult to meet the requirements of reverse-engineering system testability performance simulation verification tasks. For modern complex airborne systems with increasingly complex functions and performance, their functional performance failures are characterized in a combined and correlated form. There is a lack of a testable simulation method for designing system functional performance models, which is also the focus and pain point of current equipment testability development work.

[0004] Therefore, this invention addresses the need for digital simulation of system testability by proposing a testability simulation method based on the functional performance model of an airborne system. Based on the functional performance model of the airborne system and the system's fault mode and impact analysis design documents, it constructs test cases and simulation environment for dynamic verification of testability, realizing a leap from static qualitative analysis of functional logic to quantitative verification of dynamic performance behavior, and providing theoretical support and technical guarantee for future digital simulation of airborne system testability. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a test simulation method based on the functional performance model of airborne systems. This invention focuses on the characterization of test design characteristics of airborne systems, proposes a digital morphological representation through a fault discrimination space, and realizes the transition from static qualitative analysis of functional logic to quantitative verification of dynamic performance behavior. This solves the problem that current methods cannot conduct system test simulation based on performance behavior and combined or associated faults.

[0006] To achieve the above objectives, the present invention discloses the following technical solution:

[0007] A test simulation method based on the functional performance model of an airborne system, comprising:

[0008] S1: Obtain functional performance model information of airborne systems and conduct test simulation design;

[0009] Input the functional performance list data and failure mode and impact design parameters of the airborne system to obtain the functional performance model information of the airborne system; conduct test simulation design based on the functional performance list data of the airborne system to form the test case simulation list data of the airborne system.

[0010] S2: Use the state-space equation method to establish a functional performance model of the airborne system;

[0011] Based on the functional performance list data of the airborne system described in step S1, from a time-driven perspective, the state space equation method is used to set the state update space and sensing measurement space of the airborne system, thereby establishing the functional performance model of the airborne system; the functional performance model of the airborne system includes a first power sub-model, a second power sub-model, and a motion sub-model.

[0012] S3: Design a testable diagnostic model for the airborne system based on the state update space and perception measurement space of the airborne system.

[0013] Based on the airborne system performance status update space and perception measurement space in step S2 and the airborne system test case simulation list data in step S1, considering the test verification requirements of airborne system performance behavior, the fault discrimination space of the airborne system testability design is set as follows:

[0014]

[0015] Where Z(t) represents the fault discrimination space of the test design of the airborne system; Z i (t) represents the i-th fault discrimination vector; i is the fault discrimination vector number, belonging to 1 to s; t is the time parameter; z 11 and These represent the first fault and the j1st fault in the first fault discrimination vector, respectively; z i1 and They are the first fault and the jth fault in the i-th fault discrimination vector, respectively. i One fault; z s1 and They are the first fault and the jth fault in the s-th fault discrimination vector, respectively. s One fault;

[0016] For the working mode and remote operation process of in-flight testing design of airborne systems, a testable diagnostic model for airborne systems is established using digital morphological representation:

[0017]

[0018] Where Zs(t) is the fault discrimination space during the power-on phase of the test design of the airborne system; Zd(t) is the fault discrimination space during the normal operation phase of the test design of the airborne system; Z(t,e) is the in-flight test space equation for maintaining the airborne system; t s This is the time interval parameter between the power-on phase and the normal operation phase; t d y(t) represents the time termination parameter during normal operation; y(t) represents the time parameter output of the sensing measurement space; y(t,e) represents the time parameter and event parameter output of the sensing measurement space; u(t) represents the time parameter input variable; u(t,e) represents the time parameter and event parameter input variable; e represents the event parameter; E m To maintain the set of assurance events; E and F are the first and second parameters of the fault discrimination space matrix, respectively;

[0019] S4: Analyze the failure modes of the airborne system based on the test failure simulation list and update the failure matrix;

[0020] Based on the functional performance model of the airborne system in step S2 and the test diagnostic model of the airborne system in step S3, faults are designed in ways that affect the function of the airborne system. Fault modes of the airborne system are analyzed. By combining the perception measurement space in step S2 and the fault discrimination space in step S3, the fault injection matrix is ​​updated in the system performance state update space.

[0021]

[0022] Where x(t) is the state variable of the airborne system. Its derivative; G i (t,e) is the first fault update matrix; H i(t,e) is the second fault update matrix; A and B are the first and second parameters of the state update space matrix, respectively; C and D are the first and second parameters of the sensing measurement space matrix, respectively.

[0023] S5: Complete the simulation of the test diagnostic model of the airborne system and record the analysis results;

[0024] Based on the fault injection matrix obtained in step S4, conduct simulations of the functional performance model and test diagnostic model of the airborne system. Record the simulation results according to the fault injection simulation data recording table, and record the simulation analysis of the system functional performance model and test model, as well as the fault detection and fault isolation results.

[0025] Preferably, the state update space and sensing measurement space of the airborne system in step S2 are as follows:

[0026]

[0027] in, For the derivatives of the state variables of the airborne system.

[0028] Preferably, the functional performance model of the airborne system in step S2 is determined by the functional performance list and model information, and transformed into a system of first-order differential equations according to physical laws, forming a model in the form of state-space equations, which is used to describe the performance behavior of the airborne system.

[0029] Preferably, the first dynamic sub-model in step S2 is:

[0030]

[0031] Where u, v, and w are the airspeed components along the x, y, and z axes in the body coordinate system; θ represents the acceleration along the x, y, and z axes in the body coordinate system; r, q, and p represent the components of the angular velocity along the x, y, and z axes in the body coordinate system; g represents the acceleration due to gravity; θ represents the pitch angle; φ represents the roll angle; F x F y F z denoted as , where is the component of the resultant force of aerodynamic force and engine thrust along the three axes of the airframe coordinate system; m is the mass of the aircraft.

[0032] Preferably, the second dynamic sub-model in step S2 is:

[0033]

[0034] in, c1 represents the angular acceleration along the x, y, and z axes in the body coordinate system; c2 represents the first influence coefficient of the second dynamic sub-model; c3 represents the second influence coefficient of the second dynamic sub-model; c4 represents the fourth influence coefficient of the second dynamic sub-model; c5 represents the fifth influence coefficient of the second dynamic sub-model; c6 represents the sixth influence coefficient of the second dynamic sub-model; c7 represents the seventh influence coefficient of the second dynamic sub-model; c8 represents the eighth influence coefficient of the second dynamic sub-model; c9 represents the ninth influence coefficient of the second dynamic sub-model; L, M, and N represent the components of the external net torque along the x, y, and z axes in the body coordinate system, respectively.

[0035] Preferably, the motion sub-model in step S2 is:

[0036]

[0037] in, The pitch rate; Yaw rate; This represents the roll rate.

[0038] Preferably, the test and verification requirements for considering the performance behavior of airborne systems in step S3 are as follows:

[0039] The operational process for realizing status monitoring, fault detection, and fault isolation of airborne systems corresponds to the in-flight testability design structure of distributed-centralized airborne systems. Based on the airborne system performance status update space and sensing measurement space in step S2, the fault discrimination space Z(t) of the airborne system testability design is determined.

[0040] Preferably, the establishment of a test diagnostic model for the airborne system in step S3 is specifically as follows:

[0041] Setting up a status monitoring method for airborne systems i (t), Fault detection method zm i (t), Fault isolation method zn i (t), respectively:

[0042]

[0043]

[0044]

[0045] Among them, zl i (t) represents the status monitoring results; zm i (t) represents the fault detection result; zn i(t) represents the fault isolation result; L(y(t),u(t)) represents the observed data of state monitoring after function mapping; M(y(t),u(t)) represents the observed data of fault detection after function mapping; N(y(t),u(t)) represents the observed data of fault isolation after function mapping; L th For condition monitoring operation specifications; M th This is the operational standard for fault detection; N th This represents the operational specifications for fault isolation; Y(t) represents the functional output of the airborne system; true indicates normal operation; error indicates a fault alarm; if represents the selection criteria.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] (1) This invention focuses on the test design characteristics of airborne systems. Based on the state update space and perception measurement space in performance behavior, it establishes the fault discrimination space of airborne systems to realize the fault diagnosis and discrimination of airborne systems.

[0048] (2) This invention is aimed at the distribution form, working mode and remote transfer process of typical airborne system in-flight test design, and proposes its digital form function representation, realizing the leap from static functional logic qualitative analysis to dynamic performance behavior quantitative verification.

[0049] (3) The present invention helps to solve the problem that the current airborne system engineering design cannot carry out test simulation evaluation of airborne systems based on performance behavior and combined faults and associated faults, and has important engineering practical value. Attached Figure Description

[0050] Figure 1 This is a control block diagram of the test simulation method based on the functional performance model of an airborne system according to the present invention;

[0051] Figure 2 The fault injection simulation structure diagram of the present invention;

[0052] Figure 3 This is a schematic diagram of the fault injection phenomenon of the present invention;

[0053] Figure 4 This is a schematic diagram illustrating the successful fault detection of the present invention;

[0054] Figure 5 This is a schematic diagram illustrating the fault isolation results of the present invention. Detailed Implementation

[0055] Exemplary embodiments, features, and aspects of the present invention will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0056] This invention provides a test simulation method based on an airborne system functional performance model for a certain type of aircraft during cruise flight. The flight control system is used as the object of study in this embodiment. Figure 1 As shown, the functional performance model information of the flight control system is obtained, and test simulation design is performed. Using the state-space equation method, a functional performance model of the flight control system is established. Based on the state update space and sensing measurement space of the flight control system, a test diagnostic model of the flight control system is designed. Based on the test fault simulation list, the fault modes of the flight control system are analyzed, the fault matrix is ​​updated, and the simulation of the test diagnostic model of the flight control system is completed. The analysis results are recorded. This includes:

[0057] Step S1: Obtain the functional performance model information of the flight control system and conduct test simulation design;

[0058] Input the functional performance list data of the flight control system and the design parameters of failure modes and effects to obtain the functional performance model information of the flight control system.

[0059] Table 1 shows the functional performance list data of the flight control system. Obtain the flight control system information according to the list in the table, and check whether it meets the requirements in the information confirmation column.

[0060] Table 1. Functional Performance List of Flight Control System

[0061] 1 Clarify the input-output variable relationship of flight control Compliant □ Not Compliant □ 2 Clearly define the functions of flight control. Compliant □ Not Compliant □ 3 Define the performance parameter range of flight control Compliant □ Not Compliant □ 4 Clearly define the research areas of flight control and the description of the differential equations. Compliant □ Not Compliant □ 5 Define the modeling requirements for flight control. Compliant □ Not Compliant □

[0062] Table 2 shows the failure modes and their impact design parameters. The flight control system is decomposed into field replaceable units (LRUs), including the failure mode code, failure mode name, failure cause, and failure impact of each LRU.

[0063] Table 2 Failure Modes and Impacts Design Parameters

[0064]

[0065] Table 3 shows the functional performance model information of the flight control system, which is divided into state and perception variables and control variables. The variable names, symbols and units correspond one-to-one and are used for state monitoring and control input during the model calculation process.

[0066] Table 3. Functional Performance Model Information of Flight Control System

[0067]

[0068]

[0069] Test simulation design was carried out based on the functional performance list data of the flight control system to form test case simulation list data for the flight control system;

[0070] Step S2: Establish a functional performance model of the flight control system using the state-space equation method;

[0071] Based on the functional performance list data of the flight control system in step S1, it is transformed into a set of first-order differential equations according to physical laws, forming a model in the form of state-space equations to describe the performance behavior of the flight control system, thereby realizing the establishment of the functional performance model of the flight control system, specifically including the first power sub-model, the second power sub-model, and the motion sub-model.

[0072] The first dynamic sub-model is established as follows:

[0073]

[0074] Where u, v, and w are the airspeed components along the x, y, and z axes in the body coordinate system; θ represents the acceleration along the x, y, and z axes in the body coordinate system; r, q, and p represent the components of the angular velocity along the x, y, and z axes in the body coordinate system; g represents the acceleration due to gravity; θ represents the pitch angle; φ represents the roll angle; F x F y F z denoted as , where is the component of the resultant force of aerodynamic force and engine thrust along the three axes of the airframe coordinate system; m is the mass of the aircraft.

[0075] The second dynamic sub-model is established as follows:

[0076]

[0077] in, c1 represents the angular acceleration along the x, y, and z axes in the body coordinate system; c2 represents the first influence coefficient of the second power sub-model; c3 represents the third influence coefficient of the second power sub-model; c4 represents the fourth influence coefficient of the second power sub-model; c5 represents the fifth influence coefficient of the second power sub-model; c6 represents the sixth influence coefficient of the second power sub-model; c7 represents the seventh influence coefficient of the second power sub-model; c8 represents the eighth influence coefficient of the second power sub-model; c9 represents the ninth influence coefficient of the second power sub-model; L, M, and N represent the components of the external net torque along the x, y, and z axes in the body coordinate system, respectively.

[0078] The first influence coefficient of the second dynamic sub-model is:

[0079]

[0080] Among them, I x I y I z These are the moments of inertia of the aircraft about the x-axis, y-axis, and z-axis, respectively; I xz This is the product of inertia of the aircraft about the x-axis and z-axis.

[0081] The second influence coefficient of the second dynamic sub-model is:

[0082]

[0083] The third influence coefficient of the second dynamic sub-model is:

[0084]

[0085] The fourth influence coefficient of the second dynamic sub-model is:

[0086]

[0087] The fifth influence coefficient of the second dynamic sub-model is:

[0088]

[0089] The sixth influence coefficient of the second dynamic sub-model is:

[0090]

[0091] The seventh influence coefficient of the second dynamic sub-model is:

[0092]

[0093] The eighth influence coefficient of the second dynamic sub-model is:

[0094]

[0095] The ninth influence coefficient of the second dynamic sub-model is:

[0096]

[0097] The motion sub-model is established as follows:

[0098]

[0099] in, The pitch rate; Yaw rate; This represents the roll rate.

[0100] Linearization is applied to the first dynamic sub-model, the second dynamic sub-model, and the motion sub-model to obtain the state update space and sensing measurement space of the flight control system in longitudinal motion, thereby enabling a description of the performance behavior of the flight control system. The state update space and sensing measurement space of the flight control system are as follows:

[0101]

[0102] Where x = [Δv, Δα, Δq, Δθ] are the state variables of the flight control system, and each variable is an incremental representation of the corresponding variable in Table 3.

[0103] Step S3: Design a testable diagnostic model for the flight control system based on the state update space and perception measurement space of the flight control system.

[0104] Based on the flight control system performance state update space and sensing measurement space in step S2 and the flight control system test case simulation list data in step S1, considering the test verification requirements of the flight control system performance behavior, the operational flow for implementing state monitoring, fault detection, and fault isolation of the flight control system is defined, corresponding to the in-flight testability design structure of the distributed-centralized flight control system within the flight control system; based on the flight control system performance state update space and sensing measurement space in step S2, the fault discrimination space Z(t) of the flight control system testability design is determined as follows:

[0105]

[0106] Where Z(t) represents the fault discrimination space of the test design of the flight control system; Z i (t) represents the i-th fault discrimination vector; i is the fault discrimination vector number, belonging to 1 to s; t is the time parameter; z 11 and These represent the first fault and the j1st fault in the first fault discrimination vector, respectively; z i1 and They are the first fault and the jth fault in the i-th fault discrimination vector, respectively. i One fault; z s1 and They are the first fault and the jth fault in the s-th fault discrimination vector, respectively. s One fault.

[0107] Then, based on the working mode and remote operation process of the in-flight test design of the flight control system, a testable diagnostic model of the flight control system is established using digital representation:

[0108]

[0109] Where Zs(t) is the fault discrimination space during the power-on phase of the flight control system test design; Zd(t) is the fault discrimination space during the normal operation phase of the flight control system test design; Z(t,e) is the in-flight test space equation for maintaining the flight control system; t s This is the time interval parameter between the power-on phase and the normal operation phase; t d y(t) represents the time termination parameter during normal operation; y(t) represents the time parameter output of the sensing measurement space; y(t,e) represents the time parameter and event parameter output of the sensing measurement space; u(t) represents the time parameter input variable; u(t,e) represents the time parameter and event parameter input variable; e represents the event parameter; E m To maintain the set of events; E and F are the first and second parameters of the fault discrimination space matrix, respectively.

[0110] The state monitoring method corresponding to the test diagnostic model of the flight control system zl i (t), Fault detection method zm i (t), Fault isolation method zn i (t), respectively:

[0111]

[0112]

[0113]

[0114] Among them, zl i (t) represents the status monitoring results; zm i (t) represents the fault detection result; zn i (t) represents the fault isolation result; L(y(t),u(t)) represents the observed data of state monitoring after function mapping; M(y(t),u(t)) represents the observed data of fault detection after function mapping; N(y(t),u(t)) represents the observed data of fault isolation after function mapping; L th For condition monitoring operation specifications; M th This is the operational standard for fault detection; N th This represents the operational specifications for fault isolation; Y(t) represents the functional output of the flight control system; true indicates normal operation; error indicates a fault alarm; and if represents the judgment selection condition.

[0115] Step S4: Analyze the failure modes of the flight control system based on the test failure simulation list and update the failure matrix;

[0116] Based on the functional performance model of the flight control system in step S2 and the testable diagnostic model of the flight control system in step S3, faults are designed in a way that affects the function of the flight control system. Fault modes of the flight control system are analyzed. Combined with the perception measurement space in step S2 and the fault discrimination space in step S3, the fault injection matrix is ​​updated in the system performance state update space.

[0117]

[0118] Where x(t) is the state variable of the flight control system, Its derivative; G i (t,e) is the first fault update matrix; H i (t,e) is the second fault update matrix; A and B are the first and second parameters of the state update space matrix, respectively; C and D are the first and second parameters of the sensing measurement space matrix, respectively.

[0119] The fault mode recorded in the flight control system fault injection simulation data is pitch sensor jamming, with BY-(01-01-01.01)-01 as the alternative sample. The fault injection simulation structure diagram is as follows. Figure 2 As shown in the figure, the initial control variable settings, state updates and sensing measurement space, as well as the fault injection structure and its parameter variables, describe the performance behavior of the flight control system under fault injection. The fault injection phenomenon is as follows: Figure 3 As shown, the solid line represents the angle of attack increment curve, and the dashed line represents the pitch angle increment curve.

[0120] Table 4 shows the fault injection methods for flight control systems. Corresponding injection methods are designed based on the different ways in which the system performance model is affected by different fault modes. The simulation model parameters under fault injection are obtained, and detection and isolation criteria are defined.

[0121] Table 4 Fault Injection Methods for Flight Control Systems

[0122]

[0123] Step S5: Complete the simulation of the test diagnostic model of the flight control system and record the analysis results; based on the fault injection matrix obtained in Step S4, conduct simulations of the functional performance model and test diagnostic model of the flight control system, fill in the simulation results according to the fault injection simulation data recording table, and record the simulation status of the system functional performance model and test model, as well as the fault detection and fault isolation status. Fault detection results are as follows: Figure 4 As shown, when the lateral time axis reaches approximately 8.4 seconds, the pitch angle increment exceeds 0.4 rad, indicating a pitch sensor malfunction. Under the current fault injection, the angle of attack increment is as follows: Figure 5As shown, it did not exceed 0.1 rad, indicating that the angle of attack sensor did not exceed the judgment value and no fault was reported. Therefore, the isolation fuzzy group 1 / 2 / 3 of the fault isolation is the 1LRU-pitch sensor, and the fault isolation was successfully determined by the detection results after the fault was injected.

[0124] Compared with existing technologies, the present invention has the following beneficial effects: The embodiments of the present invention target the testability design characteristics of flight control systems, establishing a fault discrimination space for flight control systems based on the state update space and perception measurement space in performance behavior, thereby realizing fault diagnosis and discrimination of flight control systems; the embodiments propose a digital functional representation of the flight control system's in-flight test design distribution, working mode, and remote operation process, achieving a leap from static functional logic qualitative analysis to dynamic performance behavior quantitative verification; the embodiments of the present invention solve the problem faced in current flight control system engineering design of being unable to conduct testability simulation evaluation of flight control systems based on performance behavior and combined or associated faults, and have significant engineering practical value.

[0125] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A test simulation method based on the functional performance model of an airborne system, characterized in that: It includes: S1: Obtain functional performance model information of airborne systems and conduct test simulation design; Input the functional performance list data of the airborne system and the design parameters of failure modes and effects to obtain the functional performance model information of the airborne system. Test simulation design is carried out based on the functional performance list data of the airborne system to form test case simulation list data for the airborne system; S2: Use the state-space equation method to establish a functional performance model of the airborne system; Based on the functional performance list data of the airborne system described in step S1, from a time-driven perspective, the state space equation method is used to set the state update space and sensing measurement space of the airborne system, thereby establishing the functional performance model of the airborne system; the functional performance model of the airborne system includes a first power sub-model, a second power sub-model, and a motion sub-model. S3: Design a testable diagnostic model for the airborne system based on the state update space and perception measurement space of the airborne system. Based on the airborne system performance status update space and perception measurement space in step S2 and the airborne system test case simulation list data in step S1, considering the test verification requirements of airborne system performance behavior, the fault discrimination space of the airborne system testability design is set as follows: Where Z(t) represents the fault discrimination space of the test design of the airborne system; Z i (t) represents the i-th fault discrimination vector; i is the fault discrimination vector number, belonging to 1 to s; t is the time parameter; z 11 and These represent the first fault and the j1st fault in the first fault discrimination vector, respectively; z i1 and They are the first fault and the jth fault in the i-th fault discrimination vector, respectively. i One fault; z s1 and They are the first fault and the jth fault in the s-th fault discrimination vector, respectively. s One fault; For the working mode and remote operation process of in-flight testing design of airborne systems, a testable diagnostic model for airborne systems is established using digital morphological representation: Where Zs(t) is the fault discrimination space during the power-on phase of the test design of the airborne system; Zd(t) is the fault discrimination space during the normal operation phase of the test design of the airborne system; Z(t,e) is the in-flight test space equation for maintaining the airborne system; t s This is the time interval parameter between the power-on phase and the normal operation phase; t d y(t) represents the time termination parameter during normal operation; y(t) represents the time parameter output of the sensing measurement space; y(t,e) represents the time parameter and event parameter output of the sensing measurement space; u(t) represents the time parameter input variable; u(t,e) represents the time parameter and event parameter input variable; e represents the event parameter; E m To maintain the set of assurance events; E and F are the first and second parameters of the fault discrimination space matrix, respectively; S4: Analyze the failure modes of the airborne system based on the test failure simulation list and update the failure matrix; Based on the functional performance model of the airborne system in step S2 and the test diagnostic model of the airborne system in step S3, faults are designed in ways that affect the function of the airborne system. Fault modes of the airborne system are analyzed. By combining the perception measurement space in step S2 and the fault discrimination space in step S3, the fault injection matrix is ​​updated in the system performance state update space. Where x(t) is the state variable of the airborne system. Its derivative; G i (t,e) is the first fault update matrix; H i (t,e) is the second fault update matrix; A and B are the first and second parameters of the state update space matrix, respectively; C and D are the first and second parameters of the sensing measurement space matrix, respectively. S5: Complete the simulation of the test diagnostic model of the airborne system and record the analysis results; Based on the fault injection matrix obtained in step S4, conduct simulations of the functional performance model and test diagnostic model of the airborne system. Record the simulation results according to the fault injection simulation data recording table, and record the simulation analysis of the system functional performance model and test model, as well as the fault detection and fault isolation results.

2. The test simulation method based on the functional performance model of an airborne system according to claim 1, characterized in that: The state update space and sensing measurement space of the airborne system in step S2 are as follows: in, For the derivatives of the state variables of the airborne system.

3. The test simulation method based on the functional performance model of an airborne system according to claim 1, characterized in that: The functional performance model of the airborne system in step S2 is determined by the functional performance list and model information. It is transformed into a system of first-order differential equations according to physical laws, forming a model in the form of state-space equations, which is used to describe the performance behavior of the airborne system.

4. The test simulation method based on the functional performance model of an airborne system according to claim 1, characterized in that: The first dynamic sub-model in step S2 is: Where u, v, and w are the airspeed components along the x, y, and z axes in the body coordinate system; θ represents the acceleration along the x, y, and z axes in the body coordinate system; r, q, and p represent the components of the angular velocity along the x, y, and z axes in the body coordinate system; g represents the acceleration due to gravity; θ represents the pitch angle; φ represents the roll angle; F x F y F z denoted as , where is the component of the resultant force of aerodynamic force and engine thrust along the three axes of the airframe coordinate system; m is the mass of the aircraft.

5. The test simulation method based on the functional performance model of an airborne system according to claim 1, characterized in that: The second dynamic sub-model in step S2 is: in, c1 represents the angular acceleration along the x, y, and z axes in the body coordinate system; c2 represents the first influence coefficient of the second power sub-model; c3 represents the third influence coefficient of the second power sub-model; c4 represents the fourth influence coefficient of the second power sub-model; c5 represents the fifth influence coefficient of the second power sub-model; c6 represents the sixth influence coefficient of the second power sub-model; c7 represents the seventh influence coefficient of the second power sub-model; c8 represents the eighth influence coefficient of the second power sub-model; c9 represents the ninth influence coefficient of the second power sub-model; L, M, and N represent the components of the external net torque along the x, y, and z axes in the body coordinate system, respectively.

6. The test simulation method based on the functional performance model of an airborne system according to claim 1, characterized in that: The motion sub-model in step S2 is: in, The pitch rate; Yaw rate; This represents the roll rate.

7. The test simulation method based on the functional performance model of an airborne system according to claim 1, characterized in that: The testing and verification requirements for considering the performance behavior of airborne systems in step S3 are as follows: The operational process for realizing status monitoring, fault detection, and fault isolation of airborne systems corresponds to the in-flight testability design structure of distributed-centralized airborne systems. Based on the airborne system performance status update space and sensing measurement space in step S2, the fault discrimination space Z(t) of the airborne system testability design is determined.

8. The test simulation method based on the functional performance model of an airborne system according to claim 1, characterized in that: Step S3, establishing a test diagnostic model for the airborne system, specifically involves: Setting up a status monitoring method for airborne systems i (t), Fault detection method zm i (t), Fault isolation method zn i (t), respectively: Among them, zl i (t) represents the status monitoring results; zm i (t) represents the fault detection result; zn i (t) represents the fault isolation result; L(y(t),u(t)) represents the observed data of state monitoring after function mapping; M(y(t),u(t)) represents the observed data of fault detection after function mapping; N(y(t),u(t)) represents the observed data of fault isolation after function mapping; L th For condition monitoring operation specifications; M th This is the operational standard for fault detection; N th This represents the operational specifications for fault isolation; Y(t) represents the functional output of the airborne system; true indicates normal operation; error indicates a fault alarm; if represents the selection criteria.

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