2683 results about "Vibration sensor" patented technology

The invention relates to the technical field of cable operation state detection, and particularly discloses a multi-source data driven cable operation state comprehensive evaluation method, which comprises the following steps of S1, adopting a layered distributed sensing network architecture, and deploying three types of core sensors at key nodes of a cable, through space-time calibration of the multi-source heterogeneous sensor, data consistency is improved, fusion deviation is eliminated, the problem of data islands of a traditional system is solved, and a precise evaluation foundation is laid; noise suppression and dynamic correlation modeling are adopted, environmental interference is stripped, a vibration and displacement coupling relation is quantified, limitation of a single parameter is broken through, heterogeneous fault features are captured, and evaluation comprehensiveness and sensitivity are improved; a self-adaptive threshold mechanism is constructed based on environment weight and historical data, the bottleneck of a fixed threshold is broken through, an evaluation standard is corrected along with equipment aging and environment change, misjudgment is avoided, and diagnosis robustness in different scenes is enhanced.

An online testing and diagnosis method for vibration characteristics of blades of wind turbine is disclosed. Steps of testing and diagnosing blade vibration comprises: S1: installing vibration sensors at key positions of a blade, designing an adaptive data acquisition strategy, and automatically adjusting a sampling rate according to a vibration amplitude and environmental changes monitored in a real time; S2: extracting key features reflecting health status of the blade from massive data, and evaluating an impact of wind speed, temperature, and environmental factors on vibration characteristics; S3: designing a customized deep learning model for damages of the blade of a wind turbine, extracting a time sequence data and a vibration signal, identifying a damage among different types of damages and evaluating a damage degree; and S4: automatically adjusting a warning threshold based on a real-time data stream and a historical trend, and drafting a preventive maintenance plan.

The invention provides a pump equipment state monitoring and fault diagnosis method based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the steps: obtaining a vibration signal of a target type of pump equipment based on a preset vibration sensor, and marking the vibration signal; extracting features of the vibration signal based on a preset dual-channel feature extraction model; iteratively training a preset basic fault diagnosis model based on the characteristics of the vibration signal until a preset training completion condition is reached; binding a preset number of fault diagnosis models to construct a pump equipment state reasoning model; acquiring an operation vibration signal of the pump equipment of the target category, inputting the operation vibration signal into the pump equipment state reasoning model, and outputting a fault category; through time-frequency dual-channel fusion and multi-scale perception, the fault identification precision is improved; the rationality and interpretability of the result are enhanced by using physical prior constraints; and through model integration optimization, the classification stability and reliability in a complex scene are improved.

The invention relates to a multi-sensor fault feature fusion diagnosis method for electrical equipment, which comprises the following steps: synchronously acquiring operation data of the electrical equipment through a vibration sensor, a temperature sensor, a current sensor and an ultrasonic sensor, dynamically adjusting the sampling frequency according to the physical characteristics of each sensor, and the sampling rate of the temperature signal is not lower than 1Hz. Through a multi-source sensor data synchronous acquisition and time sequence alignment technology and a signal alignment method combining a dynamic time warping (DTW) algorithm and Hilbert-Huang transformation, the problem of time asynchronization of heterogeneous sensor data such as vibration and temperature is solved, so that the time alignment precision of multi-source data is improved, the feature extraction accuracy is improved, and the accuracy of feature extraction is improved. Through hierarchical feature extraction and graph convolutional network fusion, a feature incidence matrix based on mutual information is constructed, deep correlation between vibration signal TKEO features and cross-modal features such as current harmonics is mined by using GCN, the feature dimension is reduced, and the fault feature separability index is improved.

The invention relates to the technical field of road surface detection, in particular to a road surface skid resistance detection system based on multi-feature fusion, which comprises the following steps: applying broadband sweep frequency excitation by using a frequency modulation vibration sensor, matching with the inherent frequency of road surface texture to generate local resonance, and collecting the temperature, humidity and rainfall of a road surface in real time; performing fast Fourier transform on the collected vibration signals to obtain a resonance response spectrum, extracting key parameters through Gaussian fitting, and fusing frequency domain, material and environment data to form a comprehensive feature set; the frequency domain features are converted into three-dimensional energy distribution of pavement microtextures, the actual contact area ratio is calculated according to the three-dimensional energy distribution, an environment temperature and humidity compensation factor is introduced, and a dynamic friction attenuation coefficient is calculated; and comparing the calculated dynamic friction attenuation coefficient with a third-level safety threshold, and outputting a corresponding anti-skid performance level. Multi-source data fusion enables a detection result to be more fit with an actual driving scene, and misjudgment caused by single data is avoided.

The invention relates to the technical field of high-speed rail platform safety monitoring, and discloses a high-speed rail platform area intrusion detection and early warning alarm system, which comprises an acoustic and vibration sensor network deployed in a platform area and used for collecting environment signals in real time, the edge calculation unit extracts signal features and compares the signal features with a dynamically maintained environmental rhythm feature baseline to generate a detuning event mark, and the detuning mode analysis device identifies an abnormal mode through space-time correlation and triggers an alarm. According to the method, early perception of subtle anomalies is realized by constructing a multi-modal environment rhythm baseline, active perturbation injection and differential response analysis technologies are combined, the recognition capability of a silent target is remarkably improved, meanwhile, the monitoring continuity under an extreme working condition is ensured by using an elastic baseline adaptive mechanism, and the detection accuracy is improved. And a closed-loop security and protection system from passive sensing to active discrimination is formed.

The invention relates to the technical field of engineering, in particular to a geomembrane defect detection system and method under the earth and stone medium coverage condition, and the geomembrane defect detection system comprises a multi-mode adaptive sensing module, an edge calculation module, a data fusion module, a digital twinning module and a decision and application module; compared with the defects that single resistivity or geological radar detection is adopted in the prior art, interference of medium conductivity and metal impurities is likely to happen, and the deep resolution is insufficient, the scheme adopts multi-mode sensor network cooperative work, and a dynamic density layout and time-phased acquisition strategy is combined; through multi-mode complementation of an electrode array, an ultra-wideband radar, a distributed optical fiber and an MEMS vibration sensor, a dominant sensing mode is automatically switched in a conductive clay area, temperature gradient interference is avoided through night acquisition, the penetration depth is increased to 15 meters through multi-band radar fusion, and metal interference is inhibited; the detection precision and the anti-interference capability in a complex medium environment are remarkably improved, and the problems of high false alarm rate and high omission ratio of a traditional method are effectively solved.

The invention relates to the technical field of industrial manufacturing digital management, in particular to a machine vision production line performance evaluation and optimization management system, which comprises a data acquisition module for triggering a high-speed industrial camera array, a vibration sensor and an RFID reader through a central synchronous controller to synchronously acquire product images, equipment operation and material circulation data; the data processing and fusion module extracts product quality features based on CNN, and fuses multi-modal data through time sequence alignment normalization and an attention mechanism; the dynamic efficiency evaluation module calculates OEE, FPY and a production line balance rate in real time by means of a deep neural network; the optimization strategy generation module is used for reinforcing the learning agent to output optimization instructions such as equipment parameter adjustment; and the control execution module converts the instruction into an industrial protocol format, issues the instruction to the PLC, and verifies the effect to form a closed loop. According to the method, the data relevance and the evaluation real-time performance are improved, the dynamic state of the adaptive production line is optimized, and the efficiency improvement is facilitated.

The invention provides an offshore wind turbine generator fault diagnosis method and system based on multi-source sensor data fusion. The method comprises the steps that a vibration signal from at least one component of a wind turbine generator is acquired through a vibration sensor; performing time-frequency conversion on the vibration signal by applying synchronous compression wavelet transform to obtain time-frequency representation of the vibration signal; when the reconstruction error exceeds a preset threshold value, it is judged that an abnormal event exists in the vibration signal; obtaining the position of a part corresponding to the abnormal event; starting an image sensor and an acoustic sensor according to the position of the component, and acquiring an image signal and a sound signal of the component according to the image sensor and the acoustic sensor; according to the DS evidence theory, the vibration signal, the image signal and the sound signal, obtaining the confidence of the fault type; the fault type of the component is judged according to the maximum confidence allocation principle, high-resolution time-frequency analysis can be achieved through synchronous compression wavelet transform (SST), and the fault feature identification degree is improved in combination with the self-encoding neural network and the D-S evidence theory.

The invention discloses a GIS online monitoring method based on multi-state quantity integration, and relates to the field of GIS online monitoring, and the method comprises the steps: deploying an array composed of an ultrahigh frequency sensor, an ultrasonic sensor, a gas density sensor, an optical fiber temperature sensor and a vibration sensor based on a GIS equipment cavity structure zone; the method comprises the following steps: synchronously acquiring an ultrahigh-frequency electromagnetic wave signal, an ultrasonic signal, a gas density signal, a local temperature signal and a shell vibration signal in an operation process of GIS equipment so as to obtain a multi-dimensional original state quantity sequence; according to the method, multiple types of sensors are accurately distributed and controlled for the GIS cavity structure partition, and through time alignment, adaptive weight fusion and time-space convolution feature extraction, hidden dangers such as a partial discharge source can be accurately positioned, and a state quantity can be predicted by means of a correlation evolution equation. Meanwhile, mechanisms such as sensor fault automatic elimination and complementation, prediction accuracy verification and the like guarantee data reliability, equipment states can be mastered in real time, and faults can be warned in advance.

The invention relates to the technical field of combustion equipment state monitoring, in particular to a combustion state monitoring method and system based on vibration and noise fusion analysis, and the method comprises the steps: collecting a combustion signal in real time through a vibration sensor and a broadband noise sensor, extracting a characteristic frequency band through noise reduction and filtering, and obtaining a combustion state; fPGA hardware-level clock synchronization is adopted to realize time alignment of bimodal signals, a vibration-noise joint feature matrix is constructed and multidimensional coupling is carried out, a combustion state feature vector is generated through a mixed deep learning model, a combustion state is determined based on a bimodal confidence weighting decision, and a linkage control instruction is generated during interruption. The system comprises a signal acquisition synchronization unit, a feature fusion and modeling unit, a state decision and instruction generation unit and a safety linkage execution unit, executes a method and activates a standby safety system, solves the problems of asynchronous bimodal data and insufficient monitoring precision, and improves the combustion state monitoring accuracy and safety response efficiency.

The invention relates to the field of fatigue driving behavior analysis based on image recognition, in particular to a fatigue driving behavior feature extraction and analysis method based on image recognition, which comprises the following steps of: acquiring an initial state set of a driver in real time through an IMU (Inertial Measurement Unit), an RGB (Red, Green and Blue) camera and an MEMS (Micro Electro Mechanical System) vibration sensor, converting the initial state set into four images such as a head attitude angular velocity oscillogram, extracting image features and inputting the image features into corresponding preset models to obtain fused feature data, dynamically adjusting weights through scene context features, calculating cognitive load indexes and dividing processing modes; according to the method, multi-modal data fusion and dynamic weight adjustment are realized, the cognitive load of the driver can be accurately evaluated and graded intervention can be performed, and the driving safety is improved.

The invention discloses a structural health monitoring device for civil engineering. According to the technical scheme, the structural health monitoring device is characterized by comprising a multi-parameter sensing module used for collecting strain, acceleration, displacement, temperature, humidity, vibration frequency and structural surface crack image parameters of a civil engineering structure; the multi-parameter sensing module comprises a strain sensor group, an acceleration sensor group, a displacement sensor group, a temperature and humidity sensor, a vibration sensor and an image acquisition unit; the strain sensor group is composed of a plurality of fiber Bragg grating strain sensors which are installed at key stress parts of the structure in a distributed arrangement mode. According to the structural health monitoring device for civil engineering provided by the invention, by virtue of the unique multi-parameter sensing module, the advanced data processing module and the flexible and reliable data transmission module, various excellent effects are shown in the field of civil engineering structural health monitoring.

The invention discloses a remote real-time monitoring and fault early warning method for a power supply operation state, and relates to the technical field of power electronics and energy management, and the method comprises the steps: collecting the temperature data of a key part of a power supply through an optical fiber Bragg grating sensor array, and analyzing the wavelength offset; electrical parameters are collected through a differential MEMS current / voltage sensor, and a permalloy / ferrite composite shielding layer is arranged in the sensor; collecting a mechanical vibration signal through a piezoelectric ceramic vibration sensor; for device data from different manufacturers, Modbus and CAN heterogeneous protocols are matched and analyzed through a regular expression, and the Modbus and CAN heterogeneous protocols are converted into a unified RDF triple format; inputting temperature, current and vibration data into a space-time alignment module, and constructing a semantic mapping relation by adopting an ontology; calculating a sensor confidence coefficient weight, and performing weighted fusion to generate an equipment health index; operating a lightweight LSTM model at an edge node, and detecting current harmonic and vibration resonance frequency band abnormity in real time; cOMSOL multi-physical field simulation is combined at the cloud end, and the evaporation rate of the capacitor electrolyte is predicted.

The invention provides a wind tunnel structure sensitive reflection settlement position intelligent identification method, and belongs to the technical field of wind tunnels. Vibration sensors and displacement sensors are arranged at key positions of a wind tunnel structure to form a monitoring network, collected signals are preprocessed, and a settlement factor matrix is established; a dynamic load matrix is constructed to describe composite load distribution, a vibration burr identification matrix is established, real signals and noise are separated by adopting wavelet transformation, a slow settlement trend matrix is constructed to extract a long-term change rule, and a least square optimization algorithm is adopted to jointly solve each matrix parameter to establish a settlement position identification function. The settlement three-dimensional position coordinate is determined according to the multi-sensor data fusion result, the structure safety state is evaluated through the settlement risk coefficient, and the technical problems that the wind tunnel structure settlement position recognition precision is insufficient, and a real settlement signal and a noise interference signal cannot be effectively distinguished are solved.

The invention belongs to the technical field of injection mold detection, and discloses an injection mold surface defect detection method based on visual inspection, and the method comprises the following steps: S1, adaptively adjusting collection parameters according to mold process parameters, and ensuring clear collection of different process surface defect features; s2, a deformation matrix is output through the mold thermal deformation finite element model, image registration is guided in combination with a deformation field, and the defect position deviation of the thermal-state mold is corrected; the method comprises the following steps: constructing a vibration-fuzzy kernel mapping model based on vibration sensor data, and restoring a fuzzy image by using an improved Richardson-Lucy algorithm; s3, a process exclusive texture primitive library is constructed, and unified threshold positioning deviation is avoided; s4, converting process parameters into feature extraction weights through a process perception attention CNN model, and accurately capturing defect core features under different processes; according to the design, the detection method can adapt to the surface of a multi-process mold without replacing a model, and the debugging cost of cross-process detection is greatly reduced.

The invention discloses a multi-mode rainy day area road flatness detection method by means of Carla training, and relates to the technical field of road flatness detection, and the method comprises the steps: collecting real rainy day road surface images and vehicle parameters, and constructing a real data set; a rainy day scene is simulated in Carla, and terrain, wet and slippery materials and weather are configured; the method comprises the following steps: acquiring multi-modal data such as simulation point cloud and acceleration through a laser radar and a vibration sensor, and constructing a simulation data set; fusing real and simulation data to train a multi-modal neural network; extracting and fusing point cloud and acceleration features; road height variances and ranges are predicted to assess flatness. According to the method, the problem of scarcity of real data in a rainy day environment is solved by using Carla to generate synthetic data, the real acquisition cost is reduced, the detection precision is remarkably improved, various rainfall intensities and terrains can be covered, data complementation of the laser radar and the vibration sensor is realized, and the robustness in a rainy day is improved through dynamic weighting of an attention mechanism.

The invention relates to the technical field of energy storage cabinet safety monitoring, in particular to an energy storage cabinet multi-parameter intelligent monitoring device based on an MEMS sensor array, a sensor array unit in the energy storage cabinet multi-parameter intelligent monitoring device comprises distributed MEMS temperature and vibration sensors, the temperature sensors are installed in a non-vertical mode, detection deviation caused by direct blowing of airflow is avoided, and the energy storage cabinet multi-parameter intelligent monitoring device based on the MEMS sensor array is obtained. The airflow disturbance correction unit corrects temperature original data in real time and eliminates interference of airflow on module body temperature detection by means of a heat dissipation airflow vector field dynamic model and a compensation algorithm, and the multi-parameter coupling analysis unit processes vibration and temperature signals in a double-channel mode, extracts tab loosening high-frequency micro-vibration characteristics and correlates the temperature rising rate. Early fault logic is triggered through two-stage judgment, the fault decision and output unit fuses the correction temperature, the coupling strength coefficient and the speed-up ratio, graded early warning is generated through a fault tree model and transmitted to an external monitoring system, and accurate monitoring of early faults of the energy storage cabinet is achieved.

The invention relates to the technical field of electric scooters, in particular to a vehicle battery energy-saving control system of a high-strength impact-resistant scooter, which comprises an impact-resistant battery bin, a battery energy-saving control system and a battery energy-saving control system, wherein the impact-resistant battery bin comprises a multi-layer composite shell, a vibration sensor and a filling layer; the energy-saving control module comprises an intelligent detection unit, a central controller and a power optimizer; the power optimizer is used for adjusting the PWM duty ratio of the motor driving circuit according to the power mapping table; the energy recovery module is used for receiving brake force, a slope angle, a motor rotating speed and a vehicle mass estimation value, calculating an optimal recovery current through a Kalman filter, and injecting the recovery current into a battery pack by adopting a bidirectional Buck-Boost circuit; and the safety cooperative controller executes a hierarchical protection strategy, and a data bus is connected with each module to realize data interaction. Therefore, the problems that in the prior art, an electric scooter is insufficient in battery endurance, low in energy recovery efficiency, weak in impact resistance, poor in collaboration and the like are solved.

The invention relates to the technical field of hydrogen storage system testing, and discloses a one-way valve combined hydrogen storage system durability test bench and a test method. A main control computer of the rack is used for configuring test task parameters, coordinating operation of subsystems and processing test data; the high-pressure hydrogen supply subsystem comprises a pressure-adjustable hydrogen source and a pressure closed-loop control module, and can apply accurate and controllable hydrogen pressure to the inlet end of the tested system according to test task parameters; the multi-channel valve state acquisition subsystem acquires the inlet and outlet pressure of the valve, the surface temperature of the valve body and vibration spectrum data in real time through pressure, temperature and vibration sensor arrays arranged at the valves, and transmits the data to the main control computer; the valve action driving subsystem comprises a high-speed electromagnetic actuator array and a displacement feedback module and can drive all valves to execute a preset opening degree sequence and opening and closing frequency. And the durability evaluation subsystem is embedded into the main control computer, calculates the accumulated damage degree of the valve and predicts the residual life cycle.

The invention discloses a motor abnormal sound detection method and system based on contact acquisition and small sample learning, and the method comprises the steps: directly coupling a motor housing through a contact vibration sensor, collecting an original vibration signal, and generating an anti-interference vibration signal; inputting the anti-interference vibration signal into a nonlinear resonance enhancement module to generate an enhanced sound signal; performing wavelet packet decomposition on the enhanced sound signal, extracting a multi-scale frequency band energy entropy, and generating a motor abnormal sound feature matrix by combining singular value decomposition dimension reduction; on the basis of a dynamic weight distribution element learning algorithm, a small number of normal samples and abnormal samples are utilized to construct an abnormal sound classification model; and inputting the motor abnormal sound characteristic matrix into an abnormal sound classification model, detecting transient abnormality through a sliding window time sequence matching algorithm, and outputting an abnormal sound judgment result. According to the embodiment of the invention, high-precision and low-false-alarm motor abnormal sound detection under complex working conditions can be realized.

The invention discloses an automatic detection method and system based on the hidden crack characteristic of a photovoltaic module, and belongs to the technical field of automatic detection.The method comprises the steps that an unmanned aerial vehicle carries an infrared thermal imager, a polarization camera and a vibration sensor, heat distribution, surface texture and micro-vibration signals of the photovoltaic module are synchronously collected, and a multi-dimensional hidden crack characteristic data set is constructed; according to the method, the lightweight convolutional network is deployed on the unmanned aerial vehicle, local data processing and subfissure probability value output are realized, cloud dependence is reduced, delay is reduced, the subfissure probability value is weighted and calculated through the infrared thermal anomaly factor, the polarization texture anomaly factor and the edge sharpness factor, environmental noise is effectively suppressed, the detection accuracy is improved, and the detection efficiency is improved. A multi-angle reinspection task is inserted into a high-probability subfissure area, a low-risk area is quickly scanned, a medium-probability area is decided according to electric quantity and priority, when the number of reinspection areas is large, a global optimal path is recalculated, when high-probability subfissure is detected, the current path is interrupted immediately, the reinspection task is inserted, and delay is avoided.

The invention relates to the technical field of monitoring and intelligent decision analysis, and discloses a monitoring and intelligent decision system for the influence of holographic blasting vibration on slope stability, and the system comprises a data collection module, a data processing module, an intelligent decision module, and a feedback control module. The method comprises the following steps: dynamically deploying a three-dimensional vibration sensor array based on topographic features; collecting and preprocessing blasting vibration signals and geological data; fusing the physical model and the data driving model to predict vibration propagation; calculating a dynamic safety coefficient and generating a multi-objective optimization scheme; and outputting an early warning instruction and feeding back and adjusting the sensor network configuration. Through a three-dimensional dynamic monitoring network, real-time data processing, a hybrid drive prediction model, multi-target quantitative evaluation and hierarchical early warning regulation and control, the problems of incomplete sensor coverage, early warning delay, prediction deviation, evaluation deficiency and poor scene adaptability are solved, and accurate monitoring and intelligent decision making of blasting vibration influence are realized.

The invention relates to a diagnosis method for judging a loose part of rotating equipment. The diagnosis method comprises the following steps: (1) collecting vibration data; (2) spectrum characteristic analysis; (3) differential vibration testing; (4) positioning a loose part; and (5) dynamic verification and optimization. The device has the advantages that through multi-dimensional vibration data acquisition, vibration sensors are arranged at key parts such as motor feet, a bedplate and foundation bolts, vibration displacement values in the horizontal direction, the vertical direction and the axial direction are measured, multiple test points are divided to synchronously acquire data, and vibration information of rotating equipment can be comprehensively acquired. And combining frequency spectrum characteristic analysis and differential vibration comparison, comparing vibration displacement value differences of different hierarchical structures of the same part, and calculating a difference threshold value, so that the specific loose part of the A-type machine is accurately positioned, the limitation that accurate positioning is difficult to realize by traditional experience-dependent judgment or single vibration amplitude analysis is broken through, the blindness of maintenance is reduced, and the working efficiency is improved. The maintenance efficiency is improved.

The utility model discloses a detection device and a control system for settlement of a lap joint surface of a new roadbed and an old roadbed, and relates to the technical field of road engineering monitoring, the detection device comprises a first support plate, a second support plate, two solar panels, two vibration sensors and two storage batteries, a placing box is mounted on the supporting rod, a laser displacement sensor and an industrial camera are mounted in the placing box, a detection plate is mounted on the second supporting plate, and a fluorescent graduated scale is mounted on the detection plate; according to the utility model, the traditional threaded rod and threaded cylinder adjusting mode is replaced by optimizing the adjusting mechanisms, the problems that a threaded structure is easy to wear and block and the manual operation efficiency is low are effectively solved, and a composite monitoring mode of the laser displacement sensor, the industrial camera and the fluorescent graduated scale is adopted, so that the monitoring precision is improved. And a multi-dimensional auxiliary assembly is combined to improve the reliability of the system.

The invention discloses an intelligent vibration analysis and fault diagnosis method, and particularly relates to the technical field of rotating machinery state monitoring and fault diagnosis. Collecting a signal through a vibration sensor and carrying out standardization preprocessing; performing parallel analysis on the preprocessed signal by using the constructed multi-scale feature extraction and fusion network, extracting low-frequency trend and high-frequency detail features, and fusing the low-frequency trend and high-frequency detail features to generate a comprehensive feature vector; inputting the vector into a classifier based on an attention mechanism to carry out preliminary fault identification; meanwhile, performing similarity matching on the comprehensive feature vector of the current case and a fault knowledge base in which historical cases are stored; and finally, an enhanced diagnosis conclusion is generated by fusing an output result of the classifier and matched historical case experience, and a new case subjected to field verification is continuously updated to the knowledge base. According to the method, adaptive extraction of deep features of the vibration signals, interpretability enhancement of a diagnosis decision process and continuous evolution of system diagnosis knowledge are realized.

The invention discloses an ECMO equipment operation state monitoring method, and belongs to the technical field of ECMO equipment operation state monitoring. The method comprises the following steps: firstly, collecting vibration signals of a blood pump at low, medium and high test rotating speeds through a vibration sensor; carrying out mean value removal processing on the signals, and carrying out weighted synthesis on low-frequency, intermediate-frequency and high-frequency fault enhancement signals based on frequency band energy; extracting an energy abnormal synthetic value and an extreme value abnormal synthetic value for the fault enhancement signal and the envelope signal thereof; respectively splicing the two types of abnormal composite values of different frequency bands to obtain an energy abnormal splicing matrix and an extreme value abnormal splicing matrix; and finally, processing the two matrixes by using an operation state evaluation neural network, outputting an operation state score of the blood pump, and realizing accurate monitoring of the operation state of the blood pump of the ECMO equipment.

The invention discloses a tool fracture and fatigue intelligent detection method based on vibration signal analysis, and the method comprises the following steps: S1, installing a vibration sensor, and collecting the vibration signal of a tool in real time; s2, the collected tool vibration signals are preprocessed, and noise in the signals is removed; s3, performing time-frequency analysis on the preprocessed vibration signals, and extracting time-frequency features in the signals; s4, performing deep feature learning on the extracted time-frequency features to form deep features; s5, the depth features are classified and analyzed, and the health state of the cutter is output; s6, according to the health state optimization feature extraction and prediction result of the cutter, generating learning output; s7, evaluating the health state of the cutter in real time according to the learning output, and pushing alarm information; and S8, according to the alarm information, predicting the service life of the cutter and optimizing a cutter replacement and maintenance strategy. According to the method, short-time Fourier transform and Hough transform are combined, and the extreme learning machine is applied, so that intelligent detection on the fracture and fatigue of the cutter is realized.

The invention discloses a chip testboard-oriented vibration noise adaptive suppression system, and particularly relates to the technical field of chip testing. Comprising a chip testboard multi-modal sensing module, a multi-modal data feature extraction module, a vibration noise adaptive suppression decision module, a chip testboard active vibration suppression execution module and a system state monitoring and feedback module. The chip testboard multi-mode sensing module comprises a vibration sensor, an acoustic sensor and a motion state sensor, data collected by the three sensors are preprocessed through a time synchronization protocol and standardized operation, and a preprocessed chip testboard multi-mode data set is obtained; according to the invention, the DRL intelligent agent is used as a decision module, network parameters can be finely adjusted on line through a reward signal of the system state monitoring and feedback module, a new working condition can be automatically adapted, the optimal vibration suppression performance can be maintained, and extremely high robustness can be shown.

The invention discloses a system and method for health monitoring of a steel grid structure, and belongs to the technical field of structural mechanics testing, and the method comprises the steps: obtaining the position information of steel grid nodes, generating a node position distribution diagram through a self-positioning algorithm between wireless vibration sensors, recognizing a stress concentration region in the steel grid, and generating node key parameters. According to the node key parameters, the sampling frequency of the wireless vibration sensor is adjusted, and a dynamic sparse sampling instruction is generated; executing a dynamic sparse sampling instruction, and collecting steel net rack vibration data and current environment parameters; performing multi-physical field coupling analysis on the vibration data and the current environmental parameters to generate a comprehensive damage index; and when the comprehensive damage index exceeds a preset alarm threshold value, outputting an alarm signal. According to the method, the dynamic sparse sampling technology driven by the node key parameters and the multi-physics coupling analysis model are adopted, and low-energy-consumption and high-precision structural damage real-time monitoring and self-adaptive early warning can be achieved.