A smart state detection and analysis system and method for actuators

Abstract

This invention discloses an intelligent detection and analysis system and method for actuator status, relating to the field of intelligent detection technology. It aims to improve work efficiency by assessing the losses caused by delays in actuator status evaluation. The system includes tag identification of hydraulic actuators, determination of the actuator model, acquisition of different liquid-contacting components, detection of the liquid contact surfaces of different components, acquisition of pressure values ​​at the corresponding component's liquid contact surface and calculation of diagonal pressure, setting of contact surface range, acquisition of the center temperature of each component based on the contact surface range, evaluation of the damage degree of each component using a priority graph method combining diagonal pressure and center temperature, selection of detection combinations, determination of whether to perform complementary detection of hydraulic actuators, acquisition of the operating frequency and operating sound waves of the components within the detection combination, analysis of the complementary score using a logistic regression algorithm, and determination of the overall actuator status based on the complementary score.

Description

Technical Field

[0001] This invention relates to the field of intelligent detection technology, and more specifically, to an intelligent detection and analysis system and method for the state of an actuator. Background Technology

[0002] Intelligent detection technology is a technology that uses advanced sensors, data processing, artificial intelligence, and machine learning to monitor and analyze objects, environments, or systems in real time. When applied to actuator condition monitoring, intelligent detection technology can improve the efficiency of actuator condition detection and reduce losses caused by untimely repair of actuator damage.

[0003] The existing technology has the following shortcomings:

[0004] In the past, actuator testing was based on the efficiency of actuator operation. When the actuator efficiency was lower than a preset threshold, the actuator was considered to be in poor condition. This did not take into account the operating status of the internal components of the actuator. When the actuator efficiency dropped to the preset threshold, some sensitive items had already been significantly affected, resulting in a large amount of losses. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an intelligent state detection and analysis method for an actuator, which analyzes the operating status of each fluid-contacting component within the hydraulic actuator to perform real-time evaluation of the overall state of the actuator, thereby solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for intelligent detection and analysis of actuator state includes the following steps:

[0008] Step S1: Identify the label of the hydraulic actuator, determine the hydraulic actuator model, and import the hydraulic actuator model into the component database to obtain multiple liquid-contacting components of the hydraulic actuator;

[0009] Step S2: Detect the liquid contact surface of different liquid contact components, obtain the pressure value of the liquid contact surface and calculate the diagonal pressure, set the contact surface range, collect the center temperature of each liquid contact component according to the contact surface range, and use the priority diagram method to calculate the damage degree of each liquid contact component by combining the diagonal pressure and the center temperature.

[0010] Step S3: Screen the test combinations according to the degree of damage of each liquid-contacting component and determine whether to perform combined complementary test on the hydraulic actuator. When entering the combined complementary test, collect the working frequency and operating sound waves of the liquid-contacting components in the test combination.

[0011] Step S4: Analyze the operating frequency and operating sound waves of the liquid-contacting components in the detection assembly using a logistic regression algorithm to obtain a complementary score, and determine the overall state of the actuator based on the complementary score.

[0012] In a preferred embodiment, in step S1, an RFID tag is installed on the hydraulic actuator, and the data of the hydraulic actuator is read by an RFID reader. The model information of the corresponding hydraulic actuator is stored in different RFID tags, and the hydraulic actuator model is imported into the component database. The component database contains liquid-contacting components corresponding to the hydraulic actuator model, and different liquid-contacting components are marked differently.

[0013] In a preferred embodiment, in step S2, a small pressure sensor is attached to the liquid contact surface of each liquid contact component to detect the pressure value of the liquid contact surface of each liquid contact component. When the hydraulic actuator starts to work, the liquid contact area of ​​each liquid contact component and the corresponding center position of the contact surface are obtained.

[0014] In a preferred embodiment, in step S2, a straight line in any direction is randomly generated at the center position of the corresponding contact surface, and the two intersection points of the straight line with the edge of the liquid contact surface area of ​​the liquid contact component are taken as the diagonal points of the corresponding liquid contact component.

[0015] Using a small pressure sensor, pressure is measured at the diagonal points of each liquid-contacting component to obtain the diagonal pressure at the two diagonal points. The ratio of the two diagonal pressures of the liquid-contacting component is used as the diagonal pressure coefficient of the corresponding liquid-contacting component.

[0016] In a preferred embodiment, in step S2, the contact surface range is set using the percentile method, and the contact surface range is calculated by multiplying the obtained liquid contact surface area of ​​each liquid-contacting component by a preset delineation ratio: ,in This refers to the liquid contact area of ​​each liquid-contacting component. To determine the proportions, This refers to the defined contact surface area.

[0017] The temperature of the center of each liquid-contacting component is collected according to the set contact surface range. The temperature of each liquid-contacting component is geometrically expanded from the center of the liquid contact surface area of ​​each component until the range reaches the set contact surface range. The temperature of each liquid-contacting component is collected at random sampling points within the geometrically expanded contact surface area as the center temperature of the corresponding liquid-contacting component.

[0018] In a preferred embodiment, in step S2, the center temperature of each liquid-contacting component is normalized and labeled as 'a', and the absolute value of the difference between the diagonal pressure coefficient of each liquid-contacting component and 1 is labeled as 'b'. Then, the damage risk of each liquid-contacting component is scored. Where c represents the damage risk of each liquid-contacting component, and i represents the label of each liquid-contacting component;

[0019] When using the priority graph method to calculate the degree of damage to each liquid-contacting component, the components are sorted according to their risk of damage, and the percentage of damage to each component is calculated in descending order.

[0020] In a preferred embodiment, in step S3, the overall state coefficient of the hydraulic actuator is obtained by weighted summing of the damage ratio of each liquid-contacting component and the damage value of each liquid-contacting component in the last maintenance; when the overall state coefficient of the hydraulic actuator is lower than the preset state threshold, it is determined that the current state of the hydraulic actuator is abnormal.

[0021] When the overall state coefficient of the hydraulic actuator exceeds the preset state threshold, the damage ratio of each liquid-contacting component is sorted from largest to smallest. The top two liquid-contacting components are selected as the detection combination, marked, and then combined for complementary detection.

[0022] The displacement of the marked liquid-contacting components within the detection assembly is detected. The number of times the marked liquid-contacting components leave their initial positions within a certain period is counted and used as the working efficiency of the corresponding marked liquid-contacting components. The sound level meter is used to detect the operating sound waves of the marked liquid-contacting components within the detection assembly. The historical dataset is accessed to obtain the initial working efficiency and default operating sound waves of the corresponding liquid-contacting components of the hydraulic actuator.

[0023] In a preferred embodiment, in step S4, the ratio of the collected working efficiency of the marked liquid-contact component to the initial working efficiency is used as the efficiency drop rate, and the ratio of the collected operating sound wave of the marked liquid-contact component to the default operating sound wave is used as the sound wave anomaly rate.

[0024] Based on the efficiency drop rate of the marked liquid-contact components and the acoustic anomaly rate within the detection assembly, a logistic regression algorithm is used to analyze the state score of the detection assembly. The specific steps are as follows:

[0025] The efficiency drop rate and acoustic anomaly rate of each marked liquid-contact component in the detection assembly were taken as the geometric mean and used as the logistic regression parameters: Where z is the logistic regression parameter, d is the efficiency drop rate of the marked liquid-contact component in the detection assembly, and s is the acoustic anomaly rate of the corresponding marked liquid-contact component in the detection assembly. A logistic regression equation is constructed based on the logistic regression parameter: e is the natural base, and L is the logistic regression calculation result of the marked liquid-contact component in the detection assembly, which is used as the state coefficient of the corresponding marked liquid-contact component;

[0026] The state coefficients of all marked liquid-contact components in the detection assembly are summed to obtain the complementary score of the detection assembly. The complementary score of the detection assembly is compared with the preset detection assembly state threshold. If the complementary score of the detection assembly exceeds the detection assembly state threshold, the current hydraulic actuator is judged to be in an abnormal state; otherwise, the current hydraulic actuator is judged to be in a normal state.

[0027] An intelligent state detection and analysis system for an actuator, used to implement the aforementioned intelligent state detection and analysis method for an actuator, includes a data acquisition module, an initial evaluation module, a complementary measurement module, and a final evaluation module;

[0028] The data acquisition module is used to identify the label of the hydraulic actuator and acquire multiple liquid-contacting parts of the hydraulic actuator. It collects their pressure values ​​and temperatures and transmits them to the initial evaluation module. After receiving the marked part from the complementary measurement module, it collects the working frequency and operating sound waves of the liquid-contacting parts in the detection assembly and transmits them to the final evaluation module.

[0029] The initial assessment module calculates the diagonal pressure based on the pressure value of each liquid-contacting component, sets the contact surface range, and calculates the center temperature using the temperature of each liquid-contacting component. It then calculates the degree of damage to each liquid-contacting component by combining the diagonal pressure and the center temperature, and transmits the degree of damage to each liquid-contacting component to the complementary measurement module.

[0030] The complementary testing module screens the test combinations based on the degree of damage to each liquid-contacting component in the hydraulic actuator and determines whether to perform complementary testing on the hydraulic actuator. It then marks the liquid-contacting parts in the screened test combinations and transmits them to the data acquisition module.

[0031] The final evaluation module comprehensively tests the working frequency of the liquid-contacting components within the testing assembly and analyzes the complementary scores of the testing assembly. Based on the complementary scores of the testing assembly, the state of the hydraulic actuator is determined.

[0032] The technical effects and advantages of the intelligent state detection and analysis system and method for actuators of this invention are as follows:

[0033] This invention identifies hydraulic actuators by labeling them, determines their models, obtains information on different liquid-contacting components, detects the liquid contact surfaces of these components, acquires pressure values ​​at the corresponding liquid contact surfaces, and calculates diagonal pressure. This diagonal pressure is used to analyze damage to the liquid-contacting components caused by pressure imbalance. A contact surface range is defined, and the center temperature of each liquid-contacting component is collected based on this range. The degree of damage to each component is assessed by combining diagonal pressure and center temperature, and testing combinations are selected. It is then determined whether to perform complementary testing on the hydraulic actuators. Complementary testing allows for precise analysis of component damage and more accurate state analysis of the actuators. The operating frequency and sound waves of the liquid-contacting components within the testing combination are collected, and the complementary score of the testing combination is analyzed. Based on the complementary score, the overall state of the actuator is determined, significantly improving the actuator's service life and ensuring its high-efficiency operation. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of an intelligent state detection and analysis method for an actuator according to the present invention.

[0035] Figure 2 This is a flowchart of an intelligent state detection and analysis system for actuators according to the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] This invention identifies hydraulic actuators by labeling them, determines their models, obtains information on different liquid-contacting components, detects the liquid contact surfaces of these components, acquires the pressure values ​​at the corresponding liquid contact surfaces and calculates the diagonal pressure, sets the contact surface range, collects the center temperature of each liquid-contacting component based on the contact surface range, and assesses the damage level of each component by combining the diagonal pressure and center temperature. It then selects test combinations, determines whether to perform complementary testing on the hydraulic actuators, collects the operating frequencies and sound waves of the liquid-contacting components within the test combination, analyzes the complementary score of the test combination, and determines the overall condition of the actuator based on the complementary score. This significantly improves the lifespan of the actuator and ensures its high-efficiency operation.

[0038] Example 1: A method for intelligent detection and analysis of actuator state, such as... Figure 1 As shown, it includes the following steps:

[0039] Step S1: Identify the label of the hydraulic actuator, determine the hydraulic actuator model, and import the hydraulic actuator model into the component database to obtain multiple liquid-contacting components of the hydraulic actuator;

[0040] Step S2: Detect the liquid contact surface of different liquid contact components, obtain the pressure value of the liquid contact surface and calculate the diagonal pressure, set the contact surface range, collect the center temperature of each liquid contact component according to the contact surface range, and use the priority diagram method to calculate the damage degree of each liquid contact component by combining the diagonal pressure and the center temperature.

[0041] Step S3: Screen the test combinations according to the degree of damage of each liquid-contacting component and determine whether to perform combined complementary test on the hydraulic actuator. When entering the combined complementary test, collect the working frequency and operating sound waves of the liquid-contacting components in the test combination.

[0042] Step S4: Analyze the operating frequency and operating sound waves of the liquid-contacting components in the detection assembly using a logistic regression algorithm to obtain a complementary score, and determine the overall state of the actuator based on the complementary score.

[0043] The specific implementation is as follows:

[0044] In step S1, RFID tags are installed on the hydraulic actuators, and data from the hydraulic actuators is read using an RFID reader. The model information of the corresponding hydraulic actuators is stored in different RFID tags, and the hydraulic actuator model is imported into the component database. The component database contains liquid-contacting components with the corresponding hydraulic actuator model, such as hydraulic cylinders, hydraulic pumps, hydraulic valves, and filters. Different liquid-contacting components are marked differently.

[0045] It should be noted that RFID is radio frequency identification technology, a type of automatic identification technology. RFID tags can store information about the corresponding items and can be entered or read by an RFID reader. In this example, the tag is used to identify the hydraulic actuator. The component database is an information system that centrally stores and manages related mechanical, electronic, and other types of components. The fluid-contacting components inside the corresponding model of hydraulic actuator can be obtained through the component database. In this example, hydraulic cylinders, hydraulic pumps, hydraulic valves, and filters are used as examples of fluid-contacting components of the hydraulic actuator. In reality, the types of fluid-contacting components are not limited to those listed in this example.

[0046] In step S2, a small pressure sensor is attached to the liquid contact surface of each liquid contact component to detect the pressure value of the liquid contact surface of each liquid contact component. When the hydraulic actuator starts to work, the liquid contact area of ​​each liquid contact component and the corresponding center position of the contact surface are obtained. A straight line in any direction is randomly generated with the center of the corresponding contact surface. The two intersection points of the straight line and the edge of the liquid contact area of ​​the liquid contact component are taken as the diagonal points of the corresponding liquid contact component.

[0047] Using a small pressure sensor, pressure is measured at the diagonal points of each liquid-contacting component to obtain the diagonal pressure at the two diagonal points. The ratio of the two diagonal pressures of the liquid-contacting component is used as the diagonal pressure coefficient of the corresponding liquid-contacting component.

[0048] It should be explained that the closer the diagonal pressure coefficient is to 1, the more similar the pressure is to the two diagonal points of the corresponding liquid-contacting component, making it less prone to pressure loss and damage. A small pressure sensor is a device for measuring the pressure of liquids or gases, and in this example, it is used to detect the liquid pressure in a hydraulic actuator.

[0049] The contact area is determined using the percentile method. The contact area is calculated by multiplying the obtained liquid contact area of ​​each liquid-contacting component by a preset delineation ratio. ,in This refers to the liquid contact area of ​​each liquid-contacting component. To determine the proportions, This refers to the set contact surface range.

[0050] The temperature of the center of each liquid-contacting component is collected according to the set contact area range. The temperature is then geometrically expanded from the center of the liquid contact area of ​​each component until the set contact area range is reached. Within the geometrically expanded contact area, sampling points are randomly selected to collect the temperature of each liquid-contacting component, which is then used as the center temperature of the corresponding component.

[0051] The center temperature of each liquid-contacting component is normalized and labeled as 'a'. The absolute value of the difference between the diagonal pressure coefficient of each liquid-contacting component and 1 is labeled as 'b'. The damage risk of each liquid-contacting component is then scored. Where c represents the risk of damage to each liquid-contacting component, and i represents the label of each liquid-contacting component.

[0052] The damage percentage of each liquid-contacting component was calculated using the priority graph method based on the damage risk of each component, as shown in the table below:

[0053]

[0054] It should be noted that in the table above, components A, B, C, and D correspond one-to-one with the hydraulic cylinder, hydraulic pump, hydraulic valve, and filter in this example. After sorting each component that comes into contact with the liquid according to the risk of damage, they are then matched with components A, B, C, and D in descending order.

[0055] In step S3, the overall state coefficient of the hydraulic actuator is obtained by weighted summing of the damage ratio of each liquid-contacting component and the damage value of each liquid-contacting component in the last maintenance; when the overall state coefficient of the hydraulic actuator is lower than the preset state threshold, the current state of the hydraulic actuator is judged to be abnormal.

[0056] When the overall state coefficient of the hydraulic actuator exceeds the preset state threshold, the damage ratio of each liquid-contacting component is sorted from largest to smallest. The top two liquid-contacting components are selected as the detection combination, marked, and then combined for complementary detection.

[0057] An embedded controller is used to detect the displacement of the marked liquid-contacting components within the detection assembly. The number of times the marked liquid-contacting components leave their initial positions over a period of time is counted and used as the working efficiency of the corresponding marked liquid-contacting components. A sound level meter is used to detect the operating sound waves of the marked liquid-contacting components within the detection assembly. Historical datasets are accessed to obtain the initial working efficiency and default operating sound waves of the corresponding liquid-contacting components of the hydraulic actuator.

[0058] It should be noted that an embedded controller is a computer system designed to control specific functions or devices. In this example, it is used to collect the number of times the marked liquid-contact component leaves its initial position. A sound level meter is an instrument that measures sound pressure level, calculates and displays the intensity value based on the sound intensity, and in this example, it is used to acquire the operating sound waves of the marked liquid-contact component within the detection assembly. The historical database is a system for storing, managing, and retrieving historical data, which records the initial working efficiency and default operating sound waves of different liquid-contact components under normal operation of different models of hydraulic actuators. The greater the wear of the liquid-contact components inside the hydraulic actuator, the louder the operating sound waves emitted by the corresponding components.

[0059] In step S4, the ratio of the collected working efficiency of the marked liquid-contact component to the initial working efficiency is used as the efficiency drop rate, and the ratio of the collected operating sound wave of the marked liquid-contact component to the default operating sound wave is used as the sound wave anomaly rate.

[0060] Based on the efficiency drop rate of the marked liquid-contact components and the acoustic anomaly rate within the detection assembly, a logistic regression algorithm is used to analyze the state score of the detection assembly. The specific steps are as follows:

[0061] The efficiency drop rate and acoustic anomaly rate of each marked liquid-contact component in the detection assembly were taken as the geometric mean and used as the logistic regression parameters: Where z is the logistic regression parameter, d is the efficiency drop rate of the marked liquid-contact component in the detection assembly, and s is the acoustic anomaly rate of the corresponding marked liquid-contact component in the detection assembly. A logistic regression equation is constructed based on the logistic regression parameter: e is the natural base, and L is the logistic regression calculation result of the marked liquid-contact component in the detection assembly, which is used as the state coefficient of the corresponding marked liquid-contact component;

[0062] The state coefficients of all marked liquid-contact components in the detection assembly are summed to obtain the complementary score of the detection assembly. The complementary score of the detection assembly is compared with a preset detection assembly state threshold. If the complementary score of the detection assembly exceeds the detection assembly state threshold, the current hydraulic actuator is judged to be in an abnormal state; if the complementary score of the detection assembly is lower than the detection assembly state threshold, the current hydraulic actuator is judged to be in a normal state.

[0063] Example 2: An intelligent state detection and analysis system for actuators, such as... Figure 2 As shown, it includes a data acquisition module, an initial evaluation module, a complementary measurement module, and a final evaluation module;

[0064] The data acquisition module is used to identify the label of the hydraulic actuator and acquire multiple liquid-contacting parts of the hydraulic actuator. It collects their pressure values ​​and temperatures and transmits them to the initial evaluation module. After receiving the marked part from the complementary measurement module, it collects the working frequency and operating sound waves of the liquid-contacting parts in the detection assembly and transmits them to the final evaluation module.

[0065] The initial assessment module calculates the diagonal pressure based on the pressure value of each liquid-contacting component, sets the contact surface range, and calculates the center temperature using the temperature of each liquid-contacting component. It then calculates the degree of damage to each liquid-contacting component by combining the diagonal pressure and the center temperature, and transmits the degree of damage to each liquid-contacting component to the complementary measurement module.

[0066] The complementary testing module screens the test combinations based on the degree of damage to each liquid-contacting component in the hydraulic actuator and determines whether to perform complementary testing on the hydraulic actuator. It then marks the liquid-contacting parts in the screened test combinations and transmits them to the data acquisition module.

[0067] The final evaluation module comprehensively tests the working frequency of the liquid-contacting components within the testing assembly and analyzes the complementary scores of the testing assembly. Based on the complementary scores of the testing assembly, the state of the hydraulic actuator is determined.

[0068] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0069] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0070] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0071] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0072] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent detection and analysis of the state of an actuator, characterized in that, Includes the following steps, Step S1: Identify the label of the hydraulic actuator, determine the hydraulic actuator model, and import the hydraulic actuator model into the component database to obtain multiple liquid-contacting components of the hydraulic actuator; Step S2: Detect the liquid contact surface of different liquid contact components, obtain the pressure value of the liquid contact surface and calculate the diagonal pressure, set the contact surface range, collect the center temperature of each liquid contact component according to the contact surface range, and use the priority diagram method to calculate the damage degree of each liquid contact component by combining the diagonal pressure and the center temperature. Step S3: Screen the test combinations according to the degree of damage of each liquid-contacting component and determine whether to perform combined complementary test on the hydraulic actuator. When entering the combined complementary test, collect the working frequency and operating sound waves of the liquid-contacting components in the test combination. Step S4: Analyze the operating frequency and operating sound waves of the liquid-contacting components in the detection assembly using a logistic regression algorithm to obtain a complementary score, and determine the overall state of the actuator based on the complementary score.

2. The intelligent state detection and analysis method for an actuator according to claim 1, characterized in that: In step S1, RFID tags are installed on the hydraulic actuators, and data from the hydraulic actuators is read using an RFID reader. The model information of the corresponding hydraulic actuators is stored in different RFID tags, and the hydraulic actuator model is imported into the component database. The component database contains liquid-contacting components with the corresponding hydraulic actuator models, and different liquid-contacting components are marked differently.

3. The intelligent state detection and analysis method for an actuator according to claim 1, characterized in that: In step S2, a small pressure sensor is attached to the liquid contact surface of each liquid contact component to detect the pressure value of the liquid contact surface of each liquid contact component. When the hydraulic actuator starts to work, the liquid contact area of ​​each liquid contact component and the center position of the corresponding contact surface are obtained.

4. The intelligent state detection and analysis method for an actuator according to claim 3, characterized in that: In step S2, a straight line in any direction is randomly generated at the center position of the corresponding contact surface, and the two intersection points of the straight line with the edge of the liquid contact surface area of ​​the liquid contact component are taken as the diagonal points of the corresponding liquid contact component. Using a small pressure sensor, pressure is measured at the diagonal points of each liquid-contacting component to obtain the diagonal pressure at the two diagonal points. The ratio of the two diagonal pressures of the liquid-contacting component is used as the diagonal pressure coefficient of the corresponding liquid-contacting component.

5. The intelligent state detection and analysis method for an actuator according to claim 3, characterized in that: In step S2, the contact surface range is set using the percentile method. The contact surface range is calculated by multiplying the obtained liquid contact surface area of ​​each liquid-contacting component by a preset delineation ratio. ,in This refers to the liquid contact area of ​​each liquid-contacting component. To determine the proportions, This refers to the defined contact surface area. The temperature of the center of each liquid-contacting component is collected according to the set contact surface range. The temperature of each liquid-contacting component is geometrically expanded from the center of the liquid contact surface area of ​​each component until the range reaches the set contact surface range. The temperature of each liquid-contacting component is collected at random sampling points within the geometrically expanded contact surface area as the center temperature of the corresponding liquid-contacting component.

6. The intelligent state detection and analysis method for an actuator according to claim 5, characterized in that: In step S2, the center temperature of each liquid-contacting component is normalized and labeled as 'a', and the absolute value of the difference between the diagonal pressure coefficient of each liquid-contacting component and 1 is labeled as 'b'. Then, the damage risk of each liquid-contacting component is scored. Where c represents the damage risk of each liquid-contacting component, and i represents the label of each liquid-contacting component; When using the priority graph method to calculate the degree of damage to each liquid-contacting component, the components are sorted according to their risk of damage, and the percentage of damage to each component is calculated in descending order.

7. The intelligent state detection and analysis method for an actuator according to claim 6, characterized in that: In step S3, the overall state coefficient of the hydraulic actuator is obtained by weighted summing of the damage ratio of each liquid-contacting component and the damage value of each liquid-contacting component in the last maintenance; when the overall state coefficient of the hydraulic actuator is lower than the preset state threshold, the current state of the hydraulic actuator is judged to be abnormal. When the overall state coefficient of the hydraulic actuator exceeds the preset state threshold, the damage ratio of each liquid-contacting component is sorted from largest to smallest. The top two liquid-contacting components are selected as the detection combination, marked, and then combined for complementary detection. The displacement of the marked liquid-contacting components within the detection assembly is detected. The number of times the marked liquid-contacting components leave their initial positions within a certain period is counted and used as the working efficiency of the corresponding marked liquid-contacting components. The sound level meter is used to detect the operating sound waves of the marked liquid-contacting components within the detection assembly. The historical dataset is accessed to obtain the initial working efficiency and default operating sound waves of the corresponding liquid-contacting components of the hydraulic actuator.

8. The intelligent state detection and analysis method for an actuator according to claim 7, characterized in that: In step S4, the ratio of the collected working efficiency of the marked liquid-contact component to the initial working efficiency is used as the efficiency drop rate, and the ratio of the collected operating sound wave of the marked liquid-contact component to the default operating sound wave is used as the sound wave anomaly rate. Based on the efficiency drop rate of the marked liquid-contact components and the acoustic anomaly rate within the detection assembly, a logistic regression algorithm is used to analyze the state score of the detection assembly. The specific steps are as follows: The efficiency drop rate and acoustic anomaly rate of each marked liquid-contact component in the detection assembly were taken as the geometric mean and used as the logistic regression parameters: Where z is the logistic regression parameter, d is the efficiency drop rate of the marked liquid-contact component in the detection assembly, and s is the acoustic anomaly rate of the corresponding marked liquid-contact component in the detection assembly. A logistic regression equation is constructed based on the logistic regression parameter: e is the natural base, and L is the logistic regression calculation result of the marked liquid-contact component in the detection assembly, which is used as the state coefficient of the corresponding marked liquid-contact component; The state coefficients of all marked liquid-contact components in the detection assembly are summed to obtain the complementary score of the detection assembly. The complementary score of the detection assembly is compared with the preset detection assembly state threshold. If the complementary score of the detection assembly exceeds the detection assembly state threshold, the current hydraulic actuator is judged to be in an abnormal state; otherwise, the current hydraulic actuator is judged to be in a normal state.

9. A state intelligent detection and analysis system for an actuator, based on the state intelligent detection and analysis method for an actuator according to any one of claims 1-8, characterized in that, It includes a data acquisition module, an initial evaluation module, a complementary measurement module, and a final evaluation module; The data acquisition module is used to identify the label of the hydraulic actuator and acquire multiple liquid-contacting parts of the hydraulic actuator. It collects their pressure values ​​and temperatures and transmits them to the initial evaluation module. After receiving the marked part from the complementary measurement module, it collects the working frequency and operating sound waves of the liquid-contacting parts in the detection assembly and transmits them to the final evaluation module. The initial assessment module calculates the diagonal pressure based on the pressure value of each liquid-contacting component, sets the contact surface range, and calculates the center temperature using the temperature of each liquid-contacting component. It then calculates the degree of damage to each liquid-contacting component by combining the diagonal pressure and the center temperature, and transmits the degree of damage to each liquid-contacting component to the complementary measurement module. The complementary testing module screens the test combinations based on the degree of damage to each liquid-contacting component in the hydraulic actuator and determines whether to perform complementary testing on the hydraulic actuator. It then marks the liquid-contacting parts in the screened test combinations and transmits them to the data acquisition module. The final evaluation module comprehensively tests the working frequency of the liquid-contacting components within the testing assembly and analyzes the complementary scores of the testing assembly. Based on the complementary scores of the testing assembly, the state of the hydraulic actuator is determined.

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