Diagnostic-based spacecraft fault diagnosis measurement data autonomous optimization method

Abstract

The invention discloses a diagnosability-based spacecraft fault diagnosis measurement data autonomous optimization method. The method comprises the steps of obtaining a state equation of a spacecraft control system; constructing a system dynamic expression according to the state equation of the spacecraft control system; constructing a system dynamic expression determined by the diagnosis measurement data according to the system dynamic expression; according to a system dynamic expression determined by the diagnosis measurement data, obtaining random features corresponding to the diagnosis measurement data combination; according to the random features corresponding to the diagnosis measurement data combination, obtaining a diagnosability analysis result corresponding to the diagnosis measurement data combination; according to a diagnosability analysis result corresponding to the diagnosis measurement data combination, obtaining diagnosis measurement data autonomous screening logic; and obtaining an optimal diagnosis measurement data combination according to the diagnosis measurement data autonomous screening logic. According to the method, the amount of data needing to be processed for diagnosis is reduced, the fault diagnosis calculation amount is effectively reduced, and autonomous optimization of spacecraft diagnosis measurement data is achieved.

Description

Technical Field

[0001] The present invention belongs to the general technical field of spacecraft, and in particular relates to an autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability. Background Art

[0002] In order to ensure the safe, reliable and autonomous operation of spacecraft, it is necessary for spacecraft to be able to perform autonomous fault diagnosis. Spacecraft are characterized by severe resource constraints. How to reduce the amount of calculation required for diagnosis is a core issue that needs to be solved in order to rationally utilize the limited resources of spacecraft. Li et al. (Li Z, Zhang Y, Ai J, et al. A lightweight and explainable data-driven scheme for fault detection of aerospace sensors [J]. IEEE Transactions on Aerospace and Electronic Systems, 2023.) introduced an improved particle swarm optimization-CNN method based on advanced convolutional blocks to solve the problems of over-design and insufficient precision in CNN network design in fault diagnosis of satellite attitude control systems, and provided a lightweight and highly sensitive supervised anomaly detection method, but did not reduce the amount of diagnostic calculations from the perspective of data, making it difficult to further reduce the computational burden of onboard computers. Summary of the invention

[0003] The technical problem solved by the present invention is: to overcome the shortcomings of the prior art and provide a method for autonomous optimization of spacecraft fault diagnosis measurement data based on diagnosability, which effectively reduces the amount of fault diagnosis calculations by reducing the amount of data required for diagnosis, thereby achieving autonomous optimization of spacecraft diagnostic measurement data.

[0004] The object of the present invention is achieved through the following technical scheme: a method for autonomous optimization of spacecraft fault diagnosis measurement data based on diagnosability, comprising: obtaining a state equation of a spacecraft control system; constructing a system dynamic expression based on the state equation of the spacecraft control system; constructing a system dynamic expression determined by diagnostic measurement data based on the system dynamic expression; obtaining random features corresponding to a diagnostic measurement data combination based on the system dynamic expression determined by the diagnostic measurement data; obtaining a diagnosability analysis result corresponding to the diagnostic measurement data combination based on the random features corresponding to the diagnostic measurement data combination; obtaining an autonomous screening logic for diagnostic measurement data based on the diagnosability analysis result corresponding to the diagnostic measurement data combination; obtaining an optimal diagnostic measurement data combination based on the autonomous screening logic for diagnostic measurement data.

[0005] In the above-mentioned autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability, the state equation of the spacecraft control system is obtained by the following formula:

[0006] x(k+1)=A d x(k)+B d u(k)+B d,f f(k)+B d,w w(k)

[0007] y(k)=C d x(k)+D d,f f(k)+D d,v v(k);

[0008] Among them, y(k) is the system output vector at the kth moment, k is the moment, x(k+1) is the system state vector at the k+1th moment, x(k) is the system state vector at the kth moment, u(k) is the control input vector at the kth moment, f(k) is the fault vector at the kth moment, w(k) is the process noise vector at the kth moment, v(k) is the measurement noise vector at the kth moment, A d is the first parameter matrix of the system, B d is the second parameter matrix of the system, B d,f is the third parameter matrix of the system, B d,w is the fourth parameter matrix of the system, C d is the fifth parameter matrix of the system, D d,f is the sixth parameter matrix of the system, D d,v is the seventh parameter matrix of the system.

[0009] In the above-mentioned autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability, the system dynamic expression is obtained by the following formula:

[0010] d(ks,k)-H u u(ks,k)=H o x(ks)+H f f(ks,k)+H e e(ks,k);

[0011] in,

[0012]

[0013]

[0014] Where k represents the time, s represents the length of the diagnostic time window, d(ks,k) is the diagnostic measurement data vector of the system dynamic expression, u(ks,k) is the control input vector of the system dynamic expression, x(ks) is the system state vector at the ks-th time, f(ks,k) is the fault vector of the system dynamic expression, e(ks,k) is the noise vector of the system dynamic expression, H uis the first parameter matrix of the system dynamic expression, H o is the second parameter matrix of the system dynamic expression, H f is the third parameter matrix of the system dynamic expression, u(k) is the control input vector at the kth moment, f(k) is the fault vector at the kth moment, w(k) is the process noise vector at the kth moment, v(k) is the measurement noise vector at the kth moment, A d is the first parameter matrix of the system, B d is the second parameter matrix of the system, B d,f is the third parameter matrix of the system, B d,w is the fourth parameter matrix of the system, C d is the fifth parameter matrix of the system, D d,f is the sixth parameter matrix of the system, D d,v is the seventh parameter matrix of the system, y(k) is the system output vector at the k-th moment, y(ks) is the system output vector at the ks-th moment, y(k-s+1) is the system output vector at the k-s+1-th moment, u(k-s+1) is the control input vector at the k-s+1-th moment, u(ks) is the control input vector at the ks-th moment, f(ks) is the fault vector at the ks-th moment, f(k-s+1) is the fault vector at the k-s+1-th moment, w(ks) is the process noise vector at the ks-th moment, and v(ks) is the measurement noise vector at the ks-th moment.

[0015] In the above-mentioned autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability, the system dynamic expression determined by the diagnosis measurement data is obtained by the following formula:

[0016]

[0017] Where k represents the time, s represents the length of the diagnostic time window, u(ks,k) is the control input vector of the system dynamic expression, x(ks) is the system state vector at the ks-th time, f(ks,k) is the fault vector of the system dynamic expression, and e(ks,k) is the noise vector of the system dynamic expression. represents the i-th diagnostic measurement data combination, The first parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, The second parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, The third parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, The fourth parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, It means that the variables involved are determined by the i-th diagnostic measurement data combination.

[0018] In the above-mentioned autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability, the random features corresponding to the diagnosis measurement data combination are obtained by the following formula:

[0019]

[0020] Among them, E() is the mean vector, D() is the covariance matrix, f(ks,k) is the fault vector of the system dynamic expression, x(ks) is the system state vector at the ksth moment, and e(ks,k) is the noise vector of the system dynamic expression. The second parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, The third parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, The fourth parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, It means that the variables involved are determined by the i-th diagnostic measurement data combination, and i is the sequence number of the diagnostic measurement data combination.

[0021] In the above-mentioned autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability, the diagnosability analysis result corresponding to the diagnosis measurement data combination is obtained by the following formula:

[0022]

[0023] Among them, Diagnosability is the diagnosability analysis result corresponding to the i-th diagnostic measurement data combination, f(ks,k) is the fault vector of the system dynamic expression, x(ks) is the system state vector at the ks-th moment, e(ks,k) is the noise vector of the system dynamic expression, The second parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, The third parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, The fourth parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, To indicate that the variables involved are determined by the i-th diagnostic measurement data combination, i is the serial number of the diagnostic measurement data combination.

[0024] In the above-mentioned autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability, the autonomous screening logic of diagnostic measurement data is obtained by the following formula:

[0025]

[0026] in, For the optimal combination of diagnostic measurement data, Diagnosability is the diagnosability analysis result corresponding to the i-th diagnostic measurement data combination, It means that the variables involved are determined by the i-th diagnostic measurement data combination, and i is the sequence number of the diagnostic measurement data combination.

[0027] A system for autonomously optimizing spacecraft fault diagnosis measurement data based on diagnosability, comprising: a first module for obtaining the state equation of a spacecraft control system; a second module for constructing a system dynamic expression based on the state equation of the spacecraft control system; a third module for constructing a system dynamic expression determined by diagnostic measurement data based on the system dynamic expression; a fourth module for obtaining random features corresponding to a combination of diagnostic measurement data based on the system dynamic expression determined by the diagnostic measurement data; a fifth module for obtaining diagnosability analysis results corresponding to the combination of diagnostic measurement data based on the random features corresponding to the combination of diagnostic measurement data; a sixth module for obtaining autonomous screening logic for diagnostic measurement data based on the diagnosability analysis results corresponding to the combination of diagnostic measurement data; and a seventh module for obtaining an optimal combination of diagnostic measurement data based on the autonomous screening logic for diagnostic measurement data.

[0028] In the above-mentioned autonomous optimization system for spacecraft fault diagnosis measurement data based on diagnosability, the state equation of the spacecraft control system is obtained by the following formula:

[0029] x(k+1)=A d x(k)+B d u(k)+B d,f f(k)+B d,w w(k)

[0030] y(k)=C d x(k)+D d,f f(k)+D d,v v(k);

[0031] Among them, y(k) is the system output vector at the kth moment, k is the moment, x(k+1) is the system state vector at the k+1th moment, x(k) is the system state vector at the kth moment, u(k) is the control input vector at the kth moment, f(k) is the fault vector at the kth moment, w(k) is the process noise vector at the kth moment, v(k) is the measurement noise vector at the kth moment, A d is the first parameter matrix of the system, B d is the second parameter matrix of the system, B d,f is the third parameter matrix of the system, B d,w is the fourth parameter matrix of the system, C d is the fifth parameter matrix of the system, D d,f is the sixth parameter matrix of the system, D d,v is the seventh parameter matrix of the system.

[0032] In the above-mentioned autonomous optimization system for spacecraft fault diagnosis measurement data based on diagnosability, the system dynamic expression is obtained by the following formula:

[0033] d(ks,k)-H u u(ks,k)=H o x(ks)+H f f(ks,k)+H e e(ks,k);

[0034] in,

[0035]

[0036]

[0037] Where k represents the time, s represents the length of the diagnostic time window, d(ks,k) is the diagnostic measurement data vector of the system dynamic expression, u(ks,k) is the control input vector of the system dynamic expression, x(ks) is the system state vector at the ks-th time, f(ks,k) is the fault vector of the system dynamic expression, e(ks,k) is the noise vector of the system dynamic expression, H u is the first parameter matrix of the system dynamic expression, H o is the second parameter matrix of the system dynamic expression, H f is the third parameter matrix of the system dynamic expression, u(k) is the control input vector at the kth moment, f(k) is the fault vector at the kth moment, w(k) is the process noise vector at the kth moment, v(k) is the measurement noise vector at the kth moment, A d is the first parameter matrix of the system, B d is the second parameter matrix of the system, B d,f is the third parameter matrix of the system, B d,w is the fourth parameter matrix of the system, C d is the fifth parameter matrix of the system, D d,f is the sixth parameter matrix of the system, D d,v is the seventh parameter matrix of the system, y(k) is the system output vector at the k-th moment, y(ks) is the system output vector at the ks-th moment, y(k-s+1) is the system output vector at the k-s+1-th moment, u(k-s+1) is the control input vector at the k-s+1-th moment, u(ks) is the control input vector at the ks-th moment, f(ks) is the fault vector at the ks-th moment, f(k-s+1) is the fault vector at the k-s+1-th moment, w(ks) is the process noise vector at the ks-th moment, and v(ks) is the measurement noise vector at the ks-th moment.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] (1) The present invention realizes autonomous optimization of spacecraft diagnostic measurement data by analyzing the diagnosability of the spacecraft under different diagnostic information;

[0040] (2) The present invention reduces the amount of data required for diagnosis and effectively reduces the amount of fault diagnosis calculations, thus providing a solution for realizing autonomous fault diagnosis of spacecraft;

[0041] (3) The system dynamic expression determined by the diagnostic measurement data proposed in the present invention can be used to describe the dynamics of the system under different diagnostic measurement data and provide a basis for subsequent system characteristic analysis;

[0042] (4) The autonomous screening logic for diagnostic measurement data proposed in the present invention can be used for autonomous optimization of diagnostic measurement data by spacecraft, thereby reducing the computational burden of onboard computers from the data end. The principle is clear, the design process is simple, and it has strong engineering practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0044] Figure 1 It is a flow chart of a method for autonomously optimizing spacecraft fault diagnosis measurement data based on diagnosability provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to be able to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0046] To ensure the smooth implementation of future deep space exploration and other missions, it is necessary to reduce the amount of computing required for spacecraft fault diagnosis from a data perspective, study the autonomous optimization method for spacecraft diagnostic measurement data, select the data that will greatly improve diagnostic capabilities, reduce the computing burden of onboard computers from the data end, break through the strict constraints of spacecraft resources, and ensure its safe, reliable and autonomous operation.

[0047] Figure 1 1 is a flow chart of a method for autonomously optimizing spacecraft fault diagnosis measurement data based on diagnosability provided by an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0048] Obtain the state equation of the spacecraft control system;

[0049] Construct the system dynamic expression according to the state equation of the spacecraft control system;

[0050] Based on the system dynamic expression, construct the system dynamic expression determined by the diagnostic measurement data;

[0051] According to the system dynamic expression determined by the diagnostic measurement data, the random characteristics corresponding to the combination of the diagnostic measurement data are obtained;

[0052] Obtaining a diagnosability analysis result corresponding to the diagnostic measurement data combination according to the random features corresponding to the diagnostic measurement data combination;

[0053] According to the diagnosability analysis results corresponding to the diagnostic measurement data combination, the autonomous screening logic of the diagnostic measurement data is obtained;

[0054] The optimal diagnostic measurement data combination is calculated according to the diagnostic measurement data autonomous screening logic, thereby determining the measurement data used for fault diagnosis.

[0055] Specifically, the method comprises the following steps:

[0056] (1) Using the spacecraft design parameters, the state equation of the spacecraft control system (A) is obtained according to the general method. d ,B d ,B d,f ,B d,w ,C d ,D d,f ,D d,v ):

[0057] x(k+1)=A d x(k)+B d u(k)+B d,f f(k)+B d,w w(k)

[0058] y(k)=C d x(k)+D d,f f(k)+D d,v v(k)

[0059] Among them, y(k) is the system output vector at the kth moment, k represents the time, x(k+1) is the system state vector at the k+1th moment, x(k) is the system state vector at the kth moment, u(k) is the control input vector at the kth moment, f(k) is the fault vector at the kth moment, w(k) is the process noise vector at the kth moment, v(k) is the measurement noise vector at the kth moment, A d is the first parameter matrix of the system, B d is the second parameter matrix of the system, B d,f is the third parameter matrix of the system, B d,w is the fourth parameter matrix of the system, C d is the fifth parameter matrix of the system, D d,f is the sixth parameter matrix of the system, D d,v is the seventh parameter matrix of the system.

[0060] (2) Based on the state equation of the spacecraft control system obtained in (1), the system dynamic expression (H u ,H o ,H f ,H e ):

[0061] d(ks,k)-H u u(ks,k)=H o x(ks)+H f f(ks,k)+H e e(ks,k)

[0062] in,

[0063]

[0064] Where k represents the time, s represents the length of the diagnostic time window, d(ks,k) is the diagnostic measurement data vector of the system dynamic expression, u(ks,k) is the control input vector of the system dynamic expression, x(ks) is the system state vector at the ks-th time, f(ks,k) is the fault vector of the system dynamic expression, e(ks,k) is the noise vector of the system dynamic expression, H u is the first parameter matrix of the system dynamic expression, H o is the second parameter matrix of the system dynamic expression, H fis the third parameter matrix of the system dynamic expression, y(k) is the system output vector at the kth moment, y(ks) is the system output vector at the ksth moment, y(k-s+1) is the system output vector at the k-s+1th moment, u(k-s+1) is the control input vector at the k-s+1th moment, u(ks) is the control input vector at the ksth moment, f(ks) is the fault vector at the ksth moment, f(k-s+1) is the fault vector at the k-s+1th moment, w(ks) is the process noise vector at the ksth moment, and v(ks) is the measurement noise vector at the ksth moment.

[0065] (3) Based on the system dynamic expression obtained in (2), construct the system dynamic expression determined by the diagnostic measurement data

[0066]

[0067] Where k represents the time, s represents the length of the diagnostic time window, d(ks,k) is the diagnostic measurement data vector of the system dynamic expression, u(ks,k) is the control input vector of the system dynamic expression, x(ks) is the system state vector at the ks-th time, f(ks,k) is the fault vector of the system dynamic expression, and e(ks,k) is the noise vector of the system dynamic expression. represents the i-th diagnostic measurement data combination, From the matrix H u Corresponds to the measurement data used for diagnosis The rows of represent the first parameter matrix of the system dynamic expression determined by the diagnostic measurement data; From the matrix H o Corresponds to the measurement data used for diagnosis The second parameter matrix of the system dynamic expression determined by the diagnostic measurement data is composed of rows of From the matrix H f Corresponds to the measurement data used for diagnosis The third parameter matrix of the system dynamic expression determined by the diagnostic measurement data is composed of rows of From the matrix H e Corresponds to the measurement data used for diagnosis The fourth parameter matrix of the system dynamic expression determined by the diagnostic measurement data is composed of rows of It means that the variables involved are determined by the i-th diagnostic measurement data combination, and i is the sequence number of the diagnostic measurement data combination.

[0068] (4) According to the system dynamic expression determined by the diagnostic measurement data obtained in (3), assuming The mean is 0 and the variance is a matrix The normal distribution of The mean is 0 and the variance is a matrix The normal distribution of Given the random features corresponding to the i-th diagnostic measurement data combination:

[0069]

[0070] in, and Respectively The mean vector and covariance matrix of .

[0071] (5) Based on the random features corresponding to the i-th diagnostic measurement data combination obtained in (4), the diagnosability analysis results corresponding to the i-th diagnostic measurement data combination are given:

[0072]

[0073] Among them, Diagnosability is the diagnosability analysis result corresponding to the i-th diagnostic measurement data combination.

[0074] (6) Based on the diagnosability analysis results corresponding to the i-th diagnostic measurement data combination obtained in (5), the autonomous screening logic of diagnostic measurement data is given:

[0075]

[0076] in, The optimal diagnostic measurement data combination.

[0077] (7) Based on the autonomous screening logic of diagnostic measurement data obtained in (6), the optimal diagnostic measurement data combination is autonomously calculated on board to achieve autonomous optimization of spacecraft diagnostic measurement data.

[0078] The present embodiment also provides an autonomous optimization system for spacecraft fault diagnosis measurement data based on diagnosability, the system comprising: a first module for obtaining the state equation of the spacecraft control system; a second module for constructing a system dynamic expression based on the state equation of the spacecraft control system; a third module for constructing a system dynamic expression determined by the diagnostic measurement data based on the system dynamic expression; a fourth module for obtaining random features corresponding to a diagnostic measurement data combination based on the system dynamic expression determined by the diagnostic measurement data; a fifth module for obtaining a diagnosability analysis result corresponding to the diagnostic measurement data combination based on the random features corresponding to the diagnostic measurement data combination; a sixth module for obtaining an autonomous screening logic for diagnostic measurement data based on the diagnosability analysis result corresponding to the diagnostic measurement data combination; and a seventh module for obtaining an optimal diagnostic measurement data combination based on the autonomous screening logic for diagnostic measurement data.

[0079] This embodiment realizes autonomous optimization of spacecraft diagnostic measurement data by analyzing the diagnosability of the spacecraft under different diagnostic information; this embodiment effectively reduces the amount of fault diagnosis calculations by reducing the amount of data required for diagnosis, thereby providing a solution for realizing autonomous fault diagnosis of the spacecraft; the system dynamic expression determined by the diagnostic measurement data proposed in this embodiment can be used to describe the dynamics of the system under different diagnostic measurement data, and provide a basis for subsequent system feature analysis; the autonomous screening logic of diagnostic measurement data proposed in this embodiment can be used for the spacecraft to autonomously optimize diagnostic measurement data, thereby reducing the computational burden of the onboard computer from the data end. It has a clear principle, a simple design process, and strong engineering practicality.

[0080] Although the present invention has been disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solution of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for autonomous optimization of spacecraft fault diagnosis measurement data based on diagnosability, characterized in that include: Obtain the state equation of the spacecraft control system; Construct the system dynamic expression according to the state equation of the spacecraft control system; Based on the system dynamic expression, construct the system dynamic expression determined by the diagnostic measurement data; According to the system dynamic expression determined by the diagnostic measurement data, the random characteristics corresponding to the combination of the diagnostic measurement data are obtained; Obtaining a diagnosability analysis result corresponding to the diagnostic measurement data combination according to the random features corresponding to the diagnostic measurement data combination; According to the diagnosability analysis results corresponding to the diagnostic measurement data combination, the autonomous screening logic of the diagnostic measurement data is obtained; The optimal diagnostic measurement data combination is obtained according to the autonomous screening logic of the diagnostic measurement data.

2. The autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability according to claim 1 is characterized by: The state equation of the spacecraft control system is obtained by the following formula: x(k+1)=A d x(k)+B d u(k)+B d,f f(k)+B d,w w(k) y(k)=C d x(k)+D d,f f(k)+D d,v v(k); Among them, y(k) is the system output vector at the kth moment, k is the moment, x(k+1) is the system state vector at the k+1th moment, x(k) is the system state vector at the kth moment, u(k) is the control input vector at the kth moment, f(k) is the fault vector at the kth moment, w(k) is the process noise vector at the kth moment, v(k) is the measurement noise vector at the kth moment, A d is the first parameter matrix of the system, B d is the second parameter matrix of the system, B d,f is the third parameter matrix of the system, B d,w is the fourth parameter matrix of the system, C d is the fifth parameter matrix of the system, D d,f is the sixth parameter matrix of the system, D d,v is the seventh parameter matrix of the system.

3. The autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability according to claim 1 is characterized in that: The system dynamic expression is obtained by the following formula: d(k-s,k)-H u u(k-s,k)=H o x(k-s)+H f f(k-s,k)+H e e(k-s,k); in, Where k represents the time, s represents the length of the diagnostic time window, d(ks,k) is the diagnostic measurement data vector of the system dynamic expression, u(ks,k) is the control input vector of the system dynamic expression, x(ks) is the system state vector at the ks-th time, f(ks,k) is the fault vector of the system dynamic expression, e(ks,k) is the noise vector of the system dynamic expression, H u is the first parameter matrix of the system dynamic expression, H o is the second parameter matrix of the system dynamic expression, H f is the third parameter matrix of the system dynamic expression, u(k) is the control input vector at the kth moment, f(k) is the fault vector at the kth moment, w(k) is the process noise vector at the kth moment, v(k) is the measurement noise vector at the kth moment, A d is the first parameter matrix of the system, B d is the second parameter matrix of the system, B d,f is the third parameter matrix of the system, B d,w is the fourth parameter matrix of the system, C d is the fifth parameter matrix of the system, D d,f is the sixth parameter matrix of the system, D d,v is the seventh parameter matrix of the system, y(k) is the system output vector at the k-th moment, y(ks) is the system output vector at the ks-th moment, y(k-s+1) is the system output vector at the k-s+1-th moment, u(k-s+1) is the control input vector at the k-s+1-th moment, u(ks) is the control input vector at the ks-th moment, f(ks) is the fault vector at the ks-th moment, f(k-s+1) is the fault vector at the k-s+1-th moment, w(ks) is the process noise vector at the ks-th moment, and v(ks) is the measurement noise vector at the ks-th moment.

4. The autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability according to claim 1 is characterized in that: The system dynamics expression determined by the diagnostic measurement data is obtained by the following formula: Where k represents the time, s represents the length of the diagnostic time window, u(ks,k) is the control input vector of the system dynamic expression, x(ks) is the system state vector at the ks-th time, f(ks,k) is the fault vector of the system dynamic expression, and e(ks,k) is the noise vector of the system dynamic expression. represents the i-th diagnostic measurement data combination, The first parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, The second parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, The third parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, The fourth parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, It means that the variables involved are determined by the i-th diagnostic measurement data combination.

5. The autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability according to claim 1 is characterized by: The random features corresponding to the diagnostic measurement data combination are obtained by the following formula: Among them, E() is the mean vector, D() is the covariance matrix, f(ks,k) is the fault vector of the system dynamic expression, x(ks) is the system state vector at the ksth moment, and e(ks,k) is the noise vector of the system dynamic expression. The second parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, The third parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, The fourth parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, It means that the variables involved are determined by the i-th diagnostic measurement data combination, and i is the sequence number of the diagnostic measurement data combination.

6. The autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability according to claim 1 is characterized by: The diagnosability analysis results corresponding to the diagnostic measurement data combination are obtained by the following formula: in, is the diagnosability analysis result corresponding to the i-th diagnostic measurement data combination, f(ks,k) is the fault vector of the system dynamic expression, x(ks) is the system state vector at the ks-th moment, e(ks,k) is the noise vector of the system dynamic expression, The second parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, The third parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, The fourth parameter matrix representing the system dynamics expression determined by the diagnostic measurement data, It means that the variables involved are determined by the i-th diagnostic measurement data combination, and i is the sequence number of the diagnostic measurement data combination.

7. The autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability according to claim 1 is characterized by: The autonomous screening logic of diagnostic measurement data is obtained through the following formula: in, For the optimal diagnostic measurement data combination, is the diagnosability analysis result corresponding to the i-th diagnostic measurement data combination, It means that the variables involved are determined by the i-th diagnostic measurement data combination, and i is the sequence number of the diagnostic measurement data combination.

8. An autonomous optimization system for spacecraft fault diagnosis measurement data based on diagnosability, characterized in that include: The first module is used to obtain the state equation of the spacecraft control system; The second module is used to construct the system dynamic expression based on the state equation of the spacecraft control system; The third module is used to construct a system dynamic expression determined by the diagnostic measurement data based on the system dynamic expression; A fourth module is used to obtain a random feature corresponding to a combination of diagnostic measurement data according to a system dynamic expression determined by the diagnostic measurement data; A fifth module is used to obtain a diagnosability analysis result corresponding to the diagnostic measurement data combination according to the random features corresponding to the diagnostic measurement data combination; The sixth module is used to obtain the autonomous screening logic of the diagnostic measurement data according to the diagnosability analysis results corresponding to the diagnostic measurement data combination; The seventh module is used to obtain the optimal diagnosis measurement data combination according to the diagnosis measurement data autonomous screening logic.

9. The autonomous optimization system for spacecraft fault diagnosis measurement data based on diagnosability according to claim 8 is characterized in that: The state equation of the spacecraft control system is obtained by the following formula: x(k+1)=A d x(k)+B d u(k)+B d,f f(k)+B d,w w(k) y(k)=C d x(k)+D d,f f(k)+D d,v v(k); Among them, y(k) is the system output vector at the kth moment, k is the moment, x(k+1) is the system state vector at the k+1th moment, x(k) is the system state vector at the kth moment, u(k) is the control input vector at the kth moment, f(k) is the fault vector at the kth moment, w(k) is the process noise vector at the kth moment, v(k) is the measurement noise vector at the kth moment, A d is the first parameter matrix of the system, B d is the second parameter matrix of the system, B d,f is the third parameter matrix of the system, B d,w is the fourth parameter matrix of the system, C d is the fifth parameter matrix of the system, D d,f is the sixth parameter matrix of the system, D d,v is the seventh parameter matrix of the system.

10. The autonomous optimization system for spacecraft fault diagnosis measurement data based on diagnosability according to claim 8, characterized in that: The system dynamic expression is obtained by the following formula: d(k-s,k)-H u u(k-s,k)=H o x(k-s)+H f f(k-s,k)+H e e(k-s,k); in, Where k represents the time, s represents the length of the diagnostic time window, d(ks,k) is the diagnostic measurement data vector of the system dynamic expression, u(ks,k) is the control input vector of the system dynamic expression, x(ks) is the system state vector at the ks-th time, f(ks,k) is the fault vector of the system dynamic expression, e(ks,k) is the noise vector of the system dynamic expression, H u is the first parameter matrix of the system dynamic expression, H o is the second parameter matrix of the system dynamic expression, H f is the third parameter matrix of the system dynamic expression, u(k) is the control input vector at the kth moment, f(k) is the fault vector at the kth moment, w(k) is the process noise vector at the kth moment, v(k) is the measurement noise vector at the kth moment, A d is the first parameter matrix of the system, B d is the second parameter matrix of the system, B d,f is the third parameter matrix of the system, B d,w is the fourth parameter matrix of the system, C d is the fifth parameter matrix of the system, D d,f is the sixth parameter matrix of the system, D d,v is the seventh parameter matrix of the system, y(k) is the system output vector at the k-th moment, y(ks) is the system output vector at the ks-th moment, y(k-s+1) is the system output vector at the k-s+1-th moment, u(k-s+1) is the control input vector at the k-s+1-th moment, u(ks) is the control input vector at the ks-th moment, f(ks) is the fault vector at the ks-th moment, f(k-s+1) is the fault vector at the k-s+1-th moment, w(ks) is the process noise vector at the ks-th moment, and v(ks) is the measurement noise vector at the ks-th moment.

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