A method and system for diagnosing faults of a redundant electro-hydraulic servo mechanism

Abstract

The application discloses a kind of redundancy electro-hydraulic servo mechanism fault diagnosis method and system, belong to the technical field of fault diagnosis, method includes: establish about redundancy electro-hydraulic servo mechanism mathematical model;According to mathematical model, construct the bond graph model and extended Kalman filter observation model of redundancy electro-hydraulic servo mechanism;Establish about the analytical redundancy relationship formula of bond graph model, and determine the redundancy formula residual error threshold of analytical redundancy relationship formula;With analytical redundancy relationship formula and system representation parameter, construct the fault characteristic matrix of redundancy electro-hydraulic servo mechanism;With fault characteristic matrix and redundancy formula residual error isolation redundancy electro-hydraulic servo mechanism's fault parameter;The fault parameter is imported into extended Kalman filter observation model, and the fault parameter is optimized;According to the fault parameter of optimization, the fault reason of redundancy electro-hydraulic servo mechanism is positioned.Further improve fault diagnosis efficiency and the accuracy of diagnostic result.

Description

Technical Field

[0001] This invention belongs to the field of fault diagnosis technology, specifically relating to a fault diagnosis method and system for a redundant electrostatic servo mechanism. Background Technology

[0002] Redundant electrostatic servo mechanisms are key components for attitude adjustment in aerospace vehicles, and their operational status directly determines whether the vehicle can successfully complete its flight mission. As a major development direction and trend for future aerospace servo mechanisms, the operating status of redundant electrostatic servo mechanisms has become a critical factor in whether an aircraft can successfully complete its flight mission. If a malfunction occurs and cannot be handled quickly and effectively, it will directly lead to the failure of the flight mission and affect flight safety.

[0003] Electrostatic servo mechanisms mainly consist of components such as servo motors, plunger pumps, drivers, and piston actuators. Compared to other servo mechanisms, electrostatic servo mechanisms have advantages such as high efficiency, easy implementation of redundant design, and strong heavy-duty capacity. However, due to their complex structure, harsh working environment, and demanding operating conditions, they are prone to many failure modes, affecting the execution of actuation commands. Therefore, it is necessary to conduct fault diagnosis research on the failure modes of redundant electrostatic servo mechanisms, explore effective fault diagnosis methods, ensure stable operation and reduce maintenance costs, and provide important guarantees for the successful completion of flight missions.

[0004] Currently, the main fault diagnosis technology for electrostatic servo mechanisms is data-driven. Data-driven fault diagnosis requires massive amounts of equipment operation data and fault data to find features from the data. The diagnostic effect is heavily dependent on the quantity and quality of the data, resulting in low diagnostic efficiency and difficulty in combining it with knowledge of mechanical equipment fault mechanisms, leading to poor reliability of diagnostic results. Summary of the Invention

[0005] To address the technical problems of existing technologies, such as the need for massive amounts of equipment operation and fault data for data-driven fault diagnosis, the difficulty in finding features from the data, the high dependence of diagnostic effectiveness on the quantity and quality of data, low diagnostic efficiency, and the difficulty in combining with knowledge of mechanical equipment fault mechanisms, resulting in poor reliability of diagnostic results, this invention provides a fault diagnosis method and system for redundant electrostatic servo mechanisms.

[0006] First aspect

[0007] This invention provides a fault diagnosis method for a redundant electrostatic servo mechanism, comprising:

[0008] S101: Establish a mathematical model for a redundant electrostatic servo mechanism, which includes a servo motor, a hydraulic system, and a load system.

[0009] S102: Construct a bond graph model and an extended Kalman filter observation model for the redundant electrostatic servo mechanism based on the mathematical model. The bond graph model includes a servo motor bond graph model, a hydraulic system bond graph model, and a load system bond graph model.

[0010] S103: Establish the analytical redundancy relation for the bond graph model and determine the redundancy residual threshold of the analytical redundancy relation;

[0011] S104: Combining analytical redundancy relations and system characterization parameters, construct the fault characteristic matrix of the redundant electrostatic servo mechanism;

[0012] S105: Fault parameters combining fault feature matrix and redundant residual isolation excess electrostatic servo mechanism;

[0013] S106: Import the fault parameters into the extended Kalman filter observation model and optimize the fault parameters;

[0014] S107: Locate the cause of the fault in the redundant electrostatic servo mechanism based on the fault parameters obtained from optimization.

[0015] Second aspect

[0016] The present invention provides a fault diagnosis system for a redundant electrostatic servo mechanism, used to perform the fault diagnosis method for a redundant electrostatic servo mechanism in the first aspect.

[0017] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0018] In this invention, a mathematical model of a redundant electrostatic servo mechanism is first established. Based on this mathematical model, a bond graph model and an extended Kalman filter observation model are then established. The fault parameters of the electrostatic servo mechanism are isolated using the bond graph model diagnostic method. Then, the extended Kalman filter is applied to the isolated fault parameters for parameter identification and optimization, estimating the fault parameter values. Model-driven methods replace traditional data-driven methods for fault parameter identification, efficiently and accurately completing the fault diagnosis of the redundant electrostatic servo mechanism. Furthermore, during the fault parameter identification and optimization process, a fault feature matrix is ​​established by incorporating system characterization parameters. This allows for rapid location of fault parameters, avoiding the need to search for features from data, greatly improving diagnostic efficiency and reliability. Moreover, it expands the applicability of fault diagnosis while maintaining diagnostic efficiency. Attached Figure Description

[0019] The preferred embodiments will now be described in a clear and easy-to-understand manner, in conjunction with the accompanying drawings, to further explain the above-mentioned characteristics, technical features, advantages, and implementation methods of the present invention.

[0020] Figure 1This is a flowchart illustrating a fault diagnosis method for a redundant electrostatic servo mechanism provided by the present invention.

[0021] Figure 2 This is a schematic diagram of a bond graph model provided by the present invention. Detailed Implementation

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without any creative effort.

[0023] To keep the drawings concise, each figure only schematically shows the parts relevant to the invention, and these do not represent the actual structure of the product. Furthermore, to facilitate understanding, in some figures, only one of components with the same structure or function is schematically depicted, or only one is labeled. In this document, "one" not only means "only one," but can also mean "more than one."

[0024] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0025] In this document, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0026] Furthermore, in the description of this invention, the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0027] Example 1

[0028] In one embodiment, refer to the appendix to the specification. Figure 1 This document illustrates a flowchart of the fault diagnosis method for redundant electrostatic servo mechanisms provided by the present invention. (See attached specification.) Figure 2 The diagram shows a structural schematic of a bond graph model provided by the present invention.

[0029] This invention provides a fault diagnosis method for a redundant electrostatic servo mechanism, comprising:

[0030] S101: Establish a mathematical model for the redundant electrostatic servo mechanism.

[0031] The redundant electrostatic servo mechanism includes a servo motor, a hydraulic system, and a load system.

[0032] It should be noted that the redundant electrostatic servo mechanism is a mechanical system composed of a servo motor, a hydraulic system, and a load system. Each part has its own characteristics and behaviors, but they interact to achieve specific motion control tasks. Establishing a mathematical model means converting this actual mechanical system into a set of mathematical equations to better understand and predict its behavior. The mathematical model includes physical equations, motion equations, energy equations, etc. By establishing a mathematical model, the interactions between different parts, energy transfer, force and motion relationships can be described using mathematical language. Furthermore, the mathematical model analyzes how changes in different parameters affect the system's performance. In addition, the mathematical model provides a foundation for fault diagnosis, control algorithm design, and performance optimization, enabling a deeper study, operation, and description of the system's energy interaction processes.

[0033] S102: Construct a bond graph model and an extended Kalman filter observation model for the redundant electrostatic servo mechanism based on the mathematical model.

[0034] The bond graph model includes a servo motor bond graph model, a hydraulic system bond graph model, and a load system bond graph model.

[0035] Bond graph models are a graphical representation used to show the interrelationships, energy transfer, and information exchange between different components of a system. Here, establishing a bond graph model for a redundant electrostatic servo mechanism means representing the servo motor, hydraulic system, and load system as sub-models, and demonstrating the energy and signal flow relationships between them. This graphical representation helps in understanding the system's structure and dynamic characteristics, thus enabling better analysis of its operation and potential failures. The servo motor bond graph model graphically represents the servo motor and its current, torque, and other characteristics, while also showing the energy exchange between the motor and other components. The hydraulic system bond graph model graphically represents hydraulic system components such as hydraulic pumps, valves, and hydraulic cylinders, and describes the flow of fluid and pressure transmission. The load system bond graph model graphically represents the load's mass, elasticity, friction, and other characteristics, as well as its interactions with other components.

[0036] Extended Kalman Filtering (EPF) is a technique for state estimation and prediction, particularly suitable for systems with uncertainties. Here, EPF is used to observe the state and parameters of a redundant electrostatic servo mechanism. Based on measurement data and a model, EPF can estimate the system state, such as the position and speed of the servo motor, hydraulic system, and load. This technique can also be used to estimate parameters in the system, such as the motor inductance and the mass of the load—unknown variables in the system model. By establishing a bond graph model and an EPF observation model, engineers can better understand the system's structure and behavior and predict its operating state under different conditions, enabling rapid fault parameter identification and fault location.

[0037] In one possible implementation, S102 specifically includes:

[0038] S1021: The three-phase coupled circuit in the servo motor is converted into a two-phase circuit using the synchronous coordinate system. The servo motor bonding model includes the data acquisition interface se, a-axis current, q-axis current, q-axis resistance, q-axis inductance, q-axis current and variable modulus gyroscope MGY. The d-axis current in the synchronous coordinate system is simplified to 0. The data acquisition interface se is connected to the common current junction I1 representing the q-axis current. The q-axis resistance and q-axis inductance in the synchronous coordinate system are connected to the common current junction I1. The common current junction I1 is connected to the variable modulus gyroscope MGY to complete the construction of the servo motor bonding graph model.

[0039] The Variable Modulus Gyre (MGY) enables the conversion of current into electromagnetic torque and serves to transfer the back electromotive force. A common-current junction refers to a point in a circuit where multiple currents converge or branch, meaning multiple currents from different branches converge at the same node. At this node, the currents are considered "common" because they meet there, and the algebraic sum of the currents in a common-current junction is zero, based on the principle of current conservation. A common-potential junction refers to a circuit where multiple voltages are connected together, meaning multiple voltages are connected to the same node. At this node, the voltages are considered "common-potential" because they connect there, and the voltages in a common-potential junction are equal, based on the principle of equal potential levels. In other words, the voltage difference between points at the same potential is zero. In bond graph models and other modeling techniques, common-current and common-potential junctions are used to represent the current or potential relationships between different components in the modeling of redundant electrostatic servo mechanisms, thus providing a better understanding of the system's behavior.

[0040] S1022: The hydraulic system bond graph model includes data entry S1, friction coefficient fp between the shaft and bearing of the servo motor and pump, rotational inertia Jp of the shaft between the servo motor and pump, transducer TF1 representing the piston pump in the hydraulic system, transducer coefficient Dp representing the piston pump displacement, piston pump leakage coefficient ep, pipeline friction loss coefficient epipe, hydraulic cylinder leakage coefficient eh, transducer TF2 representing the hydraulic cylinder, transducer coefficient Spis representing the effective piston area of ​​the hydraulic cylinder, common flow junction I2, common potential junction O1, and common flow junction. I3 and common potential junction O2, combined with the energy interaction relationship in the hydraulic system, the friction coefficient fp, the moment of inertia Jp are connected to common potential junction I2, common potential junction I2 is connected to common potential junction O1 through converter TF2 and converter coefficient Dp, the piston pump leakage coefficient ep is connected to common potential junction O1, common potential junction O1 is connected to common flow junction I3 and common potential junction O2, the pipeline friction loss coefficient epipe is connected to common flow junction I3, the hydraulic cylinder leakage coefficient eh is connected to common potential junction O2, and common potential junction O2 is connected to converter TF2 and converter coefficient Spis.

[0041] S1023: The load system bond graph model includes the common junction I4 and the input interfaces S2, which are all connected to the common junction I4, the load mass m, the load comprehensive friction coefficient Ch, and the load elasticity coefficient k.

[0042] S1024: Connect the variable modulus gyroscope MGY to the data input S1 and the converter TF2 to the input interface S2 to connect the servo motor bond graph model, the hydraulic system bond graph model and the load system bond graph model to obtain the bond graph model.

[0043] In one possible implementation, S102 further includes:

[0044] S1025: Determine the state vector and measurement vector of the redundant electrostatic servo mechanism, and establish the state transition equation of the state vector changing with time:

[0045] x k+1 =f(x) k ,u k ,w k )

[0046] Where, x k Represents the current state vector, u k Indicates control input, w k Indicates system noise, x k+1 Represents the state vector after the transition;

[0047] S1026: Establish measurement equations for the measurement vectors, mapping the state vectors to the measurement vector space. The specific measurement equations are as follows:

[0048] zk = h(xk, vk)

[0049] Among them, z k Represents the measurement vector, v k Indicates measurement noise;

[0050] S1027: Calculate the partial derivatives of the state transition equation and the measurement equation with respect to the state vector, respectively, to obtain the state transition Jacobian matrix and the measurement Jacobian matrix;

[0051] S1028: The measurement vector of the redundant electrostatic servo mechanism is predicted using the state transition equation to obtain the state estimation vector and the state estimation covariance matrix.

[0052] S1029: Calculate the actual measurement results using the measurement equation, calculate the estimation error and Kalman gain between the predicted results and the actual measurement results, fuse the predicted results and the actual measurement results, and update the state estimation vector and the state estimation covariance matrix.

[0053] S102X: Describe the algorithm for S1025 to S1029 to complete the construction of the extended Kalman filter observation model.

[0054] It should be noted that, based on the mathematical model, bond graph models of the servo motor, hydraulic system, and load system were created. These graph models help to graphically understand the energy and signal flow between components. Based on the mathematical model, an extended Kalman filter observation model was established. This model is used for state estimation and parameter estimation, providing a more accurate system state estimate by fusing predicted and measured values.

[0055] S103: Establish the analytical redundancy relation for the bond graph model and determine the redundancy residual threshold of the analytical redundancy relation.

[0056] Among them, the Analytical Redundancy Relation (ARR) is a fault detection and isolation method based on the physical layer of the system. Its core principle is to establish constraint equations under the normal state of the system. When the system fails, the equations related to the fault parameters generate residuals. By analyzing the residuals of multiple equations, the fault range can be significantly narrowed and specific faults can be located.

[0057] In one possible implementation, S103 specifically includes:

[0058] S1031: Characterize the common potential nodes and common current nodes in the bond graph model using potential variables and current variables respectively, and obtain the analytical redundancy relational expressions corresponding to each common potential node and common current node.

[0059] Among them, the analytical redundancy relation includes the first analytical redundancy relation to the sixth analytical redundancy relation.

[0060] It should be noted that by reducing the nodal equations of the co-potential nodes and co-current nodes in the bond graph model to redundant equations representing the physical quantities of the servo mechanism, residual data of the servo system is generated. At the same time, considering the influence of disturbances and uncertainties in the operation of the servo system itself, a reasonable residual threshold range is selected, which can effectively distinguish between normal and fault states and avoid misjudgment.

[0061] In one possible implementation, S1031 specifically includes:

[0062] S1031A: The current-carrying capacity of the common junction I1 represents the q-axis current i. q The potential variables of the confluence junction I1 satisfy the algebraic sum of 0, and the first analytical redundancy relation corresponding to the confluence junction I1 is:

[0063]

[0064] Among them, U q L represents the q-axis voltage. q Indicates q-axis inductance, i q R represents the q-axis current. q Represents the q-axis resistance, ω e Indicates the electric angular velocity of the motor. Indicates the magnetic flux linkage of the motor;

[0065] S1031B: The flow rate of the common junction I2 represents the mechanical angular velocity ω output by the servo motor. m The potential variables of the confluence junction I2 satisfy the algebraic sum of 0, and the second analytical redundancy relation corresponding to the confluence junction I2 is:

[0066]

[0067] Where P represents the number of pole pairs of the motor, J p D represents the moment of inertia of the piston pump rotor, fp represents the sum of the viscous rotational friction coefficient of the motor and the viscous rotational friction coefficient of the piston pump, and D... p P represents the displacement of a plunger pump. p This indicates the pressure difference across the plunger pump.

[0068] S1031C: The potential variable of the common potential junction O1 represents the pressure difference Pp between the two ports of the plunger pump. The flow variables of the common potential junction O1 satisfy the algebraic sum of 0. The third analytical redundancy relation corresponding to the common potential junction O1 is:

[0069] D p ω m -ε p P p -q pipe =0

[0070] Where, εp q represents the leakage coefficient of a plunger pump. pipe Indicates hydraulic flow rate;

[0071] S1031D: The flow variables of the common flow junction I3 represent the pipe flow rate q. The potential variables of the common potential junction I3 satisfy the algebraic sum of 0. The fourth analytical redundancy relation corresponding to the common flow junction I3 is:

[0072] P p -ε pipe -q pipe =0

[0073] Where, ε pipe Indicates the leakage coefficient of hydraulic pipelines;

[0074] S1031E: The potential variable of the common-potential junction O2 represents the pressure difference Ph between the two ports of the hydraulic cylinder. The flow variables of the common-potential junction O2 satisfy the algebraic sum of 0. The fifth analytical redundancy relation corresponding to the common-potential junction O2 is:

[0075] q pipe -ε h P h -V pis S pis =0

[0076] Where, ε h P represents the leakage coefficient of the hydraulic cylinder. h V represents the pressure difference between the two ends of the hydraulic cylinder. pis S represents the volume of the hydraulic pipeline. pis Indicates the effective area of ​​the hydraulic cylinder piston;

[0077] S1031F: The flow variable of the common-current junction I4 represents the load velocity V. The potential variable of the common-current junction I4 satisfies the algebraic sum of 0. The sixth analytical redundancy relation corresponding to the common-current junction I4 is:

[0078] P Δ S pis -ma-C h V-kx=0

[0079] Among them, P Δ The pressure difference in the pipeline is represented by m, the load mass by a, and the piston rod acceleration by C. h denoted by , v represents the viscous friction coefficient of the hydraulic cylinder, v represents the load speed (i.e., piston rod speed), k represents the load elasticity coefficient, and x represents the piston rod displacement.

[0080] S1032: Determine the redundant residual threshold by combining the operating disturbances and uncertainties of the redundant electrostatic servo mechanism.

[0081] It should be noted that those skilled in the art can set the size of the redundant residual threshold according to actual needs, and this invention does not limit it.

[0082] S104: Combining analytical redundancy relations and system characterization parameters, construct the fault characteristic matrix of the redundant electrostatic servo mechanism.

[0083] In one possible implementation, the system characterization parameters include the q-axis resistance, q-axis inductance, motor flux linkage, moment of inertia, coefficient of friction, piston pump displacement, piston pump leakage coefficient, pipeline friction coefficient, hydraulic cylinder leakage coefficient, piston area, load mass, load damping coefficient, and load elasticity coefficient of the redundant electrostatic servo mechanism.

[0084] It should be noted that by using the equations in the analytical redundant relation and the characteristic parameters of the system, the system behavior under different fault conditions is described in an integrated way, providing a comprehensive perspective for fault diagnosis, helping to identify the system behavior under different fault conditions, and by comparing it with actual measurement data, possible causes of faults can be inferred, thereby better realizing fault diagnosis and analysis.

[0085] In one possible implementation, the fault feature matrix A is specifically:

[0086]

[0087] In this fault feature matrix, the first and second columns represent system characterization parameters, the third to eighth columns represent the first to sixth analytical redundancy relations, the ninth and tenth columns represent the detectability and isolability of the redundant static voltage servo mechanism, the numbers 0 and 1 represent the fault feature vector of the system characterization parameter in the row, and the isolability number is 1 when it is isolable.

[0088] It should be noted that the first two columns of the fault feature matrix represent system parameters, and the third to eighth columns represent the correspondence between the six redundancies and the system parameters. The number 1 in the square indicates that the system parameter in that row is in the redundancy of that column, and the number 0 indicates that the system parameter in that row is not related to the redundancy of that column. The numbers 0 and 1 in each row form the fault feature vector of the system parameter in that row. The detectability in the last two columns indicates whether the redundancy residuals react when the system parameters change. Since the system parameters in the fault feature matrix all have associated redundancies, the numbers in this column are all 1. The isolability indicates whether the fault feature vector of the system parameter is unique. If it is unique, it can be isolated by the change of residuals; if it is not unique, it cannot be isolated.

[0089] S105: Fault parameters combining the fault feature matrix and redundant residual isolation excess electrostatic servo mechanism.

[0090] In one possible implementation, S105 specifically includes:

[0091] S1051: The data features of the redundant residuals are mapped one-to-one with the fault feature vectors in the fault feature matrix, and the fault parameters are isolated from the isolation parameters.

[0092] Specifically, the fault feature matrix combines the analytical redundancy relation of the redundant electrostatic servo mechanism with system characterization parameters. Each row represents a fault condition, and each column represents a system parameter or analytical redundancy relation. By analyzing the data in the matrix, the expected redundancy residuals under different fault conditions can be calculated in advance. Redundancy residuals are the difference between actual measured data and expected values. In fault diagnosis, based on actual measured data and the system model, the expected redundancy residuals can be calculated. These redundancy residuals should be small under normal conditions, but may increase significantly when the system malfunctions. By mapping the data characteristics of the redundancy residuals to fault feature vectors in the fault feature matrix, the fault feature vector closest to the redundancy residual can be found. Each fault feature vector corresponds to a possible fault condition. By finding the fault feature vector that best matches the actual redundancy residual, potential fault parameters can be isolated. This method can, to a certain extent, determine possible fault causes, contributing to more accurate fault diagnosis and location.

[0093] S106: Import the fault parameters into the extended Kalman filter observation model to optimize the fault parameters.

[0094] In one possible implementation, S106 specifically includes:

[0095] S1061: Using the system characterization parameters corresponding to the fault parameters as the identification target, the operating data of the redundant static voltage servo mechanism is input into the extended Kalman filter observation model, and the fault parameters are optimized through recursive calculation.

[0096] Specifically, fault parameters are used as the target, and the actual operating data of the redundant static voltage servo mechanism is input into the extended Kalman filter (EPF) observation model. The recursive algorithm of the EPF continuously optimizes the estimation of fault parameters by fusing actual measurement data and model predictions. The EPF observation model iteratively updates the state estimate based on the system's dynamic model and observation equations. The target of the state estimate is the fault parameters. By comparing actual measurements with the system model, the EPF observation model calculates the optimal estimate of the fault parameters at each step, gradually reducing the difference between the estimated and actual measurements. Based on actual operating data and the system model, the estimated values ​​of the fault parameters are continuously adjusted to increasingly approximate the actual situation. Through recursive computation, the estimation of fault parameters is gradually optimized, thereby improving the accuracy and reliability of fault diagnosis.

[0097] S107: Locate the cause of the fault in the redundant electrostatic servo mechanism based on the fault parameters obtained from optimization.

[0098] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0099] In this invention, a mathematical model of a redundant electrostatic servo mechanism is first established. Based on this mathematical model, a bond graph model and an extended Kalman filter observation model are then established. The fault parameters of the electrostatic servo mechanism are isolated using the bond graph model diagnostic method. Then, the extended Kalman filter is applied to the isolated fault parameters for parameter identification and optimization, estimating the fault parameter values. Model-driven methods replace traditional data-driven methods for fault parameter identification, efficiently and accurately completing the fault diagnosis of the redundant electrostatic servo mechanism. Furthermore, during the fault parameter identification and optimization process, a fault feature matrix is ​​established by incorporating system characterization parameters. This allows for rapid location of fault parameters, avoiding the need to search for features from data, greatly improving diagnostic efficiency and reliability. Moreover, it expands the applicability of fault diagnosis while maintaining diagnostic efficiency.

[0100] Example 2

[0101] In one embodiment, the present invention provides a fault diagnosis system for a redundant electrostatic servo mechanism, used to execute the fault diagnosis method for a redundant electrostatic servo mechanism in Embodiment 1.

[0102] The fault diagnosis system for redundant electrostatic servo mechanisms provided by this invention can achieve the steps and effects of the fault diagnosis method for redundant electrostatic servo mechanisms in Embodiment 1 above. To avoid repetition, this invention will not repeat the steps.

[0103] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0104] In this invention, a mathematical model of a redundant electrostatic servo mechanism is first established. Based on this mathematical model, a bond graph model and an extended Kalman filter observation model are then established. The fault parameters of the electrostatic servo mechanism are isolated using the bond graph model diagnostic method. Then, the extended Kalman filter is applied to the isolated fault parameters for parameter identification and optimization, estimating the fault parameter values. Model-driven methods replace traditional data-driven methods for fault parameter identification, efficiently and accurately completing the fault diagnosis of the redundant electrostatic servo mechanism. Furthermore, during the fault parameter identification and optimization process, a fault feature matrix is ​​established by incorporating system characterization parameters. This allows for rapid location of fault parameters, avoiding the need to search for features from data, greatly improving diagnostic efficiency and reliability. Moreover, it expands the applicability of fault diagnosis while maintaining diagnostic efficiency.

[0105] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A fault diagnosis method for a redundant electrostatic servo mechanism, characterized in that, include: S101: Establish a mathematical model for the redundant electrostatic servo mechanism, wherein the redundant electrostatic servo mechanism includes a servo motor, a hydraulic system, and a load system; S102: Construct the bond graph model and extended Kalman filter observation model of the redundant electrostatic servo mechanism based on the mathematical model, wherein the bond graph model includes a servo motor bond graph model, a hydraulic system bond graph model and a load system bond graph model; S103: Establish the analytical redundancy relation for the bond graph model and determine the redundancy residual threshold of the analytical redundancy relation; S104: Combining the analytical redundancy relation and system characterization parameters, construct the fault characteristic matrix of the redundant electrostatic servo mechanism; S105: Combine the fault feature matrix and redundant residuals to isolate the fault parameters of the redundant electrostatic servo mechanism; S106: Import the fault parameters into the extended Kalman filter observation model and optimize the fault parameters; S107: Locate the cause of the fault in the redundant electrostatic servo mechanism based on the fault parameters obtained from optimization.

2. The fault diagnosis method for redundant electrostatic servo mechanisms according to claim 1, characterized in that, S102 specifically includes: S1021: The three-phase coupling circuit in the servo motor is converted into a two-phase circuit using a synchronous coordinate system. The servo motor bonding model includes a data acquisition interface se, a-axis current, q-axis current, q-axis resistance, q-axis inductance, q-axis current, and a variable modulus gyroscope MGY. The d-axis current in the synchronous coordinate system is simplified to 0, and the data acquisition interface se is connected to a common current junction representing the q-axis current. The q-axis resistance and q-axis inductance in the synchronous coordinate system are connected to the common current junction. Connect and connect the co-current junction Connecting to the variable modulus gyroscope MGY completes the construction of the servo motor bond graph model; S1022: The hydraulic system bond graph model includes data entry S1, the friction coefficient between the shaft and bearing between the servo motor and the pump. Moment of inertia of the shaft between the servo motor and the pump , representing the transducer of the piston pump in the hydraulic system The converter coefficient representing the displacement of the plunger pump Leakage coefficient of plunger pump Pipeline friction loss coefficient (epipe), hydraulic cylinder leakage coefficient (eh), and transducer representing the hydraulic cylinder. The converter coefficient representing the effective area of ​​the hydraulic cylinder piston. , co-flow junction Common potential , co-flow junction Harmony and common potential Based on the energy interaction relationship in the hydraulic system, the friction coefficient The moment of inertia With the aforementioned common potential junction Connection, the common potential junction Through the converter and the converter coefficients With the aforementioned common potential junction Connection, leakage coefficient of the plunger pump With the aforementioned common potential junction Connection, the common potential junction Through the co-flow junction and the aforementioned common potential junction The connection, the pipe friction loss coefficient epipe and the co-flow junction The connection, the leakage coefficient eh of the hydraulic cylinder and the common potential junction Connection, the common potential junction With the converter and the converter coefficients connect; S1023: The load system bond graph model includes a co-current junction. and both are with the aforementioned co-current junction The connected input interface S2, load mass m, load comprehensive friction coefficient Ch, and load elasticity coefficient k; S1024: Connect the variable modulus gyroscope MGY to the data input S1, and connect the converter... The input interface S2 is connected to connect the servo motor bond graph model, the hydraulic system bond graph model, and the load system bond graph model to obtain the bond graph model.

3. The fault diagnosis method for redundant electrostatic servo mechanisms according to claim 1, characterized in that, S102 further includes: S1025: Determine the state vector and measurement vector of the redundant electrostatic servo mechanism, and establish the state transition equation of the state vector changing with time: in, Represents the current state vector. Indicates control input, Indicates system noise. Represents the state vector after the transition; S1026: Establish a measurement equation for the measurement vector, mapping the state vector to the measurement vector space. The measurement equation is specifically as follows: in, Represents the measurement vector, Indicates measurement noise; S1027: Calculate the partial derivatives of the state transition equation and the measurement equation with respect to the state vector, respectively, to obtain the state transition Jacobian matrix and the measurement Jacobian matrix; S1028: The measurement vector of the redundant electrostatic servo mechanism is predicted using the state transition equation to obtain the state estimation vector and the state estimation covariance matrix. S1029: Calculate the actual measurement result using the measurement equation, calculate the estimation error and Kalman gain between the predicted result and the actual measurement result, fuse the predicted result and the actual measurement result, and update the state estimation vector and the state estimation covariance matrix; S102X: Describe the algorithm for S1025~S1029 to complete the construction of the extended Kalman filter observation model.

4. The fault diagnosis method for redundant electrostatic servo mechanisms according to claim 2, characterized in that, S103 specifically includes: S1031: The common potential nodes and common current nodes in the bond graph model are characterized by potential variables and current variables, respectively, to obtain the analytical redundancy relational expressions corresponding to each common potential node and common current node, wherein the analytical redundancy relational expressions include the first analytical redundancy relational expression to the sixth analytical redundancy relational expression; S1032: Determine the redundant residual threshold by combining the operating disturbances and uncertainties of the redundant electrostatic servo mechanism.

5. The fault diagnosis method for redundant electrostatic servo mechanisms according to claim 4, characterized in that, S1031 specifically includes: S1031A: The co-current junction The current variable represents the q-axis current. The co-current junction The potential variables satisfy the algebraic sum of 0, and the cocurrent junction The corresponding first analytical redundancy relation is: in, Represents the q-axis voltage. Indicates q-axis inductance. Represents the q-axis current. This represents the q-axis resistance. Indicates the electric angular velocity of the motor. Indicates the magnetic flux linkage of the motor; S1031B: The co-current junction The flow variable represents the mechanical angular velocity output by the servo motor. The co-current junction The potential variables satisfy the algebraic sum of 0, and the cocurrent junction The corresponding second analytical redundancy relation is: in, P Indicates the number of pole pairs of the motor. This represents the moment of inertia of the piston pump rotor. This represents the sum of the viscous rotational friction coefficient of the electric motor and the viscous rotational friction coefficient of the piston pump. Indicates the displacement of the plunger pump. This indicates the pressure difference across the plunger pump. S1031C: The commutative junction The potential variable represents the pressure difference between the two ports of the plunger pump. The common potential junction The flow variables satisfy the algebraic sum of 0, and the compotential junction... The corresponding third analytical redundancy relation is: in, Indicates the leakage coefficient of a plunger pump. Indicates hydraulic flow rate; S1031D: The co-current junction The flow variable represents the pipe flow rate. q The common potential junction The potential variables satisfy the algebraic sum of 0, and the cocurrent junction The corresponding fourth analytical redundancy relation is: in, Indicates the leakage coefficient of hydraulic pipelines; S1031E: The commutative junction The potential variable represents the pressure difference between the two ports of the hydraulic cylinder. Ph The common potential junction The flow variables satisfy the algebraic sum of 0, and the compotential junction... The corresponding fifth analytical redundancy relation is: in, Indicates the leakage coefficient of the hydraulic cylinder. This indicates the pressure difference between the two ends of the hydraulic cylinder. Indicates the volume of the hydraulic pipeline. Indicates the effective area of ​​the hydraulic cylinder piston; S1031F: The co-current junction The flow variable represents the load speed. The co-current junction The potential variables satisfy the algebraic sum of 0, and the cocurrent junction The corresponding sixth analytical redundancy relation is: in, Indicates the pipeline pressure difference. m Indicates load quality. Indicates the piston rod acceleration. This represents the viscous friction coefficient of the hydraulic cylinder. This indicates that the load speed is the piston rod speed. Indicates the load resilience coefficient. This indicates the displacement of the piston rod.

6. The fault diagnosis method for redundant electrostatic servo mechanisms according to claim 1, characterized in that, The system characterization parameters include the q-axis resistance, q-axis inductance, motor flux linkage, moment of inertia, friction coefficient, plunger pump displacement, plunger pump leakage coefficient, pipeline friction coefficient, hydraulic cylinder leakage coefficient, piston area, load mass, load damping coefficient, and load elasticity coefficient of the redundant electrostatic servo mechanism.

7. The fault diagnosis method for redundant electrostatic servo mechanisms according to claim 6, characterized in that, The fault feature matrix A is specifically as follows: In this matrix, the first and second columns of the fault feature matrix represent the system characterization parameters, the third to eighth columns represent the first to sixth analytical redundancy relations, the ninth and tenth columns represent the detectability and isolability of the redundant static voltage servo mechanism, the numbers 0 and 1 represent the fault feature vector of the system characterization parameters in the row, and the isolability value of 1 indicates that the isolability is achieved.

8. The fault diagnosis method for redundant electrostatic servo mechanisms according to claim 7, characterized in that, Specifically, S105 is: S1051: The data features of the redundant residuals are matched one-to-one with the fault feature vectors in the fault feature matrix, and the fault parameters are isolated from the isolation parameters.

9. The fault diagnosis method for redundant electrostatic servo mechanisms according to claim 1, characterized in that, Specifically, S106 is: S1061: Using the system characterization parameters corresponding to the fault parameters as the identification target, the operating data of the redundant static voltage servo mechanism is input into the extended Kalman filter observation model, and the fault parameters are optimized through recursive calculation.

10. A fault diagnosis system for a redundant electrostatic servo mechanism, characterized in that, The method for diagnosing faults in redundant electrostatic servo mechanisms according to any one of claims 1 to 9.

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