A health management method for redundant electrostatic servo mechanism based on digital twin technology

Abstract

The present invention discloses a health management method for redundant electrostatic servo mechanism based on digital twin technology. Aiming at the key failure problems existing in the operation and maintenance of redundant electrostatic servo mechanism, the main failure modes and failure mechanisms of servo mechanism under typical mission profiles and working conditions are studied, and the state evolution law of servo mechanism and the characterization parameters of working performance degradation are determined; a digital twin of servo mechanism is constructed, and the digital twin is iteratively optimized based on interactive data, and the failure mode is implanted in the twin according to the determined state evolution law of servo mechanism, so that it can map the operation state and performance degradation history of servo mechanism entity in real time; the remaining life of redundant servo mechanism is predicted and failure probability is analyzed for the digital twin. The present invention realizes the life prediction based on the digital twin of servo mechanism, adjusts the control strategy of redundant servo mechanism according to the prediction results and real-time abnormal state changes, improves the operation and maintenance efficiency of servo mechanism, and realizes the health management of redundant electrostatic servo mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of redundant electromechanical hydrostatic servo mechanisms, and in particular to a health management method for redundant electromechanical hydrostatic servo mechanisms based on digital twin technology. Background Art

[0002] The electrostatic servo mechanism, which highly integrates mechanical, electrical, magnetic, hydraulic and thermal functions, has outstanding features such as strong heavy-load capacity, high efficiency, easy redundancy, and overcomes the easy pollution of traditional aerospace servo mechanisms. It is the preferred servo control mechanism for current and future aerospace vehicles. Most aircraft will experience inventory, transportation, testing, training, flight and maintenance after they are produced. The electrostatic servo mechanism will also experience these states with the aircraft. During this period, the performance of the various components of the servo mechanism will gradually degrade, eventually leading to frequent failures and various types of failures, such as the hydraulic pump main shaft of the electrostatic servo mechanism being broken, the housing being broken, the plunger assembly being damaged, and the actuator being broken.

[0003] Since the application of electromechanical hydrostatic servomechanisms in China is still in its infancy, current research is still mainly focused on its development and performance analysis. The understanding of typical product failure mechanisms under complex functional state parameters is insufficient, and the understanding of health characterization parameters is inaccurate, so it is impossible to accurately understand the state evolution law of the servomechanism. In addition, the widely used remaining life prediction method is to use the data collected in the past to establish a life prediction model, and verify its accuracy through simulation and experiment. Although the prediction results of this static remaining life prediction model have certain reference value, the environment faced by the servomechanism during operation is complex and changeable, and the performance degradation process is relatively complicated, so the prediction results of this static model are difficult to guarantee accuracy, resulting in people being unable to timely understand the operating status of the servomechanism and adjust the control strategy to ensure that the servomechanism does not fail or can still complete the actuation instructions in extreme cases.

[0004] Therefore, we proposed a health management method for redundant electrostatic servo mechanism based on digital twin technology to solve the above problems. Summary of the invention

[0005] In view of the deficiencies of the above-mentioned prior art, the present invention provides a health management method for a redundant electrostatic pressure servo mechanism based on digital twin technology, which aims to study the failure mechanism of the servo mechanism, determine the state evolution law of the servo mechanism and the characterization parameters of the working performance degradation, and adopt the "fault injection method" to implant the failure mode into the digital twin to realize the fault prediction and control strategy adjustment of the servo mechanism, thereby improving the operation and maintenance efficiency of the servo mechanism and realizing the health management of the servo mechanism.

[0006] To achieve the above object, the present invention provides the following technical solution: a health management method for redundant electrostatic pressure servo mechanism based on digital twin technology, comprising the following steps:

[0007] S1. Study the typical faults and fault mechanisms of redundant servo mechanisms and their components;

[0008] S2. Determine the state evolution law of the servo mechanism and the characterization parameters of the working performance degradation;

[0009] S3, combining the finite element model and the lossy mathematical model to construct a digital twin of the redundant electrostatic servo mechanism;

[0010] S4. Iteratively optimize the digital twin so that it can map the operating status and performance degradation history of the servomechanism entity in real time;

[0011] S5. Adjust the control strategy of the redundant electric static pressure servo mechanism to achieve health management of the redundant electric static pressure servo mechanism.

[0012] Preferably, in step S1, the research on typical faults and fault mechanisms of the redundant servo mechanism and its components comprises the following steps:

[0013] S10. It is clarified that the redundant electrostatic servo mechanism includes but is not limited to typical mission profiles, actual use environment conditions and extreme load conditions;

[0014] S11. Analyze all failure modes, impacts and criticality of the redundant electrostatic pressure servo mechanism, and establish a complete FMECA analysis table and a redundant electrostatic pressure servo mechanism fault tree; the FMECA analysis table includes but is not limited to mechanism functions, failure modes, failure causes, failure impacts, severity categories, fault detection methods and compensation measures.

[0015] Preferably, in step S2, the determination of the state evolution law of the servo mechanism and the characterization parameters of the working performance degradation needs to be combined with the FMECA analysis table and the servo mechanism fault tree obtained in step S11;

[0016] Preferably, in step S2, the determination of the state evolution law of the servo mechanism and the characterization parameter of the working performance degradation includes the following contents:

[0017] S20, based on the servo mechanism fault knowledge base, studying the accelerated experimental method of the key fault mechanism;

[0018] S21. Design corresponding accelerated test benches according to different failure mechanisms, and reveal and verify the mechanism of failure by conducting accelerated life tests on the faulty parts on the test bench;

[0019] S22, determining the state evolution law of the servo mechanism by collecting performance parameter data of the faulty component;

[0020] S23. Determine the characterization parameters of the degradation of the working performance of the component from among numerous component parameters.

[0021] Preferably, in step S3, the construction of a digital twin of a redundant electrostatic servo mechanism includes the following contents:

[0022] S30, based on the characteristics including but not limited to the structure, working principle, operating state and performance parameters of the redundant electrostatic pressure servo mechanism, a mathematical model of the redundant electrostatic pressure servo mechanism is constructed in the Simulink module of the software Matlab;

[0023] S31, implanting the redundant electric static pressure servo mechanism failure modes studied in step S2, including but not limited to oil filter blockage and pipeline oil leakage, into a mathematical model to form a lossy mathematical model to map the operating state of the redundant electric static pressure servo mechanism entity.

[0024] Preferably, in step S3, the construction of the digital twin of the redundant electrostatic servo mechanism is based on but not limited to the overall structure and component structure of the redundant electrostatic servo mechanism, and a three-dimensional model of the redundant electrostatic servo mechanism is established in three-dimensional modeling software including but not limited to UG, ProE and SolidWorks, and the model is imported into finite element analysis software including but not limited to Ansys, HyperWorks and Abaqus to establish a finite element model that takes into account but not limited to the actual operating environment conditions and extreme loads, and is combined with the constructed lossy mathematical model to form a digital twin of the redundant electrostatic servo mechanism.

[0025] Preferably, in step S4, the iterative optimization of the digital twin so that it can map the operating state and performance degradation history of the servomechanism entity in real time includes the following:

[0026] S40, collecting performance degradation data of electrostatic servo mechanism parts from the test bench based on the servo mechanism failure mechanism and performance degradation characterization parameters studied in step S2;

[0027] S41, selecting key performance parameters that can characterize the failure history of the servo mechanism from the performance parameters of the servo mechanism;

[0028] S42, using the digital twin obtained in step S3 to interact with the servo mechanism entity in real time, analyzing the data of key performance parameters characterizing the performance degradation process of the servo mechanism selected from the interaction data, and combining it with the performance degradation curve of the redundant electrostatic pressure servo mechanism;

[0029] S43. Explore and verify the relationship between the degree of performance degradation and key performance parameters, conduct a state assessment of the redundant electrostatic servo mechanism, and quantify the degree of change in the physical state of the servo mechanism.

[0030] Preferably, in step S4, after performing hierarchical analysis on the static data of the entire life cycle and the dynamic data of the operation process of the digital twin, the performance status of the twin model is updated and combined with the operating status of the servomechanism entity to achieve iterative optimization of the digital twin, so that it can map the operating status and performance degradation history of the servomechanism entity in real time.

[0031] Preferably, in step S5, the control strategy adjustment of the redundant electrostatic pressure servo mechanism to achieve health management of the redundant electrostatic pressure servo mechanism includes the following contents:

[0032] S50, synchronizing the servo mechanism operation state and the digital twin simulation process, and controlling the mapping delay of the operation state within a certain range;

[0033] S51, performing data fusion processing on the status data collected in real time in step S4, the latest generated twin data and the recorded historical data, extracting symptom information, and matching it with the knowledge in the fault knowledge base;

[0034] S52. Perform remaining life prediction and failure probability analysis of redundant servo mechanisms based on machine learning on digital twins;

[0035] S53, adjusting the control strategy of the servo mechanism according to the prediction result and the abnormal state data of the servo mechanism collected in real time.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention realizes real-time data interaction between the digital twin and the servo mechanism, and iteratively optimizes the digital twin based on the interaction data, so that it can map the operating status and performance degradation history of the servo mechanism entity in real time; and performs remaining life prediction and failure probability analysis on the digital twin for the redundant servo mechanism, adjusts the control strategy of the redundant servo mechanism according to the prediction results and real-time abnormal state changes, improves the operation and maintenance efficiency of the servo mechanism, and realizes the health management of the redundant electrostatic pressure servo mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The present invention is a flow chart of a method for health management of a redundant electrostatic pressure servo mechanism based on digital twin technology. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the implementation regulations described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] See also Figure 1 The present invention provides a technical solution: a health management method for redundant electrostatic pressure servo mechanism based on digital twin technology, comprising the following steps:

[0041] Step 1: Study the typical faults and fault mechanisms of redundant servo mechanisms and their components; including the following:

[0042] 10) Clarify the redundant electrostatic servo mechanism including but not limited to typical mission profiles, actual operating environment conditions and extreme load conditions.

[0043] 11) Analyze all failure modes, impacts and criticality of redundant electrostatic servo mechanisms, and establish a complete FMECA analysis table and redundant electrostatic servo mechanism fault tree; the FMECA analysis table includes but is not limited to mechanism functions, failure modes, failure causes, failure impacts, severity categories, fault detection methods and compensation measures.

[0044] Step 2: determine the state evolution law of the servo mechanism and the characterization parameters of the working performance degradation;

[0045] The steps include:

[0046] 20) Accelerated experimental methods to study the key failure mechanisms.

[0047] 21) Design corresponding accelerated test benches according to different failure mechanisms, and reveal and verify the mechanism of failure by conducting accelerated life tests on faulty components on the test bench.

[0048] 22) Determine the state evolution law of the servo mechanism by collecting performance parameter data of the faulty components.

[0049] 23) Determine the characterization parameters of the degradation of working performance from a large number of component parameters.

[0050] Step 3: Combine the finite element model and the lossy mathematical model to construct a digital twin of the redundant electrostatic servo mechanism; including the following contents:

[0051] 30) Based on the characteristics including but not limited to the structure, working principle, operating status and performance parameters of the redundant electrostatic servo mechanism, a mathematical model of the redundant electrostatic servo mechanism is constructed in the Simulink module of the software Matlab.

[0052] 31) The redundant electrostatic servo mechanism failure modes studied in step 2, including but not limited to oil filter blockage and boost tank leakage, are implanted into the mathematical model to form a lossy mathematical model that maps the operating status of the redundant electrostatic servo mechanism entity.

[0053] To construct a digital twin of a redundant electrostatic servo mechanism, it is necessary to establish a three-dimensional model of the redundant electrostatic servo mechanism in three-dimensional modeling software including but not limited to UG, ProE and SolidWorks based on the overall structure and component structure of the redundant electrostatic servo mechanism, and import it into finite element analysis software including but not limited to Ansys, HyperWorks and Abaqus to establish a finite element model that takes into account but not limited to the actual operating environment conditions and extreme loads, and combine it with the established lossy mathematical model to form a digital twin of the redundant electrostatic servo mechanism.

[0054] Step 4: Iteratively optimize the digital twin so that it can map the operating status and performance degradation history of the servo mechanism entity in real time; including the following:

[0055] 40) Based on the servo mechanism failure mechanism and performance degradation characterization parameters studied in step 2, performance degradation data of electrostatic servo mechanism components should be collected from the test bench.

[0056] 41) Select key performance parameters that can characterize the failure history of the servo mechanism from its performance parameters.

[0057] 42) Use the digital twin obtained in step 3 to interact with the servo mechanism entity in real time, analyze the key performance parameters characterizing the performance degradation process of the servo mechanism selected from the interaction data, and combine it with the performance degradation curve of the redundant electrostatic servo mechanism.

[0058] 43) Explore and verify the relationship between the degree of performance degradation and key performance parameters, conduct a state assessment of the redundant electrostatic servo mechanism, and quantify the degree of change in the physical state of the servo mechanism.

[0059] Step 5: Adjust the control strategy of the redundant electrostatic pressure servo mechanism to achieve healthy management of the redundant electrostatic pressure servo mechanism. This includes the following:

[0060] 50) Synchronize the servo mechanism operation status and the digital twin simulation process to control the mapping delay of the operation status within a certain range.

[0061] 51) Perform data fusion processing on the status data collected in real time in step 4, the latest generated twin data and the recorded historical data, extract symptom information, and match it with the knowledge in the fault knowledge base.

[0062] 52) Perform remaining life prediction and failure probability analysis of redundant servo mechanisms based on machine learning on digital twins.

[0063] 53) According to the prediction results and the abnormal status data of the servo mechanism collected in real time, the control strategy of the servo mechanism is adjusted to ensure that the servo mechanism does not fail, or can still complete the actuation instructions in extreme cases such as when only a single channel is left intact, thereby improving the operation and maintenance efficiency and realizing the health management of redundant electrostatic servo mechanisms.

[0064] The present invention aims at the key problems existing in the operation and maintenance of redundant electrostatic pressure servo mechanisms, studies the main fault modes and failure mechanisms of the servo mechanisms, determines the state evolution law of the servo mechanisms and the characterization parameters of the working performance degradation; constructs a digital twin of the servo mechanism and implants the fault mode, realizes the real-time data interaction between the digital twin and the servo mechanism, and iteratively optimizes the digital twin based on the interaction data so that it can map the operating status and performance degradation history of the servo mechanism entity in real time; predicts the remaining life of the redundant servo mechanism and analyzes the failure probability of the digital twin, adjusts the control strategy of the redundant servo mechanism according to the prediction results and real-time abnormal state changes, improves the operation and maintenance efficiency of the servo mechanism, and realizes the health management of the redundant electrostatic pressure servo mechanism.

[0065] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A health management method for redundant electrostatic pressure servo mechanism based on digital twin technology, characterized in that: The following steps are involved: S1. Study the typical faults and fault mechanisms of redundant servo mechanisms and their components; S2. Determine the state evolution law of the servo mechanism and the characterization parameters of the working performance degradation; S3, combining the finite element model and the lossy mathematical model to construct a digital twin of the redundant electrostatic servo mechanism; The construction of a digital twin of a redundant electrostatic servo mechanism includes the following contents: S30, based on the characteristics of the structure, working principle, operating state and performance parameters of the redundant electrostatic pressure servo mechanism, a mathematical model of the redundant electrostatic pressure servo mechanism is constructed in the Simulink module of the software Matlab; S31. The redundant electrostatic servo mechanism failure modes including oil filter blockage and boost oil tank leakage are implanted into the mathematical model to form a lossy mathematical model, so as to map the operation state of the redundant electrostatic servo mechanism entity; The construction of the digital twin of the redundant electrostatic servo mechanism is to establish a three-dimensional model of the redundant electrostatic servo mechanism in three-dimensional modeling software including UG, ProE and SolidWorks based on the overall structure and component structure of the redundant electrostatic servo mechanism, and import it into finite element analysis software including Ansys, HyperWorks and Abaqus to establish a finite element model that takes into account the actual use environment conditions and extreme loads, and combine it with the established lossy mathematical model to form the digital twin of the redundant electrostatic servo mechanism; S4. Iteratively optimize the digital twin so that it can map the operating status and performance degradation history of the servo mechanism entity in real time; In step S4, the digital twin is iteratively optimized so that it can map the operating state and performance degradation history of the servomechanism entity in real time, including the following: S40, collecting performance degradation data of electrostatic servo mechanism parts from the test bench based on the servo mechanism failure mechanism and performance degradation characterization parameters studied in step S2; S41, selecting key performance parameters that can characterize the failure history of the servo mechanism from the performance parameters of the servo mechanism; S42, using the digital twin obtained in step S3 to interact with the servo mechanism entity in real time, analyzing the data of key performance parameters characterizing the performance degradation process of the servo mechanism selected from the interaction data, and combining it with the performance degradation curve of the redundant electrostatic pressure servo mechanism; S43. Explore and verify the relationship between the degree of performance degradation and key performance parameters, conduct a state assessment on the redundant electrostatic servo mechanism, and quantify the degree of change in the physical state of the servo mechanism; After performing hierarchical analysis on the static data of the digital twin throughout its life cycle and the dynamic data of its operation process, the performance status of the twin model is updated and combined with the operation status of the servomechanism entity to achieve iterative optimization of the digital twin, so that it can map the operation status and performance degradation history of the servomechanism entity in real time; S5. Adjust the control strategy of the redundant electric static pressure servo mechanism to achieve health management of the redundant electric static pressure servo mechanism.

2. The method for health management of redundant electrostatic pressure servo mechanism based on digital twin technology according to claim 1 is characterized in that: In step S1, the study of typical faults and fault mechanisms of the redundant servo mechanism and its components includes the following steps: S10, clarifying the redundant electrostatic servo mechanism including typical task profiles, actual use environment conditions and extreme load conditions; S11. Analyze all failure modes, impacts and criticality of the redundant electrostatic pressure servo mechanism, and establish a complete FMECA analysis table and a redundant electrostatic pressure servo mechanism fault tree; the FMECA analysis table includes mechanism function, failure mode, failure cause, failure impact, severity category, fault detection method and compensation measures.

3. The method for health management of redundant electrostatic pressure servo mechanism based on digital twin technology according to claim 2 is characterized in that: In step S2, the determination of the state evolution law of the servo mechanism and the characterization parameters of the working performance degradation needs to be combined with the FMECA analysis table and the servo mechanism fault tree obtained in step S11.

4. The method for health management of redundant electrostatic pressure servo mechanism based on digital twin technology according to claim 1, characterized in that: In step S2, the determination of the state evolution law of the servo mechanism and the characterization parameters of the working performance degradation includes the following contents: S20, based on the servo mechanism fault knowledge base, studying the accelerated experimental method of the key fault mechanism; S21. Design corresponding accelerated test benches according to different failure mechanisms, and reveal and verify the mechanism of failure by conducting accelerated life tests on the faulty parts on the test bench; S22, determining the state evolution law of the servo mechanism by collecting performance parameter data of the faulty component; S23. Determine the characterization parameters of the degradation of the working performance of the component from among numerous component parameters.

5. The method for health management of redundant electrostatic pressure servo mechanism based on digital twin technology according to claim 1, characterized in that: In step S5, the control strategy of the redundant electrostatic pressure servo mechanism is adjusted to achieve health management of the redundant electrostatic pressure servo mechanism, including the following contents: S50, synchronizing the servo mechanism operation state and the digital twin simulation process, and controlling the mapping delay of the operation state within a certain range; S51, performing data fusion processing on the status data collected in real time in step S4, the latest generated twin data, and the recorded historical data, extracting symptom information, and matching it with the knowledge in the fault knowledge base; S52. Perform remaining life prediction and failure probability analysis of redundant servo mechanisms based on machine learning on digital twins; S53, adjusting the control strategy of the servo mechanism according to the prediction result and the abnormal state data of the servo mechanism collected in real time.

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