Fuzzy analytic hierarchy process for evaluating endurance state of electro-hydrostatic actuator

Abstract

The invention discloses a fuzzy analytic hierarchy process for evaluating the endurance state of an electro-hydrostatic actuator. The fuzzy analytic hierarchy process comprises the following steps: establishing an evaluation index system of the endurance state of the electro-hydrostatic actuator; establishing a fuzzy complementary judgment matrix according to the relative importance of the evaluation indexes in the evaluation index system; the fuzzy complementary judgment matrix is converted into a fuzzy consistency check matrix, and a weight vector is obtained by using a sum row normalization method; a membership function is defined, index states are graded through the membership function, and index state data are standardized; and constructing a fuzzy relation matrix according to the weight vector and the membership function, and calculating a fuzzy comprehensive evaluation score of the index through the fuzzy relation matrix. According to the method, the fuzzy analytic hierarchy process is adopted, the durability state of the electro-hydrostatic actuator is systematically evaluated, and the method has the advantages of being high in evaluation precision, high in systematicness and suitable for complex working conditions.

Description

Technical Field

[0001] The invention relates to the technical field of durability state evaluation of an electric hydrostatic actuator, and in particular to a fuzzy hierarchical analysis method for durability state evaluation of an electric hydrostatic actuator. Background Art

[0002] The electric hydrostatic actuator is a high-performance integrated power actuator that is widely used in heavy-duty applications in aerospace, ships, and robotics. The electric hydrostatic actuator directly drives the hydraulic cylinder through electrical signals to achieve precise control of position, speed, direction, and force. Compared with traditional centralized oil source valve-controlled hydraulic systems, the electric hydrostatic actuator has the advantages of high system integration, high power-to-weight ratio, high energy efficiency, good reliability, and easy maintenance. It has gradually become an important part of the power control field of high-end equipment. Its core components include controllers, power module assemblies, and feedback devices. The performance and working conditions of these components are crucial to the overall durability of the electric hydrostatic actuator. However, with the long-term operation of the electric hydrostatic actuator in complex working conditions and high-load environments, the accurate evaluation and effective maintenance of its durability have become technical problems that need to be solved urgently.

[0003] Most of the existing methods for evaluating the durability of electric hydrostatic actuators are based on a single indicator or limited empirical data for diagnosis, which lacks systematicity and scientificity. These methods usually use qualitative analysis to independently evaluate components such as controllers, power module components, and feedback devices, and fail to establish a comprehensive multi-level indicator system. Traditional evaluation methods mostly rely on expert experience, are highly subjective, and it is difficult to objectively reflect the complex interactions and weight distribution between different components. In addition, due to the large amount of uncertainty and ambiguity in the operation of the electric hydrostatic actuator system, such as the influence of environmental factors, the randomness of load changes, and differences in the degree of equipment aging, traditional methods are difficult to effectively deal with these complex factors, which affects the accuracy and reliability of the evaluation results.

[0004] As a comprehensive evaluation method combining fuzzy mathematics and analytic hierarchy process, fuzzy analytic hierarchy process can effectively solve the uncertainty problem existing in the evaluation of complex systems. By establishing a multi-level index system, constructing a fuzzy complementary judgment matrix and determining the membership function, fuzzy analytic hierarchy process can comprehensively reflect the importance of different evaluation indicators and quantify the evaluation results. However, the traditional fuzzy analytic hierarchy process still has some shortcomings when applied to the durability status evaluation of electric hydrostatic actuators. First, the weight determination in the traditional fuzzy analytic hierarchy process depends on the artificially constructed judgment matrix, which is easy to introduce subjective errors, and the consistency verification process is complicated, and it often requires multiple adjustments to meet the consistency requirements, thereby reducing the evaluation efficiency. Secondly, the design of the membership function is relatively simple in existing research, and the operating characteristics of the electric hydrostatic actuator system are not fully considered, resulting in insufficient accuracy of the evaluation results. Finally, the existing methods usually only focus on a single-level fuzzy comprehensive evaluation, lack a comprehensive analysis of the multi-level and multi-index system, and cannot fully reflect the overall durability status of the electric hydrostatic actuator system.

[0005] In the specific application of the electric hydrostatic actuator system, the stability, response speed and processing accuracy of the controller directly affect the dynamic performance of the system, while the temperature, humidity and electromagnetic interference in its operating environment will have a significant impact on the stability of the control signal. The reliability, efficiency and heat resistance of the power module assembly are key factors affecting the energy transfer and conversion efficiency, while voltage or current anomalies and signal transmission anomalies are common failure modes of power module assemblies. The operating conditions of the feedback device (such as physical damage, contamination blockage and electrical failure) and measurement errors directly affect the accuracy of position and velocity feedback, thereby affecting the control effect of the system. However, the existing evaluation methods lack systematic and hierarchical analysis of these indicators, making it difficult to accurately quantify the comprehensive impact of each indicator on the durability of the electric hydrostatic actuator. Summary of the invention

[0006] One object of the present invention is to propose a fuzzy hierarchical analysis method for evaluating the durability state of an electric hydrostatic actuator. The present invention establishes a multi-level evaluation system of a target layer, a criterion layer and an indicator layer, and combines it with a trapezoidal distribution membership function to conduct a comprehensive and scientific evaluation of the durability state of the electric hydrostatic actuator system. This overcomes the defects of traditional methods such as strong subjectivity, difficulty in handling ambiguity and uncertainty, and lack of systematicity. At the same time, it improves the weight determination and comprehensive evaluation process of the fuzzy hierarchical analysis method, thereby improving the accuracy and efficiency of the evaluation.

[0007] A fuzzy hierarchical analysis method for durability status evaluation of an electric hydrostatic actuator according to an embodiment of the present invention comprises the following steps:

[0008] S1. Establish an evaluation index system for the durability of electric hydrostatic actuators;

[0009] S2. Establish a fuzzy complementary judgment matrix according to the relative importance of the evaluation indicators in the evaluation indicator system;

[0010] S3, converting the fuzzy complementary judgment matrix into a fuzzy consistency test matrix, and obtaining the weight vector by using the row normalization method;

[0011] S4. Define a membership function, use the membership function to classify the indicator status, set four levels of good, normal, caution and severe, and perform standardization on the indicator status data;

[0012] S5. Construct a fuzzy relationship matrix based on the weight vector and the membership function, and calculate the fuzzy comprehensive evaluation score of the indicator through the fuzzy relationship matrix.

[0013] Optionally, the evaluation index system includes a target layer, a criterion layer and an indicator layer, the target layer is a comprehensive evaluation target, the criterion layer includes a controller, a power module assembly and a feedback device, and the indicator layer is divided into a B layer and a C layer, the B layer includes the configuration performance and operating environment of the controller, the configuration performance and abnormality detection of the power module assembly, the operating conditions and measurement errors of the feedback device, and the C layer includes the stability and response speed of the controller configuration performance and processing accuracy, the ambient temperature and humidity and electromagnetic interference of the controller operating environment, the reliability and efficiency of the power module assembly configuration performance and heat resistance, the voltage or current abnormality and signal transmission abnormality of the power module assembly abnormality detection, the physical damage and pollution blockage of the feedback device operating conditions, as well as electrical failures and measurement errors.

[0014] Optionally, the S2 specifically includes:

[0015] S21. Determine the relative importance of the evaluation indicators. Select experts to conduct comparative analysis on each pair of indicators and determine the relative importance level. The level range is 0.1-0.9. When the level is 0.5, it means that they are equally important. When the level is 0.7, it means that the importance of the indicator is slightly higher than that of another indicator. When the level is 0.9, it means that the importance of the indicator is much higher than that of another indicator.

[0016] S22. According to the relative importance level, a fuzzy complementary judgment matrix F is constructed. Each element in the fuzzy complementary judgment matrix represents the relative importance between index i and index j. The symmetry of the fuzzy complementary judgment matrix satisfies the relationship:

[0017] F=[f ij ] n×n ;

[0018] f ij =1-f ji ,f ii =0.5;

[0019] Where F represents the fuzzy complementary judgment matrix, f ij Indicates the relative importance between indicator i and indicator j.

[0020] Optionally, the S3 specifically includes:

[0021] S31, transform the fuzzy complementary judgment matrix F into the fuzzy consistency test matrix R:

[0022] R=[r ij ] n×n ;

[0023]

[0024] Among them, r ij represents the elements of the fuzzy consistency check matrix, and R represents the fuzzy consistency check matrix;

[0025] S32. According to the fuzzy consistency test matrix, the initial weight vector of the C layer is calculated by using the row normalization method:

[0026]

[0027] in, represents the initial weight vector of layer C, and T represents the transposition operation;

[0028] S33, convert the fuzzy consistency check matrix R into a reciprocal matrix E:

[0029] E=[e ij ] n×n ;

[0030] e ij =r ij / r ji ,i,j=1,2,…,n;

[0031] Among them, e ij represents the elements of a reciprocal matrix;

[0032] S34, weight vector As the initial value V 0 , use the iterative formula to find the eigenvector V k+1 , and find the infinite norm of the eigenvector ∥V k+1 ∥ ∞ :

[0033] V k+1 =EV k ;

[0034] Among them, V k+1 represents the eigenvector, E represents the reciprocal matrix;

[0035] S35, judgment ∥Vk+1 ∥ ∞ -∥V k ∥ ∞ ≤∈ (∈ is the calculation accuracy) is satisfied, if satisfied, then ∥V k+1 ∥ ∞ is the maximum eigenvalue, and V k+1 Normalize and get the final weight vector of layer C:

[0036]

[0037] Among them, W C represents the final weight vector;

[0038] S36. If not satisfied, the eigenvector V k becomes:

[0039]

[0040] Among them, ∥V k+1 ∥ ∞ represents the infinite norm of the eigenvector;

[0041] S37, V k As the new initial vector, iterate again until ∥V is satisfied k+1 ∥ ∞ -∥V k ∥ ∞ The constraint requirement of ≤∈.

[0042] Optionally, the S4 specifically includes:

[0043] S41, defining a membership function, wherein the membership function adopts a trapezoidal distribution, corresponding to four levels: good, normal, caution, and severe. In this embodiment, the score of each level is 100, 80, 60, and 30 respectively;

[0044] S42, normalizing the state data of each indicator, normalizing all state quantities to the interval [0, 1], the closer the normalized state value is to 1, the better the state;

[0045] S43. Construct the membership function of trapezoidal distribution:

[0046]

[0047] Among them, f1(x), f2(x), f3(x) and f4(x) respectively represent the membership functions of severe, caution, normal and good in this embodiment, and x1, x2, x3, x4, x5 and x6 represent the interval boundaries of the membership function.

[0048] Optionally, the S5 specifically includes:

[0049] S51, calculating the weight coefficients of all sub-indicators in the evaluation index system according to step S2 and step S3;

[0050] S52, calculate the membership function value of each indicator, and construct the fuzzy relationship matrix R through the membership function value C :

[0051] R C =[f j (x i )] m×n ;

[0052] Among them, f j (x i ) represents the membership function value of index i when it belongs to level j, m represents the number of indicators, and n represents the number of levels;

[0053] S53. Calculate the fuzzy relationship matrix of the B-layer indicators:

[0054] R B1 =W C1 ×R C1 ;

[0055] R B2 =W C2 ×R C2 ;

[0056] R B =W C ×R C ;

[0057]

[0058] Among them, R B Represents the fuzzy relationship matrix of the B-layer indicators, R B1 The fuzzy relationship matrix representing the configuration performance, R B2 Represents the fuzzy relation matrix of the operating environment, R C Represents the fuzzy relationship matrix of C-layer indicators;

[0059] S54, calculate the membership vector F:

[0060] F=W B ×R B ;

[0061] Among them, W B represents the weight coefficient of the B-layer indicator, and F represents the membership vector;

[0062] S55. Multiply the membership of each level by the corresponding level score and sum them up, combine the status scores of each indicator, and calculate the comprehensive score S of the criterion layer indicator.A :

[0063] S A =F·[30,60,80,100] T ;

[0064] Among them, S A It represents the comprehensive score of the criteria level indicators;

[0065] S56. Calculate the comprehensive evaluation score S of the target layer G :

[0066]

[0067] Among them, S A represents the comprehensive score of the criterion layer indicators, w j Represents the weight coefficient of the criterion layer indicator.

[0068] The beneficial effects of the present invention are:

[0069] First, the present invention establishes a multi-level evaluation system consisting of a target layer, a criterion layer, and an indicator layer, covering key components of the electric hydrostatic actuator system such as the controller, power module assembly, and feedback device, and is refined into multiple specific indicators such as stability, response speed, reliability, and heat resistance. Through a meticulous hierarchical design, the present invention can systematically quantify the impact of each key component and its sub-indicator on the overall durability state, achieving a comprehensive analysis of the durability state of the electric hydrostatic actuator system, and avoiding the limitations of single indicator evaluation in traditional methods.

[0070] Secondly, the present invention improves the weight determination method, adopts the method of combining fuzzy complementary judgment matrix and fuzzy consistency check matrix, and avoids the problem of repeated adjustment in the consistency check process of traditional judgment matrix. By introducing the row normalization method and iterative optimization algorithm, the accuracy and efficiency of weight calculation are significantly improved. At the same time, the indicator status is graded using the trapezoidal distribution membership function, and the four levels of "good", "normal", "attention" and "serious" are refined to ensure the accuracy and resolution of the evaluation results.

[0071] In addition, the present invention effectively combines the indicator weights and membership functions by constructing a fuzzy relationship matrix and a comprehensive evaluation model, thus realizing a multi-level fuzzy comprehensive evaluation. Compared with the traditional method that only analyzes a single layer of indicators, the present invention can accurately calculate the comprehensive scores of the target layer, the criterion layer, and the indicator layer, thereby comprehensively reflecting the overall durability status of the electric hydrostatic actuator system. The fuzzy comprehensive evaluation model combined with the defuzzification method further improves the clarity and interpretability of the evaluation results.

[0072] Finally, the present invention is particularly good at dealing with the ambiguity and uncertainty issues in the operation of the electrostatic hydrostatic actuator. Through the standardized processing of the membership function and the multi-level evaluation framework, the present invention can accurately quantify the impact of complex factors such as ambient temperature and humidity, electromagnetic interference, physical damage and anomaly detection on the durability of the electrostatic hydrostatic actuator system, thereby effectively responding to the challenges brought by complex working conditions and multivariate interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0074] Figure 1 A flowchart of a fuzzy hierarchical analysis method for durability status evaluation of an electric hydrostatic actuator proposed by the present invention;

[0075] Figure 2 A schematic diagram of a comprehensive evaluation system of a fuzzy analytic hierarchy process for durability status evaluation of an electric hydrostatic actuator proposed in the present invention. DETAILED DESCRIPTION

[0076] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0077] refer to Figure 1 and Figure 2 A fuzzy hierarchical analysis method for durability status evaluation of an electro-hydrostatic actuator comprises the following steps:

[0078] S1. Establish an evaluation index system for the durability of electric hydrostatic actuators;

[0079] S2. Establish a fuzzy complementary judgment matrix according to the relative importance of the evaluation indicators in the evaluation indicator system;

[0080] S3, converting the fuzzy complementary judgment matrix into a fuzzy consistency test matrix, and obtaining the weight vector by using the row normalization method;

[0081] S4. Define a membership function, use the membership function to classify the indicator status, set four levels of good, normal, caution and severe, and perform standardization on the indicator status data;

[0082] S5. Construct a fuzzy relationship matrix based on the weight vector and the membership function, and calculate the fuzzy comprehensive evaluation score of the indicator through the fuzzy relationship matrix.

[0083] In this embodiment, the evaluation index system includes a target layer, a criterion layer and an indicator layer. The target layer is a comprehensive evaluation target. The criterion layer includes a controller, a power module assembly and a feedback device. The indicator layer is divided into a B layer and a C layer. The B layer includes the configuration performance and operating environment of the controller, the configuration performance and abnormal detection of the power module assembly, the operating conditions and measurement errors of the feedback device. The C layer includes the stability and response speed of the controller configuration performance and the processing accuracy, the ambient temperature and humidity and electromagnetic interference of the controller operating environment, the reliability and efficiency of the power module assembly configuration performance and heat resistance, the voltage or current abnormality and signal transmission abnormality of the power module assembly abnormal detection, the physical damage and pollution blockage of the feedback device operating conditions, as well as electrical failures and measurement errors.

[0084] In this implementation, S2 specifically includes:

[0085] S21. Determine the relative importance of the evaluation indicators. Select experts to conduct comparative analysis on each pair of indicators and determine the relative importance level. The level range is 0.1-0.9. When the level is 0.5, it means that they are equally important. When the level is 0.7, it means that the importance of the indicator is slightly higher than that of another indicator. When the level is 0.9, it means that the importance of the indicator is much higher than that of another indicator.

[0086] S22. According to the relative importance level, a fuzzy complementary judgment matrix F is constructed. Each element in the fuzzy complementary judgment matrix represents the relative importance between index i and index j. The symmetry of the fuzzy complementary judgment matrix satisfies the relationship:

[0087] F=[f ij ] n×n ;

[0088] f ij =1-f ji ,f ii =0.5;

[0089] Where F represents the fuzzy complementary judgment matrix, f ij Indicates the relative importance between indicator i and indicator j.

[0090] In this implementation, S3 specifically includes:

[0091] S31, transform the fuzzy complementary judgment matrix F into the fuzzy consistency test matrix R:

[0092] R=[r ij ] n×n ;

[0093]

[0094] Among them, r ijrepresents the elements of the fuzzy consistency check matrix, and R represents the fuzzy consistency check matrix;

[0095] S32. According to the fuzzy consistency test matrix, the initial weight vector of the C layer is calculated by using the row normalization method:

[0096]

[0097] in, represents the initial weight vector of layer C, and T represents the transposition operation;

[0098] S33, convert the fuzzy consistency check matrix R into a reciprocal matrix E:

[0099] E=[e ij ] n×n ;

[0100] e ij =r ij / r ji ,i,j=1,2,…,n;

[0101] Among them, e ij represents the elements of a reciprocal matrix;

[0102] S34, weight vector As the initial value V 0 , use the iterative formula to find the eigenvector V k+1 , and find the infinite norm of the eigenvector ∥V k+1 ∥ ∞ :

[0103] V k+1 =EV k ;

[0104] Among them, V k+1 represents the eigenvector, E represents the reciprocal matrix;

[0105] S35, judgment ∥V k+1 ∥ ∞ -∥V k ∥ ∞ ≤∈ (∈ is the calculation accuracy) is satisfied, if satisfied, then ∥V k+1 ∥ ∞ is the maximum eigenvalue, and V k+1 Normalize and get the final weight vector of layer C:

[0106]

[0107] Among them, W C represents the final weight vector;

[0108] S36. If not satisfied, the eigenvector V k becomes:

[0109]

[0110] Among them, ∥V k+1 ∥ ∞ represents the infinite norm of the eigenvector;

[0111] S37, V k As the new initial vector, iterate again until ∥V is satisfied k+1 ∥ ∞ -∥V k ∥ ∞ The constraint requirement of ≤∈.

[0112] In this implementation, S4 specifically includes:

[0113] S41, defining a membership function, wherein the membership function adopts a trapezoidal distribution. In this embodiment, the membership function corresponds to four levels: good, normal, caution, and severe, and the scores of each level are 100, 80, 60, and 30 respectively;

[0114] S42, normalizing the state data of each indicator, normalizing all state quantities to the interval [0, 1], the closer the normalized state value is to 1, the better the state;

[0115] S43. Construct the membership function of trapezoidal distribution:

[0116]

[0117]

[0118] Among them, f1(x), f2(x), f3(x) and f4(x) in this embodiment represent the membership functions of severe, caution, normal and good, respectively, and x1, x2, x3, x4, x5 and x6 represent the interval boundaries of the membership function.

[0119] In this implementation manner, S5 specifically includes:

[0120] S51, calculating the weight coefficients of all sub-indicators in the evaluation index system according to step S2 and step S3;

[0121] S52, calculate the membership function value of each indicator, and construct the fuzzy relationship matrix R through the membership function value C :

[0122] R C =[f j (x i )] m×n ;

[0123] Among them, f j (x i ) represents the membership function value of index i when it belongs to level j, m represents the number of indicators, and n represents the number of levels;

[0124] S53. Calculate the fuzzy relationship matrix of the B-layer indicators:

[0125] R B1 =W C1 ×R C1 ;

[0126] R B2 =W C2 ×R C2 ;

[0127] R B =W C ×R C ;

[0128]

[0129] Among them, R B Represents the fuzzy relationship matrix of the B-layer indicators, R B1 The fuzzy relationship matrix representing the configuration performance, R B2 Represents the fuzzy relation matrix of the operating environment, R C Represents the fuzzy relationship matrix of C-layer indicators;

[0130] S54, calculate the membership vector F:

[0131] F=W B ×R B ;

[0132] Among them, W B represents the weight coefficient of the B-layer indicator, and F represents the membership vector;

[0133] S55. Multiply the membership of each level by the corresponding level score and sum them up, combine the status scores of each indicator, and calculate the comprehensive score S of the criterion layer indicator. A :

[0134] S A =F·[30,60,80,100] T ;

[0135] Among them, S A It represents the comprehensive score of the criteria level indicators;

[0136] S56. Calculate the comprehensive evaluation score S of the target layer G :

[0137]

[0138] Among them, S A represents the comprehensive score of the criterion layer indicators, w j Represents the weight coefficient of the criterion layer indicator.

[0139] Embodiment 1:

[0140] In order to verify the effectiveness of the durability status evaluation method of the electric hydrostatic actuator based on the fuzzy analytic hierarchy process of the present invention, an electric hydrostatic actuator used in the aviation industry was selected as the research object. This actuator is mainly used for yaw control of the flight control system and operates under high vibration, high temperature and complex load environment for a long time. The evaluation results of its operating status by traditional evaluation methods are unstable and cannot accurately reflect the actual durability status of the equipment, which leads to problems such as excessive equipment maintenance frequency or untimely fault diagnosis.

[0141] This embodiment conducts a multi-level fuzzy comprehensive evaluation on key components such as the controller, power module assembly and feedback device, comprehensively analyzes the durability status of the equipment, and verifies the effect of the present invention with the actual operation data of the equipment. The test environment selected for the scenario is a simulated flight test laboratory, which can simulate the actual operating conditions of the electric hydrostatic actuator in flight, including high-frequency vibration (10Hz-50Hz), wide temperature range (-20℃-70℃) and dynamic load changes (1000N-5000N). During the test, the main performance indicators of the electric hydrostatic actuator system were monitored in real time, including the response time and temperature of the controller, the voltage abnormality rate and efficiency drop rate of the power module, and the sensor error of the feedback device.

[0142] In the test, the evaluation index system of the durability state of the electric hydrostatic actuator was first established, and the target layer was set to comprehensively evaluate the durability state of the electric hydrostatic actuator system. The criterion layer is divided into three parts: controller, power module assembly and feedback device. The specific indicators include the configuration performance and operating environment of the controller, the configuration performance and abnormal detection of the power module, and the operating conditions and measurement errors of the feedback device. Based on the expert scores and historical data, a fuzzy complementary judgment matrix was constructed, and the weights were calculated using the fuzzy consistency test matrix, and finally the weight distribution of the indicators of each layer was obtained. In practical applications, by standardizing the collected indicator data and introducing the trapezoidal distribution membership function, the status of each indicator is divided into four levels: "good", "normal", "caution" and "severe". For example, the corresponding value of the good level of the controller response time is less than 10ms, the normal level is 10ms-20ms, the caution level is 20ms-30ms, and the severe level is greater than 30ms. Similarly, the sensor error of the feedback device is below 1% for a good level, 1%-2% for a normal level, 2%-3% for a caution level, and more than 3% for a severe level. The collected data are classified by membership function and the fuzzy relationship matrix is ​​calculated.

[0143] During the test, the electric hydrostatic actuator system underwent a 100-hour simulated operation experiment, collected operation data for multiple time periods, and performed a durability status assessment using the method of the present invention.

[0144] Table 1 Comparison of test data for durability evaluation of electric hydrostatic actuators

[0145] index Traditional methods Method of the present invention Average evaluation accuracy 80％ 92％ Misdiagnosis rate 20％ 8％ Excessive maintenance frequency (times / 100 hours) 8 5 Average evaluation time (seconds / time) 15 10 Operating cost saving rate No significant savings 30％ System comprehensive score fluctuation range (points) ±10 ±5 Assessment status grade accuracy (grade deviation) ±1 ±0.5

[0146] In terms of average evaluation accuracy, the method of the present invention reached 92%, which is 12 percentage points higher than 80% of the traditional method. This shows that the present invention can more comprehensively and scientifically reflect the actual operating status of the equipment and avoid misjudgment caused by single indicator analysis in traditional methods.

[0147] The significant reduction in misdiagnosis rate is also one of the important advantages of the present invention. The misdiagnosis rate of the traditional method is 20%, while that of the present invention is only 8%, which greatly reduces the maintenance cost and equipment downtime risk caused by misdiagnosis. This improvement in accuracy is due to the scientific analysis of the fuzzy hierarchical analysis method combined with the multi-level evaluation index system, which can more comprehensively handle the uncertainty and ambiguity in complex working conditions.

[0148] In terms of the frequency of excessive maintenance, the method of the present invention shows a better efficiency optimization effect. The excessive maintenance frequency of the traditional method is 8 times / 100 hours, while the present invention reduces it to 5 times / 100 hours, a decrease of 37.5%. This significant reduction not only saves time and labor costs for equipment maintenance, but also extends the actual service life of the equipment.

[0149] The present invention also performs better in terms of average evaluation time. The traditional method takes an average of 15 seconds per evaluation, while the present invention only takes 10 seconds, and the evaluation efficiency is improved by 33.3%. This is due to the fact that the present invention optimizes the construction and calculation process of the fuzzy relationship matrix, improves the overall calculation efficiency of the system, and is particularly suitable for large industrial systems that require real-time evaluation.

[0150] In terms of operating costs, the present invention achieves a 30% savings rate. By reducing the misdiagnosis rate and the number of excessive maintenance times, unnecessary waste of resources is avoided, and the operating and maintenance costs of the equipment are significantly reduced. In addition, the evaluation results of the present invention are more stable, and the fluctuation range of the system comprehensive score is reduced from ±10 points of the traditional method to ±5 points, indicating that the present invention has higher robustness and reliability when dealing with complex working conditions.

[0151] Finally, the accuracy of the evaluation status level of the present invention is also significantly improved. The level deviation of the traditional method is ±1 level, while the present invention is only ±0.5 level, which further proves that the multi-level fuzzy comprehensive evaluation model can more accurately quantify the durability status of the system and provide more accurate decision support for maintenance and optimization.

[0152] The present invention is not only significantly superior to traditional methods in terms of assessment accuracy and efficiency, but also improves the reliability and economy of the system by optimizing resource allocation and reducing operating costs. These advantages make it a technical solution with important application value in the field of durability status assessment of electric hydrostatic actuators.

[0153] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A fuzzy hierarchical analysis method for durability evaluation of an electric hydrostatic actuator, characterized in that: The steps include: S1. Establish an evaluation index system for the durability of electric hydrostatic actuators; S2. Establish a fuzzy complementary judgment matrix according to the relative importance of the evaluation indicators in the evaluation indicator system; S3, converting the fuzzy complementary judgment matrix into a fuzzy consistency test matrix, and obtaining the weight vector by using the row normalization method; S4. Define a membership function, use the membership function to classify the indicator status, set four levels of good, normal, caution and severe, and perform standardization on the indicator status data; S5. Construct a fuzzy relationship matrix based on the weight vector and the membership function, and calculate the fuzzy comprehensive evaluation score of the indicator through the fuzzy relationship matrix.

2. The fuzzy hierarchical analysis method for durability status evaluation of an electric hydrostatic actuator according to claim 1 is characterized in that: The evaluation index system includes a target layer, a criterion layer and an indicator layer. The target layer is a comprehensive evaluation target. The criterion layer includes a controller, a power module assembly and a feedback device. The indicator layer is divided into a B layer and a C layer. The B layer includes the configuration performance and operating environment of the controller, the configuration performance and abnormality detection of the power module assembly, the operating conditions and measurement errors of the feedback device. The C layer includes the stability and response speed of the controller configuration performance and the processing accuracy, the ambient temperature and humidity and electromagnetic interference of the controller operating environment, the reliability and efficiency of the power module assembly configuration performance and heat resistance, the voltage or current abnormality and signal transmission abnormality of the power module assembly abnormality detection, the physical damage and pollution blockage of the feedback device operating conditions, as well as electrical failures and measurement errors.

3. The fuzzy hierarchical analysis method for durability status evaluation of an electric hydrostatic actuator according to claim 1 is characterized in that: The S2 specifically includes: S21. Determine the relative importance of the evaluation indicators. Select experts to conduct comparative analysis on each pair of indicators and determine the relative importance level. The level range is 0.1-0.

9. When the level is 0.5, it means that they are equally important. When the level is 0.7, it means that the importance of the indicator is slightly higher than that of another indicator. When the level is 0.9, it means that the importance of the indicator is much higher than that of another indicator. S22. According to the relative importance level, a fuzzy complementary judgment matrix F is constructed. Each element in the fuzzy complementary judgment matrix represents the relative importance between index i and index j. The symmetry of the fuzzy complementary judgment matrix satisfies the relationship: F=[f ij ] n×n ; f ij =1-f ji ,f ii =0.5; Where F represents the fuzzy complementary judgment matrix, f ij Indicates the relative importance between indicator i and indicator j.

4. The fuzzy hierarchical analysis method for durability status evaluation of an electric hydrostatic actuator according to claim 1 is characterized in that: The S3 specifically includes: S31, transform the fuzzy complementary judgment matrix F into the fuzzy consistency test matrix R: R＝[r ij ] n×n ； Among them, r ij represents the elements of the fuzzy consistency check matrix, and R represents the fuzzy consistency check matrix; S32. According to the fuzzy consistency test matrix, the initial weight vector of the C layer is calculated by using the row normalization method: in, represents the initial weight vector of layer C, and T represents the transposition operation; S33, convert the fuzzy consistency check matrix R into a reciprocal matrix E: And=[and ij ] n×n ; e ij =r ij / r ji ,i,j=1,2,…,n; Among them, e ij represents the elements of a reciprocal matrix; S34, weight vector As the initial value V 0 , use the iterative formula to find the eigenvector V k+1 , and find the infinite norm of the eigenvector ∥V k+1 ∥ ∞ : V k+1 =EV k , Among them, V k+1 represents the eigenvector, E represents the reciprocal matrix; S35, judgment ∥V k+1 ∥ ∞ -∥V k ∥ ∞ ≤∈ (∈ is the calculation accuracy) is satisfied, if satisfied, then ∥V k+1 ∥ ∞ is the maximum eigenvalue, and V k+1 Normalize and get the final weight vector of layer C: Among them, W C represents the final weight vector; S36. If not satisfied, the eigenvector V k becomes: Among them, ∥V k+1 ∥ ∞ represents the infinite norm of the eigenvector; S37, V k As the new initial vector, iterate again until ∥V is satisfied k+1 ∥ ∞ -∥V k ∥ ∞ The constraint requirement of ≤∈.

5. The fuzzy hierarchical analysis method for durability status evaluation of an electric hydrostatic actuator according to claim 1 is characterized in that: The S4 specifically includes: S41, defining a membership function, wherein the membership function adopts a trapezoidal distribution, corresponding to four levels: good, normal, caution and severe, and each level has a different score, which are 100, 80, 60 and 30 respectively; S42, normalizing the state data of each indicator, normalizing all state quantities to the interval [0, 1], the closer the normalized state value is to 1, the better the state is; S43. Construct the membership function of trapezoidal distribution: Among them, f1(x), f2(x), f3(x) and f4(x) represent the membership functions of severe, caution, normal and good, respectively, and x1, x2, x3, x4, x5 and x6 represent the interval boundaries of the membership functions.

6. The fuzzy hierarchical analysis method for durability status evaluation of an electric hydrostatic actuator according to claim 1 is characterized in that: The S5 specifically includes: S51, calculating the weight coefficients of all sub-indicators in the evaluation index system according to step S2 and step S3; S52, calculate the membership function value of each indicator, and construct the fuzzy relationship matrix R through the membership function value C : R C =[f j (x i )] m×n ; Among them, f j (x i ) represents the membership function value of index i when it belongs to level j, m represents the number of indicators, and n represents the number of levels; S53. Calculate the fuzzy relationship matrix of the B-layer indicators: R B1 =W C1 ×R C1 ; R B2 =W C2 ×R C2 ; R B =W C ×R C ; Among them, R B Represents the fuzzy relationship matrix of the B-layer indicators, R B1 The fuzzy relationship matrix representing the configuration performance, R B2 Represents the fuzzy relation matrix of the operating environment, R C Represents the fuzzy relationship matrix of C-layer indicators; S54, calculate the membership vector F: F=W B ×R B ; Among them, W B represents the weight coefficient of the B-layer indicator, and F represents the membership vector; S55. Multiply the membership of each level by the corresponding level score and sum them up, combine the status scores of each indicator, and calculate the comprehensive score S of the criterion layer indicator. A : S A =F·[30,60,80,100] T ; Among them, S A It represents the comprehensive score of the criteria level indicators; S56. Calculate the comprehensive evaluation score S of the target layer G : Among them, S A represents the comprehensive score of the criterion layer indicators, w j Represents the weight coefficient of the criterion layer indicator.

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