Remote fault diagnosis method for aircraft system based on hybrid observer

Abstract

The application discloses a remote fault diagnosis method for an aircraft system based on a hybrid observer and belongs to the technical field of fault diagnosis.The application fully considers the sparse available measurement values caused by a transmission network, makes up for the shortage of an existing estimation method designed based on continuous measurement values, fully utilizes a new generation of information technology, and effectively improves the reliability of an information physical system operation of an aircraft; the information physical system framework is introduced into the field of remote fault diagnosis of the aircraft system, a remote fault diagnosis scheme is established through system modeling, simulation verification and hybrid system theory, and the development of the health management technology of the aircraft system is promoted to a certain extent.

Description

Technical Field

[0001] This invention relates to the field of fault diagnosis technology, and in particular to a remote fault diagnosis method for aircraft systems based on hybrid observers. Background Technology

[0002] With the increasing penetration of next-generation information and communication technologies and networks into the industrial sector, cyber-physical systems (CPS) have effectively improved the integration and collaboration between networks and industrial processes, gradually driving the transformation of traditional industries into knowledge-based industries. In recent years, CPS have been widely applied in many critical national infrastructure sectors (such as aerospace, smart grids, health monitoring systems, and mobile robotic systems), and have had a significant impact on the national economy and daily life.

[0003] Aircraft, as typical large-scale complex systems, have extremely high safety requirements. However, their inherent complexity and the complex external environment they are exposed to make them prone to unpredictable failures. Given the close interconnectedness of components within an aircraft system, sudden failures can severely damage the entire orderly and efficient operating system, even leading to unbearable economic losses. Therefore, there is an urgent need to research and develop various effective fault detection and isolation methods to minimize the degradation of aircraft system performance and the resulting economic losses. Aircraft fault diagnosis technology has always been a key focus of the aviation industry, and scholars from various countries have made unremitting efforts over the years, achieving significant progress in many areas. However, research results in remote diagnostic technology are still insufficient.

[0004] To meet the demands of information technology and networking in next-generation physical equipment, the successful application of cyber-physical systems (CPS) has brought about a series of new ideas and methods for solving problems, showing broad development prospects. Given the superior performance of CPS, combining it with aircraft systems to form aircraft CPS will have enormous development potential, especially in the field of remote fault diagnosis for aircraft systems. It will undoubtedly provide a novel conceptual framework and methodology for remote aircraft detection and fault diagnosis. On the one hand, it can greatly improve the capability and speed of routine aircraft maintenance and emergency repairs. On the other hand, it will further promote the real-time, standardized, and shared realization of relevant information within aircraft systems.

[0005] Currently, a large amount of research is dedicated to the joint estimation of state variables and fault signals in aircraft systems, primarily due to their significant potential applications in fault detection and improving the safety and reliability of control systems. In particular, observer-based fault estimation schemes have been extensively studied and have yielded fruitful results. However, existing research typically requires that the relevant measurements of the aircraft system be continuously available. With the development of communication technologies, data communication networks are being widely integrated into aircraft cyber-physical systems to assist in data sensing, collection, processing, and transmission. Information is transmitted via data packets and is only available at discrete points in time. In this context, it is necessary to re-evaluate the performance of fault estimation methods designed based on continuously available measurements.

[0006] In recent years, state estimation based on sparse measurements has received widespread attention; however, the problem of simultaneously estimating system state and fault signals has not been adequately studied. The main technical challenges of this problem include: First, since the timing of available output measurements and the time interval between two consecutive transmission moments are unknown, it is difficult for the designed observer to achieve satisfactory estimation performance. Second, the coupling relationship between system state estimation errors and fault estimation errors further complicates the design problem. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a fault diagnosis method for aircraft cyber-physical systems that considers sparse output measurement values, thereby overcoming the shortcomings of the prior art and enabling remote fault diagnosis of aircraft cyber-physical systems.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a remote fault diagnosis method for aircraft systems based on hybrid observers, the method comprising:

[0009] S1: Establish a dynamic model for the aircraft system. The dynamic model is based on a linear continuous-time system under external disturbances and measurement noise, and the process faults are mathematically characterized based on the dynamic model.

[0010] S2: Considering that the measurement output value of the aircraft system is transmitted from the physical layer to the network layer by the digital communication network, and that the network layer can only obtain the system's measurement output value at some unknown times, give an appropriate expression for the transmission time, and make reasonable restrictions on the adjacent transmission interval based on the characteristics of the digital communication network.

[0011] S3: Verify the relevant observability performance of the aircraft system matrix;

[0012] S4: Design auxiliary variables to address the coupling problem in the joint estimation process of aircraft system state variables and fault signals;

[0013] S5: For time intervals where aircraft system output measurements cannot be obtained, a hybrid observer is designed based on hybrid system theory to simultaneously provide estimates of system state variables and fault signals.

[0014] S6: For the moment when the output measurement value of the aircraft system is available, combine the hybrid system theory to design a hybrid observer that can simultaneously provide estimates of system state variables and fault signals.

[0015] S7: Based on the stability theory of hybrid systems and the exponential input-state stability theorem, we propose a design criterion for the observer gain matrix that allows the observer to simultaneously provide estimates of system state variables and fault signals while ensuring that the estimation error system satisfies the exponential input-state stability.

[0016] S8: Combining linear matrix inequality techniques, we present the design conditions for the observer gain matrix that can be directly solved using the LMI toolbox in MATLAB software.

[0017] S9: Provide the specific mathematical expression for solving the observer gain matrix;

[0018] S10: Verify the effectiveness of the remote fault diagnosis scheme for aircraft cyber-physical systems through a simulation platform, and optimize and improve the scheme based on the simulation results.

[0019] The specific steps are as follows:

[0020] Step 1: Establish a dynamic model for the aircraft system. The dynamic model is a linear continuous-time system based on external disturbances and measurement noise. Simultaneously, the process faults are mathematically characterized based on the dynamic model. The dynamic model is shown in Equation (1):

[0021]

[0022] in These represent the state variables, control inputs, and output measurements of the aircraft system at the physical layer, respectively. This represents the first derivative of x(t) with respect to time. and Let represent the sets of n-dimensional Euclidean space and m×n-dimensional real matrices, respectively; Indicates an unknown fault signal and satisfies This represents the first derivative of f(t) with respect to time. It is a real number (which may be unknown); and Let A, B, F, D, C, and W represent bounded external disturbances and measurement noise, respectively; matrices A, B, F, D, C, and W are known real matrices with appropriate dimensions; without loss of generality, it is assumed that the (A, C) matrix pair is observable and that matrix F is full column rank.

[0023] Step 2: To achieve remote fault diagnosis, the measurement output values ​​of the aircraft system at the physical layer are transmitted from the physical layer to the network layer via a digital communication network. Furthermore, the network layer can only transmit these values ​​at certain unknown times. k The system's measured output value is obtained at any time (k is an integer).

[0024] Considering the above, a time series of measured values ​​is defined. It is unbounded and strictly increasing, among which This represents the set of positive integers. Furthermore, based on the characteristics of digital communication networks, it is assumed that there exists a positive real number T. m and T M (T m ≤T M This makes the inequality in formula (2) true:

[0025] 0≤t1≤T M T m ≤t k+1 -t k ≤T M (2) Where t1 is the initial time of the transmission of the aircraft system's measurement output value, t k and t k+1 These represent the k-th and k+1-th transmission times, respectively.

[0026] Step 3: Verify whether the invariant zeros of the aircraft system correlation matrix (A, C, F) are all stable;

[0027] Step 4: In order to provide an estimate of the fault signal while ensuring the convergence of the fault estimation error, and to resolve the coupling problem in the joint estimation process of state variables and fault signals, an auxiliary intermediate variable as shown in the following formula (3) is introduced:

[0028] θ(t)=f(t)-Kx(t)(3)

[0029] in To represent auxiliary intermediate variables, matrix K is set as K = σF T F T σ represents the transpose of matrix F, where σ is a positive number given by the designer.

[0030] Step 5: By introducing the auxiliary intermediate variable θ(t) given in the above formula (3), for the time interval where the output measurement value is unavailable, design the following hybrid observer:

[0031] When t≠t k ,and So

[0032]

[0033] in and Let x(t) and θ(t) represent the estimated values, respectively. It serves to measure the update term and inject the output estimation error between two adjacent transmission times. The estimated value of the fault signal is... matrix It is the observer gain matrix that needs to be designed;

[0034] Step 6: By introducing the auxiliary intermediate variable given in formula (3) above, and considering the time when the output measurement value can be obtained, the hybrid observer is designed as follows:

[0035] When t = t k ,and So

[0036]

[0037] in It is of dimension n y identity matrix, matrix This is the observer gain matrix that needs to be designed.

[0038] Step 7: Based on the hybrid system stability theory and the exponential input-state stability theorem, if there exists an observer gain matrix that satisfies the following conditions, then the designed observer (4)-(5) can simultaneously provide estimates of the system state variables and fault signals, and the estimation error system satisfies exponential input-state stability:

[0039] For a given positive number ε, μ, σ, c t , σ i (i = 1, 2, 3, 4) and the matrix If a positive definite matrix exists If the matrix inequality holds, then the observers (4)-(5) can simultaneously provide estimates of the system state variables and fault signals while ensuring that the estimation error system satisfies exponential input-state stability:

[0040]

[0041] in

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] The symbol “★” represents a symmetric term in a symmetric matrix, 0 and I represent the zero matrix and identity matrix with appropriate dimensions, respectively, and β=(1+μ)e ετ -1, τ∈[T] m T M ].

[0049] Step 8: Combining linear matrix inequality techniques, the design conditions and specific expressions for the observer gain matrix, which can be directly solved using the LMI toolbox in MATLAB, are given below:

[0050] For a given positive number ε, μ, σ, c t , σ i (i = 1, 2, 3, 4), if a positive definite matrix exists and matrix If the following linear matrix inequality holds, then the observers (4)-(5) can simultaneously provide estimates of the system state variables and fault signals, and the estimation error system satisfies exponential input-state stability:

[0051]

[0052] Where i = 1, 2, β1 = μ, and

[0053]

[0054]

[0055]

[0056]

[0057] Φ 12 =PF-(A+σFF) T ) T σFR

[0058]

[0059] Φ 22 =-σRF T F-(σRF T F)T +(ε+2c t )R

[0060] Φ 23i =β i F T C T Q

[0061]

[0062] Step 9: Based on the above steps, the gain matrix of the hybrid observers (4)-(5) can be obtained through the following expression:

[0063]

[0064] Step 10: Verify the effectiveness of the remote fault diagnosis scheme for the aircraft cyber-physical system through a simulation platform, and optimize and improve the scheme based on the feedback from the simulation results.

[0065] The above technical solution has at least the following beneficial effects:

[0066] (1) The present invention provides a remote fault diagnosis method for aircraft systems based on hybrid observers, which fully considers the sparse available measurement values ​​caused by the transmission network, makes up for the shortcomings of existing estimation methods based on continuous measurement values, and makes full use of the new generation of information technology to effectively improve the reliability of the aircraft cyber-physical system.

[0067] (2) The fault estimation scheme based on sparse output measurements is more suitable for modern aircraft cyber-physical systems that integrate digital transmission networks. Therefore, compared with existing fault estimation schemes based on continuous output measurements, the scheme designed in this invention has more practical engineering application value.

[0068] (3) The present invention makes full use of the available output measurement values ​​to design a hybrid observer, which improves the estimation performance of the observer and thus can obtain more accurate estimates of system state variables and fault signals.

[0069] (4) Combining the linear matrix inequality technique, the design conditions of the observer gain matrix are given, which can be solved directly through the LMI toolbox in MATLAB software, avoiding the restriction that the existing schemes need to meet strict equality conditions;

[0070] (5) The cyber-physical system framework has been introduced into the field of remote fault diagnosis of aircraft systems. Remote fault diagnosis schemes have been established through system modeling, simulation verification, hybrid system theory and other technologies, which has promoted the development of aircraft system health management technology to a certain extent.

[0071] (6) By jointly estimating the operating status and fault signals of the aircraft system at the physical layer through the network layer, real-time status monitoring was achieved, which provides certain technical support for feeding back the optimal operating decisions and maintenance suggestions to the aircraft system. Attached Figure Description

[0072] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. It should be understood that the accompanying drawings described below are only some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0073] Figure 1 This is a system block diagram of the present invention;

[0074] Figure 2 This is a block diagram of the aircraft cyber-physical system in this invention;

[0075] Figure 3 This is a flowchart illustrating the joint estimation of system state variables and fault signals in this invention;

[0076] Figure 4 The output measurement noise provided in the embodiments of the present invention;

[0077] Figure 5 Simulation diagram of the true and estimated values ​​of the aircraft system state variable x1(t) provided in an embodiment of the present invention;

[0078] Figure 6 Simulation diagrams of the true and estimated values ​​of the aircraft system state variable x2(t) provided in embodiments of the present invention;

[0079] Figure 7 Simulation diagram of the true and estimated values ​​of the aircraft system state variable x3(t) provided in an embodiment of the present invention;

[0080] Figure 8 Simulation diagram of the true and estimated values ​​of the aircraft system fault signal f(t) provided in the embodiments of the present invention. Detailed Implementation

[0081] To make the technical problems, technical solutions, and advantages of the present invention clearer, the specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the present invention.

[0082] This embodiment uses a linearized longitudinal dynamics model of an aircraft as an example to demonstrate a remote fault diagnosis method for aircraft systems based on a hybrid observer, as described in this invention.

[0083] First, a dynamic model for the aircraft system is established, based on a linear continuous-time system under external disturbances and measurement noise. The process fault is then mathematically characterized based on this model. Next, to address the coupling problem arising during the joint estimation of aircraft system state variables and fault signals, auxiliary intermediate variables are introduced. Then, based on sparsely available aircraft system output measurements and combined with hybrid system theory, a hybrid observer is designed to simultaneously provide estimates of both system state variables and fault signals. Next, the observer gain matrix is ​​directly solved using the LMI toolbox in MATLAB. Finally, the effectiveness of the remote fault diagnosis scheme for the aircraft system is verified through a simulation platform, and the scheme is optimized and improved based on the feedback from the simulation results.

[0084] A remote fault diagnosis method for aircraft systems based on hybrid observers specifically includes the following steps:

[0085] Step 1: In this embodiment, we consider a linearized longitudinal dynamics model of a certain type of aircraft, whose system state variables are x(t) = (α(t), q(t), δ... e (t))=(x1(t), x2(t), x3(t)), where α(t), q(t) and δ e (t) represent the angle of attack, pitch rate, and lift angle, respectively. In this embodiment, for simulation purposes, the control input signal is set to... Among them, the selection r(t) = 5 + sin(t). Considering the process disturbance as d(t) = sin(5t), the output measurement noise is as follows: Figure 2 As shown. And assume the fault signal of the aircraft system is:

[0086]

[0087] The longitudinal dynamics linearization model of the aircraft can be described as the system shown in Equation (1), where the corresponding system matrix is ​​shown below:

[0088]

[0089]

[0090] Step 2: The measurement output values ​​of the aircraft system at the physical layer are transmitted from the physical layer to the network layer via a digital communication network, and the network layer can only transmit these values ​​at some unknown times. k The system's measured output value (k is an integer) is obtained at any given time. Considering this, this embodiment selects a time series of measured values ​​for transmission. It is unbounded and strictly increasing, and contains positive real numbers T. m =0.1 and T M=1.2 and make the following inequality hold:

[0091] 0≤t1=0≤T M T m ≤t k+1 -t k ≤T M

[0092] Step 3: Combining the expressions of the relevant system matrix given in formulas (10) and (11), numerical calculations verify that the invariant zeros of matrix (A, C, F) are all stable.

[0093] Step 4: To address the coupling problem that arises during the joint estimation of aircraft system state variables and fault signals, and to provide an estimated value for the fault signal while ensuring the convergence of the fault estimation error system, the auxiliary intermediate variable shown in formula (3) is introduced, where matrix K is set to K = [0 0 0.667] in this embodiment;

[0094] Step 5: By combining the auxiliary intermediate variable θ(t), when considering the time interval where the output measurement value is unavailable, design the hybrid observer shown in formula (4) to provide estimates of the aircraft system state variable x(t) and the fault signal f(t);

[0095] Step 6: By combining the auxiliary intermediate variable θ(t), and taking into account the moment when the output measurement value is available, design the hybrid observer shown in formula (5) to provide estimates of the aircraft system state variable x(t) and the fault signal f(t);

[0096] Step 7: From formula (6), we can obtain that the hybrid observer (4)-(5) can simultaneously provide estimates of system state variables and fault signals and ensure that the estimation error system meets the design criteria of the observer gain matrix of exponential input-state stability.

[0097] Step 8: In this embodiment, the values ​​of the relevant given positive numbers are: ε = 0.1, μ = 0.01, σ = 0.1, c t =0.1, σ1=0.5, σ2=3, σ3=50, σ4=1. Substitute the above parameters and the corresponding system matrix into the linear matrix inequality given in formula (7);

[0098] Step 9: Obtain the observer gain matrix as shown below using the LMI toolbox in MATLAB software:

[0099]

[0100]

[0101]

[0102] Step 10: Assume the initial conditions of the aircraft system in this embodiment are (0,2,0), and simultaneously set the initial conditions of the designed hybrid observer to zero. Simulation is performed using MATLAB software for 80 seconds, yielding the following results: Figure 5-8 The simulation results are shown.

[0103] Note that this embodiment considers the fault signal shown in equation (9), where the upper bound of the fault is unknown. Therefore, the observers designed based on the upper bound of the fault signal in existing results cannot be applied to this embodiment. In addition, unlike existing research results, the hybrid observer scheme designed in this invention does not require the fault signal to belong to the L2 space, thus relaxing the restrictions of existing results.

[0104] The designed hybrid observers (4)-(5) are applied to this embodiment, and the simulation results are as follows. Figure 5-8 As can be seen, the state variables and fault signals of the aircraft system can be estimated well with satisfactory estimation accuracy. Figure 5-7 The true values ​​of the aircraft system state variables x1(t), x2(t), and x3(t) and the corresponding estimated values ​​provided by the hybrid observers (4)-(5) are given respectively. The values ​​(x1(t), x2(t), x3(t)) correspond to the solid lines in the graph. (Corresponding to the dashed line in the figure). As can be seen from the figure, even with external disturbances and measurement noise, the designed hybrid observers (4)-(5) can still provide effective estimates of the state variables of the aircraft system. Figure 8 The fault signal described by formula (9) and the corresponding estimates provided by the hybrid observers (4)-(5) are given, i.e. (f(t) corresponds to) Figure 8 The solid line in the middle, correspond Figure 8 (The dashed line in the figure). As can be seen from the figure, even with external disturbances and measurement noise, the designed hybrid observers (4)-(5) can still provide effective estimates of the fault signals of the aircraft system, thereby achieving the purpose of fault diagnosis.

[0105] The simulation results show that when the design requirements of the hybrid observer are met, it is possible to achieve joint estimation of the state variables and fault signals of the aircraft system at the physical layer, thereby achieving the purpose of fault diagnosis.

[0106] In this embodiment of the invention, a hybrid observer design scheme is used to achieve remote fault diagnosis for aircraft cyber-physical systems. A dynamic model for the aircraft system is established, which is based on a linear continuous-time system under external disturbances and measurement noise. The process fault is mathematically represented based on this dynamic model. Considering that the measured output values ​​of the aircraft system are transmitted from the physical layer to the network layer via a digital communication network, and that the network layer can only obtain the system's measured output values ​​at some unknown times, an appropriate expression for the transmission time is given, and reasonable restrictions are placed on the adjacent transmission interval based on the characteristics of the digital communication network. To address the coupling problem in the joint estimation process of aircraft system state variables and fault signals, an auxiliary intermediate variable is introduced. Based on the time interval of sparsely obtainable aircraft system output measurements, combined with the hybrid system... A hybrid observer is designed based on the theory of hybrid system stability to simultaneously provide estimates of system state variables and fault signals. Based on the theory of hybrid system stability and the exponential input-state stability theorem, a design criterion for the observer gain matrix is ​​given, which ensures that the observer can simultaneously provide estimates of system state variables and fault signals while guaranteeing that the estimation error system satisfies exponential input-state stability. Combining linear matrix inequality techniques, the design conditions for the observer gain matrix, which can be directly solved using the LMI toolbox in MATLAB, are presented, along with the mathematical expression for solving the observer gain matrix. Finally, the effectiveness of the remote fault diagnosis scheme for aircraft cyber-physical systems is verified through a simulation platform, and the scheme is optimized and improved based on the feedback from the simulation results.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A remote fault diagnosis method for aircraft systems based on hybrid observers, characterized in that, The steps are as follows: S1: Establish a dynamic model for the aircraft system. The dynamic model is based on a linear continuous-time system under external disturbances and measurement noise, and the process faults are mathematically characterized based on the dynamic model. The dynamic model is shown in equation (1): (1) in These represent the state variables, control inputs, and output measurements of the aircraft system at the physical layer, respectively. express The first derivative with respect to time; They represent Veuve space and A set of 3D real matrices; Indicates an unknown fault signal and satisfies , express The first derivative with respect to time, It is a real number, and can be unknown; Representing bounded external disturbances and measurement noise, respectively; matrix and It is a known real matrix with appropriate dimension; without loss of generality, let... Matrix pairs are observable, and the matrix It is in full order; S2: Considering that the measurement output value of the aircraft system is transmitted from the physical layer to the network layer by the digital communication network, and that the network layer can only obtain the system's measurement output value at some unknown times, give an appropriate expression for the transmission time, and make reasonable restrictions on the adjacent transmission interval based on the characteristics of the digital communication network. To achieve remote fault diagnosis, the measurement output values ​​of the aircraft system at the physical layer are transmitted from the physical layer to the network layer via a digital communication network. Furthermore, the network layer can only access these values ​​in some unknown... The system's measurement output values ​​are obtained continuously. It is an integer; Considering the above, a time series of measured values ​​is defined. It is unbounded and strictly increasing, among which Represents the set of positive integers; furthermore, based on the characteristics of digital communication networks, assume the existence of positive real numbers. and , This makes the inequality in formula (2) true: (2) in This is the initial moment of the transmission of the aircraft system's measurement output values. and These represent the k-th and k+1-th transmission times, respectively. S3: Verify the aircraft system correlation matrix Are all invariant zeros stable? S4: Design auxiliary variables to address the coupling problem in the joint estimation process of aircraft system state variables and fault signals; In order to provide an estimate of the fault signal while ensuring the convergence of the fault estimation error, and to resolve the coupling problem in the joint estimation process of state variables and fault signals, an auxiliary intermediate variable as shown in the following formula (3) is introduced: (3) in Represents auxiliary intermediate variables, matrices Set as , Representation matrix transpose, It is a positive number given by the designer. S5: For time intervals where aircraft system output measurements cannot be obtained, a hybrid observer is designed based on hybrid system theory to simultaneously provide estimates of system state variables and fault signals. By introducing the auxiliary intermediate variable given in formula (3) For time intervals where output measurements are unavailable, a hybrid observer is designed as shown below: (4) in They represent The estimated value, , , They are respectively , , The first derivative with respect to time, It serves to measure the update term and inject the output estimation error between two adjacent transmission times; the estimated value of the fault signal is... ;matrix It is the observer gain matrix that needs to be designed; S6: For the moment when the output measurement value of the aircraft system is available, combine the hybrid system theory to design a hybrid observer that can simultaneously provide estimates of system state variables and fault signals. By introducing the auxiliary intermediate variable given in formula (3), and considering the time when the output measurement value can be obtained, the following hybrid observer is designed: (5) in It is of dimension n y identity matrix, matrix It is the observer gain matrix that needs to be designed; S7: Based on the stability theory of hybrid systems and the exponential input-state stability theorem, we propose a design criterion for the observer gain matrix that allows the observer to simultaneously provide estimates of system state variables and fault signals while ensuring that the estimation error system satisfies the exponential input-state stability. Based on the hybrid system stability theory and the exponential input-state stability theorem, if there exists an observer gain matrix that satisfies the following conditions, then the designed observer (4)-(5) can simultaneously provide estimates of the system state variables and fault signals, and the estimation error system satisfies exponential input-state stability: For a given positive number and matrix , If a positive definite matrix exists If the matrix inequality holds, then the observers (4)-(5) can simultaneously provide estimates of the system state variables and fault signals while ensuring that the estimation error system satisfies exponential input-state stability: (6) in The symbol " " represents the symmetric term in a symmetric matrix. Let represent the zero matrix and the identity matrix with appropriate dimensions, respectively. ; S8: Combining linear matrix inequality techniques, we present the design conditions for the observer gain matrix that can be directly solved using the LMI toolbox in MATLAB software. S9: Provide the specific mathematical expression for solving the observer gain matrix; S10: Verify the effectiveness of the remote fault diagnosis scheme for aircraft cyber-physical systems through a simulation platform, and optimize and improve the scheme based on the simulation results.

2. The remote fault diagnosis method for aircraft systems based on hybrid observers according to claim 1, characterized in that, Step 8: Combining linear matrix inequality techniques, the design conditions and specific expressions for the observer gain matrix, which can be directly solved using the LMI toolbox in MATLAB, are given below: For a given positive number If a positive definite matrix exists and matrix If the following linear matrix inequality holds, then the observers (4)-(5) can simultaneously provide estimates of the system state variables and fault signals, and the estimation error system satisfies exponential input-state stability: (7) in ,and 。 3. The remote fault diagnosis method for aircraft systems based on hybrid observers according to claim 2, characterized in that, Step 9: Based on the above steps, the gain matrix of the hybrid observers (4)-(5) can be obtained through the following expression: (8)。

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