Dynamic self-adaptive anti-wear system and method based on behavior bionics

Abstract

The invention discloses a dynamic self-adaptive anti-wear system and method based on behavior bionics, and relates to the technical field of wear resistance of industrial equipment. The system comprises a sensing module used for collecting impact signals of particle flow impact protection equipment in real time; the control module is used for receiving the impact signal and calculating a target action of the protection equipment; and the execution module is used for adjusting the protection equipment according to the target action. The direction of the particle flow is monitored in real time by arranging the sensor array, and the movable part is adjusted, so that the impact energy is minimized. Dynamic self-adaptive adjustment is achieved through closed-loop control, and rapid changes of the wind speed and the wind direction are effectively coped with.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of anti-wear technology of industrial equipment, and particularly to a dynamic self-adaptive anti-wear system and method based on behavior simulation. BACKGROUND

[0002] At present, the passive anti-wear technologies such as welding wear-resistant coating, installing fixed anti-wear pads or shields, and designing optimized static shape are generally used in industry. These technologies have inherent defects: they cannot adapt to the changes of particle flow direction. Once the working condition changes, the particle incidence angle deviates from the design value, and the protection effect decreases sharply, and the maintenance cost is high after wear.

[0003] In recent years, bionics applications have emerged to optimize the shape of equipment to reduce resistance or wear by simulating the static form of organisms (such as shark skin and lizard scales). However, this kind of technology is essentially a one-time, offline shape design. Its output is a fixed, static geometric shape. However, this kind of anti-wear technology based on static shape optimization has fixed shape parameters after manufacturing, which cannot adapt to the dynamic changes of particle flow parameters in actual working conditions, so its applicability and effectiveness are greatly limited. SUMMARY

[0004] The purpose of the present application is to provide a dynamic self-adaptive anti-wear system and method based on behavior simulation, which aims to solve or improve at least one of the above technical problems.

[0005] To achieve the above purpose, the present application provides the following solutions: A dynamic self-adaptive anti-wear system based on behavior simulation, comprising: a perception module for real-time acquisition of impact signals of particle flow impacting a protective device; a control module receiving the impact signals and calculating target actions of the protective device; an execution module for adjusting the protective device according to the target actions.

[0006] A dynamic self-adaptive anti-wear method based on behavior simulation, comprising: real-time acquisition of impact signals of particle flow impacting a protective device by a sensor array; calculation of the incidence direction vector of the current particle flow according to the impact signals; comparison of the incidence direction vector with the current angle of the movable component in the protective device to obtain the target action of the movable component adjustment; adjustment of the movable component according to the target action to achieve self-adaptive anti-wear adjustment.

[0007] Further, the sensor array is a vibration sensor array or an acoustic emission sensor array.

[0008] Furthermore, the incident direction vector of the current particle flow is calculated based on the impact signal, including: The time difference between the signals of each sensor in the sensor array is calculated using the cross-correlation function algorithm GCC-PHAT, and the expression is as follows: ; In the formula, For GCC-PHAT, the time delay τ corresponding to the peak value of the output is the time difference between sensor signals; and The signal spectrum between the two sensors; for The conjugate of complex numbers; is a complex exponential function; j is the imaginary unit.

[0009] Obtain the coordinates and time difference of each sensor, and calculate the incident direction vector, including the following steps: Construct a system of nonlinear equations, expressed as: ; ; ; In the formula, c is the speed of sound; , and The time difference between each sensor; the coordinates of each sensor are S1( , , S2( , , S3 , , S4 , , ); The incident direction vector is obtained by solving the nonlinear equations using the least squares method.

[0010] Furthermore, the incident direction vector is compared with the current angle of the movable part in the protective device to obtain the target action of the movable part adjustment, including: The expression for calculating the target action is: ; In the formula, For the target action; The current angle of the movable part; , and Here are the weighting coefficients; the incident direction vector is... .

[0011] Furthermore, the incident direction vector is compared with the current angle of the movable part in the protective device to obtain the target action of the movable part adjustment, including: The target motion is calculated based on the incident direction vector and the current angle of the movable part, expressed as: ; ; ; ; In the formula, For the target action; The current angle of the movable part; For function Minimum The value of ; V is the particle flow density; v is the particle flow velocity; A is the impact area. The impact wear coefficient; The impact angle is; the incident direction vector is... ; Impact wear coefficient It consists of vertical component coefficients and tangential component coefficients, and its expression is: ; In the formula, , and These are the weighting coefficients.

[0012] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This invention discloses a dynamic adaptive wear-resistant system and method based on behavioral biomimicry. The system uses a sensor array to monitor the direction of particle flow in real time and adjusts movable parts to minimize impact energy. Closed-loop control enables dynamic adaptive adjustment, effectively coping with rapid changes in wind speed and direction. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the system module connections of the present invention; Figure 2 This is a schematic diagram of the method flow of the present invention; Figure 3 This is a schematic diagram of the wind turbine in an embodiment of the present invention; Figure 4 This is a schematic diagram of the wind turbine blade structure in an embodiment of the present invention; Figure 5 This is a schematic diagram of the wind turbine blades before and after adjustment in an embodiment of the present invention.

[0015] In the diagram, 1 is a vibration sensor; 2 is a movable part. Detailed Implementation

[0016] 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.

[0017] The purpose of this invention is to provide a dynamic adaptive wear-resistant system and method based on behavioral biomimicry, which aims to solve or improve at least one of the above-mentioned technical problems.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 As shown, this invention provides a dynamic adaptive wear-resistant system based on behavioral biomimicry, comprising: The sensing module is used to collect impact signals from particulate flow impact protection equipment in real time; The sensing module is specifically configured to deploy sensors near the protection equipment to detect particle flow impact signals. The deployment scheme includes: a vibration sensor array: calculating the main incident direction of the particle flow by analyzing the time and intensity differences of the impact signals received by sensors at different locations; and an acoustic emission sensor array: monitoring the time and intensity differences of the acoustic signals generated by particle impact to calculate the main incident direction of the particle flow.

[0020] The control module receives the impact signal from the sensing module and calculates the target action of the protection device. The control process of the control module is as follows: the control module connects to the sensing module, receives the impact signal sent by the sensing module, and calculates the target action of the protection device based on the preset optimization target. The optimization target is to minimize the impact force or to make the leading edge angle consistent with the incident angle; the target action is the target angle or displacement that the protection device needs to drive.

[0021] The execution module adjusts the protection device according to the target action.

[0022] The control process of the execution module is as follows: The execution module connects to the control module and receives the target action sent by the control module. Based on the target action, it generates an adjustment scheme for the protection device and adjusts the protection device according to the adjustment scheme. The device adjusted in the adjustment scheme includes one or more of the following: a rotary motor, a linear motor, a hydraulic cylinder, or a pneumatic cylinder.

[0023] The protective equipment includes movable parts, which are the areas directly impacted by particles. These movable parts are made of high-strength, wear-resistant materials and take the form of rotatable guide vanes or protective plates, airfoil sections with adjustable angle of attack, and retractable wear-resistant blocks. The wear-resistant materials include high-chromium cast iron, tungsten carbide, and ceramic composite materials. The actuation module adjusts the movable parts to rotate around a single or multiple axes or to perform linear motion, continuously changing the spatial orientation of the movable parts relative to the particle flow to achieve adaptive wear resistance adjustment.

[0024] like Figure 2 As shown, this invention provides a dynamic adaptive wear resistance method based on behavioral biomimicry, comprising: S1, which collects the impact signal of the particle flow impact protection device in real time through a sensor array; S2, Calculate the incident direction vector of the current particle flow based on the impact signal, including the following steps: The time difference between the signals of each sensor in the sensor array is calculated using the cross-correlation function algorithm GCC-PHAT, and the expression is as follows: ; In the formula, For GCC-PHAT, the time delay τ corresponding to the peak value of the output is the time difference between sensor signals; and The signal spectrum between the two sensors; for The conjugate of complex numbers; is a complex exponential function; j is the imaginary unit.

[0025] Obtain the sensor's coordinates and time difference, and calculate the incident direction vector, including the following steps: Construct a system of nonlinear equations, expressed as: ; ; ; In the formula, c is the speed of sound; , and The time difference between each sensor; the coordinates of each sensor are S1( , , S2( , , S3 , , S4 , , ); The incident direction vector is obtained by solving the nonlinear equations using the least squares method.

[0026] S3, compare the incident direction vector with the current angle of the movable part to obtain the target action of the movable part adjustment, including the following steps: The target motion is calculated based on the incident direction vector and the current angle of the movable part, expressed as: ; ; ; ; In the formula, For the target action; The current angle of the movable part; For function Minimum The value of ; V is the particle flow density; v is the particle flow velocity; A is the impact area. The impact wear coefficient; The impact angle is; the incident direction vector is... .

[0027] Impact wear coefficient It consists of vertical component coefficients and tangential component coefficients, and its expression is: ; In the formula, , and The weighting coefficients are determined through experimental optimization. The preferred values ​​in this invention are... .

[0028] The target action calculation can be expressed as: ; In the formula, , and The weighting coefficients are determined through experimental optimization. The preferred values ​​in this invention are... .

[0029] Adjustments are made to the movable parts based on the target motion to achieve adaptive wear resistance adjustment. Specific Implementation like Figure 3 As shown, this embodiment uses a wind turbine as an example. A vibration sensor 1 is installed on the wind turbine blade.

[0031] like Figure 4 As shown, a movable part 2 is provided at the front end of the wind turbine blade.

[0032] like Figure 5 As shown, this is the state of the wind turbine blades after they have been adjusted according to the impact direction of the particle flow.

[0033] The specific steps in this embodiment are as follows: Approximately 0.3 meters upstream of the protected area on the leading edge of the wind turbine blade, four piezoelectric vibration sensors (PCB 352C33) are evenly distributed in a circumferential pattern, spaced 90° apart. The sensors are fixed to the blade surface with high-strength waterproof adhesive and connected to a data acquisition device via shielded cables. Data acquisition utilizes an NI-9234 module with a sampling frequency set to 50 kHz to fully capture the high-frequency vibration signals generated by sand impact. Its anti-aliasing filter cutoff frequency is set to 20 kHz.

[0034] Control Module: An industrial-grade Siemens S7-1200 PLC is used as the control core. The internally stored control program executes the following algorithm: First, bandpass filtering of the four sensor signals from 1 kHz to 10 kHz is applied to effectively remove low-frequency noise interference such as blade vibration. Second, the generalized cross-correlation algorithm GCC-PHAT is used to calculate the time delay estimate (TDOA) between each pair of sensor signals. Third, based on the known sensor spatial coordinates (forming a circle with a diameter of 200 mm) and the speed of sound (c = 340 m / s, 20°C air), the incident direction vector (α, β) of the particle flow is solved using the least squares method. Finally, based on the incident direction vector (α, β) and the preset optimization objective of minimizing impact energy, the formula... The weighting coefficients n1=0.6, n2=0.3, and n3=0.1 were determined through wind tunnel experiments to calculate the target angle that the actuator needs to drive.

[0035] The execution module employs a Panasonic A6 series 400W servo motor paired with an RV-80E planetary reducer, achieving a reduction ratio of 1:50 and an output torque of up to 120 N·m, fully meeting the drive requirements under aerodynamic loads on the blade. The motor is mounted on the internal main beam structure of the blade via a flange, and the output shaft is connected to the rotating shaft of the movable wear-resistant component via a coupling. The motor uses an absolute encoder, achieving a positioning accuracy of 0.1°.

[0036] Movable component 2: An airfoil leading-edge sheath made of a high-toughness tungsten carbide composite material, 15cm wide, with an arc length covering a 120° range of the blade leading edge. It is connected to the blade body structure via two high-strength stainless steel shafts and driven by the actuation module, allowing for precise rotation within a ±30° range, thereby dynamically changing the leading-edge angle of attack.

[0037] Workflow: When a stream of sand particles impacts the leading edge of a wind turbine blade, an array of vibration sensors positioned at the front captures the impact signal. This signal is digitized by a data acquisition card and transmitted to the PLC. The PLC's internal control program runs the TDOA algorithm in real time to calculate the main incident direction of the sand flow (e.g., α=15.3°, β=8.7°). Subsequently, the decision algorithm calculates the optimal target angle (e.g., θ=5.2°) based on the current sheath angle. =12.5°). The control signal drives the servo motor to rotate (corresponding to an angle difference of 7.3°), which in turn drives the sheath to precisely adjust to the target angle through the reducer and transmission mechanism, ensuring that the leading edge always faces the particle flow at the optimal angle, thereby minimizing impact energy. The system continuously runs the above-mentioned perception-decision-execution closed-loop control at a frequency of 100 Hz, thereby achieving high-precision dynamic adaptive adjustment and effectively coping with rapid changes in wind speed and direction.

[0038] This invention represents a technological leap from traditional "passive wear resistance" to "active avoidance." Wind tunnel experiments have verified that, in typical wind and sand environments, it can significantly reduce the blade leading edge wear rate by more than 70%, greatly extending the maintenance cycle and service life of wind turbine generators in harsh environments.

[0039] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0040] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A dynamic adaptive wear-resistant system based on behavioral biomimicry, characterized in that, include: The sensing module is used to collect impact signals from particulate flow impact protection equipment in real time; The control module receives the impact signal and calculates the target action of the protection device; The execution module adjusts the protection device according to the target action.

2. A dynamic adaptive wear resistance method based on behavioral biomimicry, characterized in that, include: The impact signal of the particle flow impact protection device is collected in real time through a sensor array. Calculate the incident direction vector of the current particle flow based on the impact signal; The incident direction vector is compared with the current angle of the movable part in the protective device to obtain the target action of the movable part adjustment; The movable component is adjusted according to the target action to achieve adaptive wear resistance adjustment.

3. The dynamic adaptive wear resistance method based on behavioral biomimicry according to claim 2, characterized in that, The sensor array is a vibration sensor array or an acoustic emission sensor array.

4. The dynamic adaptive wear resistance method based on behavioral biomimicry according to claim 2, characterized in that, The step of calculating the incident direction vector of the current particle flow based on the impact signal includes: The time difference between the signals of each sensor in the sensor array is calculated using the cross-correlation function algorithm GCC-PHAT, and the expression is as follows: ； In the formula, For GCC-PHAT, the time delay τ corresponding to the peak value of the output is the time difference between sensor signals; and The signal spectrum between the two sensors; for The conjugate of complex numbers; It is a complex exponential function; j is the imaginary unit; Obtain the coordinates of each sensor and the time difference, and calculate the incident direction vector, including the following steps: Construct a system of nonlinear equations, expressed as: ； ； ； In the formula, c is the speed of sound; , and The time difference between each sensor; the coordinates of each sensor are S1( , , S2( , , S3 , , S4 , , ); The incident direction vector is obtained by solving the nonlinear equations using the least squares method.

5. The dynamic adaptive wear resistance method based on behavioral biomimicry according to claim 2, characterized in that, The step of comparing the incident direction vector with the current angle of the movable component in the protective device to obtain the target action of the movable component adjustment includes: The expression for calculating the target action is: ； In the formula, For the target action; The current angle of the movable part; , and Here are the weighting coefficients; the incident direction vector is... .

6. The dynamic adaptive wear resistance method based on behavioral biomimicry according to claim 2, characterized in that, The step of comparing the incident direction vector with the current angle of the movable component in the protective device to obtain the target action of the movable component adjustment includes: Based on the incident direction vector and the current angle of the movable part, the target motion is calculated as follows: ； ； ； ； In the formula, For the target action; The current angle of the movable part; For function Minimum The possible values ​​of ; V is the particle flow density; v is the particle flow velocity; A is the impact area. The impact wear coefficient; The impact angle is; the incident direction vector is... ; Impact wear coefficient It consists of vertical component coefficients and tangential component coefficients, and its expression is: ； In the formula, , and These are the weighting coefficients.

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