An aircraft system fault detection method fusing iterative algorithms

Abstract

The present application relates to the technical field of aircraft system fault detection, in particular to an aircraft system fault detection method based on fusion iterative algorithm. The present application provides a joint estimation method of nonlinear aircraft system state variables and fault signals based on m+1 adaptive observers, which considers the widely existing nonlinear characteristics in actual systems, makes up for the deficiencies of the existing estimation methods designed for linear aircraft systems, and has more extensive and far-reaching theoretical significance and practical application value. Secondly, the estimation values of system state variables and fault signals are provided, which can detect the existence of faults in time and lay the foundation for real-time detection of the dynamic behavior of the aircraft system and completion of specific control tasks. At the same time, by designing m+1 adaptive observers and giving iterative algorithm 1, the influence of the derivative of the fault signal on the estimation performance of the designed observer can be effectively suppressed, and the estimation performance of the proposed estimation method can be greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aircraft system fault detection, and particularly relates to an aircraft system fault detection method fusing an iterative algorithm. BACKGROUND

[0002] With the progress of science and technology, modern aviation technology is also constantly innovating, and the integration of various aircraft systems and the complexity of mission requirements are increasing. The internal components, structural modules, sensor integration and intelligentization are also increasingly high. However, in the process of flight, the aircraft system will face complex and harsh external environment, and many unfavorable factors will affect the performance and reliability of the aircraft to varying degrees and cause faults. Due to the internal structure of the aircraft system and its complexity, the high coupling between components makes the fault not only destroy the overall stability of the system and reduce the performance of the system, but also may trigger a chain reaction and cause disastrous consequences. As can be seen, the threat of faults to aircraft flight safety cannot be underestimated. On the other hand, with the increasing demand for safety, reliability, maneuverability and maintainability of aircraft systems, aircraft fault detection has attracted widespread attention in the aerospace field, and relevant research helps to diagnose and eliminate the impact of faults in the early stage of fault occurrence, which has far-reaching practical significance and is an important guarantee for improving the survivability of aircraft systems.

[0003] At the same time, the estimation of aircraft system state variables often plays an important role in real-time detection of system dynamic behavior and completion of specific control tasks. For example, residual analysis based on state estimation results is a common fault detection method. This method compares and analyzes the estimated values of the aircraft system state and the actual measured output data to obtain the corresponding residual signal, and further analyzes the system operation condition by evaluating the residual signal. In the past few decades, joint estimation schemes for different aircraft system state variables and fault signals have been extensively studied. In particular, observer-based estimation schemes have received widespread attention and have achieved numerous outstanding results. However, in existing research results, it is usually assumed that the aircraft system can be constructed as a linear system. In addition, in practical applications, external disturbances exist widely, and their influence on estimation performance cannot be ignored. In this case, it is necessary to reevaluate the effectiveness of the estimation methods proposed in existing research results.

[0004] So far, the state estimation problem for nonlinear aircraft systems has been extensively studied, while the problem of simultaneously estimating the system states and the fault signals has not been fully investigated. In addition, the effect of the derivative of the fault signal on the estimation performance has not been effectively addressed. The main technical challenges of this problem are as follows. First, the nonlinear characteristics existing in the aircraft system, and the coupling relationship between the state estimation error and the fault estimation error make the design problem very challenging. In addition, due to the lack of effective suppression methods for the effect of the derivative of the time-varying fault signal on the estimation error, it is difficult to obtain satisfactory estimation performance. SUMMARY

[0005] The technical problem solved by the present application is to overcome the deficiencies of the prior art, and to provide a fault detection method for an aircraft system based on an iterative algorithm, which realizes the joint estimation of the state variables and the fault signals of a nonlinear aircraft system, effectively suppresses the effect of the derivative of the fault signal on the estimation performance, and improves the estimation accuracy.

[0006] To solve the above technical problems, the technical scheme adopted by the present application is as follows:

[0007] A fault detection method for an aircraft system based on an iterative algorithm, comprising the following steps:

[0008] S1: establishing a dynamic model for a nonlinear aircraft system, the dynamic model being a nonlinear continuous-time system with external disturbances, and mathematically representing the fault signals based on the dynamic model;

[0009] S2: verifying the conditions required to be satisfied by the rank of the correlation matrix for the fault signal distribution matrix and the system output matrix;

[0010] S3: verifying the observable condition of the relevant system matrix;

[0011] S4: approximating the unknown nonlinear function in the aircraft system using fuzzy logic technology;

[0012] S5: designing an adaptive rate for the ideal constant parameter vector contained in the fuzzy logic technology to approximate it;

[0013] S6: obtaining an expression for the estimated value of the unknown nonlinear function in the aircraft system based on the fuzzy logic technology and the above adaptive rate;

[0014] S7: based on the output value of the system and the estimated value of the unknown nonlinear function, combining the iterative idea, designing m+1 adaptive observers to provide the estimated value of the state variables and the fault signals of the nonlinear aircraft system, while effectively suppressing the effect of the derivative of the fault signal on the estimation performance;

[0015] S8: Give the expression of the state variables and the fault signals of the nonlinear aircraft system based on the designed m+1 adaptive observers;

[0016] S9: Define a series of estimation error variables based on the designed adaptive observers;

[0017] S10: For the above error variables, get the expression of the corresponding error system based on the dynamics model of the nonlinear aircraft system and the designed adaptive observers;

[0018] S11: Give the observer gain matrix design condition in the form of linear matrix inequality based on Lyapunov stability theory and matrix inequality technique;

[0019] S12: Give the specific mathematical expression of the observer gain matrix solution;

[0020] S13: Give an iterative algorithm that can simultaneously obtain the estimates of the state variables and the fault signals of the nonlinear aircraft system;

[0021] S14: Verify the effectiveness of the fault detection scheme proposed for the nonlinear aircraft system through a simulation platform, and optimize and improve the scheme based on the simulation results.

[0022] The specific steps are as follows:

[0023] Step 1: Establish a dynamics model for the nonlinear aircraft system, which is a nonlinear continuous-time system with external disturbances, and mathematically represent the fault signals based on the dynamics model. The dynamics model is as follows:

[0024]

[0025] where, x(t), u(t), f(t), w(t), and y(t) represent the state vector of the aircraft system, the control input signal, the nonlinear function in the system, the bounded external disturbance, and the measured output value of the system, respectively. The symbol "∈" represents the belonging relation, x(t), u(t), f(t), w(t), and y(t) represent the state vector of the aircraft system, the control input signal, the nonlinear function in the system, the bounded external disturbance, and the measured output value of the system, respectively. The symbol "∈" represents the belonging relation, x(t), u(t), f(t), w(t), and y(t) represent the state vector of the aircraft system, the control input signal, the nonlinear function in the system, the bounded external disturbance, and the measured output value of the system, respectively. The symbol "∈" represents the belonging relation, x(t), u(t), f(t), w(t), and y(t) represent the state vector of the aircraft system, the control input signal, the nonlinear function in the system, the bounded external disturbance, and the measured output value of the system, respectively. The symbol "∈" represents the belonging relation, x(t), u(t), f(t), w(t), and y(t) represent the state vector of the aircraft system, the control input signal, the nonlinear function in the system, the bounded external disturbance, and the measured output value of the system, respectively. The symbol "∈" represents the belonging relation, x(t), u(t), f(t), w(t), and y(t) represent the state vector of the aircraft system, the control input signal, the nonlinear function in the system, the bounded external disturbance, and the measured output value of the system, respectively. The symbol "∈" represents the belonging relation, x(t), u(t), f(t), w(t), and y(t) represent the state vector of the aircraft system, the control input signal, the nonlinear function in the system, the bounded external disturbance, and the measured output value of the system, respectively. The symbol "∈" represents the belonging relation,

[0026] Step 2: Verify that rank(CE) = n for the fault signal distribution matrix E and the system output matrix Cf rank(CE) < rank(C) and rank(CE) < rank(A) are satisfied, where the symbol "rank(CE)" denotes the rank of the matrix CE, and it is verified that C satisfies the row full rank;

[0027] Step 3: Verify whether the matrix pair (A, C) satisfies the observable condition;

[0028] Step 4: For the nonlinear function g(t) in the above system (1), use fuzzy logic technology to approximate it, and obtain the following expression:

[0029] g(x) = Φ T (x)θ * + τ(x) (2)

[0030] where Φ * (x) denotes the transpose of Φ(x), and are the fuzzy base function and the ideal constant parameter vector, respectively, and the symbol denotes a diagonal matrix with diagonal elements ; τ(x) denotes the minimum fuzzy estimation error, which is generally bounded;

[0031] Step 5: In order to use fuzzy logic technology to approximate the unknown nonlinear function g(t) in the nonlinear aircraft system (1), for the ideal constant parameter vector θ x , design the parameter adaptive rate shown below to estimate it:

[0032]

[0033] where is the estimate value of θ -1 ; α and Υ are normal numbers and positive definite matrices given by the designer, respectively; the matrix Q is a positive definite matrix obtained from the design conditions given below; denotes the pseudo-inverse of the matrix C; denotes the estimate value of y(t), which is provided by the adaptive observer designed below;

[0034] Step 6: Based on the above adaptive rate, obtain the estimate value of the unknown nonlinear function g(x):

[0035]

[0036] Step 7: For the nonlinear aircraft system shown in (1), in order to provide satisfactory estimate values of the system state and the fault signal and effectively suppress the influence of external disturbances on the estimation performance, design the following m+1 adaptive observers:

[0037] The 0th adaptive observer:

[0038]

[0039] where and respectively represent the estimation values of the system state variable x(t) and the fault signal f(t) provided by the 0th adaptive observer; represents the estimation value of the system measurement value y(t) provided by the 0th adaptive observer; the matrix and is the gain matrix of the adaptive observer to be designed.

[0040] The 1st adaptive observer:

[0041]

[0042] where and respectively represent the estimation values of the system state variable x(t) and the fault signal f(t) provided by the 1st adaptive observer; represents the estimation value of the system measurement value y(t) provided by the 1st adaptive observer.

[0043] The jth adaptive observer:

[0044]

[0045] where 1 and respectively represent the estimation values of the system state variable x(t) and the fault signal f(t) provided by the jth adaptive observer; represents the estimation value of the system measurement value y(t) provided by the jth adaptive observer.

[0046] The mth adaptive observer:

[0047]

[0048] where 1 and respectively represent the estimation values of the system state variable x(t) and the fault signal f(t) provided by the mth adaptive observer; represents the estimation value of the system measurement value y(t) provided by the mth adaptive observer.

[0049] Step 8: Based on the above adaptive observers (5)-(8), the estimation values of the system state and the fault are obtained as shown below:

[0050]

[0051] Step 9: For a positive integer m, define the following estimation error variable in combination with the estimation value provided by the adaptive observer described above:

[0052]

[0053] Based on the error variable defined in (10) above, define the following estimation error variable for state, parameter and fault estimation:

[0054]

[0055] Step 10: Obtain the expression of the state error system as follows:

[0056]

[0057] and the expression of the fault estimation error system as follows:

[0058]

[0059] Further, obtain the expression of the estimation error system as follows:

[0060]

[0061] where

[0062] Step 11: By solving the linear matrix inequality shown as follows, the gain matrices K, L and F of the designed adaptive observer (5)-(8) can be obtained.

[0063] For given positive numbers δ1, δ2, δ3, δ4, if there exist symmetric positive definite matrices matrix and such that the linear matrix inequality shown as follows is satisfied:

[0064]

[0065] where

[0066]

[0067] The symbol "*" indicates the symmetric term in the symmetric matrix, the superscript "T" represents the transpose of the matrix, and the symbol represents the unit matrix with appropriate dimensions, represents the unit matrix with dimension n x . Then it can be concluded that the designed adaptive observer (5)-(8) and the adaptive rate (3) can make the following performance index satisfy:

[0068] All the signals in the estimation error system (14) are bounded;

[0069] When d(t)=0, the average estimation error sequence of the state variable and the fault signal will tend to zero, i.e.:

[0070]

[0071] Step 12: Further, the gain matrix F of the observer and the matrix K can be obtained as follows:

[0072] K=Q -1 P, L=FCK-G (18)

[0073] Step 13: The estimation values of the state variable and the fault signal of the nonlinear aircraft system (1) can be obtained simultaneously by the following iterative algorithm, and the flow chart of the algorithm is shown as follows: Figure 2

[0074] Iterative algorithm 1:

[0075] ① By running the adaptive observer in equation (5), obtain and and set m=1;

[0076] ② By running the adaptive observer of equations (6)-(8), obtain and and calculate the following values:

[0077]

[0078] ③ For a given sufficiently small positive number ε, for if

[0079]

[0080] then, set m=m+1, and return to step ②; otherwise, output the estimation values of x(t) and f(t) as follows:

[0081]

[0082] Step 14: The effectiveness of the fault detection scheme proposed for the nonlinear aircraft system is verified by a simulation platform, and the scheme is optimized and improved based on the simulation results.

[0083] The beneficial effects produced by the above technical solutions are as follows:

[0084] ​The application provides a joint estimation method for state variables and fault signals of a nonlinear aircraft system based on m+1 adaptive observers, considers the nonlinear characteristics widely existing in actual systems, makes up for the deficiency of the existing estimation method designed for a linear aircraft system, has more extensive and profound theoretical significance and practical application value, secondly, simultaneously provides estimation values of system state variables and fault signals, can timely detect the existence of faults, and lays a foundation for real-time detection of dynamic behaviors of the aircraft system and completion of specific control tasks, and simultaneously, through the design of m+1 adaptive observers and the given iterative algorithm 1, the influence of the derivative of the fault signal on the estimation performance of the designed observer can be effectively inhibited, and then the estimation performance of the proposed estimation method is greatly improved. BRIEF DESCRIPTION OF DRAWINGS

[0085] Figure 1 is a structural diagram of the application;

[0086] Figure 2 is a flowchart of the iterative algorithm proposed in the application;

[0087] Figure 3 is the estimation value of x1(t) under different iteration numbers provided in the embodiment of the application and the true value;

[0088] Figure 4 is the estimation value of x2(t) under different iteration numbers provided in the embodiment of the application and the true value;

[0089] Figure 5 is the estimation value of x3(t) under different iteration numbers provided in the embodiment of the application and the true value;

[0090] Figure 6 is the estimation value of f(t) under different iteration numbers provided in the embodiment of the application and the true value. DETAILED DESCRIPTION

[0091] The specific embodiments of the application are further described in detail below in combination with the drawings and embodiments. The following embodiments are used to illustrate the application, but are not used to limit the scope of the application.

[0092] In this embodiment, a fusion iterative algorithm aircraft system fault detection method is used for a nonlinear longitudinal dynamic system model of a certain type of aircraft.

[0093] A fusion iterative algorithm aircraft system fault detection method comprises the following steps:

[0094] Step 1: In this embodiment, the state vector of the nonlinear longitudinal dynamic system model of a certain type of aircraft considered is x(t)=[α(t),q(t,)δ e t()T ] = x[1t(, ) x2 t(, ) x3 t( T ), where a(t), q(t), d e (t) represent the angle of attack, pitch rate and elevator angle, respectively. The dynamic model is given as follows:

[0095]

[0096] y(t) = Cx(t)

[0097] where u(t) = K v y(t) + r(t), r(t) = 5 + sin(0.1t), w(t) = 0.1 sin(t), and

[0098]

[0099] K v = [2.5555 -2.5316]

[0100] The initial condition of the system is (1.0, 1.2, 0.5), and the initial condition of the designed observer is set as The fault signal is:

[0101]

[0102] Step 2: For the fault signal distribution matrix E and the system output matrix C, it is obtained that rank(CE) = 1, i.e., rank(CE) = n f , and C satisfies the row full rank;

[0103] Step 3: The matrix pair (A, C) satisfies the observable condition;

[0104] Step 4: For the unknown nonlinear function g(x), the fuzzy logic technology is used to approximate it, and the following expression is obtained:

[0105] g(x) = Φ T (x) θ * + τ(x)

[0106] where τ(x) is the minimum fuzzy estimation error, and

[0107]

[0108]

[0109] Step 5: For the ideal constant parameter vector θ * , the parameter adaptive law shown below is designed to estimate it:

[0110]

[0111] where

[0112]

[0113] Step 6: Based on the above adaptive rate, the estimated value of the unknown nonlinear function g(x) is obtained as follows:

[0114]

[0115] Step 7: For the above nonlinear aircraft system, in order to provide satisfactory estimates of the system state and fault signals and effectively suppress the influence of fault derivatives on the estimation performance, the adaptive observer shown in equations (5)-(8) is designed, where m=5;

[0116] Step 8: Based on the adaptive observer (5)-(8), the estimates of the system state and faults are obtained as follows:

[0117]

[0118] Step 9: For a positive integer m=5, in combination with the estimates provided by the adaptive observer, the following estimation error variables are defined:

[0119]

[0120] Based on the above error variables, the following estimation error variables are defined for state, parameter and fault estimation:

[0121]

[0122] Step 10: The expression of the state estimation error system shown in equation (12) and the expression of the fault estimation error system shown in equation (13) are obtained, and further, the expression of the estimation error system shown in equation (14) is obtained;

[0123] Step 11: For a given positive number δ1=δ2=δ3=δ4=1, the gain matrix of the observer is obtained by solving the linear matrix inequality in equation (15);

[0124] Step 12: In this embodiment, the gain matrix of the observer can be obtained as follows:

[0125]

[0126] F=[0.2265 0.1634]

[0127] L=[-35.4856 51.0423]

[0128] Step 13: the estimation values of the state variables and the fault signals of the nonlinear aircraft system in this embodiment are obtained simultaneously by the iterative algorithm 1;

[0129] Step 14: the simulation is performed by the MATLAB software, the total simulation time is 100 seconds, and the simulation result as shown in Fig. 6 is obtained. Figures 3-6

[0130] The designed adaptive observer is applied to this embodiment, and the simulation result as shown in Fig. 6 can be seen. Figures 3-6 It can be seen that the state variables and the fault signals of the aircraft system can be effectively estimated, and the estimation accuracy is satisfactory. In addition, it can be seen from the simulation graph that with the increase of m, the estimation values of the state variables and the fault signals are constantly approaching the corresponding true values. Therefore, even if there is external disturbance, the designed adaptive observer can still provide effective estimation values for the fault signals of the nonlinear aircraft system, and with the increase of the iteration number, the estimation accuracy is constantly improved, thereby achieving the purpose of fault detection.

[0131] In this embodiment, it can be seen that when the adaptive observer design condition is satisfied, the joint estimation of the system state variables and the fault signals can be finally realized, and satisfactory estimation performance can be obtained.

[0132] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope defined by the claims of the present application.​

Claims

1. An aircraft system fault detection method fusing iterative algorithms, characterized in that, The method comprises the following steps: S1: establishing a dynamic model for a nonlinear aircraft system, the dynamic model being a nonlinear continuous-time system with external disturbances, and mathematically representing a fault signal based on the dynamic model; S2: verifying conditions required for the rank of a correlation matrix to be satisfied for a fault signal distribution matrix and a system output matrix; S3: verifying an observable condition for a correlation system matrix; S4: approximating an unknown nonlinear function in the aircraft system by using a fuzzy logic technique; S5: designing an adaptive rate for an ideal constant parameter vector contained in the fuzzy logic technique to approximate the ideal constant parameter vector; S6: obtaining an expression for an estimated value of the unknown nonlinear function in the aircraft system based on the fuzzy logic technique and the adaptive rate; S7: based on an output value of the system and the estimated value of the unknown nonlinear function, combining an iterative idea, and designing m+1 adaptive observers to provide estimated values of state variables and the fault signal of the nonlinear aircraft system, while effectively suppressing an influence of derivatives of the fault signal on estimation performance; S8: combining the estimated values provided by the designed m+1 adaptive observers to obtain an expression for the estimated values of the state variables and the fault signal of the nonlinear aircraft system; S9: defining a series of estimation error variables based on the designed adaptive observers; S10: obtaining an expression for a corresponding error system for the error variables based on the dynamic model of the nonlinear aircraft system and the designed adaptive observers; S11: obtaining a design condition for an observer gain matrix in a linear matrix inequality form based on Lyapunov stability theory and matrix inequality technology; S12: obtaining a specific mathematical expression for solving the observer gain matrix; S13: obtaining an iterative algorithm for simultaneously obtaining the estimated values of the state variables and the fault signal of the nonlinear aircraft system; S14: verifying effectiveness of the fault detection scheme proposed for the nonlinear aircraft system through a simulation platform, and optimizing and improving the scheme based on simulation results; The step 11 is specifically as follows: The gain matrix K, L and F of the designed adaptive observer are obtained by solving the linear matrix inequality shown as follows: For given positive numbers δ1, δ2, δ3, δ4, if there exists a symmetric positive definite matrix matrix and such that the following linear matrix inequality holds: Wherein Matrices A, E, and C represent the correlation matrices of a known system with appropriate dimensions. Let α and β be the pseudoinverse of matrix C. These are positive constants and positive definite matrices, respectively, provided by the designer. Representation matrix The inverse matrix of a matrix, the symbol "★" denotes the symmetric term in a symmetric matrix, the superscript "T" denotes the transpose of the matrix, and the symbol... "" represents the identity matrix with appropriate dimensions, and "0" represents the zero matrix with appropriate dimensions. Indicates a dimension of n x The identity matrix; then we can derive the designed adaptive observer (5)-(8) and the adaptive rate (3), which can satisfy the following performance indices: It is assumed that all signals in the estimation error system (14) are bounded; When d(t) = 0, the average estimation error sequence of the state variable and the fault signal will tend to zero; The step 12 is further to obtain the gain matrix F and K = Q -1 P, L = FCK-G (18) where the matrix Q -1 denotes the inverse matrix of the matrix Q.

2. The method of claim 1, wherein the fusion iterative algorithm is a consensus algorithm. In the step 1, the dynamic model for the nonlinear aircraft system is established, the dynamic model being a nonlinear continuous-time system with external disturbances, and the fault signal is mathematically represented based on the dynamic model; the dynamic model is as follows: y(t)=Cx(t) (1) where, x(t), u(t), f(t), d(t), y(t), respectively, denote the state vector of the aircraft system, the control input signal, the nonlinear function in the system, the bounded external disturbance, and the measured output value of the system; the symbol "∈" denotes the belonging relation, denotes the n-dimensional Euclidean space; denotes the unknown process fault signal in the aircraft system, and ||f(t)|| and are bounded, denotes the first-order derivative of f(t) with respect to time t, ||f(t)|| and denote the Euclidean norm of f(t) and , respectively; the matrices A, B, E, W, C denote known system-related matrices with appropriate dimensions.

3. A method of detecting faults in an aircraft system using a fusion iterative algorithm as claimed in claim 2, wherein, In step 2, it is verified that rank(CE) = n for the fault signal distribution matrix E and the system output matrix C f whether or not rank(CE) = n is satisfied, the symbol "rank(CE)" representing the rank of the matrix CE, and it is verified that C satisfies the row full rank.

4. The aircraft system fault detection method of claim 3, wherein In the step 4, the specific operation is as follows: In the step 4, the specific operation is as follows: where Φ T (x) denotes the transpose of Φ(x), and are the fuzzy basis functions and the ideal constant parameter vector, respectively, and the notation denotes a diagonal matrix with diagonal elements ; τ(x) denotes the minimum fuzzy estimation error.

5. A method of detecting faults in an aircraft system using a fusion iterative algorithm as claimed in claim 4, wherein, In the step 5, the specific operation is as follows: Step 5: To approximate the unknown nonlinear function g(x) in the nonlinear aircraft system (1) using fuzzy logic technique, for the ideal constant parameter vector The parameter adaptive rate is designed as shown below to estimate it: wherein is an estimate of ; a and are a normal number and a positive definite matrix respectively given by the designer. The matrix Q is a positive definite matrix, which is obtained by the design condition given later. denotes the pseudo-inverse of the matrix C. denotes the estimate of y(t), which is provided by the adaptive observer designed later.

6. A method of detecting faults in an aircraft system using a fusion iterative algorithm as claimed in claim 5, wherein, In the step 6, the specific operation is as follows: Step 6: Based on the above adaptive rate, get the estimated value of the unknown nonlinear function g(x) The specific expression is as follows:

7. A method of detecting faults in an aircraft system using a fusion iterative algorithm as claimed in claim 6, wherein, In the step 7, the specific operation is as follows: Step 7: For the nonlinear aircraft system shown in (1), to provide satisfactory estimates of the system states and fault signals and to effectively suppress the influence of external disturbances on the estimation performance, the following m+1 adaptive observers are designed: The 0th adaptive observer: wherein and denote the estimates of the system state variable x(t) and the fault signal f(t) provided by the 0th adaptive observer; denotes the estimate of the system measurement y(t) provided by the 0th adaptive observer; the matrix and is the gain matrix of the adaptive observer to be designed; The 1st adaptive observer: wherein and respectively represent the estimation values of the system state variable x(t) and the fault signal f(t) provided by the first adaptive observer; represents the estimation value of the system measurement value y(t) provided by the first adaptive observer; The jth adaptive observer: where 1 < j < m, and x^j(t) and f^j(t) represent the estimates of the system state variable x(t) and the fault signal f(t) provided by the jth adaptive observer, respectively; y^j(t) represents the estimate of the system measurement y(t) provided by the jth adaptive observer. The mth adaptive observer: where 1 < j < m, and x^m(t) and f^m(t) represent the estimates of the system state variable x(t) and the fault signal f(t) provided by the mth adaptive observer, respectively; y^m(t) represents the estimate of the system measurement y(t) provided by the mth adaptive observer.

8. A method of detecting faults in an aircraft system using a fusion iterative algorithm as claimed in claim 7, wherein, In the step 8, the specific operation is as follows: Step 8: Based on the above adaptive observers (5)-(8), the following estimates of the system states and faults are obtained:

9. A method of detecting faults in an aircraft system using a fusion iterative algorithm as claimed in claim 8, wherein, In the step 9, the specific operation is as follows: Step 9: For a positive integer m, the following estimate error variable is defined in combination with the estimates provided by the above adaptive observers: Based on the error variable defined in the above (10), the following estimate error variable is defined for the state, parameter and fault estimates:

10. The method of claim 9, wherein the fusion iterative algorithm is a consensus algorithm. In the step 10, the specific operation is as follows: Step 10: The following expression of the state error system is obtained: And the expression of the fault estimation error system is: The following expression of the estimation error system is obtained: wherein

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