Overload detection method and system for wire feeder motor

Abstract

The application discloses a kind of overload detection method and system of wire feeding motor, it is related to the overload detection technical field of motor.It includes the establishment of the electromagnetic-thermal-mechanical stress multi-field coupling model of wire feeding motor, input first input data, calculate magnetic field distortion degree and temperature rise gradient, form composite criterion;Based on composite criterion, analyze current fluctuation characteristics in wire feeding process, extract wire feeding resistance fluctuation spectrum using spectral analysis;According to wire feeding resistance fluctuation spectrum, load dynamic decoupling is carried out, and equivalent friction coefficient is real-time backstepping;The overload state of wire feeding motor is determined by the dynamic change of the composite criterion and the equivalent friction coefficient;Based on the determination result of the overload state, adjust the parameters in the electromagnetic-thermal-mechanical stress multi-field coupling model.The application uses wire feeding resistance fluctuation spectrum to dynamically backstepping friction coefficient, and effectively determines overload state by composite criterion, avoids the problem that single parameter is easily misjudged in traditional overload detection method.

Description

Technical Field

[0001] This invention relates to the field of overload detection technology for electric motors, and in particular to an overload detection method and system for a wire feeding motor. Background Technology

[0002] Wire feed motors are widely used in welding equipment, automated production lines, and additive manufacturing, and their stability and reliability directly affect welding quality and production efficiency. As the manufacturing industry continues to demand higher precision and efficiency in welding processes, the performance and intelligent control level of wire feed motors are also constantly improving. Existing overload detection methods for wire feed motors mainly rely on monitoring single parameters such as current, voltage, or speed, for example, judging overload conditions based on current thresholds or identifying load anomalies based on voltage fluctuations.

[0003] However, these traditional methods struggle to accurately reflect the complex operating conditions during wire feeding, especially under the combined effects of multiple factors such as high-frequency current fluctuations, nonlinear friction, and temperature changes, which can easily lead to misjudgments or omissions. Furthermore, some studies have attempted to obtain load information using mechanical stress sensors, but their practical application is limited by their complex installation, high cost, and significant modifications to the equipment structure. Summary of the Invention

[0004] Given that existing technologies for detecting overload in wire feeding motors still suffer from problems such as low accuracy, poor real-time performance, and high false alarm rate, this invention is proposed.

[0005] Therefore, the problem to be solved by this invention is how to effectively improve the accuracy of overload detection.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides an overload detection method for a wire feeding motor, comprising: establishing an electromagnetic-thermal-mechanical stress multi-field coupling model of the wire feeding motor; inputting first input data; calculating the magnetic field distortion degree and temperature rise gradient to form a composite criterion; the first input data including real-time current, temperature, and stress data of the wire feeding motor; based on the composite criterion, analyzing the current fluctuation characteristics during wire feeding and extracting the wire feeding resistance fluctuation spectrum using spectral analysis; performing dynamic load decoupling based on the wire feeding resistance fluctuation spectrum and back-calculating the equivalent friction coefficient in real time; determining the overload state of the wire feeding motor through the composite criterion and the dynamic change of the equivalent friction coefficient; and adjusting the parameters in the electromagnetic-thermal-mechanical stress multi-field coupling model based on the determination result of the overload state and feeding them back to the step of extracting the wire feeding resistance fluctuation spectrum.

[0008] As a preferred embodiment of the overload detection method for the wire feeding motor described in this invention, the establishment of the electromagnetic-thermal-mechanical stress multi-field coupling model includes: establishing an electromagnetic field model inside the wire feeding motor based on current data using Maxwell's equations, and initializing the electromagnetic field parameters; calculating the heat source generated by electromagnetic loss according to the electromagnetic field model, and establishing a temperature field model through the Fourier heat conduction equation, and initializing the temperature field parameters; establishing a mechanical stress field model based on the temperature field gradient and electromagnetic force using the generalized Hooke's law and thermal stress coupling analysis, and initializing the mechanical stress field parameters; and establishing the electromagnetic-thermal-mechanical stress fields sequentially, and establishing data sharing and parameter transfer relationships between each field to enable coupling relationships between the fields, thus forming a complete multi-field coupling model.

[0009] In a preferred embodiment of the overload detection method for the wire feeding motor described in this invention, the composite criterion is formed by weighting the magnetic field distortion degree and the temperature rise gradient.

[0010] As a preferred embodiment of the overload detection method for the wire feeding motor described in this invention, the step of analyzing the current fluctuation characteristics during wire feeding and extracting the wire feeding resistance fluctuation spectrum using spectral analysis includes: performing spectral analysis based on preprocessed current fluctuation data, calculating the spectral density of the current fluctuation, and obtaining the spectral characteristics of the current fluctuation; selecting spectral features based on the spectral characteristics of the current fluctuation, combined with the information of magnetic field distortion and temperature rise gradient in the composite criterion, and constructing the wire feeding resistance fluctuation spectrum; the spectral feature selection includes: using a feature selection algorithm based on mutual information to calculate the mutual information between magnetic field distortion, temperature rise gradient, and each spectral feature, measuring the dependency relationship, and selecting spectral features; and optimizing and reducing the dimensionality of the selected spectral features.

[0011] As a preferred embodiment of the overload detection method for the wire feeding motor described in this invention, the load dynamic decoupling based on the wire feeding resistance fluctuation spectrum includes the following steps: based on the wire feeding resistance fluctuation spectrum, the spectrum is decomposed using wavelet transform to divide the spectral signal into sub-signals of different scales and frequency bands; the spectral energy of each sub-signal is calculated, and sensitive frequency bands are selected in combination with the wire feeding current fluctuation characteristics; based on the sensitive frequency bands, a dynamic decoupling method based on generalized least squares is used to separate the friction coefficient-related frequency bands and other interference frequency bands to obtain the net friction influence signal.

[0012] As a preferred embodiment of the overload detection method for the wire feeding motor described in this invention, the generation of the equivalent friction coefficient includes: establishing a nonlinear back-calculation model of the equivalent friction coefficient based on the decoupled net friction influence signal, and using the recursive least squares method for parameter identification to dynamically calculate the real-time equivalent friction coefficient.

[0013] As a preferred embodiment of the overload detection method for the wire feeding motor described in this invention, the determination of the overload state of the wire feeding motor includes: constructing a composite criterion model based on the equivalent friction coefficient, combined with the magnetic field distortion degree and the temperature rise gradient, and calculating the composite criterion value. The upper and lower limit thresholds for judgment are set based on the working characteristics of the wire feeding motor and the typical parameter change trends under overload conditions. and .

[0014] In a preferred embodiment of the overload detection method for the wire feeding motor described in this invention, the determination of the overload state of the wire feeding motor further includes: based on a calculated composite criterion value. Based on the set judgment threshold, an overload state is determined: when When the wire feeding motor is in an overload state, it is determined that the motor is overloaded. When the wire feeding motor is in normal condition, it is determined that the motor is in normal condition; when When the wire feeding motor is in a critical state, further analysis is conducted based on the dynamic trend of the equivalent friction coefficient: if the equivalent friction coefficient shows a continuous upward trend and the difference between the composite criterion and the upper limit threshold is less than the difference threshold... If the load is high, an early warning of potential overload risks will be issued; otherwise, the current state will be maintained.

[0015] Secondly, the present invention provides an overload detection system for a wire feeding motor, comprising: a composite criterion calculation module, used to establish an electromagnetic-thermal-mechanical stress multi-field coupling model of the wire feeding motor, inputting first input data, calculating magnetic field distortion and temperature rise gradient, and forming a composite criterion; the first input data includes real-time current, temperature, and stress data of the wire feeding motor; a spectrum analysis module, used to analyze the current fluctuation characteristics during wire feeding based on the composite criterion, and extract the wire feeding resistance fluctuation spectrum using spectrum analysis; a decoupling and friction coefficient back-calculation module, used to perform dynamic load decoupling based on the wire feeding resistance fluctuation spectrum, and back-calculate the equivalent friction coefficient in real time; an overload state determination module, used to determine the overload state of the wire feeding motor through the composite criterion and the dynamic change of the equivalent friction coefficient; and an adjustment and feedback module, used to adjust the parameters in the electromagnetic-thermal-mechanical stress multi-field coupling model based on the overload state determination result, and feed the results back to the wire feeding resistance fluctuation spectrum extraction stage.

[0016] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the overload detection method for the wire feeding motor as described in the first aspect of the present invention.

[0017] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the overload detection method for the wire feeding motor as described in the first aspect of the present invention are implemented.

[0018] The beneficial effects of this invention are as follows: This invention combines a multi-field coupling model with online identification of the friction coefficient to achieve accurate detection and real-time response to the overload state of the wire feeding motor; it uses the fluctuation spectrum of the wire feeding resistance to dynamically back-calculate the friction coefficient, and effectively determines the overload state through composite criteria, avoiding the problem of easy misjudgment of a single parameter in traditional overload detection methods. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0020] Figure 1 This is a flowchart illustrating the overload detection method for the wire feeding motor.

[0021] Figure 2 This is a schematic diagram for determining overload conditions.

[0022] Figure 3 This is a structural diagram of the overload detection system for the wire feeding motor. Detailed Implementation

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Example 1

[0027] Reference Figures 1-3This is the first embodiment of the present invention, which provides an overload detection method for a wire feeding motor, such as... Figure 1 As shown, it includes:

[0028] S1: Establish an electromagnetic-thermal-mechanical stress multi-field coupling model for the wire feeding motor, input the first input data, calculate the magnetic field distortion degree and temperature rise gradient, and form a composite criterion.

[0029] The first input data includes the real-time current, temperature, and stress data of the wire feeding motor.

[0030] After collecting the first input data, the first input data is filtered and normalized to remove noise and unify the data scale, providing a high-quality data foundation for subsequent calculations.

[0031] In one embodiment of this application, an electromagnetic-thermal-mechanical stress multi-field coupling model of the wire feeding motor is established, specifically including the following operations:

[0032] Based on historical input data, an electromagnetic-thermal-mechanical stress multi-field coupling model is established, and the coupling parameters, including electromagnetic field parameters, temperature field parameters and mechanical stress field parameters, are initialized to enable coupling relationships between the fields.

[0033] Based on the coupling parameters of the electromagnetic-thermal-mechanical stress multi-field coupling model, an adaptive algorithm is used to calculate the coupling coefficient, dynamically adjusting the mutual influence between the electromagnetic field, temperature field, and mechanical stress field to ensure the accuracy and stability of the multi-field coupling model.

[0034] Specifically, based on current data, an electromagnetic field model of the wire feeding motor is established using Maxwell's equations, and electromagnetic field parameters, including permeability, eddy current loss coefficient, and current density distribution, are initialized.

[0035] Based on the electromagnetic field model, the heat source generated by electromagnetic loss is calculated, and a temperature field model is established through the Fourier heat conduction equation. Temperature field parameters are initialized, including thermal conductivity, heat capacity and heat dissipation boundary conditions.

[0036] Based on the temperature field gradient and electromagnetic force, a mechanical stress field model is established using the generalized Hooke's law and thermal stress coupling analysis, and the mechanical stress field parameters, including elastic modulus, Poisson's ratio and thermal expansion coefficient, are initialized.

[0037] By establishing the electromagnetic-thermal-mechanical stress fields sequentially and establishing data sharing and parameter transfer relationships between each field, a complete multi-field coupling model is formed, enabling coupling relationships between the fields.

[0038] Furthermore, the calculation process for the coupling coefficient includes the following steps:

[0039] Sensitivity analysis was performed on electromagnetic field parameters, temperature field parameters, and mechanical stress field parameters to determine the influence weight of each parameter on the coupling effect. The initial values ​​of the parameters were adaptively adjusted using the gradient descent algorithm. The calculation formula is as follows:

[0040]

[0041]

[0042] in, Coupling parameters For state change Sensitivity State variables in the electromagnetic-thermal-mechanical stress multi-field coupling model The gradient of change, Coupling parameters The minute change.

[0043] In the above process, the sensitivity of each coupling parameter to the changes in the state of multiple fields is calculated to obtain the parameter sensitivity, which can be used for adaptive adjustment of the coupling coefficient in subsequent steps. It can be seen that the above calculation process improves the traditional local sensitivity analysis method by introducing the gradient of parameter change and the gradient of state change, which can reflect the dynamic influence of each parameter on different state variables and effectively improve the dynamic response capability of the coupled model.

[0044] An adaptive algorithm is used to dynamically calculate the coupling coefficient between the electromagnetic field, temperature field, and mechanical stress field based on the sensitivity analysis results. The coupling coefficient is updated according to the real-time changes in the operating state of the wire feeding motor. The calculation formula is as follows:

[0045]

[0046] in, This is the coupling coefficient between the electromagnetic field, temperature field, and mechanical stress field. The coupling coefficient from the previous iteration. The adaptive learning rate is used to control the update magnitude of the coupling coefficients. The state change quantity obtained by experimental measurement. The state change quantity calculated for the model.

[0047] In the above process, the formula is based on the results of parameter sensitivity analysis and adopts an adaptive incremental adjustment strategy to dynamically update the coupling coefficient. By integrating the adaptive learning rate and sensitivity and introducing a state difference feedback mechanism, the dynamic optimization and adaptive adjustment of the coupling coefficient are realized, overcoming the error accumulation problem caused by the fixed or linearized coupling coefficient in the traditional model.

[0048] Considering the nonlinear coupling effect between fields, a generalized regression neural network (GRNN) is used to establish nonlinear coupling relationships, and an adaptive algorithm is used to update the network weights online, so that the coupling model has nonlinear adaptive adjustment capabilities.

[0049] A preferred approach is to use a generalized regressive neural network (GRNN) to establish nonlinear coupling relationships, including:

[0050]

[0051]

[0052] in, State variables in a multi-field coupled model of magnetic-thermal-mechanical stress The total state variables, Coupling parameters The non-linear activation function is in the form of the sigmoid function. The slope parameter of the sigmoid function controls the nonlinearity of the activation function. Here, N represents the model error term, and N is the sum of parameters for all coupled fields.

[0053] In the above process, through three steps—sensitivity analysis, dynamic coupling coefficient calculation, and nonlinear coupling relationship modeling—adaptive adjustment and nonlinear regulation of coupling parameters in the multi-field coupling model were achieved, overcoming the error accumulation problem caused by fixed or linearized coupling coefficients in traditional models.

[0054] Preferably, based on the electromagnetic-thermal-mechanical stress multi-field coupling model and adaptive coupling coefficient, the magnetic field distortion degree and temperature rise gradient are calculated, and a composite criterion is constructed through logical operations, including the following steps:

[0055] Based on the obtained current, temperature, and stress, the electromagnetic-thermal-mechanical stress multi-field coupling model is applied to calculate the magnetic field strength, temperature gradient, and mechanical stress distribution.

[0056] Calculate the degree of magnetic field distortion and temperature gradient :

[0057] Among them, magnetic field distortion The calculation formula is as follows:

[0058]

[0059] in, For measured values, This is the ideal value;

[0060]

[0061] in, The change in temperature It represents the corresponding spatial distance; the temperature gradient is used to assess the rate of change of the thermal field, thereby predicting the potential risks of overload and thermal damage.

[0062] A better approach is to dynamically adjust the weights of magnetic field distortion and temperature rise gradient based on the adaptive coupling coefficient, ensuring that both contribute reasonably to overload detection.

[0063] Finally, a composite criterion is constructed through logical operations. Based on the relationship between magnetic field distortion and temperature gradient, an overload determination result is generated:

[0064]

[0065] This composite criterion not only considers the influence of various physical fields, but also adapts to different operating conditions and load conditions.

[0066] S2: Based on the composite criterion, analyze the current fluctuation characteristics during the wire feeding process, and extract the wire feeding resistance fluctuation spectrum using spectrum analysis.

[0067] Preferably, the wire feeding resistance fluctuation spectrum is used as the input parameter for online identification of the friction coefficient.

[0068] Optionally, by acquiring the current fluctuation data of the wire feeding motor in real time, the acquired current fluctuation data can be preprocessed using an adaptive noise filtering algorithm to filter out environmental noise and high-frequency interference, ensuring the accuracy of subsequent spectrum analysis.

[0069] Among them, the adaptive noise filtering algorithm adaptively adjusts the filtering parameters according to the time-frequency characteristics of the current fluctuation data to improve the noise suppression effect and the retention of fluctuation characteristics.

[0070] In this embodiment, in order to more accurately extract the spectral characteristics of current fluctuations during wire feeding, this step performs spectral analysis based on the preprocessed current fluctuation data, calculates the spectral density of the current fluctuations, and obtains the main frequency components and their amplitudes. Specifically, this includes the following operations:

[0071] First, the preprocessed current fluctuation data is subjected to spectral analysis using short-time Fourier transform to calculate the spectral density of the current fluctuation. In order to achieve a balance between time and frequency resolution, an adaptive window function is introduced in this embodiment, that is, the window length and overlap rate of the STFT are adaptively adjusted according to the frequency distribution characteristics of the current fluctuation.

[0072] When frequency components are concentrated in the lower frequency band, a longer window length is used to obtain higher frequency resolution; when frequency components are distributed in the higher frequency band, a shorter window length is used to improve time resolution, thereby better capturing transient fluctuation characteristics.

[0073] In addition, to further improve the accuracy of spectral density calculation, this step introduces a multi-window function superposition strategy, that is, to calculate the spectral density by superimposing multiple window functions of different types (such as Hanning window, Hamming window and Blackman window) and taking a weighted average value, so as to reduce spectral leakage effect and fluctuation error.

[0074] Based on the spectral density results, the main frequency components and their amplitudes of the current fluctuations are extracted to form the spectral characteristics of the current fluctuations.

[0075] In this embodiment, peak detection of the spectral density is performed by setting an adaptive threshold, and the frequency and amplitude corresponding to each peak are extracted. The adaptive threshold is set based on the fundamental frequency and harmonic characteristics of the current fluctuation data, which makes peak detection more sensitive and the false detection rate lower.

[0076] It is worth noting that the spectral features extracted in this step not only include the main frequency components and their amplitudes, but also key information such as spectral energy distribution, harmonic characteristics, and phase difference, so as to provide a richer data foundation for constructing the wire feeding resistance fluctuation spectrum in subsequent steps.

[0077] In this embodiment, to more accurately construct the wire feeding resistance fluctuation spectrum, this step selects spectral features based on the spectral characteristics of the current fluctuation, combined with information on magnetic field distortion and temperature rise gradient in the composite criterion, to eliminate environmental interference and irrelevant frequency components, ultimately forming the wire feeding resistance fluctuation spectrum. Specifically, this includes the following operations:

[0078] Based on the spectral characteristics of the current fluctuations, and combined with the information on magnetic field distortion and temperature rise gradient in the composite criteria, spectral feature screening is performed.

[0079] In this embodiment, a feature selection algorithm based on mutual information is used to calculate the mutual information between magnetic field distortion, temperature rise gradient and each spectral feature to measure their dependence and thus screen out spectral features that are strongly correlated with wire feeding resistance fluctuations.

[0080] It is worth noting that, in order to improve the accuracy and robustness of feature selection, this step introduces a recursive feature elimination (RFE) strategy, which gradually removes frequency components that have little impact on spectral features and dynamically updates the mutual information calculation, ultimately retaining the spectral features most relevant to wire feed resistance fluctuations.

[0081] In addition, to avoid feature misselection caused by fluctuations in magnetic field distortion and temperature gradient data, this step introduces a multi-scale feature selection method, which calculates mutual information and performs weighted averaging under different time windows and frequency scales to improve the stability and accuracy of feature selection.

[0082] To further improve the recognizability and computational efficiency of the selected spectral features, this step optimizes and reduces the dimensionality of the spectral features.

[0083] In this embodiment, the adaptive principal component analysis (APCA) method is used to reduce the dimensionality of the spectral features. That is, based on the variance contribution rate of each spectral feature, the number of principal components is adaptively determined, and the high-dimensional features are linearly mapped and reduced in dimensionality to retain the main information while reducing redundant features.

[0084] Finally, based on the optimized and dimensionality-reduced spectral characteristics, the wire feeding resistance fluctuation spectrum is constructed.

[0085] As can be seen, this invention classifies spectral features according to different frequency bands and amplitudes, and uses an adaptive weighting algorithm to calculate the contribution of spectral components in each frequency band, ultimately constructing a spectral model closely related to the fluctuation of wire feeding resistance.

[0086] In addition, to further improve the accuracy of the fluctuation spectrum, this step introduces a multi-channel spectrum fusion strategy, which involves fusing the spectral characteristics of different measurement channels to eliminate the influence of single-channel measurement errors and environmental interference, thereby constructing a more accurate wire feed resistance fluctuation spectrum.

[0087] S3: Based on the aforementioned wire feeding resistance fluctuation spectrum, perform dynamic load decoupling and back-calculate the equivalent friction coefficient in real time.

[0088] S3.1: Based on the aforementioned wire feeding resistance fluctuation spectrum, separate the friction influencing factors and extract the sensitive frequency band of the equivalent friction coefficient.

[0089] In this embodiment, in order to more accurately deduce the equivalent friction coefficient, this step separates the friction influencing factors based on the wire feeding resistance fluctuation spectrum and extracts the sensitive frequency band of the equivalent friction coefficient, specifically including the following operations:

[0090] First, based on the aforementioned wire feeding resistance fluctuation spectrum, wavelet transform is used to decompose the spectrum, dividing the spectral signal into sub-signals of different scales and frequency bands, in order to further analyze the correlation between different frequency bands and the friction coefficient.

[0091] Next, the spectral energy of each sub-signal is calculated, and sensitive frequency bands highly correlated with the friction coefficient are selected by combining the characteristics of wire feeding current fluctuation.

[0092] For example, spectral energy can be calculated through the following process:

[0093] Perform a Fourier transform on each sub-signal to obtain its spectral representation; calculate the square of the spectral amplitude and integrate over the entire spectral range to obtain the spectral energy of the sub-signal.

[0094] Correlation analysis was performed on the spectral energy of each sub-signal and the characteristics of wire feeding current fluctuations to screen out sensitive frequency bands that are highly correlated with the friction coefficient; the correlation between spectral energy and current fluctuation characteristics was calculated using the Pearson correlation coefficient, and frequency bands with high correlation coefficients were screened out.

[0095] S3.2: Based on the aforementioned sensitive frequency band, perform dynamic load decoupling and establish an equivalent friction coefficient back-calculation model.

[0096] S3.2.1: Based on the aforementioned sensitive frequency band, a dynamic decoupling method based on generalized least squares is used to separate the friction coefficient-related frequency band from other interference frequency bands to obtain the net friction influence signal.

[0097] S3.2.2: During the decoupling process, the influence coefficients of different frequency bands are dynamically adjusted by combining the current fluctuation characteristics and mechanical stress state using the generalized least squares method, so as to improve the accuracy and stability of decoupling.

[0098] Optionally, a Kalman filter can be used to smooth the decoupling results.

[0099] S3.2.3: Based on the net friction influence signal after decoupling, a nonlinear back-calculation model of the equivalent friction coefficient is established, and the recursive least squares method is used for parameter identification to dynamically calculate the real-time equivalent friction coefficient.

[0100] Existing methods for calculating the friction coefficient are susceptible to interference from multiple factors, such as current fluctuations and mechanical stress, under non-stationary operating conditions, resulting in low calculation accuracy and difficulty in reflecting the dynamic changes of the friction coefficient in real time. This invention, through sensitive frequency band screening, dynamic decoupling using the generalized least squares method, and parameter identification using the recursive least squares method, not only effectively separates the friction-related frequency bands from the interference frequency bands, but also improves the accuracy and stability of decoupling by dynamically adjusting the influence coefficients, thereby obtaining a more accurate and real-time updated equivalent friction coefficient. Compared with traditional frequency domain filtering, this scheme can dynamically adapt to different operating conditions with complex current fluctuation characteristics and multiple interference frequency bands, reducing error accumulation and making the friction coefficient calculation more stable and reliable.

[0101] Optionally, the equivalent friction coefficient is substituted into the electromagnetic-thermal-mechanical stress multi-field coupling model to update the friction parameters in the mechanical stress state, making the multi-field coupling model closer to the actual situation. Based on the updated friction parameters, the mechanical stress state, including tangential stress and normal stress, is recalculated, and the calculation results are fed back to the optimization and adjustment process in S5 to achieve dynamic compensation and adjustment of friction factors during wire feeding.

[0102] S4: Determine the overload state of the wire feeding motor by using the composite criterion and the dynamic change of the equivalent friction coefficient.

[0103] Preferably, when the combination of magnetic field distortion, temperature rise gradient and equivalent friction coefficient exceeds a set threshold, the wire feeding motor is determined to be in an overload state.

[0104] Specifically, based on the equivalent friction coefficient obtained in S3, and combined with the magnetic field distortion and temperature rise gradient obtained in S2, a composite criterion model is constructed. The construction of the composite criterion model is based on a weighted combination of the magnetic field distortion, temperature rise gradient, and equivalent friction coefficient.

[0105] Based on the working characteristics of the wire feeding motor and the typical parameter change trends under overload conditions, a judgment threshold is set.

[0106] Preferably, the judgment threshold can be adaptively updated based on the motor's historical operating data and fault records through statistical analysis or machine learning methods, thereby further improving the accuracy and robustness of the judgment.

[0107] Optionally, to avoid false positives, upper and lower threshold values ​​can be set. and .

[0108] Furthermore, based on the calculated composite criterion value Based on the set threshold, an overload state is determined, such as... Figure 2 As shown:

[0109] when When this occurs, the wire feeding motor is determined to be in an overload state;

[0110] when At this time, it is determined that the wire feeding motor is in normal condition;

[0111] when When the wire feeding motor is in a critical state, further analysis is conducted based on the dynamic trend of the equivalent friction coefficient: if the equivalent friction coefficient shows a continuous upward trend and the difference between the composite criterion and the upper limit threshold is less than the difference threshold... If the load is high, an early warning of potential overload risks will be issued; otherwise, the current state will be maintained.

[0112] By dynamically determining the status, not only is the accuracy of overload condition identification improved, but early warnings can also be given and misjudgments and delayed responses can be avoided.

[0113] S5: Based on the determination result of the overload state, adjust the parameters in the electromagnetic-thermal-mechanical stress multi-field coupling model and feed them back to the wire feeding resistance fluctuation spectrum extraction stage to optimize the accuracy and response speed of subsequent overload detection.

[0114] Upon determining the overload state, a corresponding parameter adjustment strategy is generated, including parameters from multiple dimensions such as electromagnetic, thermal, and mechanical stress. The parameter adjustment strategy includes the following:

[0115] Based on the magnetic field distortion under overload conditions, the electromagnetic coupling coefficient is dynamically adjusted to reduce the impact of electromagnetic interference on the fluctuation spectrum; based on the change of temperature rise gradient, the thermal conductivity coefficient and temperature compensation coefficient are corrected to more accurately reflect the influence of temperature on mechanical stress; combined with the dynamic change of the equivalent friction coefficient, the friction attenuation coefficient and elastic modulus are updated to optimize the calculation accuracy of mechanical stress state.

[0116] The key parameters such as electromagnetic coupling coefficient, thermal conductivity coefficient, friction attenuation coefficient and elastic modulus are corrected in real time according to the parameter adjustment strategy. The updated parameters are used to recalculate the mechanical stress state and are compared with the calculation results of the previous cycle to verify the update effect.

[0117] In one embodiment of the present invention, the parameters in the updated electromagnetic-thermal-mechanical stress multi-field coupling model are fed back to the wire feeding resistance fluctuation spectrum extraction step in step S2:

[0118] Based on the updated parameters, the resistance fluctuation spectrum is recalculated to more accurately reflect the actual operating state of the wire feeding motor. A secondary screening of the spectrum energy concentration and sensitive frequency bands is performed to eliminate interference bands introduced by parameter errors, thereby improving the accuracy of the spectrum analysis. During the feedback process, an incremental update method is used to reduce interference in the spectrum extraction stage, and the accuracy and response speed of overload detection are continuously optimized through iterative iteration. Simultaneously, the spectrum extraction results before and after feedback are compared; if the differences are significant, a parameter adjustment strategy is triggered to regenerate the parameters for further optimization.

[0119] Furthermore, this embodiment also provides an overload detection system for a wire feeding motor, including,

[0120] The composite criterion calculation module is used to establish a multi-field coupled electromagnetic-thermal-mechanical stress model of the wire feeding motor. It inputs the first input data, calculates the magnetic field distortion degree and temperature rise gradient, and forms a composite criterion. The first input data includes the real-time current, temperature and stress data of the wire feeding motor.

[0121] The spectrum analysis module is used to analyze the current fluctuation characteristics during wire feeding based on the composite criterion, and to extract the wire feeding resistance fluctuation spectrum using spectrum analysis.

[0122] The decoupling and friction coefficient back-calculation module is used to perform dynamic load decoupling based on the wire feeding resistance fluctuation spectrum and back-calculate the equivalent friction coefficient in real time.

[0123] The overload condition determination module is used to determine the overload condition of the wire feeding motor by means of the composite criterion and the dynamic change of the equivalent friction coefficient.

[0124] The adjustment and feedback module is used to adjust the parameters in the electromagnetic-thermal-mechanical stress multi-field coupling model based on the determination result of the overload state, and feed the results back to the stage of extracting the wire feeding resistance fluctuation spectrum.

[0125] This embodiment also provides a computer device applicable to the overload detection method of a wire feeding motor, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the overload detection method of the wire feeding motor as proposed in the above embodiment.

[0126] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0127] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the overload detection method for the wire feeding motor as proposed in the above embodiments.

[0128] In summary, this invention achieves accurate detection and real-time response to the overload state of the wire feeding motor by combining a multi-field coupling model with online identification of the friction coefficient; it uses the fluctuation spectrum of the wire feeding resistance to dynamically back-calculate the friction coefficient and effectively determines the overload state through composite criteria, thus avoiding the problem of easy misjudgment of a single parameter in traditional overload detection methods.

[0129] 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. An overload detection method for a wire feeding motor, characterized in that: include: An electromagnetic-thermal-mechanical stress multi-field coupling model of the wire feeding motor is established. The first input data is input, the magnetic field distortion degree and temperature rise gradient are calculated, and a composite criterion is formed. The first input data includes the real-time current, temperature, and stress data of the wire feeding motor; Based on the composite criterion, the current fluctuation characteristics during the wire feeding process are analyzed, and the wire feeding resistance fluctuation spectrum is extracted using spectrum analysis. Based on the aforementioned wire feeding resistance fluctuation spectrum, load dynamic decoupling is performed, and the equivalent friction coefficient is calculated in real time. The overload state of the wire feeding motor is determined by the composite criterion and the dynamic change of the equivalent friction coefficient. Based on the determination result of the overload state, the parameters in the electromagnetic-thermal-mechanical stress multi-field coupling model are adjusted and fed back to the stage of extracting the wire feeding resistance fluctuation spectrum. The analysis of current fluctuation characteristics during wire feeding and the extraction of wire feeding resistance fluctuation spectrum using spectral analysis include: performing spectral analysis on preprocessed current fluctuation data to calculate the spectral density of current fluctuations and obtain the spectral characteristics of current fluctuations; selecting spectral features based on the spectral characteristics of current fluctuations, combined with information on magnetic field distortion and temperature rise gradient in the composite criterion, to construct the wire feeding resistance fluctuation spectrum; the spectral feature selection includes: using a feature selection algorithm based on mutual information to calculate the mutual information between magnetic field distortion, temperature rise gradient, and each spectral feature, measuring the dependency relationship, and selecting spectral features; and optimizing and reducing the dimensionality of the selected spectral features. Based on the aforementioned wire feeding resistance fluctuation spectrum, the load dynamic decoupling includes the following steps: Based on the aforementioned wire feeding resistance fluctuation spectrum, the spectrum is decomposed using wavelet transform, dividing the spectral signal into sub-signals of different scales and frequency bands; the spectral energy of each sub-signal is calculated, and sensitive frequency bands are selected by combining the characteristics of the wire feeding current fluctuation; based on the sensitive frequency bands, a dynamic decoupling method based on generalized least squares is used to separate the friction coefficient-related frequency bands from other interference frequency bands, obtaining the net friction influence signal; The generation of the equivalent friction coefficient includes: establishing a nonlinear back-calculation model of the equivalent friction coefficient based on the decoupled net friction influence signal, and using the recursive least squares method for parameter identification to dynamically calculate the real-time equivalent friction coefficient.

2. The overload detection method for the wire feeding motor as described in claim 1, characterized in that: The establishment of the electromagnetic-thermal-mechanical stress multi-field coupling model includes: Based on current data, an electromagnetic field model of the wire feeding motor was established using Maxwell's equations, and the electromagnetic field parameters were initialized. Based on the electromagnetic field model, the heat source generated by electromagnetic loss is calculated, and a temperature field model is established through the Fourier heat conduction equation to initialize the temperature field parameters. Based on the temperature field gradient and electromagnetic force, a mechanical stress field model is established using the generalized Hooke's law and thermal stress coupling analysis, and the mechanical stress field parameters are initialized. By establishing the electromagnetic-thermal-mechanical stress fields sequentially and establishing data sharing and parameter transfer relationships between each field, a complete multi-field coupling model is formed, enabling coupling relationships between the fields.

3. The overload detection method for the wire feeding motor as described in claim 2, characterized in that: The composite criterion is formed by weighting the magnetic field distortion degree and the temperature rise gradient.

4. The overload detection method for the wire feeding motor as described in claim 1, characterized in that: The determination of the overload state of the wire feeding motor includes: Based on the equivalent friction coefficient, combined with the magnetic field distortion and temperature rise gradient, a composite criterion model is constructed, and the composite criterion value is calculated. ; The upper and lower limit thresholds are set based on the operating characteristics of the wire feeding motor and the typical parameter change trends under overload conditions. and .

5. The overload detection method for the wire feeding motor as described in claim 4, characterized in that: The determination of the overload state of the wire feeding motor also includes: Based on the calculated composite criterion value Based on the set threshold, overload status is determined: when When this occurs, the wire feeding motor is determined to be in an overload state; when At this time, it is determined that the wire feeding motor is in normal condition; when When the wire feeding motor is in a critical state, further analysis is conducted based on the dynamic trend of the equivalent friction coefficient: if the equivalent friction coefficient shows a continuous upward trend and the difference between the composite criterion and the upper limit threshold is less than the difference threshold... If the load is high, an early warning of potential overload risks will be issued; otherwise, the current state will be maintained.

6. An overload detection system for a wire feeding motor, based on the overload detection method for a wire feeding motor according to any one of claims 1 to 5, characterized in that: Also includes: The composite criterion calculation module is used to establish a multi-field coupled electromagnetic-thermal-mechanical stress model of the wire feeding motor. It inputs the first input data, calculates the magnetic field distortion degree and temperature rise gradient, and forms a composite criterion. The first input data includes the real-time current, temperature and stress data of the wire feeding motor. The spectrum analysis module is used to analyze the current fluctuation characteristics during wire feeding based on the composite criterion, and to extract the wire feeding resistance fluctuation spectrum using spectrum analysis. The decoupling and friction coefficient back-calculation module is used to perform dynamic load decoupling based on the wire feeding resistance fluctuation spectrum and back-calculate the equivalent friction coefficient in real time. The overload condition determination module is used to determine the overload condition of the wire feeding motor by means of the composite criterion and the dynamic change of the equivalent friction coefficient. The adjustment and feedback module is used to adjust the parameters in the electromagnetic-thermal-mechanical stress multi-field coupling model based on the determination result of the overload state, and feed the results back to the stage of extracting the wire feeding resistance fluctuation spectrum.

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