24 results about "Vibration spectra" patented technology

The invention relates to the technical field of vehicle assembly vibration fatigue tests, in particular to a road load spectrum-based accelerated random vibration spectrum determination method, which comprises the following steps of: selecting i-circle data of each characteristic road surface under full-load, half-load and no-load working conditions as original data of accelerated random vibration spectrum calculation; calculating an impact response spectrum and a fatigue damage spectrum of the i-circle data of each characteristic pavement; respectively accumulating the fatigue damage spectrum statistical values of the three working conditions; packaging the maximum values of the impact response spectrums under the three working conditions; according to a fatigue damage accumulation theory, calculating a fatigue damage spectrum of the total target mileage according to the number of cycles of three working conditions; packaging the maximum value of the impact response spectrum of the whole circle of characteristic pavement under the three working conditions to obtain the maximum value of the overall target mileage impact response spectrum; synthesizing an accelerated random vibration spectrum by using the fatigue damage spectrum of the total target mileage according to a damage equivalence principle; and finally, carrying out ERS calculation on the generated accelerated random vibration spectrum, and checking the correctness and rationality of the accelerated random vibration spectrum.

The present application relates to fine particle sensing technology, and particularly relates to a microcavity vibration spectrum system and method for measuring inherent vibration spectrum of fine particles. The microcavity vibration spectrum system comprises an inherent vibration excitation light path unit and a microcavity detection light path unit; the inherent vibration excitation light path unit is sequentially provided with a pulsed light source, a vibrating mirror, an objective lens and a particle to be measured; and the microcavity detection light path unit is sequentially provided with a tunable laser light source, an adjustable attenuator, a polarization controller, a coupling optical waveguide, an optical microcavity, a photodetector and an oscilloscope. The present application can realize precise measurement of sound waves caused by particle vibration of mesoscale particles and biological particles with inherent vibration frequency in the sub-megahertz-gigahertz frequency range.

The application discloses a kind of sensor chip high reliability high accuracy test system and method for extreme environment, belong to sensor test technical field.The test method includes multiple physical field coupling environment loading, automobile working condition excitation injection, anti-interference synchronous acquisition, anti-drift real-time error compensation, dynamic performance degradation analysis and drift immunity test report generation steps.Anti-drift real-time error compensation is monitored by distributed auxiliary sensor array in real time temperature gradient kurtosis, vibration spectrum entropy and electromagnetic shielding effectiveness, freezes CNN convolution layer weight after triggering drift flag, only carries out incremental federal compensation to fully connected layer, and introduces the physical constraint correction compensation output based on FEM simulation.The application can effectively inhibit the mismatch of compensation coefficient caused by environmental drift, improve the long-term stability and precision of sensor chip test under extreme environment, meet the AEC-Q100 full life cycle reliability verification requirement.

A system (100), apparatus (110) and method for estimating a remaining useful life of at least one bearing are provided. The method includes receiving a request from a source (115, 125, 130) to analyze defects in a bearing, determining a vibration spectrum of the bearing from received operational data, monitoring impacts of defects on the bearing over a period of time based on the determined vibration spectrum, determining a characteristic value for which impacts of defects on the bearing are above a threshold range from the vibration spectrum, determining an impact force during operation of the at least one bearing based on the determined characteristic value and one or more parameters obtained from a virtual bearing model, determining a remaining useful life of the bearing based on the determined impact force during the period of time, and generating a notification on an output device indicating the remaining useful life of the bearing.

This application provides a cavity-enhanced mid-infrared photothermal vibration measurement system and method, relating to the field of photothermal vibration spectrum detection of materials, aiming to improve the accuracy and sensitivity of detection. The measurement system includes a cavity-enhanced substrate, a mid-infrared laser, a laser vibrometer, and a lock-in amplifier. The cavity-enhanced substrate includes a substrate and an asymmetric Fabry-Perot resonator. The asymmetric Fabry-Perot resonator includes a reflective layer, an intermediate dielectric layer, and a layer to be tested, sequentially stacked on the substrate. The mid-infrared laser applies mid-infrared laser light to the layer to be tested, causing the layer to generate a photothermal vibration signal. The laser vibrometer detects the photothermal vibration signal and converts it into a detection electrical signal. The lock-in amplifier extracts the detection electrical signal to determine the photothermal vibration spectrum of the layer to be tested. The ratio of the thickness of the intermediate dielectric layer to the thickness of the layer to be tested ranges from 0.8 to 10. The refractive index of the material of the layer to be tested is less than the refractive index of the material of the intermediate dielectric layer, but greater than the refractive index of air.

The present application relates to a kind of based on self-assembled monolayer interface heat conduction regulation and control physical characteristic engineering construction method.The method comprises the following steps: constructing interface heat transport simulation system;Molecular dynamics simulation is carried out to interface heat transport simulation system, obtains physical dynamic information matrix;All physical dynamic information matrix is subjected to data statistical operation operation, obtains first physical characteristic dataset;The vibration spectrum physical information of the self-assembled monolayer obtained is subjected to peak searching operation and vibration spectrum overlapping area calculation, obtains second physical characteristic dataset and third physical characteristic dataset;Physical characteristic dataset is summarized, and finally constructs based on self-assembled monolayer interface heat conduction regulation and control physical characteristic engineering.Compared with prior art, the present application realizes the construction of physical characteristic engineering based on the interface heat conduction characteristics of self-assembled monolayer, and has important significance for carrying out data-driven self-assembled monolayer interface heat conduction characteristics regulation and control design.

The present application relates to a kind of numerical control machine tool health diagnosis method and system based on vibration spectrum coupling, belong to the field of fault diagnosis.Therein, the method includes obtaining original electric energy by energy self-adaptive capture and converting into stable electric energy, obtain coupling coefficient matrix;Based on coupling coefficient matrix, stability evaluation is carried out, and dynamic stability margin is obtained;Based on dynamic stability margin, early fault risk prediction model is constructed, and fault development time prediction value is obtained;Based on three-order resonance modal coupling coefficient, fault coupling frequency combination is screened out, dynamic parameter compensation is carried out based on fault coupling frequency combination, and targeted parameter compensation amount is obtained;Based on targeted parameter compensation amount, corresponding parameter is compensated, and global knowledge evolution is carried out.The present application realizes vibration spectrum coupling analysis, energy self-capture, dynamic stability evaluation and knowledge evolution mechanism, realizes sensitive capture of early fault, predictive maintenance and adaptive compensation, and improves the intelligent level of machine tool health management.

The invention relates to a dynamic closed-loop energy efficiency management method and system for a numerical control machine tool, and belongs to the field of energy efficiency management. The method comprises the steps that original electric energy and total generation power are obtained through energy self-adaptive capture, and the original electric energy is converted into stable electric energy; vibration signals are obtained through stable electric energy, vibration spectrum entropy mutation detection is carried out based on the vibration signals, and an effective machining period is obtained; obtaining effective machining energy based on the effective machining time period; establishing an adaptive communication protocol according to the effective processing energy; and performing optimization based on an adaptive communication protocol to obtain optimized total energy consumption and reference total energy consumption in a preset period, and obtaining an energy efficiency improvement net value in the preset period. According to the method, the machining state recognition precision is improved through vibration spectrum entropy sudden change detection, self-powered sensing is achieved through multi-source energy collection, intelligent communication is triggered based on the battery state and energy efficiency change, and therefore accurate, real-time and energy-saving energy efficiency monitoring and optimization are achieved without external intervention.

This invention relates to the field of highway pavement inspection technology, specifically a method for detecting the friction performance of highway pavements based on intelligent sensors. The method includes: collecting vibration spectra, surface texture depth sequences, and environmental meteorological readings using a multimodal intelligent sensor array deployed on the highway pavement to form a raw pavement dataset. A sparse coding algorithm optimized based on the projection characteristics of tribological feature subspaces is used to extract vibration feature vectors. These vectors are then reconstructed in three dimensions and fused with temporal features to obtain texture feature maps and pavement environmental state vectors. Multiple features are input into a multi-source fusion network to generate a comprehensive state representation. Finally, a deep convolutional neural network model decodes and outputs estimated friction coefficients and spatial distribution maps. Based on this, risk level assessments and maintenance recommendations are generated and uploaded to a management platform. This method accurately preserves friction-related features, enables collaborative analysis of multi-source data, fully reflects the spatial distribution of pavement friction, and improves the accuracy and completeness of the detection results.

The invention provides an optical cavity enhanced mid-infrared light thermal vibration measurement system and method, relates to the field of material photo-thermal vibration spectrum detection, and aims to improve the accuracy and sensitivity of detection. The measurement system comprises an optical cavity enhanced substrate, an intermediate infrared laser, a laser vibration meter and a lock-in amplifier. The optical cavity enhancement base comprises a substrate and an asymmetric Fabry-Perot resonant cavity, and the asymmetric Fabry-Perot resonant cavity comprises a reflecting layer, an intermediate dielectric layer and a to-be-detected object layer which are sequentially stacked on the substrate. The mid-infrared laser acts mid-infrared laser on the to-be-detected object layer, so that the to-be-detected object layer generates a photo-thermal vibration signal. The laser vibration meter detects a photo-thermal vibration signal and converts the photo-thermal vibration signal into a detection electric signal. And the lock-in amplifier extracts the detection electric signal to determine the photo-thermal vibration spectrum of the to-be-detected object layer. And the ratio of the thickness of the intermediate dielectric layer to the thickness of the to-be-detected object layer is 0.8-10. The refractive index of the material of the to-be-detected object layer is smaller than the refractive index of the material of the intermediate dielectric layer and larger than the refractive index of air.

This invention discloses a fault diagnosis method for crane maintenance management, relating to the field of crane equipment operation and maintenance technology. It includes collecting a multi-source sensor data sequence consisting of vibration spectrum of the hoisting mechanism, wire rope tension fluctuations, and temperature changes at hinge points within the crane's operating cycle. The multi-source sensor data sequence is time-synchronized and outlier-removed to generate a standardized synchronization data matrix. The synchronization data matrix is ​​then fed into a hierarchical fault feature extraction process, outputting degradation feature vectors for key components and anomaly pattern identifiers. The remaining effective working range length of the components is calculated using the degradation feature vectors, and the fault initiation time point is located using the anomaly pattern identifier. These two types of information are merged and encoded to generate a maintenance trigger signal, which is then pushed to the crane control terminal display interface. This method achieves standardized organization and hierarchical feature mining of multi-source operating data, quantifies the service status of components, and identifies the fault initiation time, providing a standardized status basis for crane maintenance management.

A kind of test system of aircraft surface structure gap and seal piece flow-induced sound vibration, wherein the test system is substantially a longitudinally extending multi-pipe segment closed structure, the multi-pipe segment closed structure is mounted on a support, and includes gas supply section, noise elimination and flow stabilizing and pressure stabilizing section, test section and gas release section connected in sequence;Wherein the test section includes sample mounting part, the test sample container of the sample mounting part is detachably connected, the pressure and temperature in the test sample container are controlled;The test section further includes microphone and vibration meter.The system can simulate aircraft cruising speed flow field and can simulate temperature and pressure difference environment when aircraft is running, obtain flow-induced noise spectrum, flow-induced vibration spectrum and transfer characteristics of typical structure gap and seal piece in simulated aircraft working environment, and key data in the field of civil aircraft vibration reduction and noise reduction control technology.

The invention provides a thermal power oil liquid quality evaluation method based on multi-source data fusion, and relates to the technical field of industrial data processing, and the method comprises the steps: obtaining multi-source heterogeneous data such as an oil liquid microstructure acoustic scattering spectrum, a thermodynamic entropy flow, a microscopic vibration spectrum, an external environment and an operation condition; a multi-scale degradation chain type fusion evaluation strategy is adopted, data time sequence evolution is dynamically captured, contribution weights are corrected, real-time self-adaptive evaluation of dynamic working conditions is achieved, and the accuracy and generalization ability of the model are remarkably improved; meanwhile, a degradation chain reaction risk index and driving factor weight distribution are generated, and a refined linkage control strategy including maintenance task, execution time, material personnel and equipment parameter adjustment is generated in combination with deep reasoning of a maintenance knowledge graph and historical records; and super-early warning, dynamic traceability and intelligent intervention are executed, and an evaluation result is converted into a structured instruction set capable of guiding operation and maintenance, so that the interpretability, operability and practicability of a decision are greatly improved.

This invention relates to the field of highway pavement inspection technology, specifically a method for detecting the friction performance of highway pavements based on intelligent sensors. The method includes: collecting vibration spectra, surface texture depth sequences, and environmental meteorological readings using a multimodal intelligent sensor array deployed on the highway pavement to form a raw pavement dataset. A sparse coding algorithm optimized based on the projection characteristics of tribological feature subspaces is used to extract vibration feature vectors. These vectors are then reconstructed in three dimensions and fused with temporal features to obtain texture feature maps and pavement environmental state vectors. Multiple features are input into a multi-source fusion network to generate a comprehensive state representation. Finally, a deep convolutional neural network model decodes and outputs estimated friction coefficients and spatial distribution maps. Based on this, risk level assessments and maintenance recommendations are generated and uploaded to a management platform. This method accurately preserves friction-related features, enables collaborative analysis of multi-source data, fully reflects the spatial distribution of pavement friction, and improves the accuracy and completeness of the detection results.

The present invention relates to a monitoring device (10) arranged to be operably connected to a medicament delivery device (12), said medicament delivery device (12) comprising a manually operated movable dose activation element (20), said monitoring device (10) comprising an electronic circuit (42), said electronic circuit (42) comprising an optical sensor (68), wherein said optical sensor (68) is positioned in relation to said dose activation element (20) so as to detect a manual operation of said dose activation element (20) from an initial position; said electronics circuit further comprising a vibration sensor (70), wherein said vibration sensor (70) is positioned in relation to said medicament delivery device so as to detect specific vibration spectra produced by the medicament delivery device when said dose activating element (20) is operated, wherein said electronic circuit (42) comprises computing elements capable of correlating the information from the optical sensor (68) with the information from the vibration sensor (70) when said dose activation element (20) is operated in order to register when a correct dose has been set.

This application provides a rapid fault triggering device and method for a cantilever structure sensor, belonging to the field of sensor technology. It includes a base plate, a mounting base, a mounting plate, and a collision clamp. The mounting base and mounting plate are vertically connected to the upper surface of the base plate. The mounting plate moves axially along the sensor under test. The collision clamp is mounted on the mounting plate. The sensor under test passes sequentially through the mounting base, the mounting plate, and the collision clamp. The collision clamp has a through hole for the sensor to pass through, the diameter of which is larger than the outer diameter of the sensor. The method includes mounting the sensor under test on the mounting base, mounting the collision clamp on the mounting plate, adjusting the position of the collision clamp and the gap between the through hole and the sensor; conducting vibration tests using the HB5830 test method and vibration spectrum; and swinging the collision test fixture under vibration using the cantilever structure to amplify the test magnitude through the collision effect, thereby achieving rapid fault triggering. This application significantly improves R&D efficiency and reduces time costs and experimental resource waste.

This invention discloses an intelligent access control system, belonging to the field of security monitoring. Addressing the problem that existing access control systems fail in power outages and cannot perceive user intent, this invention includes: an energy wake-up module that uses a piezoelectric transducer to convert the mechanical energy of knocking into electrical energy, switching between full perception, emergency alarm, or energy accumulation modes based on the supercapacitor voltage; a multimodal perception module that collects facial, touch pressure, and environmental data and performs adaptive compensation; a state interpretation module that identifies coercive states by associating facial micro-expressions with touch tremors and identifies attack states by associating vibration spectra with non-biological contact; and a decision execution module that executes normal unlocking, silent alarm, or audible and visual locking accordingly. This invention achieves zero-power standby, passive start-up, surface unlocking and covert alarm in coercive scenarios, and active defense in attack scenarios, significantly improving the energy reliability and security intelligence level of the access control system.

The invention discloses a molecular terahertz spectrum measurement method and system based on plasmon nano optical tweezers, and belongs to the technical field of molecular spectrum detection and nano manipulation. A local enhanced light field generated by a plasmon nano structure is utilized to capture and fix nanoparticles of a single target molecule or a loaded molecule in an aqueous solution; generating frequency-tunable terahertz waves by adopting a double-laser beat frequency technology, and coupling the terahertz waves to the local light field to excite a captured target; monitoring the optical signal change caused thereby in real time; acquiring a characteristic vibration spectrum of the target molecule by scanning the terahertz frequency; the plasmon nano optical tweezers and the optical beat frequency terahertz technology are combined, so that the terahertz vibration spectrum of the single molecule or nano particle level in the aqueous solution environment is directly measured, and the core problem that the traditional terahertz technology cannot be used for liquid-phase biological detection due to strong absorption of water is solved.

The invention relates to the technical field of seawater cooling analysis, in particular to a seawater cooling comprehensive analysis system based on multi-sensor monitoring, which comprises a multi-source sensing array module for synchronously outputting a pipe wall temperature gradient, an axial vibration spectrum, an electrochemical noise signal and a real-time water quality parameter; the dirt-corrosion coupling module is used for calculating a dynamic dirt thermal resistance value based on the energy attenuation characteristics of the pipe wall temperature gradient and the axial vibration spectrum, analyzing the current peak density of the electrochemical noise signal, and generating a corrosion heat transfer influence factor in combination with the dynamic dirt thermal resistance value; the self-adaptive calibration engine module is used for compensating and calibrating the electrochemical noise signal according to the real-time water quality parameter and generating a multi-domain early warning threshold value; and the intelligent decision module is used for comparing the corrosion heat transfer influence factor with a multi-domain early warning threshold value and outputting an energy efficiency optimization instruction set. Quantitative indexes of corrosion heat transfer influence factors are formed, and the perception sensitivity and engineering interpretability of the corrosion-heat exchange degradation process are improved.

A cross-modal vibration spectrogram-visible light image fusion fan fault detection method comprises the following steps: step 1, vibration signals and blade images in the fan operation process are synchronously acquired through a vibration sensor and an industrial camera, vibration sequences are converted into pseudo-color time-spectrograms, and the pseudo-color time-spectrograms are used as time-spectrograms; standardization processing and defect bounding box labeling are carried out on the two types of images, and a cross-modal fan fault detection data set is constructed; and step 2, constructing a double-branch backbone network structure, respectively applying two-way decoupling differential feature extraction to the visible light image and the vibration spectrogram, expanding the direction perception capability while maintaining the independence of modal features, and providing a high-quality semantic base for subsequent fusion. The technical problem to be solved by the invention is to provide the cross-modal vibration spectrogram-visible light image fused fan fault detection method, and multiple types of defects such as blade cracks, corrosion and abrasion are accurately identified in real time, so that the operation and maintenance efficiency of a wind turbine generator is improved, and the equipment safety is guaranteed.

The invention discloses a method and a system for constructing a digital twinborn model of a phase modifier based on real-time data fitting. The method comprises the following steps: acquiring real-time operation data of the phase modifier; based on vibration data in operation data of the phase modifier, a vibration spectral density characteristic quantity is extracted through wavelet transform processing, then reactive power in the operation data serves as an input variable, the vibration spectral density characteristic quantity serves as an output variable, function fitting is carried out through a least square method, and a phase modifier is obtained. Constructing a digital twinborn model between reactive power and vibration spectral density; inputting reactive power in operation data collected in real time into the digital twinborn model, and generating a predicted value of the vibration spectral density; calculating a standard deviation between the real-time vibration spectral density and the predicted value; when the standard deviation exceeds a preset threshold value, it is judged that the phase modifier is in an abnormal operation state, and an alarm mechanism is triggered. The method has the advantages of improving the monitoring efficiency of the running state of the phase modifier, reducing the operation and maintenance cost of the phase modifier and the like.

This invention relates to the field of elevator fault diagnosis technology, specifically to an elevator predictive diagnosis system and method based on multi-source sensor fusion and deep learning. The system comprises: a multi-source sensor acquisition module for simultaneously acquiring acoustic fingerprints, broadband vibration spectra, and operating condition data during elevator operation; an edge computing terminal for time synchronization, noise reduction, and feature extraction of the acquired multi-source data; a cloud-based or local intelligent diagnostic platform for fusing the multi-source sensor data and incorporating deep learning models with attention mechanisms, adaptive transfer learning based on operating conditions, and uncertainty quantification; and a human-computer interaction and early warning module for visually presenting fault diagnosis results, remaining life prediction values, and their confidence intervals. This system and method achieve deep fusion of multi-source heterogeneous sensor information and accurate fault identification, possess adaptive transfer learning capabilities based on operating conditions, provide remaining life prediction with uncertainty quantification, and improve detection intelligence and human-computer interaction experience.

This invention provides a machine vision-based nozzle clogging identification method and system, belonging to the field of image data analysis and processing technology. It employs multi-nozzle thermal vision data, deformation vision data, and acoustic data to collect and extract multi-physics dynamic features, monitoring the overall state of material flow, thermodynamic behavior, and structural changes within the nozzle. Thermal vision data analyzes heat propagation anomalies through temperature field sequences, deformation vision data captures structural responses through surface deformation sequences, and acoustic data identifies material flow obstacles through vibration spectra, improving detection accuracy. A multi-modal temporal fusion network is constructed for deep learning and fusion analysis of multi-physics dynamic features. Based on the root cause and mechanism type of clogging, clogging risk levels are classified and self-healing control strategies are implemented, achieving accurate identification and judgment of minor and initial clogging, improving clogging monitoring accuracy, reducing production interruptions caused by false alarms, and enhancing the efficiency and economy of the injection molding process.