A method and system for predicting and managing faults of electromechanical static pressure servo mechanisms

Abstract

The present invention provides a method and system for fault prediction and management of an electromechanical hydrostatic servo mechanism. Aiming at key issues existing in the research on failure mechanisms, fault prediction and health management of electromechanical hydrostatic servo mechanisms throughout their life cycle, the present invention establishes a simulation model close to the physical electromechanical hydrostatic servo mechanism, studies the failure modes and failure mechanisms under typical mission profiles and measured environmental stress modes, constructs a full-condition fault data set based on the combination of simulation models and physical experiments, analyzes and extracts health characterization parameters, predicts performance degradation trends, and uses PHM systems. This constructs a technical framework for fault prediction and health management of key systems of typical equipment, develops a tool platform for fault prediction and health status assessment, and realizes fault prediction and health management of the electromechanical hydrostatic servo mechanism throughout its life cycle.

Description

Technical Field

[0001] The present invention relates to the field of electromechanical and hydraulic technology, and in particular to a method and system for predicting and managing faults of an electromechanical static pressure servo mechanism. Background Art

[0002] With the requirements of high reliability and high precision being put forward in the field of modern aerospace, the thrust vector control system (TVCSubsystem) composed of servo mechanisms and various levels of engine thrust systems, as a key component of the aircraft operation system, has received more and more attention and attention. The servo mechanisms in the system often operate under very harsh environmental conditions. The randomness of the ascent altitude and wind load at different stages will lead to the complexity and variability of its working load. The new era has put forward higher requirements for the performance and reliability of the servo mechanism. The electro-hydrostatic servo mechanism (EHA), which highly integrates mechanical, electrical, magnetic, hydraulic and thermal functions, is the most promising servo solution. It has outstanding features such as strong heavy-load capacity, easy redundancy and overcoming the contamination of traditional servo valves. It has become the preferred servo control mechanism in the new generation of aerospace field.

[0003] The performance of each component of the electromechanical hydrostatic servo mechanism will gradually degrade during the entire life cycle of its mission, eventually leading to frequent failures with diverse types of failures. The application of electromechanical hydrostatic servo mechanisms in China is still in its infancy, and current research is mainly focused on its development and performance analysis. There is insufficient understanding of typical product failure modes and failure mechanisms under complex functional status parameters, inaccurate understanding of health characterization parameters, lack of performance degradation models, and lack of full-condition fault data sets to support diagnosis and prediction models, resulting in low credibility of health assessment and fault prediction results. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the deficiencies in the prior art, the present invention provides a method and system for predicting and managing faults of an electromechanical hydrostatic servo mechanism, which solves the current problem of low reliability of health assessment and fault prediction results for electromechanical hydrostatic servo mechanisms.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for predicting and managing faults of an electromechanical hydrostatic servo mechanism, comprising:

[0008] A simulation model is established based on the material properties, functions, manufacturing processes, and assembly processes of each component of the electromechanical hydrostatic servo mechanism, and the simulation model is integrated into a system based on the connection relationship of each component;

[0009] By means of experiments, the statistical distribution patterns of important parameters of electromechanical hydrostatic servo mechanisms of the same batch are found and stored, and the statistical distribution patterns are used to replace the important parameters in the model built in the previous step, and one of the values ​​is called according to probability during simulation.

[0010] Inputting actuation commands simultaneously into the physical entity and simulation model of the electromechanical hydrostatic servo mechanism, comparing the operating data of the physical entity model with the operating data of the simulation model, and optimizing the parameters of the simulation model so that the simulation model is sufficiently close to the physical entity without loss;

[0011] According to the failure mechanism and evolution law of key components and parts of electromechanical hydrostatic servo mechanism, and considering the expert information of electromechanical hydrostatic servo mechanism in the process of development, testing and use, a mathematical model of the degradation of fault characteristic parameters of electromechanical hydrostatic servo mechanism over time is established through experiments.

[0012] Establish a servo mechanism failure probability prediction model based on performance degradation and data fusion to obtain the time-varying fault characteristic parameter values ​​and their distribution;

[0013] Changing the parameters of the simulation model to simulate the occurrence of a fault, repeatedly measuring the fault characteristic parameter thresholds and their probability distribution of the servo mechanism under a specific fault;

[0014] Based on the established servo mechanism failure probability prediction model, Monte Carlo simulation is used to compare the current state characteristic parameters with the failure threshold to obtain the estimated values ​​of the servo mechanism failure probability and remaining life;

[0015] A PHM system for electromechanical hydrostatic servo mechanism is built based on physical entities and simulation models, and the multi-source heterogeneous data of the servo system is fully mined and analyzed. The health management of the servo system is achieved by building a fault prediction and health assessment experimental platform for the servo system.

[0016] Preferably, a simulation model is established based on the material properties, functions, manufacturing processes, and assembly processes of each component of the electromechanical hydrostatic servo mechanism, and the simulation model is integrated into a system according to the connection relationship of each component, including: the simulation model is completed on different platforms according to the characteristics of each part, the servo motor and control system are completed in the matlab / simulink platform, the hydraulic pipeline and actuator are modeled with the help of CAD, and fluid and stress analysis are achieved through ANASYS Fluent and ANASYS.

[0017] Preferably, the statistical distribution law of important parameters of electromechanical hydrostatic servo mechanism entities of the same batch is found and stored through experiments, and is used to replace the invariant parameters in the model built in the previous step, and one of the values ​​is called according to probability during simulation, including: the important parameters include: coil resistance, shear elastic modulus of the motor shaft, efficiency of the hydraulic pump, viscosity of the hydraulic oil, and friction coefficient between the actuator piston and the rigid body; the important parameters are used to replace the important parameters in the model built in the previous step, and one of the values ​​is called according to probability during simulation.

[0018] Preferably, the action instructions are simultaneously input into the physical entity and the simulation model of the electromechanical hydrostatic servo mechanism, the operating data of the physical entity model and the operating data of the simulation model are compared, and the parameters of the simulation model are optimized; including: inputting the same actuation instructions into the physical entity and the simulation model, selecting the servo motor current, servo motor voltage, instruction speed, actuator displacement, actuator speed, actuator extension chamber pressure, actuator retraction chamber pressure, and using the difference between these physical quantities in the physical entity and the simulation model as indicators, and using the convergence of the indicators as the evaluation criteria for parameter optimization.

[0019] Preferably, the method of establishing a mathematical model of the degradation of the fault characteristic parameters of the electromechanical hydrostatic servo mechanism over time comprises: first, selecting a typical electromechanical hydrostatic servo mechanism failure mode, analyzing its failure mechanism, finding out the influencing factors through the failure mechanism, conducting accelerated life experiments on the key components that have failed, accelerating their failure, and finding out the relationship between the degree of failure and time; second, embedding components with different degrees of failure into the electromechanical hydrostatic servo mechanism entity, finding out the values ​​of the corresponding fault characteristic parameters, and fitting the relationship between the fault characteristic parameters and the degree of failure in a two-dimensional coordinate system, with the fault degree as the horizontal axis and the fault characteristic parameters as the vertical axis; and combining the two relationships obtained above to derive a mathematical model of the degradation of the fault characteristic parameters over time.

[0020] Preferably, the health management of the servo system is achieved by constructing a fault prediction and health assessment experimental platform for the servo system, including: establishing a hardware platform including the servo system, a digital simulation model computer, a fault prediction and health management computer, a sensor data acquisition and signal conditioning system, and a multi-source data interface, to achieve drive control of the servo mechanism and load application of real working conditions, injection of software and hardware patterns of faults, and import analysis of external data and system fault diagnosis and health management;

[0021] Establish a software platform for data integration and fusion unit, data mining and analysis unit, engineering assessment unit, fault diagnosis and health assessment unit and decision support unit to perform data storage and management, data analysis and processing, engineering assessment, fault diagnosis and health assessment, as well as intelligent reconstruction and life extension decision support for the servo system throughout its life cycle.

[0022] The present invention also provides a fault prediction and management system for an electromechanical static pressure servo mechanism, comprising:

[0023] Digital simulation model building module: used to build simulation models based on the material properties, functions, manufacturing processes, and assembly processes of each component of the electromechanical hydrostatic servo mechanism, and integrate the simulation models into a system based on the connection relationships of each component;

[0024] Model important parameter statistical distribution module: used to store the statistical distribution law of important parameters of the simulation model that are consistent with the experiment, and call one of the values ​​according to probability during simulation;

[0025] Model parameter optimization module: used to input actuation instructions into the physical entity and simulation model of the electromechanical hydrostatic servo mechanism simultaneously, compare the operating data of the physical entity model with the operating data of the simulation model, and optimize the parameters of the simulation model to make the simulation model fully approximate the physical entity without loss;

[0026] Fault probability prediction module: This module is used to establish a mathematical model for the degradation of the fault characteristic parameters of the electromechanical hydrostatic servo mechanism over time, based on the failure mechanism and evolution law of the key components and parts of the electromechanical hydrostatic servo mechanism, while taking into account expert information during the development, testing, and use of the electromechanical hydrostatic servo mechanism.

[0027] Establish a servo mechanism failure probability prediction model based on performance degradation and data fusion to obtain the time-varying fault characteristic parameter values ​​and their distribution;

[0028] Changing the parameters of the simulation model to simulate the occurrence of a fault, repeatedly measuring the fault characteristic parameter thresholds and their probability distribution of the servo mechanism under a specific fault;

[0029] Based on the established servo mechanism failure probability prediction model, Monte Carlo simulation is used to compare the current state characteristic parameters with the failure threshold to obtain the estimated values ​​of the servo mechanism failure probability and remaining life;

[0030] Health management module: used to build a PHM system for electromechanical hydrostatic servo mechanisms based on physical entities and simulation models, fully mine and analyze the multi-source heterogeneous data of the servo system, and achieve health management of the servo system by building a fault prediction and health assessment experimental platform for the servo system.

[0031] The present invention also provides a terminal for predicting and managing faults of an electromechanical static pressure servo mechanism, comprising: a memory and a processor; the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute a method for predicting and managing faults of an electromechanical static pressure servo mechanism as described above.

[0032] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the method for predicting and managing a fault of an electromechanical static pressure servo mechanism as described above is executed.

[0033] Beneficial effects

[0034] The present invention provides a method and system for predicting and managing faults in an electromechanical hydrostatic servo mechanism. This method has the following beneficial effects:

[0035] The present invention provides a technical solution to address the key issues in the research on failure mechanisms, fault prediction and health management of electromechanical hydrostatic servo mechanisms throughout their life cycle. It establishes a simulation model with parameters that conform to statistical distribution laws, which is close to the physical electromechanical hydrostatic servo mechanism (EHA). It studies the failure modes and failure mechanisms under typical mission profiles and measured environmental stress modes, constructs a full-condition fault data set based on the combination of simulation models and physical experiments, analyzes and extracts health characterization parameters, predicts performance degradation trends, and uses PHM systems. It builds a technical framework for fault prediction and health management of key systems of typical equipment, develops a fault prediction and health status assessment tool platform, and realizes fault prediction and health management of electromechanical hydrostatic servo mechanisms throughout their life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flow chart of a method for predicting and managing faults of an electromechanical hydrostatic servo mechanism provided by the present invention;

[0037] Figure 2 This is a structural diagram of a fault prediction and management system for an electromechanical hydrostatic servo mechanism provided by the present invention. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0039] like Figure 1 As shown, an embodiment of the present invention provides a method for predicting and managing faults of an electromechanical hydrostatic servo mechanism, comprising:

[0040] S1. Establish a simulation model based on the material properties, functions, manufacturing processes, and assembly processes of each component of the electromechanical hydrostatic servo mechanism, and integrate the simulation model into a system based on the connection relationships between the components;

[0041] S2. By means of experiments, find out the statistical distribution of important parameters of electromechanical hydrostatic servo mechanism entities in the same batch and store them, use them to replace the important parameters of the model built in the previous step, and call one of the values ​​according to probability during simulation;

[0042] S3. The actuation command is simultaneously input into the physical entity and the simulation model of the electromechanical hydrostatic servo mechanism, the operation data of the physical entity model and the operation data of the simulation model are compared, and the parameters of the simulation model are optimized so that the simulation model is sufficiently close to the physical entity without loss;

[0043] S4. Based on the failure mechanisms and evolution patterns of key components and parts of electromechanical hydrostatic servo mechanisms, and taking into account expert information on the development, testing, and use of electromechanical hydrostatic servo mechanisms, a mathematical model for the degradation of characteristic failure parameters of electromechanical hydrostatic servo mechanisms over time was established experimentally.

[0044] S5. Establish a servomechanism failure probability prediction model based on performance degradation and data fusion to obtain the time-varying fault characteristic parameter values ​​and their distribution;

[0045] In one embodiment, the establishment of a servo mechanism failure probability prediction model based on performance degradation and data fusion may adopt a data-driven prediction method such as a fusion neural network;

[0046] S6. Changing the parameters of the simulation model to simulate the occurrence of a fault, repeatedly measuring the fault characteristic parameter threshold and probability distribution of the servo mechanism under a specific fault;

[0047] S7. Based on the established servo mechanism failure probability prediction model, Monte Carlo simulation is used to compare the current state characteristic parameters with the failure threshold to obtain an estimate of the servo mechanism failure probability and remaining life;

[0048] S8. Build a PHM system for electromechanical hydrostatic servo mechanisms based on physical entities and simulation models, fully mine and analyze the multi-source heterogeneous data of the servo system, and achieve health management of the servo system by building a fault prediction and health assessment experimental platform for the servo system.

[0049] In one embodiment, a simulation model is established based on the material properties, functions, manufacturing processes, and assembly processes of each component of the electromechanical hydrostatic servo mechanism, and the simulation model is integrated into a system according to the connection relationship of each component, including: the simulation model is completed on different platforms according to the characteristics of each part, the servo motor and control system are completed in the matlab / simulink platform, the hydraulic pipeline and actuator are modeled with the help of CAD, and fluid and stress analysis are achieved through ANASYS Fluent and ANASYS.

[0050] In one embodiment, the statistical distribution patterns of important parameters of electromechanical hydrostatic servo mechanism entities of the same batch are found and stored through experiments, and are used to replace the invariant parameters in the model built in the previous step, and one of the values ​​is called according to probability during simulation, including: the important parameters include: coil resistance, shear elastic modulus of the motor shaft, efficiency of the hydraulic pump, viscosity of the hydraulic oil, and friction coefficient between the actuator piston and the rigid body; the statistical distribution patterns of important parameters of the electromechanical hydrostatic servo mechanism entities of the same batch are found and stored, and are used to replace the invariant parameters in the model built in the previous step, and one of the values ​​is called according to probability during simulation;

[0051] In one embodiment, the simulation model is different from the traditional simulation model. In order to enable the model to simulate other electromechanical hydrostatic servo mechanism entities of the same batch, the important parameters of the model, such as coil resistance, shear elastic modulus of the motor shaft, efficiency of the hydraulic pump, viscosity of the hydraulic oil, friction coefficient between the actuator piston and the rigid body, etc., should be consistent with the statistical distribution measured by the experiment, just like the EHA entities of the batch.

[0052] In one embodiment, the method of simultaneously inputting motion instructions into a physical entity and a simulation model of the electromechanical hydrostatic servo mechanism, comparing the operating data of the physical entity model with the operating data of the simulation model, and optimizing the parameters of the simulation model includes: inputting the same motion instructions into the physical entity and the simulation model, selecting servo motor current, servo motor voltage, command speed, actuator displacement, actuator speed, actuator extension chamber pressure, and actuator retraction chamber pressure, using the difference between these physical quantities in the physical entity and the simulation model as an index, and using the convergence of the index as an evaluation criterion for parameter optimization, so as to fully approximate the simulation model to the physical entity before any loss occurs.

[0053] In one embodiment, the method of establishing a mathematical model for degradation of fault characteristic parameters of an electromechanical hydrostatic servo mechanism over time comprises: first, selecting a typical electromechanical hydrostatic servo mechanism failure mode, analyzing its failure mechanism, finding influencing factors such as stress, temperature, vibration, etc. through the failure mechanism, and conducting accelerated life experiments on key components where the failure occurs, for example, accelerating their failure by increasing stress, increasing temperature, increasing vibration amplitude or frequency, and finding the relationship between the degree of failure and time; second, embedding components with different degrees of failure into an electromechanical hydrostatic servo mechanism entity, finding the values ​​of the corresponding fault characteristic parameters, and fitting the relationship between the fault characteristic parameters and the degree of failure in a two-dimensional coordinate system, with the fault degree as the horizontal axis and the fault characteristic parameters as the vertical axis; and combining the two relationships obtained above to derive a mathematical model for degradation of the fault characteristic parameters over time.

[0054] In one embodiment, for a specific fault level of a specific EHA fault, the same fault level as that in the EHA entity is implanted in the simulation body, and the fault characteristic parameter value of the simulation body is tested. When the difference between the fault characteristic parameters of the simulation body and the fault characteristic parameters of the EHA entity is large, the fault level implanted in the simulation body is adjusted. When the difference between the fault characteristic parameters of the two reaches a specific threshold, the experiment is repeated multiple times in the simulation body with the current fault level to obtain the probability distribution of the system's fault characteristic parameters at the current fault level. The same processing method is used for the next fault level of the physical EHA. The obtained results are fitted to obtain the fault characteristic parameter thresholds and their probability distributions of the servo mechanism at various fault levels.

[0055] In one embodiment, the latest measured operating data of the EHA physical entity is collected and fault characteristic parameters are extracted. Based on the established servo mechanism failure probability prediction model, Monte Carlo simulation is used to compare the current state characteristic parameters with the failure threshold to obtain the estimated values ​​of the servo mechanism failure probability and remaining life.

[0056] In one embodiment, the health management of the servo system is achieved by constructing a fault prediction and health assessment experimental platform for the servo system, including: establishing a hardware platform including the servo system, a digital simulation model computer, a fault prediction and health management computer, a sensor data acquisition and signal conditioning system, and a multi-source data interface, to achieve drive control of the servo mechanism and load application of real working conditions, injection of software and hardware models of faults, and import analysis of external data for system fault diagnosis and health management;

[0057] Establish a software platform for data integration and fusion unit, data mining and analysis unit, engineering assessment unit, fault diagnosis and health assessment unit and decision support unit to perform data storage and management, data analysis and processing, engineering assessment, fault diagnosis and health assessment, as well as intelligent reconstruction and life extension decision support for the servo system throughout its life cycle.

[0058] like Figure 2 As shown, an embodiment of the present invention further provides a fault prediction and management system for an electromechanical hydrostatic servo mechanism, comprising:

[0059] Digital simulation model building module: used to build simulation models based on the material properties, functions, manufacturing processes, and assembly processes of each component of the electromechanical hydrostatic servo mechanism, and integrate the simulation models into a system based on the connection relationships of each component;

[0060] Model important parameter statistical distribution module: used to store the statistical distribution law of important parameters of the simulation model that are consistent with the experiment, and call one of the values ​​according to probability during simulation;

[0061] Model parameter optimization module: used to input actuation instructions into the physical entity and simulation model of the electromechanical hydrostatic servo mechanism simultaneously, compare the operating data of the physical entity model with the operating data of the simulation model, and optimize the parameters of the simulation model to make the simulation model fully approximate the physical entity without loss;

[0062] Fault probability prediction module: This module is used to establish a mathematical model for the degradation of the fault characteristic parameters of the electromechanical hydrostatic servo mechanism over time, based on the failure mechanism and evolution law of the key components and parts of the electromechanical hydrostatic servo mechanism, while taking into account expert information during the development, testing, and use of the electromechanical hydrostatic servo mechanism.

[0063] Establish a servo mechanism failure probability prediction model based on performance degradation and data fusion to obtain the time-varying fault characteristic parameter values ​​and their distribution;

[0064] Changing the parameters of the simulation model to simulate the occurrence of a fault, repeatedly measuring the fault characteristic parameter thresholds and their probability distribution of the servo mechanism under a specific fault;

[0065] Based on the established servo mechanism failure probability prediction model, Monte Carlo simulation is used to compare the current state characteristic parameters with the failure threshold to obtain the estimated values ​​of the servo mechanism failure probability and remaining life;

[0066] Health management module: used to build a PHM system for electromechanical hydrostatic servo mechanisms based on physical entities and simulation models, fully mine and analyze the multi-source heterogeneous data of the servo system, and achieve health management of the servo system by building a fault prediction and health assessment experimental platform for the servo system.

[0067] An embodiment of the present invention also provides an electromechanical hydrostatic servo mechanism fault prediction and management terminal, comprising: a memory and a processor; the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute a method for predicting and managing an electromechanical hydrostatic servo mechanism fault as described above.

[0068] The embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the method for predicting and managing a fault of an electromechanical static pressure servo mechanism as described above is executed.

[0069] The present invention provides a technical solution to address the key issues in the research on failure mechanisms, fault prediction and health management of electromechanical hydrostatic servo mechanisms throughout their life cycle. It establishes a simulation model with parameters that conform to statistical distribution laws, which is close to the physical electromechanical hydrostatic servo mechanism (EHA). It studies the failure modes and failure mechanisms under typical mission profiles and measured environmental stress modes, constructs a full-condition fault data set based on the combination of simulation models and physical experiments, analyzes and extracts health characterization parameters, predicts performance degradation trends, and uses PHM systems. It builds a technical framework for fault prediction and health management of key systems of typical equipment, develops a fault prediction and health status assessment tool platform, and realizes fault prediction and health management of electromechanical hydrostatic servo mechanisms throughout their life cycle.

[0070] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting and managing faults of an electromechanical hydrostatic servo mechanism, characterized in that: include: A simulation model is established based on the material properties, functions, manufacturing processes, and assembly processes of each component of the electromechanical hydrostatic servo mechanism, and the simulation model is integrated into a system based on the connection relationship of each component; By means of experiments, the statistical distribution patterns of important parameters of electromechanical hydrostatic servo mechanism entities of the same batch are found and stored, and the statistical distribution patterns are used to replace the important parameters in the model built in the previous step, and one of the values ​​is called according to probability during simulation; the important parameters include: coil resistance, shear elastic modulus of the motor shaft, efficiency of the hydraulic pump, viscosity of the hydraulic oil, and friction coefficient between the actuator piston and the rigid body; the statistical distribution patterns of important parameters of the electromechanical hydrostatic servo mechanism entities of the same batch are found and stored, and ... used to replace the important parameters in the model built in the previous step, and one of the values ​​is called according to probability during simulation; Inputting actuation commands simultaneously into the physical entity and simulation model of the electromechanical hydrostatic servo mechanism, comparing the operating data of the physical entity model with the operating data of the simulation model, and optimizing the parameters of the simulation model so that the simulation model is sufficiently close to the physical entity without loss; According to the failure mechanism and evolution law of key components and parts of electromechanical hydrostatic servo mechanism, and considering the expert information of electromechanical hydrostatic servo mechanism in the process of development, testing and use, a mathematical model of the degradation of fault characteristic parameters of electromechanical hydrostatic servo mechanism over time is established through experiments. The method of establishing a mathematical model for the degradation of fault characteristic parameters of an electromechanical hydrostatic servo mechanism over time comprises: first, selecting a typical electromechanical hydrostatic servo mechanism failure mode, analyzing its failure mechanism, identifying influencing factors through the failure mechanism, conducting accelerated life tests on key components that have failed, accelerating their failure, and identifying the relationship between the degree of failure and time; second, embedding components with different degrees of failure into the electromechanical hydrostatic servo mechanism entity, identifying the values ​​of corresponding fault characteristic parameters, and fitting the relationship between the change of the fault characteristic parameters and the degree of failure in a two-dimensional coordinate system, with the degree of failure as the horizontal axis and the fault characteristic parameter as the vertical axis; and combining the two relationships obtained above to derive a mathematical model for the degradation of the fault characteristic parameters over time. Establish a servo mechanism failure probability prediction model based on performance degradation and data fusion to obtain the time-varying fault characteristic parameter values ​​and their distribution; Changing the parameters of the simulation model to simulate the occurrence of a fault, repeatedly measuring the fault characteristic parameter thresholds and their probability distribution of the servo mechanism under a specific fault; Based on the established servo mechanism failure probability prediction model, Monte Carlo simulation is used to compare the current state characteristic parameters with the failure threshold to obtain the estimated values ​​of the servo mechanism failure probability and remaining life; A PHM system for electromechanical hydrostatic servo mechanism is built based on physical entities and simulation models, and the multi-source heterogeneous data of the servo system is fully mined and analyzed. The health management of the servo system is achieved by building a fault prediction and health assessment experimental platform for the servo system.

2. A method for predicting and managing electromechanical static pressure servo mechanism faults according to claim 1, characterized in that: The simulation model is established based on the material properties, functions, manufacturing processes, and assembly processes of each component of the electromechanical hydrostatic servo mechanism, and the simulation model is integrated into a system according to the connection relationship of each component. The simulation model is completed on different platforms according to the characteristics of each part, the servo motor and control system are completed in the MATLAB / Simulink platform, the hydraulic pipeline and actuator are modeled with the help of CAD, and fluid and stress analysis is achieved through ANASYS Fluent and ANASYS.

3. The electromechanical static pressure servo mechanism fault prediction and management method according to claim 1, characterized in that: The method comprises inputting the actuation instructions into the physical entity and the simulation model of the electromechanical hydrostatic servo mechanism at the same time, comparing the operating data of the physical entity model with the operating data of the simulation model, and optimizing the parameters of the simulation model; the method comprises: inputting the same actuation instructions into the physical entity and the simulation model, selecting the current of the servo motor, the voltage of the servo motor, the instruction speed, the displacement of the actuator, the speed of the actuator, the pressure of the actuator extension cavity, and the pressure of the actuator retraction cavity, using the difference between these physical quantities in the physical entity and the simulation model as an indicator, and using the convergence of the indicator as the evaluation standard for parameter optimization.

4. The electromechanical static pressure servo mechanism fault prediction and management method according to claim 1, characterized in that: The health management of the servo system is achieved by constructing a fault prediction and health assessment experimental platform for the servo system, including: establishing a hardware platform including the servo system, a digital simulation model computer, a fault prediction and health management computer, a sensor data acquisition and signal conditioning system, and a multi-source data interface, to achieve drive control of the servo mechanism and load application of real working conditions, injection of software and hardware models of faults, and import analysis of external data and system fault diagnosis and health management; Establish a software platform for data integration and fusion unit, data mining and analysis unit, engineering assessment unit, fault diagnosis and health assessment unit and decision support unit to perform data storage and management, data analysis and processing, engineering assessment, fault diagnosis and health assessment, as well as intelligent reconstruction and life extension decision support for the servo system throughout its life cycle.

5. A system for predicting and managing electromechanical static pressure servo mechanism faults, characterized in that: include: Digital simulation model building module: used to build simulation models based on the material properties, functions, manufacturing processes, and assembly processes of each component of the electromechanical hydrostatic servo mechanism, and integrate the simulation models into a system based on the connection relationships of each component; The module for statistical distribution of important model parameters is used to store the statistical distribution of important simulation model parameters that are consistent with the experiment, and to call one of the values ​​based on probability during simulation. The important parameters include: coil resistance, shear elastic modulus of the motor shaft, efficiency of the hydraulic pump, viscosity of the hydraulic oil, and friction coefficient between the actuator piston and the rigid body. Use it to replace the important parameters in the model built in the previous step, and call one of the values ​​according to probability during simulation; Model parameter optimization module: used to input actuation instructions into the physical entity and simulation model of the electromechanical hydrostatic servo mechanism simultaneously, compare the operating data of the physical entity model with the operating data of the simulation model, and optimize the parameters of the simulation model to make the simulation model fully approximate the physical entity without loss; Fault probability prediction module: This module is used to establish a mathematical model for the degradation of the fault characteristic parameters of the electromechanical hydrostatic servo mechanism over time, based on the failure mechanism and evolution law of the key components and parts of the electromechanical hydrostatic servo mechanism, while taking into account expert information during the development, testing, and use of the electromechanical hydrostatic servo mechanism. The method of establishing a mathematical model for the degradation of fault characteristic parameters of an electromechanical hydrostatic servo mechanism over time comprises: first, selecting a typical electromechanical hydrostatic servo mechanism failure mode, analyzing its failure mechanism, identifying influencing factors through the failure mechanism, conducting accelerated life tests on key components that have failed, accelerating their failure, and identifying the relationship between the degree of failure and time; second, embedding components with different degrees of failure into the electromechanical hydrostatic servo mechanism entity, identifying the values ​​of corresponding fault characteristic parameters, and fitting the relationship between the change of the fault characteristic parameters and the degree of failure in a two-dimensional coordinate system, with the degree of failure as the horizontal axis and the fault characteristic parameter as the vertical axis; and combining the two relationships obtained above to derive a mathematical model for the degradation of the fault characteristic parameters over time. Establish a servo mechanism failure probability prediction model based on performance degradation and data fusion to obtain the time-varying fault characteristic parameter values ​​and their distribution; Changing the parameters of the simulation model to simulate the occurrence of a fault, repeatedly measuring the fault characteristic parameter thresholds and their probability distribution of the servo mechanism under a specific fault; Based on the established servo mechanism failure probability prediction model, Monte Carlo simulation is used to compare the current state characteristic parameters with the failure threshold to obtain the estimated values ​​of the servo mechanism failure probability and remaining life; Health management module: used to build a PHM system for electromechanical hydrostatic servo mechanisms based on physical entities and simulation models, fully mine and analyze the multi-source heterogeneous data of the servo system, and achieve health management of the servo system by building a fault prediction and health assessment experimental platform for the servo system.

6. A terminal for predicting and managing electromechanical static pressure servo mechanism faults, characterized in that: The terminal includes: a memory and a processor; the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute a fault prediction and management method for an electromechanical hydrostatic servo mechanism as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the method for predicting and managing faults of an electromechanical hydrostatic servo mechanism according to any one of claims 1 to 4 is executed.

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