A method and system for real-time safety performance evaluation of unmanned aerial vehicle (UAV) electric actuators

Abstract

This invention discloses a real-time safety performance evaluation method and system for unmanned aerial vehicle (UAV) electric actuators, belonging to the field of UAV safety analysis and evaluation technology. The method includes: defining a generalized displacement vector and establishing a kinematic matrix based on multi-source data; constructing a unified dynamic equation by combining external disturbance loads and equivalent external loads of the actuator; defining a state vector based on the unified dynamic equation and constructing a deviation state equation for discrete solution; calculating the theoretical minimum actuator benchmark and forming a capability degradation index; defining the working condition and performing simulation analysis based on a 3D finite element model; outputting the equivalent principal stress of the critical point and defining a window to calculate the worst-case margin and select the most critical point; extracting the stress sequence of the most critical point; using a four-point method for rainflow counting to identify stress closed loops; calculating the stress amplitude; dividing the stress into bins; outputting damage increments; and updating the cumulative total damage. This invention achieves coupled characterization of actuator behavior and stress state under complex working conditions, improving the accuracy of state solution and degradation identification.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) safety analysis and assessment technology, and in particular to a method and system for real-time safety performance assessment of UAV electric actuators. Background Technology

[0002] With the large-scale application of UAVs in aerial surveying, energy inspection, logistics transportation, and complex mission flights, flight control systems have placed higher demands on the response accuracy, load-bearing capacity, and operational reliability of electric actuators. As a key execution unit in the attitude control, control surface drive, and mission execution chain, the performance status of electric actuators directly affects flight stability, handling quality, and flight safety. Existing related technologies have gradually evolved from traditional mechanical strength verification based on experience margins to a comprehensive evaluation model that integrates sensor monitoring, state estimation, fault diagnosis, and life prediction. However, existing UAV electric actuator evaluation technologies still have significant limitations. Existing methods mostly focus on identifying position deviations, current anomalies, temperature rise changes, or single fault symptoms, emphasizing control performance monitoring, while insufficiently considering the coupling relationship between external loads and internal structural stress responses. It is difficult to construct a closed-loop analysis chain from flight conditions to actuation loads and then to stress evolution at structural critical points, thus failing to meet the application requirements of UAV electric actuators in high-reliability, lightweight, and real-time safety evaluation scenarios. Summary of the Invention

[0003] In view of the aforementioned existing problems, the present invention is proposed.

[0004] Therefore, this invention provides a method and system for real-time safety performance evaluation of UAV electric actuators, which solves the problem that existing technologies do not adequately consider the coupling relationship between external loads and internal structural stress responses, and are difficult to construct a closed-loop analysis link from flight conditions to actuation loads and stress evolution at structural critical points.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a method for real-time safety performance evaluation of unmanned aerial vehicle (UAV) electric actuators, comprising,

[0007] Collect multi-source data from the electric actuators of UAVs and establish a 3D finite element model to define a set of hazard points;

[0008] Calculate the external disturbance load and equivalent external load of the UAV based on multi-source data of the UAV's electric actuator;

[0009] Based on multi-source data, a generalized displacement vector is defined and a kinematic matrix is ​​established. A unified dynamic equation is constructed by combining external disturbance loads and motion equivalent external loads.

[0010] Define the state vector based on the unified dynamic equation and construct the deviation state equation for discrete solution, calculate the theoretical minimum action momentum benchmark and form the capability degradation index;

[0011] Define the working conditions and perform simulation analysis based on the 3D finite element model. Output the equivalent principal stress of the critical point and delineate the window to calculate the worst-case margin and select the most critical point.

[0012] The stress sequence of the most dangerous point is extracted, and the four-point rainflow counting method is used to identify the stress closed loop. The stress amplitude is calculated and the damage increment is output in bins to update the cumulative total damage.

[0013] The overall health score is calculated based on total damage and capability degradation indicators, and drones are classified and given early warnings.

[0014] As a preferred embodiment of the real-time safety performance evaluation method for UAV electric actuators described in this invention, the following steps are included: collecting multi-source data from the UAV electric actuator and establishing a 3D finite element model to define a set of hazard points: collecting multi-source signal data from the UAV electric actuator, aligning the multi-source signal data in time, and dividing the data into fixed time grids. ;

[0015] A 3D finite element model is established based on the electric actuator parameters, and a set of hazard points is defined based on the 3D finite element model. Among them, dangerous points It covers weak points in the transmission chain, including gear tooth roots, shaft shoulder fillets, lead screw roots, bearing housing transitions, housing welds, and bolt hole edges.

[0016] As a preferred embodiment of the real-time safety performance evaluation method for UAV electric actuators described in this invention, the method includes: calculating the external disturbance load and equivalent external load index of the UAV based on multi-source data from the UAV electric actuator; converting the motion of the UAV control surfaces, including control surface deflection angle, control surface angular velocity, and acceleration, based on multi-source signal data; and simultaneously calculating the dynamic pressure of the UAV. ;

[0017] Based on the motion of the UAV control surfaces and the hinge torque of the UAV dynamic pressure structure ;

[0018] The hinge torque is converted into an equivalent axial force at the output end and an external disturbance load is constructed.

[0019] Calculate the current temperature compensation and motor output torque, map the motor output torque to the lead screw axial force and convert it into an equivalent inelastic stroke.

[0020] Construct the input matrix D and obtain the equivalent external load of the action.

[0021] As a preferred embodiment of the real-time safety performance evaluation method for UAV electric actuators described in this invention, the method includes: defining a generalized displacement vector and establishing a kinematic matrix based on multi-source data, and constructing a unified dynamic equation by combining external disturbance loads and equivalent external loads of the actuator, including:

[0022] Define a generalized displacement vector, calculate the equivalent mass, coupled stiffness, and coupled damping to establish the kinematic matrix, including the mass matrix M, stiffness matrix K, and damping matrix C;

[0023] A unified dynamic equation is constructed by combining the generalized displacement vector and the kinematic matrix;

[0024] The generalized acceleration vector is obtained by discretizing the unified dynamic equation using semi-implicit Euler integrals.

[0025] The generalized velocity vector and generalized displacement vector at the current time k are updated using the generalized acceleration vector.

[0026] As a preferred embodiment of the real-time safety performance evaluation method for UAV electric actuators described in this invention, the method includes: defining a state vector based on a unified dynamic equation and constructing a deviation state equation for discrete solution; calculating the theoretical minimum actuation benchmark and forming a capability degradation index, including:

[0027] The state vector is defined based on the generalized velocity vector and the generalized displacement vector. ;

[0028] Substitute the equivalent external load of the action into the discrete solution process and rewrite it in the form of a first-order state space.

[0029] The static equilibrium point is calculated using the static equilibrium point translation method. and static equivalent travel And define the deviation state equation ;

[0030] The state deviation equation is approximated by forward difference in the time grid to obtain a discrete recursive model;

[0031] Define the discrete cost function J based on the discrete recursive model;

[0032] The discrete Riccati equation is solved by minimizing the discrete cost function J, and the LQR feedback gain matrix is ​​calculated based on the solution of the discrete Riccati equation. ;

[0033] Determining the optimal momentum of the deviation based on the negative value of the feedback gain matrix. And calculate the theoretical minimum action reference. ;

[0034] Capability degradation index is defined based on the theoretical minimum action time benchmark and equivalent inelastic stroke. .

[0035] As a preferred embodiment of the real-time safety performance evaluation method for UAV electric actuators described in this invention, the following steps are taken: defining the working condition and performing simulation analysis based on a 3D finite element model, outputting the equivalent principal stress of the critical point, defining the window, calculating the worst-case margin, and selecting the most critical point. For each set of working conditions, input the condition into a 3D finite element model for simulation analysis, and output the critical point under the m-th working condition. The three-dimensional stress tensor;

[0036] Calculate the principal stresses and define the equivalent principal stresses based on the three-dimensional stress tensor of the critical point;

[0037] Calculate the stress margin at each critical point based on the equivalent principal stress, and calculate the worst-case margin by defining a window based on the stress margin. ;

[0038] If the worst-case margin is less than 0, it is determined that a dangerous stress overshoot has occurred, and the dangerous point corresponding to the worst-case margin is extracted as the most dangerous point.

[0039] As a preferred embodiment of the real-time safety performance evaluation method for UAV electric actuators described in this invention, the following steps are taken: The stress sequence of the most dangerous point is extracted using a four-point rainflow counting method to identify stress closed loops; the stress amplitude is calculated, and the damage increment is updated by binning; the cumulative total damage index is recorded, and the stress sequence of the most dangerous point within the window is recorded; the mean of the stress sequence within the window is calculated; the mean is subtracted from the stress sequence to form a mean-free stress sequence; and a turning point sequence is constructed using a local extremum extraction method. ;

[0040] The four-point method is used to identify stress closed cycles and calculate stress amplitude for the inflection point sequence;

[0041] The stress amplitude is divided into boxes using a fixed stress amplitude division method, and the allowable number of cycles for each box is calculated using a two-segment SN curve. ;

[0042] Calculate the damage increment for each window and recursively obtain the total damage.

[0043] As a preferred embodiment of the real-time safety performance evaluation method for UAV electric actuators described in this invention, the method for calculating comprehensive health based on total damage and capability degradation indicators refers to calculating mechanical fatigue health and actuation capability health based on total damage and capability degradation indicators.

[0044] A comprehensive health score is calculated by geometric fusion of mechanical fatigue health score and operational capability health score. .

[0045] As a preferred embodiment of the real-time safety performance evaluation method for UAV electric actuators described in this invention, the method includes: graded early warning for UAVs.

[0046] worst-case margin Less than or equal to 0 or total damage If the value is greater than or equal to 1, it is judged as a dangerous level, the highest alarm is immediately output and the drone is advised to stop flying;

[0047] If the worst-case margin If the value is greater than 0 and less than the set stress threshold, it is judged as a warning level, and it is recommended that the drone end its mission and return to base.

[0048] worst-case margin When the stress level is greater than or equal to the set stress threshold, the overall health level is considered. To make a judgment, based on the overall health level If the value is less than or equal to the health threshold, a maintenance check of the drone is prompted.

[0049] Secondly, this invention provides a real-time safety performance evaluation system for unmanned aerial vehicle (UAV) electric actuators, comprising,

[0050] The data acquisition module is used to collect multi-source data from the UAV's electric actuators and establish a 3D finite element model to define a set of hazard points;

[0051] The dynamics mapping module is used to calculate the external disturbance load and the equivalent external load of the UAV based on multi-source data of the UAV's electric actuator, define the generalized displacement vector and establish the kinematic matrix based on the multi-source data, and construct a unified dynamic equation by combining the external disturbance load and the equivalent external load of the actuation.

[0052] The state analysis module is used to define state vectors based on the unified dynamic equations and construct deviation state equations for discrete solution, calculate the theoretical minimum action momentum benchmark and form a capability degradation index.

[0053] The damage accumulation module is used to define the working conditions and perform simulation analysis based on the 3D finite element model. It outputs the equivalent principal stress of the critical point, defines the window, calculates the worst-case margin, selects the most critical point, extracts the stress sequence of the most critical point, uses the four-point method rainflow counting to identify the stress closed loop, calculates the stress amplitude, divides the output into boxes, and updates the total accumulated damage.

[0054] The assessment and early warning module is used to calculate the overall health status based on total damage and capability degradation indicators, and to provide graded early warnings for drones.

[0055] The beneficial effects of this invention are as follows: By collecting multi-source data from electric actuators and combining it with a 3D finite element model to define a set of dangerous points, this invention achieves a unified correlation between operational status information and structural weak points, providing a clear object for subsequent safety assessment; by reconstructing external disturbance loads and equivalent external loads of the actuator and establishing a unified dynamic equation, it achieves coupled characterization of actuator behavior and stress state under complex working conditions, improving the accuracy of state solution and degradation identification; by extracting the stress at the most dangerous point, conducting rainflow counting and cumulative damage updates, it achieves simultaneous assessment of instantaneous safety margin and long-term fatigue damage; finally, by combining capability degradation indicators and total damage to calculate comprehensive health, it can achieve graded early warning for UAV electric actuators, thereby improving the comprehensiveness, reliability, and engineering practicality of real-time safety assessment. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0057] Figure 1 This is a flowchart of the real-time safety performance evaluation method for the UAV electric actuator in Example 1.

[0058] Figure 2 This is a structural diagram of the real-time safety performance evaluation system for the UAV electric actuator in Example 1. Detailed Implementation

[0059] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0060] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0061] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0062] Example 1, referring to Figure 1 and Figure 2This is the first embodiment of the present invention, which provides a method for real-time safety performance evaluation of unmanned aerial vehicle (UAV) electric actuators, including the following steps:

[0063] S1. Collect multi-source data from the UAV's electric actuators and establish a 3D finite element model to define a set of hazard points;

[0064] S1.1 Acquire multi-source signal data from the UAV's electric actuators, including commanded displacement. (From flight control commands), output position (From the output encoder), motor angle Motor current ,temperature ,airspeed and vibration acceleration Multi-source signal data is time-aligned and divided into fixed-time grids. ;

[0065] S1.2. Establish a 3D finite element model based on the electric actuator parameters, and define a set of danger points based on the 3D finite element model. Among them, dangerous points Select 8-15 items to cover the weak points of the transmission chain, including gear tooth roots, shaft shoulder fillets, lead screw roots, bearing housing transitions, housing welds, and bolt hole edges;

[0066] Specifically, the 3D finite element model first needs to establish a three-dimensional model of the electric actuator to reflect its structural geometric features. Then, local mapping modeling is performed for each critical point, and the three-dimensional geometric information, boundary constraint parameters, material constitutive parameters, and load input interfaces of each critical point are uniformly deployed in the 3D finite element model, so that the 3D finite element model can solve the structural response for a given working condition input.

[0067] S2. Calculate the external disturbance load and equivalent external load of the UAV based on the multi-source data of the UAV's electric actuator;

[0068] S2.1 Calculate the UAV control surface motion based on multi-source signal data, including control surface deflection angle, angular velocity, and acceleration:

[0069]

[0070] in For the deflection angle of the control surface, This is the rudder angle conversion factor. , The equivalent radius of the corresponding motion mechanism. For rudder angular velocity, For rudder angle acceleration, The output position at time k is the output distance of the electric actuator. This represents the width of the time grid.

[0071] Synchronous calculation of UAV dynamic pressure :

[0072]

[0073] in The density is the air density, calculated based on the ambient temperature. This is the low-speed protection constant. Effective airspeed;

[0074] Based on the motion of the UAV control surfaces and the hinge torque of the UAV dynamic pressure structure :

[0075]

[0076] in where c is the rudder surface area and c is the chord length. Zero offset hinge coefficient, For the deflection derivative, This is the rate derivative;

[0077] Specifically, zero-offset hinge coefficient The preferred value is 0.05, which is on the order of magnitude of the common control surface hinge derivative and can be adjusted by ground wind tunnel / flight fitting, deflection derivative. The preferred value is 0.02, used to capture the additional hinge torque during rapid steering, which can significantly reduce the underestimation of external loads under high-speed maneuvers and the rate derivative. The preferred value is generally close to 0; if there is a balancing bias, it will be solidified into a small amount.

[0078] Convert the hinge torque into an equivalent axial force at the output end and construct an external disturbance load:

[0079]

[0080] in The lever arm is the point of motion. For equivalent axial force, External disturbance load;

[0081] S2.2 Calculate current temperature compensation and motor output torque:

[0082]

[0083] in For current-temperature compensation, The no-load current at the reference temperature. The room temperature is 25 degrees Celsius. This is the temperature drift coefficient. The torque constant of the motor is... This is the output torque of the motor;

[0084] The motor output torque is mapped to the lead screw axial force and converted into an equivalent inelastic stroke:

[0085]

[0086] in For the guide, For lead screw efficiency, For reducer efficiency, The reduction ratio, It provides axial power to the lead screw. For equivalent inelastic travel, This is the equivalent axial stiffness of the actuating segment. , For elastic modulus, For the equivalent cross-sectional area under stress, This is the equivalent stress length;

[0087] Construct the input matrix D and obtain the equivalent external load of the action:

[0088]

[0089] in This is the equivalent external load for the action.

[0090] S3. Define a generalized displacement vector and establish a kinematic matrix based on multi-source data, and construct a unified dynamic equation by combining external disturbance loads and equivalent external loads of motion.

[0091] S3.1 Convert the motor angle into displacement and define the generalized displacement vector:

[0092]

[0093] in To convert motor angles into displacement. The linear displacement at the output end is obtained directly through measurement using the output encoder. It is a generalized displacement vector;

[0094] Calculate the equivalent mass, coupled stiffness, and coupled damping to establish the kinematic matrix, including the mass matrix M, stiffness matrix K, and damping matrix C:

[0095]

[0096] in For the equivalent mass on the motor side, For output-side equivalent mass, This refers to the equivalent coupling stiffness between the motor side and the output side. For equivalent coupling damping;

[0097] It should be noted that the equivalent mass on the motor side By the rotational inertia of the motor side (From motor / gearbox datasheets or CAD quality attributes) Converted to a unique equivalent quality:

[0098]

[0099] Output-side equivalent quality Then, by considering the rotational inertia of the output side... Measure and convert the data;

[0100] Equivalent coupling stiffness between the motor side and the output side This is obtained through a static loading experiment, with the following steps:

[0101] Mount the actuator onto the test bench, connect a tension / compression sensor in series with the output terminal, fix the motor side, and apply a step force to the output terminal. Record steady-state relative displacement Step external force Relative displacement with steady state The ratio is used as the equivalent coupling stiffness ;

[0102] Equivalent Coupled Damping The result is obtained through a free decay experiment, with the following steps:

[0103] After applying and releasing the displacement disturbance at the output, record the relative displacement peak sequence and calculate the damping ratio:

[0104]

[0105] in For logarithmic reduction, and This is a sequence of relative displacement peaks, where n is the number of points in the sequence. The damping ratio;

[0106] Calculate the equivalent coupled damping using the damping ratio. :

[0107]

[0108] in To reduce quality;

[0109] S3.2. Construct a unified dynamic equation by combining the generalized displacement vector and the kinematic matrix:

[0110]

[0111] in For generalized velocity vectors, It is a generalized acceleration vector. This is a preloaded vector, obtained by querying assembly parameters;

[0112] Discretizing the generalized acceleration vector using semi-implicit Euler integrals of the unified dynamic equations:

[0113]

[0114]

[0115] in Let k be the generalized velocity vector at time k-1. Let be the generalized displacement vector at time k-1, with initial values ​​of 0;

[0116] Airborne embedded systems cannot directly solve continuous ODEs; numerical integration must be performed at discrete sampling points. However, if the discrete equation is directly applied at time k, problems will arise. , as well as The implicit equations are coupled, so semi-implicit Euler integrals are used to use the known state of the stiffness and damping terms at the previous time step k-1 to avoid implicit coupling in the current step. For rigid systems, explicit Euler is prone to divergence. Semi-implicit Euler "borrows" the values ​​of the previous time step in damping / stiffness, which is equivalent to introducing slight numerical damping and significantly improving stability. In real-time simulation of aerospace / mechanical actuators, semi-implicit Euler is the standard low-computation choice, similar to a simplified version of Newmark-β commonly used in flight control.

[0117] The generalized velocity vector and generalized displacement vector at the current time k are updated using the generalized acceleration vector.

[0118] S4. Define the state vector based on the unified dynamic equation and construct the deviation state equation for discrete solution, calculate the theoretical minimum action momentum benchmark and form the capability degradation index.

[0119] S4.1 Define the state vector based on the generalized velocity vector and the generalized displacement vector. :

[0120]

[0121] in For the linear velocity at the output end, Speed ​​is calculated from motor angle;

[0122] Substitute the equivalent external load of the action into the discrete solution process and rewrite it in first-order state-space form:

[0123]

[0124]

[0125]

[0126] in This is a first-order state-space equation, where I is the identity matrix. This is the system state matrix, reflecting the dynamic characteristics of the structure itself. The motion input matrix represents how the equivalent inelastic stroke affects the system state. This is the disturbance input matrix, representing how external disturbance loads affect the system state. This is a constant bias term formed by the preloaded vector;

[0127] S4.2 Calculate the static equilibrium point using the static equilibrium point translation method. and static equivalent travel And define the deviation state equation Specifically, the static condition is determined as follows: , , Then the unified dynamic equation under static conditions is:

[0128]

[0129] In the technical solution of this invention, in order to achieve minimum operational balance under healthy conditions, the minimum operational principle for static balance is set as follows: if the preload vector is sufficient to balance the static zero position, then priority is given to taking... ,at this time:

[0130]

[0131]

[0132] in This is the equilibrium point;

[0133] State deviation is defined based on the state equilibrium point. and actuation deviation input :

[0134]

[0135]

[0136] At this time, the deviation state equation for:

[0137]

[0138] The state deviation equation is approximated by forward difference in the time grid to obtain a discrete recursive model:

[0139]

[0140]

[0141] in Let I be the state deviation equation at time k+1, and I be the identity matrix.

[0142] S4.3. Define the discrete cost function J based on the discrete recursive model:

[0143]

[0144] in This is the state weight matrix, used to characterize the degree of penalty for deviant states. , This is a penalty coefficient for the displacement deviation on the motor side. This is a penalty coefficient for the output-side displacement deviation. This is a penalty coefficient for speed deviation on the motor side. This is a penalty coefficient for output-side speed deviation. The input weight matrix is ​​used to characterize the degree of penalty for the momentum. , This is the penalty coefficient for inputting actuation deviation;

[0145] S4.4 Solve the discrete Riccati equation by minimizing the discrete cost function J. Specifically, first solve for the state weight matrix Q and the input weight matrix R by minimizing the discrete cost function, and then define the discrete matrix:

[0146]

[0147] in For discrete state matrices, For discrete input matrices, The discrete perturbation matrix;

[0148] Solve the discrete Riccati equation using the discrete matrix, the state weight matrix Q, and the input weight matrix R:

[0149]

[0150] in This is a stable symmetric positive definite solution to the discrete Riccati equation;

[0151] Calculate the LQR feedback gain matrix by solving the discrete Riccati equation. :

[0152]

[0153] Determining the optimal momentum of the deviation based on the negative value of the feedback gain matrix. And calculate the theoretical minimum action reference. :

[0154]

[0155] At this point, if preferred If it is 0, then ;

[0156] S4.5. Define the capability degradation index based on the theoretical minimum action time benchmark and equivalent inelastic stroke. :

[0157]

[0158] in To prevent small positive numbers with a denominator of zero, a value of 10 is preferred. -6 .

[0159] S5. Define the working condition and perform simulation analysis based on the 3D finite element model. Output the equivalent principal stress of the critical point and delineate the window to calculate the worst-case margin and select the most critical point.

[0160] S5.1 Definition The system consists of several working conditions, each including commanded displacement input, commanded speed input, drive-side torque, external load, and drive parameters. Each working condition is input into a 3D finite element model for simulation analysis, and the output is the critical point under the m-th working condition. The three-dimensional stress tensor:

[0161]

[0162] in For a three-dimensional stress tensor, , as well as These are three-dimensional normal stresses, Let be the shear stress acting along the y direction on the cross section with normal x. and This is also the shear stress in the corresponding direction;

[0163] It should be noted that when performing simulation analysis, the 3D finite element model starts the finite element solution separately for each set of working conditions, calculates the displacement field, strain field and stress field of the critical point structure in the finite element model throughout the working process, outputs the stress simulation results of the whole model, and reads the stress components of each critical point, including three normal stress components and three shear stress components.

[0164] Determine the principal stresses and define the equivalent principal stresses based on the three-dimensional stress tensor of the critical point:

[0165]

[0166]

[0167] in For the maximum principal stress, The intermediate principal stress, The minimum principal stress is obtained by sorting the maximum, intermediate, and minimum principal stresses using three-dimensional normal stress. Equivalent principal stress;

[0168] S5.2 Calculate the stress margin for each critical point based on the equivalent principal stress:

[0169]

[0170] in The upper limit of the allowable stress at the j-th critical point. , For the material's yield strength, This is the reduction factor. The stress margin for the j-th critical point;

[0171] Calculate the worst-case margin by defining a window based on the stress margin:

[0172]

[0173] in As a worst-case scenario margin, The window length is preferably 2 seconds.

[0174] S5.3 If the worst-case margin is less than 0, it is determined that a dangerous stress overshoot has occurred, and the dangerous point corresponding to the worst-case margin is extracted as the most dangerous point.

[0175] S6. Extract the stress sequence of the most dangerous point, use the four-point method rainflow counting to identify the stress closed loop, calculate the stress amplitude, output the damage increment in the box, and update the cumulative total damage.

[0176] S6.1 Record the stress sequence of the most dangerous point within the window. The number of sampling points for the stress sequence within the window is obtained according to the set sampling frequency. Calculate the mean of the stress sequence within the window, subtract the mean from the stress sequence to form the mean-free stress sequence, and construct the inflection point sequence using the local extremum extraction method. Specifically, if the product of the stress difference between the r-th sampling point and the (r-1)-th sampling point and the stress difference between the (r+1)-th sampling point and the r-th sampling point is less than or equal to 0, then the r-th sampling point is taken as a turning point.

[0177] The four-point method is used to identify stress closed loops and calculate stress amplitudes for the inflection point sequence:

[0178] From the turning point sequence Select four consecutive turning points , , as well as Define two stress ranges before and after. and :

[0179]

[0180]

[0181] like Less than or equal to Then the two turning points in the middle and Consider it a closed loop;

[0182] Calculate the stress amplitude of the closed loop:

[0183]

[0184] S6.2. Stress amplitude is divided into boxes using a fixed stress amplitude box:

[0185] Define the width of the stress amplitude sub-box. The preferred value is 5MPa. If the binning is too coarse, the fatigue amplitude resolution will be lost. If the binning is too fine, the burden of airborne storage and table lookup will increase. For the local dangerous point stress level of the UAV electric actuator, the 5MPa level binning can balance accuracy and computing power.

[0186] The box number is determined based on the box division to determine the stress amplitude. :

[0187]

[0188] At this time, the center value of the i-th sub-box is :

[0189]

[0190] S6.3 Calculate the allowable number of cycles for each sub-box using a two-segment SN curve. :

[0191]

[0192] in The reference fatigue point for the material. As a reference inflection point, and These are the fatigue point stress amplitude and the inflection point stress amplitude, respectively. and These correspond to the number of cycles at the reference fatigue point and the number of cycles at the inflection point, respectively. The material's reference fatigue point and reference inflection point can be determined through offline fatigue experiments. and The slope of the two-segment power law;

[0193] Specifically, The preferred value is 3. The preferred value is 5, as fatigue life in the high-stress range typically corresponds to a gentler slope. A value of 3 reflects the characteristic of a sharp but not extremely steep decrease in cycle life under relatively large values. Below the fatigue inflection point, life becomes more sensitive to stress. Take a steeper 5;

[0194] It should also be noted that the reference fatigue point refers to a representative stress amplitude-life data point selected in the high-stress life region during offline fatigue testing. This point is used to solidify the position of the high-stress segment of the two-segment SN curve. The reference inflection point refers to the boundary data point where the slope of the fatigue life changes significantly with the stress amplitude during offline fatigue testing. This point is used as the segment connection point of the two-segment SN curve. When acquiring the reference fatigue point, firstly, multiple constant amplitude stress loading experiments are set up for the material or structural specimens corresponding to the critical details. The number of cycles when the specimen reaches the failure criterion under each stress amplitude is recorded. Then, the cycle life of each level is statistically processed to form a representative stress amplitude-life dataset. Based on this, experimental points with small life dispersion and representativeness in the high-stress region are selected as reference fatigue points. By performing segmented fitting on all experimental data, the turning point that minimizes the fitting error between the two segments is identified, and the stress amplitude-life data point corresponding to this position is solidified as the reference inflection point.

[0195] S6.4 Calculate the damage increment for each window and recursively obtain the total damage:

[0196]

[0197]

[0198] in Let be the damage increment for the t-th window. The number of stress amplitude subdivisions. Let be the number of iterations for the i-th sub-bin in the t-th window. Total damage, This is the sliding window overlap correction factor. , The window update cycle is preferably 0.5 seconds.

[0199] S7. Calculate the overall health status based on total damage and capability degradation indicators, and conduct graded early warning for drones;

[0200] S7.1 Calculate mechanical fatigue health and actuation capacity health based on total damage and capacity degradation indices:

[0201]

[0202]

[0203] in For mechanical fatigue health, The total damage up to time k. For motor function health, The attenuation coefficient;

[0204] A comprehensive health score is calculated by geometric fusion of mechanical fatigue health score and operational capability health score. :

[0205]

[0206] in and These are the fusion coefficients for mechanical fatigue and actuation capability, respectively. 1.

[0207] S7.2, Worst-case margin Less than or equal to 0 or total damage If the value is greater than or equal to 1, it is judged as a dangerous level, the highest alarm is immediately output and the drone is advised to stop flying;

[0208] If the worst-case margin If the value is greater than 0 and less than the set stress threshold, it is judged as a warning level, and it is recommended that the drone end its mission and return to base.

[0209] worst-case margin When the stress level is greater than or equal to the set stress threshold, the overall health level is considered. To make a judgment, based on the overall health level If the value is less than or equal to the health threshold, a maintenance check of the drone is prompted.

[0210] The preferred stress threshold is 0.05 times the minimum allowable stress limit of the danger point, indicating that it is close to the maximum stress tolerance limit. The preferred health threshold is 0.7, indicating that the various components of the UAV actuator have shown significant degradation in mechanical fatigue and actuation capability.

[0211] This embodiment also provides a real-time safety performance evaluation system for unmanned aerial vehicle (UAV) electric actuators, including:

[0212] The data acquisition module is used to collect multi-source data from the UAV's electric actuators and establish a 3D finite element model to define a set of hazard points;

[0213] The dynamics mapping module is used to calculate the external disturbance load and the equivalent external load of the UAV based on multi-source data of the UAV's electric actuator, define the generalized displacement vector and establish the kinematic matrix based on the multi-source data, and construct a unified dynamic equation by combining the external disturbance load and the equivalent external load of the actuation.

[0214] The state analysis module is used to define state vectors based on the unified dynamic equations and construct deviation state equations for discrete solution, calculate the theoretical minimum action momentum benchmark and form a capability degradation index.

[0215] The damage accumulation module is used to define the working conditions and perform simulation analysis based on the 3D finite element model. It outputs the equivalent principal stress of the critical point, defines the window, calculates the worst-case margin, selects the most critical point, extracts the stress sequence of the most critical point, uses the four-point method rainflow counting to identify the stress closed loop, calculates the stress amplitude, divides the output into boxes, and updates the total accumulated damage.

[0216] The assessment and early warning module is used to calculate the overall health status based on total damage and capability degradation indicators, and to provide graded early warnings for drones.

[0217] In summary, this invention achieves a unified correlation between operational status information and structural weak points by collecting multi-source data from electric actuators and defining a set of dangerous points using a 3D finite element model, providing a clear target for subsequent safety assessments. By reconstructing external disturbance loads and equivalent external loads for actuation and establishing unified dynamic equations, it achieves coupled characterization of actuation behavior and stress state under complex working conditions, improving the accuracy of state solution and degradation identification. By extracting the stress at the most dangerous point, conducting rainflow counting, and updating cumulative damage, it achieves simultaneous assessment of instantaneous safety margin and long-term fatigue damage. Finally, by combining capability degradation indicators and total damage to calculate comprehensive health, it can realize graded early warning for UAV electric actuators, thereby improving the comprehensiveness, reliability, and engineering practicality of real-time safety assessments.

[0218] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for real-time safety performance evaluation of unmanned aerial vehicle (UAV) electric actuators, characterized in that: include, Collect multi-source data from the electric actuators of UAVs and establish a 3D finite element model to define a set of hazard points; Calculate the external disturbance load and equivalent external load of the UAV based on multi-source data of the UAV's electric actuator; Based on multi-source data, a generalized displacement vector is defined and a kinematic matrix is ​​established. A unified dynamic equation is constructed by combining external disturbance loads and motion equivalent external loads. Define the state vector based on the unified dynamic equation and construct the deviation state equation for discrete solution, calculate the theoretical minimum action momentum benchmark and form the capability degradation index; Define the working conditions and perform simulation analysis based on the 3D finite element model. Output the equivalent principal stress of the critical point and delineate the window to calculate the worst-case margin and select the most critical point. The stress sequence of the most dangerous point is extracted, and the four-point rainflow counting method is used to identify the stress closed loop. The stress amplitude is calculated and the damage increment is output in bins to update the cumulative total damage. The overall health score is calculated based on total damage and capability degradation indicators, and drones are classified and given early warnings.

2. The real-time safety performance evaluation method for UAV electric actuators as described in claim 1, characterized in that: The process of collecting multi-source data from the UAV's electric actuators and establishing a 3D finite element model to define a set of hazard points refers to collecting multi-source signal data from the UAV's electric actuators, aligning the multi-source signal data in time, and dividing it into fixed time grids. ; A 3D finite element model is established based on the electric actuator parameters, and a set of hazard points is defined based on the 3D finite element model. Among them, dangerous points It covers weak points in the transmission chain, including gear tooth roots, shaft shoulder fillets, lead screw roots, bearing housing transitions, housing welds, and bolt hole edges.

3. The real-time safety performance evaluation method for UAV electric actuators as described in claim 2, characterized in that: The process involves calculating the external disturbance load and equivalent external load of the UAV based on multi-source data from the UAV's electric actuators, converting the UAV's control surface motion (including control surface deflection angle, angular velocity, and acceleration) based on multi-source signal data, and simultaneously calculating the UAV's dynamic pressure. ; Based on the motion of the UAV control surfaces and the hinge torque of the UAV dynamic pressure structure ; The hinge torque is converted into an equivalent axial force at the output end and an external disturbance load is constructed. Calculate the current temperature compensation and motor output torque, map the motor output torque to the lead screw axial force and convert it into an equivalent inelastic stroke. Construct the input matrix D and obtain the equivalent external load of the action.

4. The real-time safety performance evaluation method for UAV electric actuators as described in claim 3, characterized in that: The process of defining a generalized displacement vector and establishing a kinematic matrix based on multi-source data, and constructing a unified dynamic equation by combining external disturbance loads and equivalent external loads of motion, includes: Define a generalized displacement vector, calculate the equivalent mass, coupled stiffness, and coupled damping to establish the kinematic matrix, including the mass matrix M, stiffness matrix K, and damping matrix C; A unified dynamic equation is constructed by combining the generalized displacement vector and the kinematic matrix; The generalized acceleration vector is obtained by discretizing the unified dynamic equation using semi-implicit Euler integrals. The generalized velocity vector and generalized displacement vector at the current time k are updated by using the generalized acceleration vector.

5. The real-time safety performance evaluation method for UAV electric actuators as described in claim 4, characterized in that: The process of defining a state vector based on the unified dynamic equation and constructing a deviation state equation for discrete solution, calculating the theoretical minimum action momentum benchmark, and forming a capability degradation index includes: The state vector is defined based on the generalized velocity vector and the generalized displacement vector. ; Substitute the equivalent external load of the action into the discrete solution process and rewrite it in the form of a first-order state space. The static equilibrium point is calculated using the static equilibrium point translation method. and static equivalent travel And define the deviation state equation ; The state deviation equation is approximated by forward difference in the time grid to obtain a discrete recursive model; Define the discrete cost function J based on the discrete recursive model; The discrete Riccati equation is solved by minimizing the discrete cost function J, and the LQR feedback gain matrix is ​​calculated based on the solution of the discrete Riccati equation. ; Determining the optimal momentum of the deviation based on the negative value of the feedback gain matrix. And calculate the theoretical minimum action reference. ; Capability degradation index is defined based on the theoretical minimum action time benchmark and equivalent inelastic stroke. .

6. The real-time safety performance evaluation method for UAV electric actuators as described in claim 5, characterized in that: The defined working condition is used to perform simulation analysis based on a 3D finite element model, outputting the equivalent principal stress at the critical point, defining a window to calculate the worst-case margin, and selecting the most critical point as defined. For each set of working conditions, input the condition into a 3D finite element model for simulation analysis, and output the critical point under the m-th working condition. The three-dimensional stress tensor; Calculate the principal stresses and define the equivalent principal stresses based on the three-dimensional stress tensor of the critical point; Calculate the stress margin at each critical point based on the equivalent principal stress, and calculate the worst-case margin based on the defined window. ; If the worst-case margin is less than 0, it is determined that a critical stress overshoot has occurred, and the critical point corresponding to the worst-case margin is extracted as the most critical point.

7. The real-time safety performance evaluation method for UAV electric actuators as described in claim 6, characterized in that: The stress sequence for extracting the most dangerous point is identified using a four-point rainflow counting method to identify stress closed loops. The stress amplitude is calculated, and the damage increment is updated by binning. The cumulative total damage index records the stress sequence of the most dangerous point within the window. The mean of the stress sequence within the window is calculated, and the mean is subtracted from the stress sequence to form a mean-free stress sequence. A turning point sequence is constructed using a local extremum extraction method. ; The four-point method is used to identify stress closed loops and calculate stress amplitudes for the inflection point sequence; The stress amplitude is divided into boxes using a fixed stress amplitude division method, and the allowable number of cycles for each box is calculated using a two-segment SN curve. ; Calculate the damage increment for each window and recursively obtain the total damage.

8. The real-time safety performance evaluation method for UAV electric actuators as described in claim 7, characterized in that: The calculation of comprehensive health based on total damage and capacity degradation index refers to the calculation of mechanical fatigue health and operational capacity health based on total damage and capacity degradation index; A comprehensive health score is calculated by geometric fusion of mechanical fatigue health score and operational capability health score. .

9. The real-time safety performance evaluation method for UAV electric actuators as described in claim 8, characterized in that: The tiered early warning system for drones includes: worst-case margin Less than or equal to 0 or total damage If the value is greater than or equal to 1, it is judged as a dangerous level, the highest alarm is immediately output and the drone is advised to stop flying; If the worst-case margin If the value is greater than 0 and less than the set stress threshold, it is judged as a warning level, and it is recommended that the drone end its mission and return to base. worst-case margin When the stress level is greater than or equal to the set stress threshold, the overall health level is considered. To make a judgment, based on the overall health level If the value is less than or equal to the health threshold, a maintenance check of the drone is prompted.

10. A real-time safety performance evaluation system for unmanned aerial vehicle (UAV) electric actuators, based on the real-time safety performance evaluation method for UAV electric actuators according to any one of claims 1 to 9, characterized in that: include, The data acquisition module is used to collect multi-source data from the UAV's electric actuators and establish a 3D finite element model to define a set of hazard points; The dynamics mapping module is used to calculate the external disturbance load and the equivalent external load of the UAV based on multi-source data of the UAV's electric actuator, define the generalized displacement vector and establish the kinematic matrix based on the multi-source data, and construct a unified dynamic equation by combining the external disturbance load and the equivalent external load of the actuation. The state analysis module is used to define state vectors based on the unified dynamic equations and construct deviation state equations for discrete solution, calculate the theoretical minimum action momentum benchmark and form a capability degradation index. The damage accumulation module is used to define the working conditions and perform simulation analysis based on the 3D finite element model. It outputs the equivalent principal stress of the critical point, defines the window, calculates the worst-case margin, selects the most critical point, extracts the stress sequence of the most critical point, uses the four-point method rainflow counting to identify the stress closed loop, calculates the stress amplitude, divides the output into boxes, and updates the total accumulated damage. The assessment and early warning module is used to calculate the overall health status based on total damage and capability degradation indicators, and to provide graded early warnings for drones.

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