Flight control servo actuation system fault identification method and system

Abstract

The present invention provides a flight control servo actuator system fault identification method and system, relating to the field of aviation system detection technology. The method comprises: obtaining a historical data set; training a fault identification model based on the historical data set to obtain a trained fault identification model; constructing a performance model of the system to be identified based on the physical architecture and design parameter model of the system to be identified; performing a sensitivity analysis on the performance model to obtain an identification parameter set, and screening the performance model to obtain an observable parameter set and an unobservable parameter set; inputting data from the observable parameter set and data from the system output parameter set into the trained fault identification model to obtain an unobservable parameter prediction data set; and judging the unobservable parameter prediction data set in combination with a set threshold range to obtain a fault identification result. By comparing the unobservable parameter prediction data set with the set threshold range, the present invention achieves accurate positioning of the faulty unit.

Description

Technical Field

[0001] The present invention relates to the technical field of aviation system detection, and in particular to a flight control servo actuation system fault identification method and system. Background Art

[0002] During the use of the flight control servo actuation system, due to the large number of system components and the complex parameter cross-linking relationship between the units, unit failure will cause parameter jumps and lead to abnormal system output. Therefore, the design parameters need to be monitored during the system operation. When a fault occurs, safety design measures such as redundancy switching and compensation can be implemented to ensure the normal operation of the system.

[0003] Fault-tolerance implementations such as redundant switching in systems rely on parameter monitoring, fault behavior analysis, identification of the corresponding faulty unit, and switching. Currently, unit fault detection primarily utilizes multi-signal flow graphs. Faults are reported upon occurrence, and unit switching is implemented through the control center. This approach significantly supports the safe operation of the system. However, due to the electromechanical-hydraulic coupling in flight control servo actuation systems, some faults cannot be directly measured. For example, wear of the electro-hydraulic valve core or blockage between the electro-hydraulic valve and the mode conversion valve can reduce pressure supply capacity and thus require direct disconnection of the oil channel after the fault occurs. Furthermore, flight control servo actuation system faults vary widely, including coupling failures and time-dependent faults. These types of faults cannot be accurately characterized using models, resulting in inadequate fault identification and analysis. Combined with these two factors, traditional fault identification and location methods are ineffective in accurately locating faults in flight control servo actuation systems. Potential faults and coupled faults cannot be fully identified, leading to safety risks during mission execution. Summary of the Invention

[0004] The object of the present invention is to provide a flight control servo actuation system fault identification method and system, which realizes accurate positioning of the faulty unit by comparing the unobservable parameter prediction data set and the set threshold range.

[0005] A method for identifying a fault in a flight control servo actuator system, comprising:

[0006] Acquire a historical data set; the historical data set includes a historical observable parameter data set, a historical unobservable parameter set, and a historical system output parameter data set;

[0007] Constructing a fault identification model, taking the historical observable parameter data set and the historical system output parameter data set as inputs of the fault identification model, taking the historical unobservable parameter set as labels, and training the fault identification model to obtain the trained fault identification model;

[0008] Construct a performance model of the system to be identified based on the physical architecture and design parameter model of the system to be identified;

[0009] Performing a sensitivity analysis on the performance model to obtain an identification parameter set; the identification parameter set includes an input command, an oil supply pressure, an actuator output speed, an actuator output displacement, a servo valve spool displacement, an actuator zero-point coordinate value, and a sensor gain;

[0010] The identified parameter set is screened to obtain an observable parameter set and an unobservable parameter set; the observable parameter set includes an input instruction, oil supply pressure, actuator output speed, and actuator output displacement; and the unobservable parameter set includes a servo valve spool displacement, actuator zero-point coordinates, and sensor gain;

[0011] Acquiring input instruction data, oil supply pressure data, actuator output speed data, actuator output displacement data, and a system output parameter data set of the system to be identified based on the identification parameter set; the system output parameter data set includes the system output speed and the system output displacement;

[0012] Inputting the input command data, oil supply pressure data, actuator output speed data, actuator output displacement data, the system output speed, and the system output displacement into the trained fault identification model to obtain a predicted value of the servo valve spool displacement, a predicted value of the actuator zero-point coordinate value, and a predicted value of the sensor gain;

[0013] The predicted value of the actuator 0-point coordinate value is judged. If the predicted value of the actuator 0-point coordinate value is within the actuator set threshold range, the unit in the to-be-identified system corresponding to the actuator 0-point coordinate value is normal; if the predicted value of the actuator 0-point coordinate value exceeds the actuator set threshold range, the unit in the to-be-identified system corresponding to the actuator 0-point coordinate value is faulty.

[0014] The sensor gain prediction value is judged. If the sensor gain prediction value is within a sensor set threshold range, the unit in the to-be-identified system corresponding to the sensor gain is normal. If the sensor gain prediction value exceeds the sensor set threshold range, the unit in the to-be-identified system corresponding to the sensor gain is faulty.

[0015] The predicted value of the servo valve spool displacement is judged. If the predicted value of the servo valve spool displacement is within the servo valve set threshold range, the unit in the system to be identified corresponding to the servo valve spool is normal. If the predicted value of the servo valve spool displacement exceeds the servo valve set threshold range, the unit in the system to be identified corresponding to the servo valve spool is faulty.

[0016] Optionally, the performance model includes an input instruction set, a system output parameter set, and a system parameter set;

[0017] The input instruction set expression is:

[0018] u={u1,u2,…,u i};

[0019] The system output parameter set expression is:

[0020] y={y1,y2};

[0021] The system parameter set expression is:

[0022] x′={x′1,x′2,…,x′ k};

[0023] Where: u represents the input instruction set, y represents the system output parameter set, x' represents the system parameter set, i represents the number of dimensions of the input instruction set, k represents the number of dimensions of the system parameter set, u i represents the i-th input instruction in the input instruction set, y1 represents the system output speed, y2 represents the system output displacement, x' k Represents the kth system parameter in the system parameter set.

[0024] Optionally, the identification parameter set expression is:

[0025] x={x1,x2,x3,x4,x5,x6,x7};

[0026] The observable parameter set expression is:

[0027] x o ={x1,x2,x3,x4};

[0028] The unobservable parameter set expression is:

[0029] x uo ={x5,x6,x7};

[0030] Where: x represents the identification parameter set, x o represents the observable parameter set, x uo represents the unobservable parameter set, x1 represents the input command, x2 represents the oil supply pressure, x3 represents the actuator output speed, x4 represents the actuator output displacement, x5 represents the actuator 0 point coordinate value, x6 represents the sensor gain, and x7 represents the servo valve spool displacement.

[0031] Optionally, the observable parameter data set, the unobservable parameter data set and the system output parameter data set are all Real data.

[0032] The present invention also provides a flight control servo actuation system fault identification system, which includes:

[0033] A first data acquisition module is configured to acquire a historical data set, wherein the historical data set includes a historical observable parameter data set, a historical unobservable parameter data set, and a historical system output parameter data set;

[0034] a model training module, configured to construct a fault identification model, using the historical observable parameter dataset and the historical system output parameter dataset as inputs to the fault identification model, using the historical unobservable parameter set as labels, and training the fault identification model to obtain the trained fault identification model;

[0035] A performance model module is used to construct a performance model of the system to be identified based on the physical architecture and design parameter model of the system to be identified;

[0036] a sensitivity analysis module for performing a sensitivity analysis on the performance model to obtain an identification parameter set; the identification parameter set includes an input instruction, oil supply pressure, actuator output speed, actuator output displacement, servo valve spool displacement, actuator zero point coordinate value, and sensor gain;

[0037] a parameter screening module for screening the identified parameter set to obtain an observable parameter set and an unobservable parameter set; the observable parameter set includes input instructions, oil supply pressure, actuator output speed, and actuator output displacement; the unobservable parameter set includes servo valve spool displacement, actuator zero point coordinates, and sensor gain;

[0038] a second data acquisition module, which acquires input instruction data, oil supply pressure data, actuator output speed data, actuator output displacement data, and a system output parameter data set of the system to be identified based on the identification parameter set; the system output parameter data set includes the system output speed and the system output displacement;

[0039] a data prediction module, inputting the input command data, oil supply pressure data, actuator output speed data, actuator output displacement data, the system output speed, and the system output displacement into the trained fault identification model to obtain a predicted value of the servo valve spool displacement, a predicted value of the actuator zero-point coordinate value, and a predicted value of the sensor gain;

[0040] a first judgment module, judging the predicted value of the actuator 0-point coordinate value; if the predicted value of the actuator 0-point coordinate value is within a threshold range set for the actuator, then the unit in the to-be-identified system corresponding to the actuator 0-point coordinate value is normal; if the predicted value of the actuator 0-point coordinate value exceeds the threshold range set for the actuator, then the unit in the to-be-identified system corresponding to the actuator 0-point coordinate value is faulty;

[0041] A second judgment module judges the sensor gain prediction value. If the sensor gain prediction value is within a sensor set threshold range, the unit in the to-be-identified system corresponding to the sensor gain is normal; if the sensor gain prediction value exceeds the sensor set threshold range, the unit in the to-be-identified system corresponding to the sensor gain is faulty.

[0042] The third judgment module is used to judge the predicted value of the servo valve spool displacement. If the predicted value of the servo valve spool displacement is within the servo valve set threshold range, the unit in the system to be identified corresponding to the servo valve spool is normal; if the predicted value of the servo valve spool displacement exceeds the servo valve set threshold range, the unit in the system to be identified corresponding to the servo valve spool is faulty.

[0043] Optionally, the performance model includes an input instruction set, a system output parameter set, and a system parameter set;

[0044] The input instruction set expression is:

[0045] u={u1,u2,…,u i};

[0046] The system output parameter set expression is:

[0047] y={y1,y2};

[0048] The system parameter set expression is:

[0049] x′={x′1,x′2,…,x′ k};

[0050] Where: u represents the input instruction set, y represents the system output parameter set, x' represents the system parameter set, i represents the number of dimensions of the input instruction set, k represents the number of dimensions of the system parameter set, u i represents the i-th input instruction in the input instruction set, y1 represents the system output speed, y2 represents the system output displacement, x' k Represents the kth system parameter in the system parameter set.

[0051] Optionally, the identification parameter set expression is:

[0052] x={x1,x2,x3,x4,x5,x6,x7};

[0053] The observable parameter set expression is:

[0054] x o ={x1,x2,x3,x4};

[0055] The unobservable parameter set expression is:

[0056] x uo ={x5,x6,x7};

[0057] Where: x represents the identification parameter set, x o represents the observable parameter set, x uo represents the unobservable parameter set, x1 represents the input command, x2 represents the oil supply pressure, x3 represents the actuator output speed, x4 represents the actuator output displacement, x5 represents the actuator 0 point coordinate value, x6 represents the sensor gain, and x7 represents the servo valve spool displacement.

[0058] Optionally, the observable parameter data set, the unobservable parameter data set and the system output parameter data set are all Real data.

[0059] The effects of the present invention are as follows:

[0060] The flight control servo actuation system fault identification method of the present invention first obtains the key parameters affecting the system output based on sensitivity analysis, so that the system output is more targeted and accurate, and screens to obtain observable parameters and unobservable parameters. It further combines the trained fault identification model to predict the unobservable parameter set of the system to be identified, and obtains an unobservable parameter prediction data set. Based on the unobservable parameter data set, a set threshold range is obtained. Based on the set threshold range, the unobservable parameter prediction data set is judged to obtain the identification result, and the faulty unit is accurately located, avoiding problems such as failure coupling and unclear parameter association. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of a method for identifying a fault in a flight control servo actuator system according to the present invention;

[0062] Figure 2 is a schematic diagram of a performance model of the flight control servo actuation system of the present invention;

[0063] Figure 3 1 is a schematic diagram of the system output when the sensor gain of the present invention is abnormal;

[0064] Figure 4 This is a schematic diagram of the system output when the sensor gain of the present invention is normal. DETAILED DESCRIPTION

[0065] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.

[0066] Figure 1 This is a flow chart of the flight control servo actuator system fault identification method of the present invention. Figure 1 The present invention provides a method for identifying a fault in a flight control servo actuator system, which comprises:

[0067] S1, obtain historical data sets. The historical data sets include historical observable parameter data sets, historical unobservable parameter data sets, and historical system output parameter data sets.

[0068] S2, build a fault recognition model, use the historical observable parameter data set and the historical system output parameter data set as the input of the fault recognition model, use the historical unobservable parameter set as the label, train the fault recognition model, and obtain a trained fault recognition model.

[0069] S3, constructing a performance model of the system to be identified based on the physical architecture and design parameter model of the system to be identified.

[0070] S4, performing sensitivity analysis on the performance model to obtain an identification parameter set. The identification parameter set includes input command, oil supply pressure, actuator output speed, actuator output displacement, servo valve spool displacement, actuator zero point coordinate value, and sensor gain.

[0071] S5, the identified parameter set is screened to obtain an observable parameter set and an unobservable parameter set. The observable parameter set includes the input command, oil supply pressure, actuator output speed, and actuator output displacement; the unobservable parameter set includes the servo valve spool displacement, actuator zero point coordinates, and sensor gain.

[0072] S6. Based on the identification parameter set, input command data, oil supply pressure data, actuator output speed data, actuator output displacement data, and a system output parameter dataset are obtained for the system to be identified. The system output parameter dataset includes the system output speed and the system output displacement. Preferably, the observable parameter dataset, the unobservable parameter dataset, and the system output parameter dataset are all Real data.

[0073] S7, input the input command data, oil supply pressure data, actuator output speed data, actuator output displacement data, system output speed and system output displacement into the trained fault identification model to obtain the servo valve spool displacement prediction value, actuator 0 point coordinate value prediction value and sensor gain prediction value.

[0074] S8, judge the predicted value of the actuator 0-point coordinate value. If the predicted value of the actuator 0-point coordinate value is within the actuator set threshold range, the unit in the system to be identified corresponding to the actuator 0-point coordinate value is normal. If the predicted value of the actuator 0-point coordinate value exceeds the actuator set threshold range, the unit in the system to be identified corresponding to the actuator 0-point coordinate value is faulty.

[0075] S9, judging the sensor gain prediction value. If the sensor gain prediction value is within the sensor set threshold range, the unit in the system to be identified corresponding to the sensor gain is normal. If the sensor gain prediction value exceeds the sensor set threshold range, the unit in the system to be identified corresponding to the sensor gain is faulty.

[0076] S10, judge the predicted value of the servo valve spool displacement. If the predicted value of the servo valve spool displacement is within the servo valve set threshold range, the unit in the system to be identified corresponding to the servo valve spool is normal. If the predicted value of the servo valve spool displacement exceeds the servo valve set threshold range, the unit in the system to be identified corresponding to the servo valve spool is faulty.

[0077] Preferably, the actuator setting threshold range and the sensor setting threshold range are determined based on experimental data or based on expert knowledge.

[0078] Specifically, the performance model includes an input instruction set, a system output parameter set, and a system parameter set.

[0079] The input instruction set expression is:

[0080] u={u1,u2,…,u i}.

[0081] The system output parameter set expression is:

[0082] y={y1,y2}。

[0083] The system parameter set expression is:

[0084] x′={x′1,x′2,…,x′ k};

[0085] Where: u represents the input instruction set, y represents the system output parameter set, x' represents the system parameter set, i represents the number of dimensions of the input instruction set, k represents the number of dimensions of the system parameter set, u i represents the i-th input instruction in the input instruction set, y1 represents the system output speed, y2 represents the system output displacement, x' k Represents the kth system parameter in the system parameter set.

[0086] Preferably, the system parameter set includes input instructions, oil supply pressure, actuator output speed, actuator output displacement, servo valve spool displacement, actuator 0 point coordinate value, sensor gain, internal leakage between the actuator drive chamber and the oil return chamber, external leakage between the actuator drive chamber and the oil return chamber, electromagnetic coil current input and electromagnetic coil voltage input.

[0087] Furthermore, the identification parameter set expression is:

[0088] x={x1,x2,x3,x4,x5,x6,x7}.

[0089] The observable parameter set expression is:

[0090] x o ={x1,x2,x3,x4}.

[0091] The expression of the unobservable parameter set is:

[0092] x uo ={x5,x6,x7};

[0093] Where: x represents the identification parameter set, x o represents the observable parameter set, x uo represents the unobservable parameter set, x1 represents the input command, x2 represents the oil supply pressure, x3 represents the actuator output speed, x4 represents the actuator output displacement, x5 represents the actuator 0 point coordinate value, x6 represents the sensor gain, and x7 represents the servo valve spool displacement.

[0094] The present invention also provides a flight control servo actuation system fault identification system, which includes:

[0095] The first data acquisition module is used to acquire a historical data set; the historical data set includes a historical observable parameter data set, a historical unobservable parameter set and a historical system output parameter data set.

[0096] The model training module is used to build a fault recognition model. It takes the historical observable parameter data set and the historical system output parameter data set as the input of the fault recognition model, and uses the historical unobservable parameter set as the label to train the fault recognition model to obtain a trained fault recognition model.

[0097] The performance model module is used to build a performance model of the system to be identified based on the physical architecture and design parameter model of the system to be identified.

[0098] The sensitivity analysis module is used to perform sensitivity analysis on the performance model and obtain an identification parameter set. The identification parameter set includes input command, oil supply pressure, actuator output speed, actuator output displacement, servo valve spool displacement, actuator zero point coordinate value, and sensor gain.

[0099] The parameter screening module is used to filter the identified parameter set to obtain observable and unobservable parameter sets. The observable parameter set includes input command, oil supply pressure, actuator output speed, and actuator output displacement. The unobservable parameter set includes servo valve spool displacement, actuator zero point coordinates, and sensor gain.

[0100] The second data acquisition module acquires input instruction data, oil supply pressure data, actuator output speed data, actuator output displacement data, and a system output parameter data set of the system to be identified based on the identification parameter set. The system output parameter data set includes the system output speed and the system output displacement.

[0101] The data prediction module inputs the input command data, oil supply pressure data, actuator output speed data, actuator output displacement data, system output speed and system output displacement into the trained fault identification model to obtain the servo valve spool displacement prediction value, actuator 0 point coordinate value prediction value and sensor gain prediction value.

[0102] The first judgment module judges the predicted value of the actuator's 0-point coordinate value. If the predicted value of the actuator's 0-point coordinate value is within the actuator's set threshold range, the unit in the system to be identified corresponding to the actuator's 0-point coordinate value is normal. If the predicted value of the actuator's 0-point coordinate value exceeds the actuator's set threshold range, the unit in the system to be identified corresponding to the actuator's 0-point coordinate value is faulty.

[0103] The second judgment module judges the sensor gain prediction value. If the sensor gain prediction value is within the sensor set threshold range, the unit in the system to be identified corresponding to the sensor gain is normal. If the sensor gain prediction value exceeds the sensor set threshold range, the unit in the system to be identified corresponding to the sensor gain is faulty.

[0104] The third judgment module is used to judge the predicted value of the servo valve spool displacement. If the predicted value of the servo valve spool displacement is within the set threshold range of the servo valve, the unit in the system to be identified corresponding to the servo valve spool is normal. If the predicted value of the servo valve spool displacement exceeds the set threshold range of the servo valve, the unit in the system to be identified corresponding to the servo valve spool is faulty.

[0105] Optionally, the performance model includes an input instruction set, a system output parameter set, and a system parameter set.

[0106] The input instruction set expression is:

[0107] u={u1,u2,…,u i}.

[0108] The system output parameter set expression is:

[0109] y={y1,y2}。

[0110] The system parameter set expression is:

[0111] x′={x′1,x′2,…,x′ k}.

[0112] Where: u represents the input instruction set, y represents the system output parameter set, x' represents the system parameter set, i represents the number of dimensions of the input instruction set, k represents the number of dimensions of the system parameter set, u i represents the i-th input instruction in the input instruction set, y1 represents the system output speed, y2 represents the system output displacement, x' k Represents the kth system parameter in the system parameter set.

[0113] Optionally, the identification parameter set expression is:

[0114] x={x1,x2,x3,x4,x5,x6,x7}.

[0115] The observable parameter set expression is:

[0116] x o ={x1,x2,x3,x4}.

[0117] The expression of the unobservable parameter set is:

[0118] x uo ={x5,x6,x7}.

[0119] Where: x represents the identification parameter set, x o represents the observable parameter set, x uo represents the unobservable parameter set, x1 represents the input command, x2 represents the oil supply pressure, x3 represents the actuator output speed, x4 represents the actuator output displacement, x5 represents the actuator 0 point coordinate value, x6 represents the sensor gain, and x7 represents the servo valve spool displacement.

[0120] Optionally, the observable parameter data set, the unobservable parameter data set and the system output parameter data set are all Real data.

[0121] Specifically, taking a certain flight control servo actuation system as an example, the flight control servo actuation system receives instructions from the flight control computer and controls the driving direction and flow rate of the hydraulic circuit by controlling the solenoid valve to achieve the extension and retraction of the actuator, thereby achieving control of the aircraft's control surfaces and attitude.

[0122] Build a performance model of the flight control servo actuation system. The performance model of the flight control servo actuation system is a description of the system's physical architecture design and parameters. By combining the Modelica language, after the system receives instructions, it controls the displacement of the reversing valve core through the control loop, and then controls the switching of the hydraulic oil circuit to achieve displacement control of the actuator. The performance model is as follows: Figure 2 shown.

[0123] According to the performance model established, input instructions are given, and the oil supply pressure, actuator zero-point coordinate value, and sensor gain parameters are set. The system output response under different input and parameter setting conditions is observed. The output under different parameters is analyzed, and the speed at 0.5s and the displacement at 1.0s are selected, as shown in Table 1.

[0124] Table 1 Velocity at 0.5s and displacement at 1.0s

[0125]

[0126]

[0127]

[0128] Based on the data in Table 1, combined with the system design and usage characteristics, the observable parameter set includes input command, oil supply pressure, actuator output speed and actuator output displacement; the unobservable parameter set includes the actuator zero point coordinate and sensor gain, and the system output parameter set includes the system output speed and system output displacement. The system output parameter data when the input command is 1.9 is as follows: Figure 3 shown.

[0129] The input command, oil supply pressure, actuator output speed, actuator output displacement, system output speed and system output displacement are input into the trained fault identification model to obtain the actuator zero point coordinate and sensor gain.

[0130] The actuator 0-point coordinate at time 1.0 is 3.55015, and the sensor gain is 1.25. The sensor gain exceeds its corresponding set threshold range, and the corresponding sensor is faulty. The sensor is corrected by adding a proportional link. The proportional value of the correction link is 1.599688. After correction, the system output displacement is 19.5 cm. Figure 4 As shown, the system responds normally.

[0131] The embodiments described above are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. A method for identifying a fault in a flight control servo actuator system, characterized in that: It includes: Acquire a historical data set; the historical data set includes a historical observable parameter data set, a historical unobservable parameter set, and a historical system output parameter data set; Constructing a fault identification model, taking the historical observable parameter data set and the historical system output parameter data set as inputs of the fault identification model, taking the historical unobservable parameter set as labels, and training the fault identification model to obtain the trained fault identification model; Construct a performance model of the system to be identified based on the physical architecture and design parameter model of the system to be identified; Performing a sensitivity analysis on the performance model to obtain an identification parameter set; the identification parameter set includes an input command, an oil supply pressure, an actuator output speed, an actuator output displacement, a servo valve spool displacement, an actuator zero-point coordinate value, and a sensor gain; The identification parameter set is screened to obtain an observable parameter set and an unobservable parameter set; the observable parameter set includes an input instruction, an oil supply pressure, an actuator output speed, and an actuator output displacement; The unobservable parameter set includes the servo valve spool displacement, the actuator zero point coordinate and the sensor gain; Acquiring input instruction data, oil supply pressure data, actuator output speed data, actuator output displacement data, and system output parameter data set of the system to be identified based on the identification parameter set; The system output parameter data set includes system output speed and system output displacement; Inputting the input command data, oil supply pressure data, actuator output speed data, actuator output displacement data, the system output speed, and the system output displacement into the trained fault identification model to obtain a predicted value of the servo valve spool displacement, a predicted value of the actuator zero-point coordinate value, and a predicted value of the sensor gain; The predicted value of the actuator 0-point coordinate value is judged. If the predicted value of the actuator 0-point coordinate value is within the actuator set threshold range, the unit in the to-be-identified system corresponding to the actuator 0-point coordinate value is normal; if the predicted value of the actuator 0-point coordinate value exceeds the actuator set threshold range, the unit in the to-be-identified system corresponding to the actuator 0-point coordinate value is faulty. The sensor gain prediction value is judged. If the sensor gain prediction value is within a sensor set threshold range, the unit in the to-be-identified system corresponding to the sensor gain is normal. If the sensor gain prediction value exceeds the sensor set threshold range, the unit in the to-be-identified system corresponding to the sensor gain is faulty. The predicted value of the servo valve spool displacement is judged. If the predicted value of the servo valve spool displacement is within the servo valve set threshold range, the unit in the system to be identified corresponding to the servo valve spool is normal. If the predicted value of the servo valve spool displacement exceeds the servo valve set threshold range, the unit in the system to be identified corresponding to the servo valve spool is faulty.

2. The flight control servo actuation system fault identification method according to claim 1, characterized in that: The performance model includes an input instruction set, a system output parameter set, and a system parameter set; The input instruction set expression is: ； The system output parameter set expression is: ； The system parameter set expression is: ； Where: Represents the input instruction set, Represents the system output parameter set, represents the system parameter set, i represents the number of dimensions of the input instruction set, is the number of dimensions of the system parameter set, represents the i-th input instruction in the input instruction set, Indicates the system output speed, represents the system output displacement, Represents the kth system parameter in the system parameter set.

3. The flight control servo actuation system fault identification method according to claim 1, characterized in that: The identification parameter set expression is: ； The observable parameter set expression is: ； The unobservable parameter set expression is: ； Where: Represents the identification parameter set, represents the set of observable parameters, represents the set of unobservable parameters, Indicates input command, Indicates the oil supply pressure, Indicates the actuator output speed, represents the actuator output displacement, Indicates the coordinate value of the actuator 0 point, represents the sensor gain, Indicates the displacement of the servo valve spool.

4. The flight control servo actuation system fault identification method according to claim 1, characterized in that: The observable parameter set, the unobservable parameter set and the system output parameter data set are all Real data.

5. A flight control servo actuation system fault identification system, characterized in that: It includes: A first data acquisition module is configured to acquire a historical data set, wherein the historical data set includes a historical observable parameter data set, a historical unobservable parameter data set, and a historical system output parameter data set; a model training module, configured to construct a fault identification model, using the historical observable parameter dataset and the historical system output parameter dataset as inputs to the fault identification model, using the historical unobservable parameter set as labels, and training the fault identification model to obtain the trained fault identification model; A performance model module is used to construct a performance model of the system to be identified based on the physical architecture and design parameter model of the system to be identified; a sensitivity analysis module for performing a sensitivity analysis on the performance model to obtain an identification parameter set; the identification parameter set includes an input instruction, oil supply pressure, actuator output speed, actuator output displacement, servo valve spool displacement, actuator zero point coordinate value, and sensor gain; a parameter screening module, configured to screen the identified parameter set to obtain an observable parameter set and an unobservable parameter set; the observable parameter set includes an input instruction, an oil supply pressure, an actuator output speed, and an actuator output displacement; The unobservable parameter set includes the servo valve spool displacement, the actuator zero point coordinate and the sensor gain; a second data acquisition module, which acquires input instruction data, oil supply pressure data, actuator output speed data, actuator output displacement data and system output parameter data set of the system to be identified based on the identification parameter set; The system output parameter data set includes system output speed and system output displacement; a data prediction module, inputting the input command data, oil supply pressure data, actuator output speed data, actuator output displacement data, the system output speed, and the system output displacement into the trained fault identification model to obtain a predicted value of the servo valve spool displacement, a predicted value of the actuator zero-point coordinate value, and a predicted value of the sensor gain; a first judgment module, judging the predicted value of the actuator 0-point coordinate value; if the predicted value of the actuator 0-point coordinate value is within a threshold range set for the actuator, then the unit in the to-be-identified system corresponding to the actuator 0-point coordinate value is normal; if the predicted value of the actuator 0-point coordinate value exceeds the threshold range set for the actuator, then the unit in the to-be-identified system corresponding to the actuator 0-point coordinate value is faulty; A second judgment module judges the sensor gain prediction value. If the sensor gain prediction value is within a sensor set threshold range, the unit in the to-be-identified system corresponding to the sensor gain is normal; if the sensor gain prediction value exceeds the sensor set threshold range, the unit in the to-be-identified system corresponding to the sensor gain is faulty. The third judgment module is used to judge the predicted value of the servo valve spool displacement. If the predicted value of the servo valve spool displacement is within the servo valve set threshold range, the unit in the system to be identified corresponding to the servo valve spool is normal; if the predicted value of the servo valve spool displacement exceeds the servo valve set threshold range, the unit in the system to be identified corresponding to the servo valve spool is faulty.

6. The flight control servo actuation system fault identification system according to claim 5, characterized in that: The performance model includes an input instruction set, a system output parameter set, and a system parameter set; The input instruction set expression is: ； The system output parameter set expression is: ； The system parameter set expression is: ； Where: Represents the input instruction set, Represents the system output parameter set, represents the system parameter set, i represents the number of dimensions of the input instruction set, is the number of dimensions of the system parameter set, represents the i-th input instruction in the input instruction set, Indicates the system output speed, represents the system output displacement, Represents the kth system parameter in the system parameter set.

7. The flight control servo actuation system fault identification system according to claim 5, characterized in that: The identification parameter set expression is: ； The observable parameter set expression is: ； The unobservable parameter set expression is: ； Where: Represents the identification parameter set, represents the set of observable parameters, represents the set of unobservable parameters, Indicates input command, Indicates the oil supply pressure, Indicates the actuator output speed, represents the actuator output displacement, Indicates the coordinate value of the actuator 0 point, represents the sensor gain, Indicates the displacement of the servo valve spool.

8. The flight control servo actuation system fault identification system according to claim 5, characterized in that: The observable parameter set, the unobservable parameter set and the system output parameter data set are all Real data.

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