1808 results about "Vibration response" patented technology

The invention relates to an electric power material intelligent detection method based on multi-modal data fusion, and aims to improve the accuracy and automation level of material state recognition. According to the method, in the electric power material operation or circulation process, multi-modal data such as images, infrared thermal imaging, radio frequency identification, vibration response and environmental parameters are acquired through a unified time index, and a structured time sequence data set is constructed. And after normalization and exception elimination processing, multi-dimensional feature vectors including structural strength, temperature distribution, label continuity and dynamic stability are extracted, and weighted statistics and correlation calculation are executed to generate a comprehensive state index. And further through comparison with a historical reference, identifying an abnormal state according to a deviation threshold value, outputting corresponding labels and feature information, and obtaining material state evaluation and disposal suggestions based on rule reasoning. According to the method, accurate monitoring and abnormal early warning of the electric power materials under the driving of the multi-source data are realized, and the method has good practicability and expansibility.

The invention provides an intelligent networked automobile chassis drive-by-wire simulation method and system based on multi-domain modeling, and relates to the technical field of adaptive control, and the method comprises the steps: obtaining structural parameters and kinetic parameters of an intelligent networked automobile chassis, constructing a finite element grid, building a multi-flexible-body kinetic model corresponding to the intelligent networked automobile chassis, and constructing a multi-flexible-body dynamic model corresponding to the intelligent networked automobile chassis; introducing a coupling coordination equation, establishing a multi-flexible-body kinetic equation, and solving to obtain vibration response and stress-strain distribution under different working conditions; generating a plurality of finite element models, performing multi-physical field fusion and multi-error source fusion models, performing online error identification and active compensation, performing all-condition drive-by-wire chassis closed-loop control, and generating a drive-by-wire chassis closed-loop integrated simulation model; constructing a road three-dimensional parameterized model, generating a vehicle-road coupling model, generating a simulation test case, identifying a performance short board, constructing a knowledge graph and an optimization rule base, and generating an optimal control strategy.

The invention relates to the technical field of discharge detection, in particular to a high-precision detection method and device for partial discharge of totally-enclosed GIS equipment. The method comprises the following steps: carrying out metal shell three-dimensional structure modeling on the GIS equipment, and carrying out electromagnetic shielding evaluation so as to obtain a multi-point electromagnetic shielding evaluation value; acquiring an original discharge signal of each sampling point based on the electromagnetic wave sensor, and performing interference suppression processing and back propagation attenuation compensation according to a multi-point electromagnetic shielding evaluation value, so as to obtain an attenuation compensation discharge signal waveform; micro-vibration response parameters are collected based on an MEMS micro-vibration sensor, actual influence waveform point detection marking and physical vibration interference filtering are carried out on attenuation compensation discharge signal waveforms, and therefore pure optimized discharge signal waveforms are obtained. According to the invention, through efficient and accurate partial discharge detection, real-time early warning of the state of the GIS equipment is realized, and the stability and safety of the equipment are improved.

The invention relates to the technical field of structure monitoring, in particular to a safety monitoring method and device for a steel structure. The method comprises the following steps: collecting historical key environment data from a local meteorological station, distributing weights according to the influence degree of the key environment data on a steel structure, and generating an environment index; evaluating vibration response characteristics of the steel structure under the dynamic load in combination with the environment index, and generating a vibration response index; performing static and dynamic analysis on the steel structure in combination with the vibration response index to obtain a structural stability index; and generating a comprehensive safety coefficient by combining an environment index, a vibration response index and a structural stability index, dividing risk levels, and providing specific maintenance and transformation suggestions for different levels. According to the safety monitoring method and device for the steel structure, the safety coefficient can provide a more comprehensive steel structure health condition index, and potential risks in multiple aspects can be identified, so that potential safety hazards can be found in time.

The invention relates to the technical field of geological landslide monitoring, in particular to a tower footing geological landslide monitoring and early warning system and method. The method comprises the following steps: arranging an inclination angle sensor, a Beidou-GNSS module, a soil moisture content probe and a micro-seismic accelerometer on a tower footing and the periphery of the tower footing, and collecting the inclination amount, three-dimensional displacement, soil moisture content and vibration response information of the tower footing to form a monitoring original data set; timestamp unification, WGS-84 projection coordinate system conversion and multi-frequency vibration elimination are carried out on the monitored original data set, and an air-ground integrated displacement profile is constructed; the whole process realizes high integration and automation in the aspects of data acquisition, processing, fusion and transmission, so that the timeliness, accuracy and data integrity of landslide risk monitoring are ensured, and scientific decision support is provided for disaster prevention and reduction.

The invention relates to the technical field of aircraft structure detection, and discloses an aircraft structure crack intelligent identification method based on deep learning. The method comprises the following steps: acquiring original vibration response signals and electromagnetic field distribution data on the surface and inside of an aircraft structure in parallel through a multi-source sensor network; synchronously processing the data by using a multi-scale convolutional neural network, and extracting time-frequency domain abnormal fluctuation features and space magnetic field distortion features; constructing a cross-modal correlation model, analyzing a topological dependency relationship of the two types of features through a graph attention mechanism, and generating a fused damage sensitive feature vector; inputting the vector into a pre-trained deep belief network to obtain a probability distribution mapping relation for different crack types; and according to the mapping relation, carrying out adaptive weighted fusion on original multi-sensor data, inhibiting environmental noise and structural background interference, and separating and reconstructing an accurate three-dimensional morphology map of the target crack. According to the method, multi-source data information can be effectively fused to improve the accuracy of aircraft structure crack identification.

The invention discloses a large-span bridge vibration response prediction method based on data driving, and relates to the technical field of bridge vibration response prediction, and the method comprises the following steps: arranging a plurality of sensors at a plurality of key structure parts of a bridge, collecting vibration response data of the bridge during operation in real time based on an initial sampling frequency set based on the inherent frequency of the bridge structure; performing frequency domain analysis on the collected vibration response data, extracting a main excitation frequency component under the current working condition, and constructing a current excitation frequency spectrum; the method comprises the following steps: calling structural modal parameters obtained in a design stage, a static load test or a modal test of a bridge, extracting the natural vibration frequency of each order of structure, and constructing a bridge inherent frequency feature library; according to the method, the resonance state is dynamically identified through frequency domain analysis and the fuzzy risk index, the sampling frequency is intelligently adjusted, high-frequency response accurate capture and resource efficiency optimization are realized, and the sensitivity, the real-time performance and the safety of a bridge health monitoring system are remarkably improved.

The invention discloses a pre-twisted damper fatigue life prediction method for an extra-high voltage ground wire, and particularly relates to the technical field of power transmission line vibration prevention. Collecting multi-source vibration and environment data, and constructing a vibration characteristic time sequence; amplitude, frequency and strain energy are extracted based on the multi-frequency vibration response, and a multi-frequency collaborative vibration model is established; obtaining a damper structure and material parameters, establishing a finite element model, coupling the finite element model with a vibration model, and simulating local stress response; a Rainflow counting method and a Miner damage criterion are adopted to construct a fatigue damage factor distribution matrix; predicting the residual fatigue life of the damper based on the damage evolution trend, and evaluating the replacement opportunity; according to the method, accurate fatigue life prediction and optimal replacement strategy recommendation of the pre-twisted damper under complex working conditions can be realized, and the method has high precision, high adaptability and engineering practicability.

The invention relates to the technical field of intelligent transportation control, in particular to a tomato transportation speed self-adaptive adjustment method based on path road condition feedback, which comprises the following steps: acquiring road images, vibration waveforms and altitude data through a multi-source sensing unit, and constructing real-time road condition information; identifying a driving mode and extracting a corresponding bumping parameter; detecting the maturity grade of the tomato by combining multispectrum and thermal imaging, and querying a maturity-compressive strength corresponding table to calculate a cargo damage threshold value; constructing a dynamic mapping model under multiple working conditions, predicting vibration response and converting the vibration response into equivalent pressure; comparing the equivalent pressure with a damage threshold value to obtain a safety margin, constructing a speed adjustment decision tree and generating a maximum allowable speed value of each road section; and dynamically generating a segmented variable-speed control instruction based on the speed decision matrix, and controlling a throttle valve and a braking system to cooperatively change speed. The method has the advantages of accurate working condition identification, dynamic fruit adaptation, closed-loop speed regulation and control and the like, and is suitable for fine speed control of a high-sensitivity fruit and vegetable transportation scene.

The invention relates to the technical field of material performance detection, in particular to an industrial ceramic product performance detection method and system, and the method comprises the following steps: S1, applying a composite dynamic load to the surface of an industrial ceramic product; s2, three-dimensional vibration response signals, surface thermal field distribution data and electromagnetic eddy current response parameters are collected; s3, extracting a crack propagation characteristic frequency; s4, constructing a heat-force coupling field three-dimensional model; s5, constructing a material performance degradation gradient matrix; and S6, determining a critical failure area of the industrial ceramic product, and outputting a performance detection report. According to the method, through collaborative fusion of multi-source data synchronous acquisition, thermal-mechanical coupling field modeling and electromagnetic impedance analysis, precise monitoring and quantitative evaluation of crack propagation, thermal stress concentration and microdefect degradation of the industrial ceramic product under the composite dynamic load are realized, so that the defect identification precision is improved, and a critical failure area is effectively judged.

The invention provides a porcelain insulator pulse excitation detection method based on multi-mode coupling analysis, and belongs to the technical field of intelligent detection of power equipment, and the method comprises the following steps: applying a mechanical pulse excitation signal with an adjustable amplitude; collecting a vibration response signal and constructing a temperature-frequency compensation model; separating the vibration mode of each section: constructing an equivalent mass-rigidity-damping model of a plurality of sections of insulators; extracting instantaneous frequency from the separated multi-mode signal, capturing the non-stationary characteristic of the vibration signal, and capturing the local rigidity change; and calculating a crack quantitative index and outputting a diagnosis result, including establishing a crack depth-frequency offset mapping database, and acquiring an actual bending form of the vibration mode by identifying the position of the crack. The method has the advantages that the three-dimensional characteristic spectrum of the crack is constructed through joint analysis of multi-dimensional data such as the vibration mode, the instantaneous frequency and the curvature distribution, the limitation of traditional single parameter detection is broken through, and refined distinguishing of defect property cracks, aging and the like is achieved.

The invention discloses a disk array fault early warning method and related equipment, and relates to the technical field of computer storage, and the method comprises the steps: responding to a preset period triggering condition, and applying a preset mechanical excitation signal to a target disk array; collecting a vibration response frequency spectrum and an acoustic response waveform of the target disk array under the action of the mechanical excitation signal; performing fusion analysis on the vibration response frequency spectrum and the acoustic response waveform through a preset feature fusion model to obtain a fault probability prediction vector; and determining a real-time fault early warning instruction for the target disk array according to the fault probability prediction vector and a preset health reference vector. Through periodic mechanical excitation and multi-mode signal fusion analysis, high-precision, non-intrusive and real-time fault early warning of the disk array can be realized, the reliability of the disk array is improved, and the operation and maintenance cost is reduced.

The invention relates to an existing building glass curtain wall operation and maintenance method and system based on a digital base. The method comprises the following steps that curtain wall vibration response data, visible light image data and three-dimensional point cloud data are obtained; based on the curtain wall vibration response data, comparing the current modal parameter with the historical modal parameter, and determining the dynamic characteristics of the curtain wall panel; identifying the curtain wall apparent damage type and the corresponding damage degree based on the visible light image data; based on the three-dimensional point cloud data, a curtain wall live-action three-dimensional point cloud model is constructed and mapped into a numerical model, a local stiffness reduction coefficient is calculated based on curtain wall panel dynamic characteristics, curtain wall apparent damage types and corresponding damage degrees, and a local stiffness matrix of a corresponding unit or node in the numerical model is corrected; and performing finite element analysis on the corrected numerical model to realize performance evaluation and deduction of the glass curtain wall. Compared with the prior art, the method has the advantages that the performance change of the glass curtain wall can be accurately and dynamically sensed and predicted, and the like.

The invention relates to a vehicle vibration response prediction method based on multi-modal feature depth fusion, which belongs to the field of computer vision and signal processing, and is characterized by comprising the following steps: constructing a vehicle vibration excitation and response triple data set based on multi-modal data; constructing a vehicle vibration response prediction model based on multi-modal feature deep fusion; training, verifying and testing the vehicle vibration response prediction model; and predicting a vehicle vibration response signal. According to the method, the problems that the traditional road excitation measurement precision is relatively poor and the calculation complexity and the structure fineness are difficult to balance by vibration signal prediction are solved, and the image feature extraction advantage of an EfficientNet framework and the time sequence feature extraction advantage of a Transformer framework are applied to the regression prediction of the vehicle vibration response signal; multi-modal features are fused through a bidirectional cross attention mechanism obeying Dirichlet distribution, and high prediction accuracy is obtained. And a method with relatively high prediction accuracy is provided for the field of vehicle vibration response signal prediction.

The invention relates to the technical field of wind resistance design of bridge structures, discloses a bridge tower aerodynamic damping determination method and device based on unscented Kalman filtering, and aims to solve the technical problem that existing aerodynamic damping identification is not accurate enough. The scheme mainly comprises the following steps: determining an initial nonlinear aerodynamic damping model comprising a plurality of unknown model parameters of the bridge tower, and constructing a state equation comprising the model parameters; obtaining a vortex-induced vibration response time history of the bridge tower model by using a wind tunnel test, and constructing an observation equation; inputting the state equation and the observation equation into an unscented Kalman filter, and determining values of model parameters by using the unscented Kalman filter; and substituting the values of the model parameters into the initial nonlinear aerodynamic damping model to obtain a nonlinear aerodynamic damping model, and determining aerodynamic damping by using the nonlinear aerodynamic damping model. Accurate identification of aerodynamic damping is achieved, the requirement for the actual measurement response time history is lowered, and the method is particularly suitable for accurately evaluating the wind-induced response of the structure.

The invention discloses a large-model-based steel bar engineering quality detection method, system and equipment and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring vibration response data and three-dimensional point cloud data of a to-be-detected reinforcement project; inputting the vibration response data into a pre-trained vibration characteristic analysis model to obtain a stress distribution map and a stress transmission path, and performing segmentation processing on the three-dimensional point cloud data along the stress transmission direction to obtain segmented point cloud data; based on the segmented point cloud data, extracting morphological characteristics of each section of reinforcing steel bar in the to-be-detected reinforcing steel bar project, and establishing a stress-geometry coupled digital twinborn model according to the stress distribution map and the morphological characteristics; performing dynamic mapping on the digital twinborn model and a pre-calibrated BIM design model, and determining steel bar deformation parameters of the stress abnormal region; and based on the steel bar deformation parameters, generating a quality detection report of the to-be-detected steel bar project. By implementing the technical scheme provided by the invention, the accuracy of reinforcing steel bar engineering quality detection can be improved.

The invention relates to the technical field of fan defect detection, in particular to a fan blade inspection method based on an unmanned aerial vehicle, which comprises the following steps: acquiring static basic data of a fan; constructing a blade reference model based on the static basic data of the fan, and collecting dynamic vibration response data according to a preset short-time dynamic vibration excitation rule; identifying a blade high-risk area according to the dynamic vibration response data, and generating a layered surrounding route in combination with a blade reference model; the unmanned aerial vehicle is controlled to collect blade multi-angle images according to the layered surrounding route, the blade multi-angle images are processed to recognize surface defects, and visual confidence is generated; and mapping the dynamic vibration response data to a blade reference model by using a space-time alignment model, extracting power spectral density characteristics of corresponding positions, calculating a damage index by fusing visual confidence, and outputting an inspection report. The objective of the invention is to solve the technical problem that blade internal damage is difficult to identify in a static inspection mode.

The invention relates to the technical field of engineering mechanical structure health monitoring, and discloses a carry-scraper structural member crack real-time detection system and method based on vibration characteristics, and the carry-scraper structural member crack real-time detection system based on vibration characteristics comprises a sensing acquisition module which is used for acquiring structural member vibration response and obtaining an original signal; the signal processing module is used for carrying out wavelet denoising on the original signal and extracting effective vibration data; the feature extraction and dynamic calibration module is used for extracting multi-domain features and performing dynamic calibration in combination with working conditions; the crack recognition and intelligent diagnosis module is used for inputting an intelligent model to recognize a crack state; and the crack positioning and alarm feedback module is used for positioning cracks and giving an alarm in combination with the diagnosis result. According to the invention, the multi-modal sensor array is constructed, piezoelectric and MEMS sensors are cooperatively arranged, a magnetic snap-in type installation structure is combined, broadband response signals are stably collected under different working conditions, and then front-end signal optimization is completed in cooperation with a multi-scale wavelet denoising and standardization mechanism.

The invention discloses a loss tuning method of a power transformer, which is applied to a transformer body sleeved with a winding and comprises the following steps: applying scanning current excitation containing fundamental waves and harmonic waves to the winding, synchronously acquiring a body vibration signal and converting the body vibration signal into a frequency spectrum; extracting a formant from the frequency spectrum, matching the formant with a theoretical electromagnetic force wave and a structure inherent frequency library, and identifying a coupling formant to be optimized; aiming at each formant, installing a vibration exciter in a corresponding area, sending out an anti-phase periodic pulse force, and dynamically and finely adjusting a pulse force parameter by monitoring a vibration response in real time and taking equivalent mechanical impedance minimization as a target; when the optimal damping state is achieved, the vibration exciter output rod is locked, and static pre-tightening force is formed; and after all formants are adjusted and optimized in sequence and the prestress is locked, final pressing and fixing of the transformer body are completed in the state that the pretightening force is kept. According to the invention, the dynamic loss source of the individual transformer can be actively inhibited and cured before assembly and curing, the operation loss and noise are effectively reduced, and the structural stability is improved.

The invention provides a vibration signal space-time reconstruction method based on a multi-modal condition diffusion model, and relates to the technical field of vibration signal reconstruction, and the method comprises the steps: firstly collecting structure vibration response through multiple sensors, constructing a multi-dimensional vibration signal matrix, and automatically recognizing a space continuous missing region and a time random missing region; performing coarse reconstruction on the missing region by adopting self-adaptive multi-scale interpolation so as to recover the basic trend and frequency band characteristics of the signal; a pseudo-missing mask is further applied to complete data, a training sample is constructed through a self-supervision strategy, and the model is guided to learn spatio-temporal correlation features and missing modes; in a training stage, a diffusion model is used as a generation framework, Gaussian noise disturbance is applied to a missing region, four types of condition embedding of time, space, trend and frequency domain are introduced in a denoising inversion process, signal periodicity, multi-sensor space coupling, low-frequency change and a physical frequency spectrum structure are respectively described, and the noise is reduced; and high-fidelity signal reconstruction under multi-modal information joint constraint is realized.

The invention discloses a subway depot upper cover building vibration response prediction method based on deep learning, and the method comprises the steps: constructing a feature library containing vibration signals and working condition data, generating enhanced data through a mechanical model, and fusing the enhanced data into a data set; a mixed deep learning model embedded with physical prior is constructed, and training and dual-objective parameter optimization are carried out; a prediction result is output after working conditions of real-time data are recognized through the lightweight model; parameters are finely adjusted through regular incremental learning, and transfer learning adaptation is carried out when working conditions suddenly change; verifying precision and rationality, and adjusting the weight of a regular term or suggesting to add a sensor; according to the method, measured data sparseness is made up by enhancing data fusion; the double-branch architecture overcomes the deep nonlinear mapping problem, and physical constraints are prevented from violating physical rules; the incremental learning reduces the cost, and the transfer learning solves the time-varying vibration capture problem; precision is improved through closed-loop verification, accurate real-time prediction of vibration response is achieved, and safety and comfort of a building are guaranteed.

The invention belongs to the technical field of vibration control, and provides a CFD numerical simulation-based TMDI control bridge rigid main beam low-frequency vertical vortex vibration research method, which comprises the following steps of: establishing a rigid main beam-TMDI vertical coupling vibration equation in a wind field, carrying out order reduction processing on the coupling vibration equation by constructing a state equation, embedding a solving process into Fluent software by utilizing a user-defined function, and carrying out low-frequency vertical vortex vibration research on a rigid main beam-TMDI control bridge rigid main beam. And the vertical vortex-induced vibration response of the controlled main beam is obtained by alternately solving the flow field domain and the structural domain. Compared with a theoretical method, the fluid-solid coupling effect can be directly simulated, a vortex-induced force model does not need to be assumed, and the result is more reliable; compared with a wind tunnel test method, various parameters of the structure and the TMDI can be accurately set, physical space constraint is avoided, machining errors do not exist, the method is not limited to low wind speed, and the vibration suppression effect can be verified more conveniently; compared with an on-site actual measurement method, the method does not need to passively wait for vertical vortex vibration of a real bridge for a long time, can obtain richer information, facilitates the development of vibration suppression mechanism analysis, and greatly reduces the research difficulty and cost.

The invention provides an aeolian vibration analysis method for a power transmission line steel tube tower, and the method comprises the steps: S1, obtaining the operation monitoring data of the power transmission line steel tube tower, and historical wind speed meteorological data; s2, sensitivity analysis is carried out on a plurality of different parameters possibly influencing the amplitude of the tower, and main parameters causing vortex-induced vibration response of the steel tube tower component are determined; s3, simulating a typical steel tube power transmission tower component model by using the refined three-dimensional finite element model, and analyzing the natural vibration frequency, the vibration mode curve, the critical wind speed and the maximum amplitude of the component; and S4, establishing vortex-induced vibration models of a single steel pipe and adjacent steel pipes, realizing a dynamic grid effect in the aeolian vibration process based on a nested grid technology, developing a fluid-solid coupling calculation UDF program in the vortex-induced vibration process, and embedding the fluid-solid coupling calculation UDF program into FLUENT software to reproduce a vortex-induced vibration response process of a complex working condition. According to the method, theoretical analysis and numerical simulation are combined to effectively solve the problem of aeolian vibration response evaluation of the high-voltage transmission line steel tube tower, and the calculation efficiency and precision are high.

The invention provides a marine diesel engine shafting longitudinal-torsional coupling vibration calculation method. The method specifically comprises the following steps: S1, calculating crankshaft longitudinal-torsional coupling rigidity; s2, calculating torsional vibration excitation at the propeller by using a CFD method; s3, establishing a ship propulsion shafting coupling free vibration kinetic equation through the calculated longitudinal-torsional coupling stiffness; s4, calculating a longitudinal-torsional coupling inherent frequency and a vibration mode of the ship propulsion shafting through a coupling free vibration kinetic equation of the ship propulsion shafting; and S5, substituting the propeller excitation calculated in the step S2 into the ship propulsion shafting coupling forced vibration kinetic equation to calculate the longitudinal-torsional coupling forced vibration response of the ship propulsion shafting. According to the marine diesel engine shafting longitudinal-torsional coupling vibration calculation method, the crankshaft longitudinal-torsional coupling rigidity is calculated based on a material mechanical deformation energy method, and related exciting force considering the crankshaft longitudinal-torsional coupling effect is obtained by combining related formulas of marine diesel engine shafting dynamics; and finally, ship shafting longitudinal-torsional coupling vibration inherent characteristics and ship shafting longitudinal-torsional coupling forced vibration response are obtained through calculation.

The invention relates to a bridge non-stationary wind speed intelligent prediction and abnormal vibration early warning method and system, and a storage medium, and the method comprises the steps: constructing a first training set based on historical wind speed-vibration response data with a timestamp; and meanwhile, constructing a second training set based on the historical wind speed feature vector marked with the abnormal state. And respectively training the CNN-LSTM network and the KNN classification model by using the first training set and the second training set. And inputting real-time wind speed data into the CNN-LSTM network to obtain a non-stationary wind speed prediction feature vector in a future time period. And the prediction feature vector is imported into a t-SNE-KNN dimension reduction classification algorithm, unstable abnormal wind and normal wind are distinguished, and an early warning boundary and safe and dangerous areas are established. Compared with the prior art, the method has the advantages that an accurate intelligent prediction and abnormal vibration early warning decision method can be constructed for extreme wind events such as downburst, the problems in the prior art are solved, and reliable guarantee is provided for safe operation of a large-span bridge under the extreme wind events.

The invention discloses a vortex-induced vibration response prediction method for a double-layer steel truss bridge based on a machine learning algorithm, and the method comprises the steps: carrying out the wind tunnel test of a section model of the double-layer steel truss bridge, and carrying out the test analysis of the influence rule of various subsidiary facilities and additional pneumatic measures on the vortex-induced vibration of the section of the double-layer steel truss bridge; the vortex vibration generation mechanism of the bridge forming state segment model is analyzed through numerical simulation, and the influence rule of additional pneumatic measure parameter change on the vortex vibration of the section of the double-layer steel truss bridge is explored; based on a machine learning algorithm, a section amplitude prediction model under different wind speeds and a vortex vibration characteristic parameter prediction model under different additional pneumatic measures are established to predict the vortex vibration response of the double-layer steel truss bridge. According to the method, wind tunnel test, numerical simulation and machine learning methods are combined, the vortex-induced vibration response of the double-layer steel truss bridge is predicted, the stability is good, the prediction accuracy is high, and therefore reference is provided for bridge design, operation and maintenance.

The invention discloses an intelligent quality inspection system for medicinal empty capsules, particularly relates to the field of intelligent detection, and comprises a multi-dimensional optical characteristic acquisition module, a dynamic thermodynamic response analysis module, a near infrared spectrum component inversion module, a multi-source data fusion decision module and a self-adaptive feedback optimization module. Omnibearing nondestructive detection of the capsule is realized through non-contact multispectral imaging and vibration response analysis, traditional destructive sampling loss is thoroughly avoided, and the multisource data fusion module integrates optical texture features, mechanical frequency domain energy density and near infrared spectrum inversion values based on the D-S evidence theory, so that the detection accuracy is improved. And the self-adaptive optimization module dynamically adjusts an optical wavelength weight, a mechanical sweep frequency range and a spectrum modeling parameter by applying reinforcement learning, is self-adaptive to fluctuation of different batches of gelatin materials and environmental interference, remarkably reduces a misjudgment rate and ensures continuous and stable operation of a production line.

Provided in the present invention application is a test identification method and apparatus for vibration-involved dynamic parameters of a bearing pedestal and using additional masses. The method comprises: simplifying a dynamic model of a bearing pedestal into a single-degree-of-freedom system; applying an excitation force to the bearing pedestal, testing a response, plotting a frequency spectrogram, and obtaining a resonance frequency; adding additional masses to the bearing pedestal, and testing inherent frequencies under different additional masses; calculating vibration-involved masses on the basis of test results; plotting a curve of the vibration-involved masses changing with the additional masses, performing fitting by using the least-squares method, and performing extrapolation, so as to obtain a vibration-involved mass for when the additional mass is 0 and use same as a vibration-involved mass of the bearing pedestal; adding additional masses to the bearing pedestal, and testing damped vibration responses under different additional masses, so as to obtain logarithmic decrements and damping factors; plotting a curve of the damping factors changing with the additional masses, obtaining a damping factor in the same manner to use as a damping factor of the bearing pedestal, and performing calculation on the basis of the damping factor and the mass to obtain a vibration-involved stiffness coefficient of the bearing pedestal. The present invention using the solution can realize accurate identification of vibration-involved dynamic parameters of a bearing pedestal.

The invention discloses a steel-concrete composite beam sound and vibration calculation method based on FEM-BEM-SEA, and belongs to the technical field of rail transit bridge vibration and noise reduction. The method comprises the following steps: determining the upper limit cut-off frequency of boundary element calculation; determining a lower limit starting frequency of statistical energy analysis; determining a boundary frequency based on the upper limit cut-off frequency and the lower limit start frequency; constructing a bridge finite element model of a bridge structure, and calculating vibration response in a full-frequency-band range when the train passes through the bridge; inputting the vibration response into the bridge boundary element model, and calculating an acoustic response within a range below the boundary frequency when the train passes through the bridge; establishing a bridge statistical energy analysis model of the bridge structure, and calculating acoustic response in a range above the boundary frequency; and jointly using the vibration response and the acoustic response as an acoustic vibration calculation result of the steel-concrete composite beam. According to the method, the problem that high-precision calculation and low calculation workload cannot be met at the same time during sound and vibration calculation of an existing bridge structure is solved.

The invention discloses an optical cable fitting fatigue damage detection method and system, and relates to the technical field of power transmission line state monitoring, and the method comprises the following steps: S1, constructing a wind field and structure coupling observation baseline, obtaining a full-time-domain vibration response signal of an optical cable fitting in a non-uniform wind field environment, generating a phase consistency distribution map, and obtaining a phase consistency distribution map; establishing a corresponding relation between the multi-path reflection source group and the time correlation sequence as a traceable reference for phase analysis; and S2, based on the phase consistency distribution map, performing causal beam demixing processing, performing arrival time difference densification calculation and curvature spectrum separation analysis on each sound wave propagation path, and extracting crack propagation pointing data. According to the method, through wind field-structure coupling observation, path unmixing, phase regression and error checking, multi-path propagation recognition and correction are achieved, crack propagation topology is reconstructed, polarization rotation and a phase suppression mechanism are combined, and direction recognition stability and detection adaptivity are improved.