Dual-mode self-sensing electromagnetic-frictional electric smart superstructure and design method thereof

Abstract

The application provides a dual-mode self-sensing electromagnetic-friction intelligent superstructure and a design method thereof. The electromagnetic-friction intelligent superstructure comprises a cover plate, a bottom plate, an inner wall, a first permanent magnet, a second permanent magnet, a third permanent magnet, an induction coil set, a first friction layer and a second friction layer. The electromagnetic-friction intelligent superstructure provided by the application makes the total equivalent stiffness of the third permanent magnet near the static equilibrium position approximately zero, forming a quasi-zero stiffness system. Through the structural design of the electromagnetic-friction intelligent superstructure, the vibration isolation function can be realized. When the third permanent magnet is excited to vibrate, the induced electromotive force generated by the relative motion between the third permanent magnet and the induction coil set realizes the conversion of vibration energy into electric energy. In addition, through the analysis of the induced electromotive force generated by the induction coil set, the open-circuit voltage generated by the first friction layer and the second friction layer, the self-sensing monitoring function of the electromagnetic-friction intelligent superstructure can be realized.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic device technology, and in particular to a dual-modal self-sensing electromagnetic-triboelectric intelligent superstructure and its design method. Background Technology

[0002] Vibration control, energy harvesting, and health monitoring are three important technical means to ensure the safety of engineering structures and equipment. In building seismic isolation layers, bridge bearings, precision equipment pedestals, or suspension points of rail transit equipment, vibration cannot be completely avoided. Vibration control is used to introduce controllable forces to offset or dissipate vibration energy; energy harvesting is used to convert excess mechanical energy from vibration into electrical energy; and health monitoring is used to detect and diagnose structural damage or abnormalities.

[0003] In existing technologies, vibration control is achieved by setting linear vibration isolators, energy harvesting is achieved by setting piezoelectric or electromagnetic energy harvesters, and health monitoring is achieved by setting a separate structural health monitoring system.

[0004] However, the aforementioned linear vibration isolators struggle to balance low-frequency vibration isolation performance with static load-bearing capacity, and the vibration energy is ultimately dissipated as heat. The aforementioned energy harvesters operate on a narrow frequency band and must be installed separately as supplementary equipment. The aforementioned structural health monitoring systems require the additional installation of numerous sensors and their power and communication cables, resulting in a complex system structure and high manufacturing and maintenance costs.

[0005] Therefore, how to provide an intelligent superstructure that integrates low-frequency broadband vibration reduction, vibration energy recovery, and structural health self-sensing is a technical problem to be solved in this field. Summary of the Invention

[0006] The main objective of this invention is to provide a dual-modal self-sensing electromagnetic-triboelectric intelligent superstructure and its design method. The aim is to provide an intelligent superstructure integrating low-frequency broadband vibration reduction, vibration energy recovery, and structural health self-sensing.

[0007] To achieve the above objectives, the present invention provides a dual-modal self-sensing electromagnetic-triboelectric intelligent superstructure, comprising: Cover plate; Base plate; The inner wall is connected to the cover plate at its top and to the bottom plate at its bottom. The inner wall is tubular and has a through hole in the middle. The cover plate, the bottom plate and the inner wall together form a receiving cavity. A first permanent magnet is located inside the receiving cavity and is disposed at the bottom of the cover plate; The second permanent magnet is located inside the receiving cavity and is disposed on the top of the base plate; A third permanent magnet is disposed between the first permanent magnet and the second permanent magnet, and is located in the middle of the receiving cavity. The first permanent magnet and the third permanent magnet are arranged with their poles facing each other, and the vertical axis of the first permanent magnet, the vertical axis of the second permanent magnet, and the vertical axis of the third permanent magnet are the same axis. An induction coil assembly, wherein the induction coil assembly is disposed opposite to the third permanent magnet; A first friction layer is disposed on the outer surface of the third permanent magnet relative to the induction coil assembly; A second friction layer is disposed on the inner surface of the inner wall and is disposed opposite to the first friction layer; The third permanent magnet reciprocates along the vertical axis, the induction coil group generates an induced electromotive force based on the reciprocating motion of the third permanent magnet, the first friction layer and the second friction layer generate an open-circuit voltage based on the reciprocating motion of the third permanent magnet, the induced electromotive force is related to the vibration velocity of the third permanent magnet in the reciprocating motion, and the open-circuit voltage is related to the displacement of the third permanent magnet in the reciprocating motion. The repulsive force between the third permanent magnet and the first and second permanent magnets constitutes the negative magnetic stiffness acting on the third permanent magnet, and the gap fit between the inner wall and the third permanent magnet constitutes the linear positive stiffness acting on the third permanent magnet, thus forming a quasi-zero stiffness system.

[0008] Optionally, the electromagnetic-triboelectric smart superstructure further includes a slider; The slider is disposed between the first permanent magnet and the second permanent magnet, in the middle of the receiving cavity, and the third permanent magnet is embedded in the slider.

[0009] Optionally, the electromagnetic-triboelectric smart superstructure further includes an outer wall; The top of the outer wall is connected to the cover plate, the bottom of the outer wall is connected to the bottom plate, the outer wall is tubular and the inner wall is embedded inside the outer wall.

[0010] Optionally, the induction coil assembly is wound around the outer surface of the outer wall.

[0011] Optionally, the electromagnetic-triboelectric smart superstructure further includes a storage circuit, which is electrically connected to the induction coil group; The storage circuit is used to store the induced electromotive force generated by the induction coil group.

[0012] Optionally, the electromagnetic-triboelectric intelligent superstructure further includes a health monitoring module, which is electrically connected to the induction coil group, the first friction layer, and the second friction layer; The health monitoring module is used to generate a speed sensing signal based on the induced electromotive force generated by the induction coil group, and to generate a displacement sensing signal based on the open-circuit voltage generated by the first friction layer and the second friction layer. The velocity sensing signal and the displacement sensing signal are preprocessed. Extract the spectral centroid offset, harmonic distortion rate, and wavelet energy entropy from the preprocessed velocity sensing signal; Extract the peak displacement and contact time duty cycle from the preprocessed displacement sensing signal; The target feature vector is obtained by normalizing the spectral centroid offset, the harmonic distortion rate, the wavelet energy entropy, the peak displacement, and the contact time duty cycle. The target feature vector is input into a preset vector classifier to generate monitoring results.

[0013] Furthermore, to achieve the above objectives, the present invention also provides a design method for a dual-modal self-sensing electromagnetic-triboelectric intelligent superstructure, the method comprising: Generate candidate design parameter combinations; Based on the candidate design parameter combinations, a finite element model is constructed; The trained proxy model is invoked to predict the performance of the finite element model, and the multi-objective fitness value corresponding to the finite element model is generated. Determine whether the convergence condition is met; If the convergence condition is not met, return to the step: Generate candidate design parameter combinations; If the convergence condition is met, the candidate design parameter combination is determined as the target candidate design parameter combination, and based on the target candidate parameter combination, the electromagnetic-triboelectric smart superstructure as described above is designed. The design parameter combination includes permanent magnet parameters, friction layer parameters, and coil parameters. The permanent magnet parameters include the diameter and thickness of the first permanent magnet, the diameter and thickness of the second permanent magnet, the diameter and thickness of the third permanent magnet, and the spacing between the permanent magnets. The friction layer parameters include the friction layer material, friction layer thickness, and friction layer area. The coil parameters include the number of turns of the induction coil group.

[0014] Optionally, the step of calling the trained proxy model to perform performance prediction on the finite element model and generating a multi-objective fitness value corresponding to the finite element model includes: The physical field data corresponding to the finite element model are input into the trained surrogate model to obtain the vibration transmissibility, energy harvesting power density, and induced voltage signal-to-noise ratio. The multi-objective fitness function value corresponding to the finite element model is calculated using the following formula: F = w1·(1 / T _avg ) + w2·P _avg + w3·SNR _avg Where F represents the multi-objective fitness value, T _avg P represents the vibration transmissibility. _avg SNR represents the energy-harvesting power density. _avg The signal-to-noise ratio of the induced voltage is represented by w1, w2, and w3, where w1+w2+w3=1.

[0015] Optionally, the method further includes: Acquire training data and labels. The training data includes physical field data corresponding to multiple finite element models. The labels include peak open-circuit voltage, vibration transmissibility, energy harvesting power density, and induced voltage signal-to-noise ratio corresponding to multiple finite element models. The training data and the labels are input into a preset proxy model, and the proxy model is trained iteratively. If the prediction accuracy of the proxy model is greater than or equal to the preset accuracy, the trained proxy model is obtained.

[0016] Optionally, determining whether the convergence condition is met includes: If the relative rate of change between the multi-objective fitness values ​​of consecutive N iterations is less than a preset threshold, then the convergence condition is satisfied, where N is a positive integer greater than 1. If the relative rate of change between the multi-objective fitness values ​​of adjacent consecutive N iterations is greater than or equal to a preset threshold, then the convergence condition is not met.

[0017] This application provides a dual-modal self-sensing electromagnetic-triboelectric intelligent superstructure and its design method. The electromagnetic-triboelectric intelligent superstructure includes: a cover plate; a base plate; an inner wall, the top of which is connected to the cover plate, and the bottom of which is connected to the base plate. The inner wall is tubular and has a through hole in the middle. The cover plate, the base plate, and the inner wall enclose a cavity. A first permanent magnet is located in the cavity and is disposed at the bottom of the cover plate. A second permanent magnet is located in the cavity and is disposed at the top of the base plate. A third permanent magnet is disposed between the first and second permanent magnets and is located in the middle of the cavity. The first and third permanent magnets are arranged with their poles facing each other, and the vertical axes of the first, second, and third permanent magnets are the same. The system comprises an induction coil assembly, which is positioned opposite to a third permanent magnet; a first friction layer, which is disposed on the outer surface of the third permanent magnet relative to the induction coil assembly; and a second friction layer, which is disposed on the inner surface of the inner wall and is positioned opposite to the first friction layer. The third permanent magnet reciprocates along a vertical axis. The induction coil assembly generates an induced electromotive force (EMF) based on the reciprocating motion of the third permanent magnet. The first and second friction layers generate an open-circuit voltage based on the reciprocating motion of the third permanent magnet. The induced EMF is related to the vibration velocity of the third permanent magnet during its reciprocating motion, and the open-circuit voltage is related to the displacement of the third permanent magnet during its reciprocating motion. The repulsive force between the third permanent magnet and the first and second permanent magnets constitutes the negative magnetic stiffness acting on the third permanent magnet, and the gap fit between the inner wall and the third permanent magnet constitutes the linear positive stiffness acting on the third permanent magnet, thus forming a quasi-zero stiffness system.

[0018] The electromagnetic-triboelectric intelligent superstructure provided in this application comprises a third permanent magnet and two other permanent magnets whose repulsive forces between their poles constitute a magnetic negative stiffness acting on the third permanent magnet; the gap fit between the inner wall and the third permanent magnet constitutes a radial elastic constraint, providing linear positive stiffness; the linear positive stiffness and the magnetic negative stiffness are connected in parallel, making the total equivalent stiffness of the third permanent magnet approximately zero near its static equilibrium position, thus forming a quasi-zero stiffness system that can achieve vibration isolation. When the third permanent magnet is excited to vibrate, the induced electromotive force generated by the relative motion between the third permanent magnet and the induction coil group realizes the conversion of vibration energy into electrical energy.

[0019] Furthermore, by analyzing the induced electromotive force generated by the induction coil group and the open-circuit voltage generated by the first and second friction layers, the self-sensing monitoring function of the electromagnetic-triboelectric intelligent superstructure can be realized.

[0020] Furthermore, the induced electromotive force is related to the vibration velocity of the third permanent magnet in reciprocating motion, and the open-circuit voltage is related to the displacement of the third permanent magnet in reciprocating motion, thus forming a velocity-displacement complementary dual-mode sensing system.

[0021] In summary, this application provides an intelligent superstructure that integrates low-frequency broadband vibration reduction, vibration energy recovery, and structural health self-sensing. Attached Figure Description

[0022] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is one of the structural schematic diagrams of the electromagnetic-triboelectric smart superstructure in the embodiments of this application; Figure 2 This is the second schematic diagram of the electromagnetic-triboelectric smart superstructure in the embodiments of this application; Figure 3 This is the third schematic diagram of the electromagnetic-triboelectric smart superstructure in the embodiments of this application; Figure 4 This is a comparison diagram of the bandwidth broadening and nonlinear resonant amplification mechanisms in the embodiments of this application; Figure 5 This is a schematic diagram of the mapping curve between harmonic distortion rate and displacement amplitude in an embodiment of this application; Figure 6 This is a schematic diagram of the health monitoring process in an embodiment of this application; Figure 7 This is a flowchart illustrating the design method of the electromagnetic-triboelectric smart superstructure in the embodiments of this application; Figure 8 This is a schematic diagram illustrating the application process of the design method for electromagnetic-triboelectric smart superstructures in the embodiments of this application; Figure 9 This is a schematic diagram of the architecture of the system designed in the embodiments of this application.

[0024] Explanation of reference numerals in the attached figures: 1. Cover plate; 2. Base plate; 31. Inner wall; 32. Outer wall; 4. First permanent magnet; 5. Second permanent magnet; 6. Third permanent magnet; 7. Induction coil assembly; 81. First friction layer; 82. Second friction layer; 9. Slider. Detailed Implementation

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0026] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0027] Please see Figures 1 to 3 The electromagnetic-triboelectric smart superstructure provided in this application includes: Cover plate 1; Base plate 2; The inner wall 31 is connected to the cover plate 1 at its top and to the bottom plate 2 at its bottom. The inner wall 31 is tubular and has a through hole in the middle. The cover plate 1, the bottom plate 2 and the inner wall 31 together form a receiving cavity. The first permanent magnet 4 is located inside the receiving cavity and is disposed at the bottom of the cover plate 1; The second permanent magnet 5 is located inside the receiving cavity and is disposed on the top of the base plate 2; The third permanent magnet 6 is disposed between the first permanent magnet 4 and the second permanent magnet 5, and is located in the middle of the receiving cavity. The first permanent magnet 4 and the third permanent magnet 6 are arranged with their poles facing each other, and the third permanent magnet 6 and the second permanent magnet 5 are arranged with their poles facing each other. The vertical axis of the first permanent magnet 4, the vertical axis of the second permanent magnet 5 and the vertical axis of the third permanent magnet 6 are the same axis. Induction coil group 7, which is arranged opposite to the third permanent magnet 6; A first friction layer 81 is disposed on the outer surface of the third permanent magnet 6 relative to the induction coil group 7; The second friction layer 82 is disposed on the inner surface of the inner wall 31 and is disposed opposite to the first friction layer 81.

[0028] It should be understood that the third permanent magnet 6 reciprocates along the vertical axis, and the induction coil group 7 generates an induced electromotive force based on the reciprocating motion of the third permanent magnet 6. The first friction layer 81 and the second friction layer 82 generate an open-circuit voltage based on the reciprocating motion of the third permanent magnet 6. The induced electromotive force is related to the vibration velocity of the third permanent magnet 6 in the reciprocating motion, and the open-circuit voltage is related to the displacement of the third permanent magnet 6 in the reciprocating motion.

[0029] It should be noted that the aforementioned induced electromotive force can be understood as a velocity sensing signal, also known as an EMG signal; the aforementioned open-circuit voltage can be understood as a displacement sensing signal, also known as a TENG signal. The EMG signal has high velocity sensitivity for low-frequency, small-amplitude vibrations, but low displacement sensitivity; the TENG signal has high displacement sensitivity. The fusion of these two signals allows for a joint characterization of the vibration response amplitude and rate of change, revealing the nonlinear amplification mechanism of the sensing signal-to-noise ratio in quasi-zero stiffness systems, and improving the accuracy of condition diagnosis.

[0030] Please see Figure 1 , Figure 1 A two-dimensional coordinate system is also shown, where the Z-axis represents the horizontal direction and the X-axis represents the vertical direction. The third permanent magnet 6 reciprocates along the X-axis. When the third permanent magnet 6 reciprocates along the X-axis, the first friction layer 81 and the second friction layer 82 generate an open-circuit voltage that is positively correlated with the displacement of the third permanent magnet 6 based on the reciprocating motion of the third permanent magnet 6. The induction coil group 7 generates an induced electromotive force that is positively correlated with the vibration velocity of the third permanent magnet 6 based on the reciprocating motion of the third permanent magnet 6.

[0031] Optionally, the first permanent magnet 4, the second permanent magnet 5, and the third permanent magnet 6 are made of N52 neodymium iron boron permanent magnets.

[0032] Optionally, the first friction layer 81 is a copper film, the second friction layer 82 is an FEP film, and the surface of the FEP film is provided with micro-nano structures to enhance the contact electrification effect.

[0033] The electromagnetic-triboelectric intelligent superstructure provided in this application embodiment has a magnetic negative stiffness acting on the third permanent magnet 6 due to the repulsive force between the third permanent magnet 6 and the first and second permanent magnets 4 and 5. The gap fit between the inner wall 31 and the third permanent magnet 6 constitutes a radial elastic constraint, providing linear positive stiffness. The linear positive stiffness and the magnetic negative stiffness are connected in parallel, making the total equivalent stiffness of the third permanent magnet 6 approximately zero near its static equilibrium position, thus forming a quasi-zero stiffness system. The above-mentioned quasi-zero stiffness configuration improves the displacement sensitivity of the third permanent magnet 6 by 2-5 times, thereby making the signal-to-noise ratio of the induced voltage of the same induction coil under weak excitation 6 dB higher than that of the energy-harvesting coil in a conventional linear vibration isolation structure.

[0034] The electromagnetic-triboelectric intelligent superstructure provided in this application embodiment makes the total equivalent stiffness of the third permanent magnet 6 approximately zero near its static equilibrium position, forming a quasi-zero stiffness system. Through the above-mentioned structural design of the electromagnetic-triboelectric intelligent superstructure, vibration isolation functionality can be achieved. When the third permanent magnet 6 is excited to vibrate, the induced electromotive force generated by the relative motion between the third permanent magnet 6 and the induction coil group 7 realizes the conversion of vibration energy into electrical energy. Furthermore, by analyzing the induced electromotive force generated by the induction coil group 7 and the open-circuit voltage generated by the first friction layer 81 and the second friction layer 82, the self-sensing monitoring function of the electromagnetic-triboelectric intelligent superstructure can be realized. In summary, this application provides an embodiment of an intelligent superstructure integrating low-frequency broadband vibration reduction, vibration energy recovery, and structural health self-sensing.

[0035] It should be understood that the electromagnetic intelligent superstructure provided in this embodiment can be verified for quasi-zero stiffness vibration isolation, dual-mode amplification, energy capture performance, and self-sensing monitoring.

[0036] The verification results for quasi-zero stiffness vibration isolation are as follows: The electromagnetic intelligent superstructure was subjected to a frequency sweep test on a low-frequency vibration table. The frequency range of the frequency sweep test was 0.5-100Hz, and the acceleration was 0.1g. The measured resonant frequency was approximately 7.5Hz, and the vibration transmissibility in the 8Hz-50Hz frequency band was less than 0.5.

[0037] The verification results for dual-modal amplification are as follows: During the aforementioned vibration table frequency sweep test, EMG and TENG signals were simultaneously acquired. For example... Figure 4 As shown, near the resonant frequency of the quasi-zero stiffness (QZS) system, the EMG induced voltage signal-to-noise ratio is improved by approximately 8 dB compared to the linear system, and the TENG open-circuit voltage peak value is improved by approximately 6 dB compared to the linear system. This indicates that both the EMG induced voltage and the TENG open-circuit voltage are significantly amplified. This confirms the nonlinear amplification mechanism of the dual-mode signal-to-noise ratio in the quasi-zero stiffness system formed by the electromagnetic-triboelectric smart superstructure provided in this embodiment. Here, the EMG signal can be understood as a velocity sensing signal, and the TENG signal can be understood as a displacement sensing signal.

[0038] The verification results of the energy harvesting performance are as follows: under 10Hz and 0.1g sinusoidal excitation, the peak open-circuit voltage of the coil is 0.62V, and the output power after rectification and filtering is about 4.8mW. The peak open-circuit voltage of the first friction layer 81 and the second friction layer 82 is about 3.2V. The combined power supply of the two mechanisms can meet the power supply requirements of the low-power wireless sensing node.

[0039] The verification results of the self-sensing monitoring were as follows: In the simulated bolt loosening experiment, the harmonic distortion rate (THD) of the induced voltage monotonically increased from 2.1% to 8.7%. In the simulated elastic beam damage experiment, the wavelet energy entropy increased from 0.42 to 0.78. The accuracy rate for identifying the four states—normal, loose connection, elastic beam damage, and magnetic degradation—reached 92.5%.

[0040] Please see Figure 5 , Figure 5 This is a schematic diagram of the mapping curve between harmonic distortion rate and displacement amplitude in an embodiment of this application. For example... Figure 5 As shown, the quantitative relationship curve between cubic nonlinearity and harmonic distortion rate is divided into three regions: the linear region (small displacement, THD is approximately constant), the weakly nonlinear region (THD increases slowly with displacement), and the strongly nonlinear region (THD increases rapidly with displacement). Different degrees of connection loosening correspond to different THD offset trajectories. The greater the loosening, the lower the threshold for THD to enter the strongly nonlinear region, thus achieving quantitative diagnosis of connection loosening.

[0041] The quantitative mapping relationship between harmonic distortion rate (THD) and the degree of connection loosening has the following physical mechanism: Connection loosening leads to a decrease in the effective damping of the system near the static equilibrium position, thereby increasing the displacement response amplitude of the third permanent magnet 6 under the same excitation. When the displacement response amplitude enters the cubic nonlinear dominance region, the induced voltage waveform is distorted, and THD increases monotonically with the displacement amplitude. The quasi-zero stiffness configuration formed by the electromagnetic-triboelectric intelligent superstructure provided in this embodiment allows the third permanent magnet 6 to obtain a large displacement response (2-5 times amplified compared to a linear system) even under weak excitation, thus enabling THD to enter a monitorable variation range under excitation conditions, achieving quantitative diagnosis of connection loosening. Therefore, different degrees of loosening correspond to different THD offset trajectories; that is, the greater the loosening, the lower the system damping ratio, and the lower the threshold for THD to enter the strong nonlinear region.

[0042] Therefore, through zero-stiffness vibration isolation verification, dual-modal amplification verification, energy harvesting performance verification, and self-sensing monitoring verification, it can be demonstrated that the embodiments of this application provide an intelligent superstructure that integrates low-frequency broadband vibration reduction, vibration energy recovery, and structural health self-sensing.

[0043] Please see Figure 1 Optionally, the electromagnetic-triboelectric smart superstructure further includes a slider 9; The slider 9 is disposed between the first permanent magnet 4 and the second permanent magnet 5, in the middle position of the receiving cavity, and the third permanent magnet 6 is embedded in the slider 9.

[0044] The electromagnetic-triboelectric intelligent superstructure provided in this application also includes a slider 9, in which a third permanent magnet 6 is embedded. When the third permanent magnet is excited to vibrate, the slider 9 drives the third permanent magnet 6 to move relative to the induction coil group 7.

[0045] Please see Figure 1 Optionally, the electromagnetic-triboelectric smart superstructure further includes an outer wall 32; The top of the outer wall 32 is connected to the cover plate 1, the bottom of the outer wall 32 is connected to the bottom plate 2, the outer wall 32 is tubular and the inner wall 31 is embedded inside the outer wall 32.

[0046] Please see Figure 3 Optionally, the induction coil group 7 is wound around the outer surface of the outer wall 32.

[0047] It should be understood that, in one optional application scenario, multiple electromagnetic-triboelectric smart superstructures are arranged in an array.

[0048] Optionally, the electromagnetic-triboelectric smart superstructure further includes a storage circuit, which is electrically connected to the induction coil group; The storage circuit is used to store the induced electromotive force generated by the induction coil group.

[0049] It should be noted that the above-mentioned storage circuit includes a rectifier bridge, filter capacitors, a power management chip, and a supercapacitor.

[0050] In this embodiment, the relative motion between the third permanent magnet and the induction coil group generates an induced electromotive force (EMF). This EMF flows through a storage circuit, which stores the EMF generated by the relative motion between the third permanent magnet and the induction coil group, thus achieving energy capture and storage, and subsequently powering the low-power sensing node.

[0051] Optionally, the electromagnetic-triboelectric intelligent superstructure further includes a health monitoring module, which is electrically connected to the induction coil group, the first friction layer, and the second friction layer; The health monitoring module is used to generate a speed sensing signal based on the induced electromotive force generated by the induction coil group, and to generate a displacement sensing signal based on the open-circuit voltage generated by the first friction layer and the second friction layer. The velocity sensing signal and the displacement sensing signal are preprocessed. Extract the spectral centroid offset, harmonic distortion rate, and wavelet energy entropy from the preprocessed velocity sensing signal; Extract the peak displacement and contact time duty cycle from the preprocessed displacement sensing signal; The target feature vector is obtained by normalizing the spectral centroid offset, the harmonic distortion rate, the wavelet energy entropy, the peak displacement, and the contact time duty cycle. The target feature vector is input into a preset vector classifier to generate monitoring results.

[0052] Please see Figure 6 , Figure 6 This is a schematic diagram of the health monitoring process in an embodiment of this application. Figure 6 The illustrated health monitoring process can be applied to a health monitoring module that collects the induced electromotive force generated by the induction coil group and the open-circuit voltage generated by the first and second friction layers without the need for any additional sensors.

[0053] Since the induced electromotive force is positively correlated with the vibration frequency of the third permanent magnet, a velocity sensing signal can be generated based on the induced electromotive force; since the open-circuit voltage is positively correlated with the displacement of the third permanent magnet, a displacement sensing signal can be generated based on the open-circuit voltage.

[0054] Furthermore, the velocity sensing signals and displacement sensing signals undergo preprocessing. It should be understood that this preprocessing includes detrending, bandpass filtering, and segmentation of the signals.

[0055] Spectral analysis is performed on the preprocessed velocity sensing signal to obtain the spectral centroid offset, which reflects the frequency change of the controlled structure's modalities; harmonic analysis is performed on the preprocessed velocity sensing signal to obtain the harmonic distortion rate, which reflects the contact nonlinearity caused by loose connections; wavelet packet decomposition is performed on the preprocessed velocity sensing signal to obtain the wavelet energy entropy.

[0056] Peak displacement and contact time duty cycle are extracted from the preprocessed displacement sensing signal.

[0057] The spectral centroid offset, harmonic distortion rate, wavelet energy entropy, peak displacement, and contact time duty cycle are normalized to obtain the target feature vector, which reflects the frequency band energy distribution changes caused by early impact damage. The target feature vector is then input into a preset vector classifier to generate monitoring results.

[0058] Optionally, the vector classifier described above is an RBF-SVM classifier. The monitoring results include health status categories and confidence levels such as normal, loose connections, structural damage, and magnetic property degradation.

[0059] It should be understood that the health monitoring module described above executes the health monitoring process using its own captured electrical energy, thus achieving passive, self-sensing health monitoring.

[0060] In this embodiment, by analyzing displacement sensing signals and velocity sensing signals, the self-sensing monitoring function of the electromagnetic intelligent superstructure can be realized.

[0061] Please see Figure 7 , Figure 7 This is a flowchart illustrating the design method of the electromagnetic-triboelectric smart superstructure in the embodiments of this application, as shown below. Figure 7 As shown, the design method for electromagnetic-triboelectric smart superstructures includes: Step 701: Generate candidate design parameter combinations.

[0062] Step 702: Construct a finite element model based on the candidate design parameter combination.

[0063] Step 703: Call the trained surrogate model to predict the performance of the finite element model and generate the multi-objective fitness value corresponding to the finite element model.

[0064] Step 704: Determine whether the convergence condition is met.

[0065] Step 705: If the convergence condition is not met, return to step: Generate candidate design parameter combinations.

[0066] Step 706: If the convergence condition is met, the candidate design parameter combination is determined as the target candidate design parameter combination, and an electromagnetic-triboelectric smart superstructure is designed based on the target candidate parameter combination.

[0067] The design parameter combination includes permanent magnet parameters, friction layer parameters, and coil parameters. The permanent magnet parameters include the diameter and thickness of the first permanent magnet, the diameter and thickness of the second permanent magnet, the diameter and thickness of the third permanent magnet, and the spacing between the permanent magnets. The friction layer parameters include the friction layer material, friction layer thickness, and friction layer area. The coil parameters include the number of turns of the induction coil group.

[0068] In an optional embodiment, the first, second, and third permanent magnets have a diameter of 45 mm and a thickness of 18 mm, and the spacing between the permanent magnets is 32 mm; each of the first and second coils has 650 turns, a wire diameter of 0.2 mm, and an inner diameter of 48 mm; the first friction layer is a copper film, the second friction layer is an FEP film, the friction layer area is 4 cm², and the friction layer thickness is 120 μm.

[0069] Optionally, the parameter optimization agent can be invoked to execute step 701. The parameter optimization agent employs a Bayesian optimization algorithm, iteratively searching among multiple combinations of first design parameters. The initial sampling points are 50, and the maximum number of iterations is 500 rounds. Optionally, the multiple combinations of first design parameters can be at least 2000 sets of design parameter combinations generated in the design parameter space by Latin hypercube sampling.

[0070] Optionally, the configuration generation agent can be invoked to execute step 702. The configuration generation agent adopts a conditional diffusion model, using the parameter vector output by the parameter optimization agent as a condition to generate the corresponding three-dimensional superstructure configuration. The diffusion model is based on the denoised diffusion probability model (DDPM) architecture, and the training data consists of 2000 sets of three-dimensional geometric models from finite element simulations.

[0071] Optionally, the evaluation agent can be invoked to execute step 703. The evaluation agent invokes a proxy model to predict the performance of the three-dimensional superstructure configuration and calculates the multi-objective fitness function value. The proxy model is a multi-fidelity neural network, trained using both high-fidelity finite element simulation data and low-fidelity analytical model data.

[0072] Optionally, steps 704-706 can be executed by calling the meta-agent. The meta-agent monitors the optimization process and checks the convergence of the Pareto front every 50 rounds. When the hypervolume change of the Pareto front is less than 1% for 100 consecutive rounds, convergence is determined, the target candidate parameter combination is output, and the electromagnetic-triboelectric intelligent superstructure is designed.

[0073] This embodiment applies an AI multi-agent reverse design framework to the design of electromagnetic-triboelectric intelligent superstructures. Through multi-agent collaboration involving parameter optimization agents, configuration generation agents, evaluation agents, and meta-agents, autonomous iterative optimization is achieved, thereby reducing the design cycle of the electromagnetic-triboelectric intelligent superstructure and improving design efficiency.

[0074] Optionally, the step of calling the trained proxy model to perform performance prediction on the finite element model and generating a multi-objective fitness value corresponding to the finite element model includes: The physical field data corresponding to the finite element model are input into the trained surrogate model to obtain the vibration transmissibility, energy harvesting power density, and induced voltage signal-to-noise ratio. The multi-objective fitness function value corresponding to the finite element model is calculated using the following formula: F = w1·(1 / T _avg ) + w2·P _avg + w3·SNR _avg Where F represents the multi-objective fitness value, T _avg P represents the vibration transmissibility._avg SNR represents the energy-harvesting power density. _avg The signal-to-noise ratio of the induced voltage is represented by w1, w2, and w3, where w1+w2+w3=1.

[0075] It should be understood that the multi-objective fitness function value is calculated by weighted summation of vibration transmissibility, energy capture power density, and induced voltage signal-to-noise ratio, where w1, w2, and w3 can be customized.

[0076] The training method for the proxy model is explained in detail below: Optionally, the method further includes: Acquire training data and labels. The training data includes physical field data corresponding to multiple finite element models. The labels include peak open-circuit voltage, vibration transmissibility, energy harvesting power density, and induced voltage signal-to-noise ratio corresponding to multiple finite element models. The training data and the labels are input into a preset proxy model, and the proxy model is trained iteratively. If the prediction accuracy of the proxy model is greater than or equal to the preset accuracy, the trained proxy model is obtained.

[0077] Optionally, the training data includes physical field data corresponding to at least 2,000 finite element simulation models, and the physical field data includes the swept frequency response of the electromagnetic field-structural mechanics-circuit three-field coupling.

[0078] Optionally, the above preset precision is 0.95.

[0079] Optionally, determining whether the convergence condition is met includes: If the relative rate of change between the multi-objective fitness values ​​of consecutive N iterations is less than a preset threshold, then the convergence condition is satisfied, where N is a positive integer greater than 1. If the relative rate of change between the multi-objective fitness values ​​of adjacent consecutive N iterations is greater than or equal to a preset threshold, then the convergence condition is not met.

[0080] Optionally, the preset threshold is 1%, and N is 100. That is, if the hypervolume change of the Pareto front is less than 1% for 100 consecutive rounds, the convergence condition is satisfied.

[0081] For a better understanding of the overall technical solution, please refer to [link / reference]. Figure 8 and Figure 9 It should be understood that the design method provided in the embodiments of this application can be applied to a design system, and the architecture of the design system is as follows: Figure 9 As shown, it includes a data layer, an agent model layer, a multi-agent collaborative optimization layer, and a physical implementation layer.

[0082] Please refer to this simultaneously. Figure 8 , Figure 8 The main operational steps include: S1 Target performance definition, including determining the target vibration isolation frequency band, target energy harvesting power density, and target monitoring sensitivity, executed by the data layer. S2 Proxy model training, executed by the proxy model layer, used for iterative training of the proxy model. S3 Multi-agent collaborative optimization, executed by the multi-agent collaborative optimization layer, generates candidate parameters and combinations through parameter optimization agents, generates candidate 3D configurations through configuration generation agents, evaluates agent prediction performance and computational fitness, and determines whether convergence conditions are met through meta-agents. S4 Physical verification and active learning, executed by the physical implementation layer, used for prototype manufacturing, vibration isolation testing, energy harvesting testing, and monitoring testing.

[0083] In this embodiment, physical experimental data is continuously fed back to the surrogate model through an active learning mechanism to achieve self-evolution and realize design automation.

[0084] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A dual-modal self-sensing electromagnetic-triboelectric intelligent superstructure, characterized in that, include: The cover plate, the bottom plate, and the inner wall are provided. The top of the inner wall is connected to the cover plate, and the bottom of the inner wall is connected to the bottom plate. The cover plate, the bottom plate, and the inner wall together form a receiving cavity. A first permanent magnet and a second permanent magnet are both located within the receiving cavity, with the first permanent magnet disposed at the bottom of the cover plate and the second permanent magnet disposed at the top of the base plate. A third permanent magnet is disposed between the first permanent magnet and the second permanent magnet, wherein the first permanent magnet and the third permanent magnet are arranged with their poles facing each other, and the third permanent magnet and the second permanent magnet are arranged with their poles facing each other. An induction coil assembly, wherein the induction coil assembly is disposed opposite to the third permanent magnet; A first friction layer and a second friction layer are provided. The first friction layer is disposed on the outer surface of the third permanent magnet relative to the induction coil assembly, and the second friction layer is disposed on the inner surface of the inner wall and is disposed opposite to the first friction layer.

2. The dual-modal self-sensing electromagnetic-triboelectric intelligent superstructure according to claim 1, characterized in that, The electromagnetic-triboelectric intelligent superstructure also includes a slider; The slider is disposed between the first permanent magnet and the second permanent magnet, in the middle of the receiving cavity, and the third permanent magnet is embedded in the slider.

3. The dual-modal self-sensing electromagnetic-triboelectric intelligent superstructure according to claim 1, characterized in that, The electromagnetic-triboelectric intelligent superstructure also includes an outer wall; The top of the outer wall is connected to the cover plate, the bottom of the outer wall is connected to the bottom plate, the outer wall is tubular and the inner wall is embedded inside the outer wall.

4. The dual-modal self-sensing electromagnetic-triboelectric intelligent superstructure according to claim 3, characterized in that, The induction coil assembly is wound around the outer surface of the outer wall.

5. The dual-modal self-sensing electromagnetic-triboelectric intelligent superstructure according to claim 1, characterized in that, The electromagnetic-triboelectric intelligent superstructure also includes a storage circuit, which is electrically connected to the induction coil group. The storage circuit is used to store the induced electromotive force generated by the induction coil group.

6. The dual-modal self-sensing electromagnetic-triboelectric intelligent superstructure according to claim 1, characterized in that, The electromagnetic-triboelectric intelligent superstructure also includes a health monitoring module, which is electrically connected to the induction coil group, the first friction layer and the second friction layer. The health monitoring module is used to generate a speed sensing signal based on the induced electromotive force generated by the induction coil group, and to generate a displacement sensing signal based on the open-circuit voltage generated by the first friction layer and the second friction layer. The velocity sensing signal and the displacement sensing signal are preprocessed. Extract the spectral centroid offset, harmonic distortion rate, and wavelet energy entropy from the preprocessed velocity sensing signal; Extract the peak displacement and contact time duty cycle from the preprocessed displacement sensing signal; The target feature vector is obtained by normalizing the spectral centroid offset, the harmonic distortion rate, the wavelet energy entropy, the peak displacement, and the contact time duty cycle. The target feature vector is input into a preset vector classifier to generate monitoring results.

7. A design method for a dual-modal self-sensing electromagnetic-triboelectric intelligent superstructure, characterized in that, include: Generate candidate design parameter combinations; Based on the candidate design parameter combinations, a finite element model is constructed; The trained proxy model is invoked to predict the performance of the finite element model, and the multi-objective fitness value corresponding to the finite element model is generated. Determine whether the convergence condition is met; If the convergence condition is not met, return to the step: Generate candidate design parameter combinations; If the convergence condition is met, the candidate design parameter combination is determined as the target candidate design parameter combination, and based on the target candidate parameter combination, the electromagnetic-triboelectric smart superstructure as described in any one of claims 1-6 is designed. The design parameter combination includes permanent magnet parameters, friction layer parameters, and coil parameters. The permanent magnet parameters include the diameter and thickness of the first permanent magnet, the diameter and thickness of the second permanent magnet, the diameter and thickness of the third permanent magnet, and the spacing between the permanent magnets. The friction layer parameters include the friction layer material, friction layer thickness, and friction layer area. The coil parameters include the number of turns of the induction coil group.

8. The method according to claim 7, characterized in that, The process of calling the trained proxy model to perform performance prediction on the finite element model and generating the multi-objective fitness value corresponding to the finite element model includes: The physical field data corresponding to the finite element model are input into the trained surrogate model to obtain the vibration transmissibility, energy harvesting power density, and induced voltage signal-to-noise ratio. The multi-objective fitness function value corresponding to the finite element model is calculated using the following formula: F = w1·(1 / T _avg ) + w2·P _avg + w3·SNR _avg Where F represents the multi-objective fitness value, T _avg P represents the vibration transmissibility. _avg SNR represents the energy-harvesting power density. _avg The signal-to-noise ratio of the induced voltage is represented by w1, w2, and w3, where w1+w2+w3=1.

9. The method according to claim 8, characterized in that, The method further includes: Acquire training data and labels. The training data includes physical field data corresponding to multiple finite element models. The labels include peak open-circuit voltage, vibration transmissibility, energy harvesting power density, and induced voltage signal-to-noise ratio corresponding to multiple finite element models. The training data and the labels are input into a preset proxy model, and the proxy model is trained iteratively. If the prediction accuracy of the proxy model is greater than or equal to the preset accuracy, the trained proxy model is obtained.

10. The method according to claim 7, characterized in that, The determination of whether the convergence condition is met includes: If the relative rate of change between the multi-objective fitness values ​​of consecutive N iterations is less than a preset threshold, then the convergence condition is satisfied, where N is a positive integer greater than 1. If the relative rate of change between the multi-objective fitness values ​​of adjacent consecutive N iterations is greater than or equal to a preset threshold, then the convergence condition is not met.

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