948 results about "Signal characteristic" patented technology

The invention relates to the technical field of low-altitude safety emergency, and discloses a low-altitude safety emergency system based on large model application. The system comprises a low-altitude situation awareness module which constructs a dynamic situation prediction model based on historical flight data, collects a low-altitude target track coordinate sequence, radar echoes and communication signal characteristics in real time, and outputs a theoretical security situation value. The multi-source data fusion analysis module performs three-dimensional difference analysis on theoretical and actually measured situation values, which relates to time domain trend deviation, space domain coverage offset and event sequence matching degree, and generates a target-level difference feature matrix; and the risk propagation path deduction module inputs the matrix into a geographic topology analysis network, generates a probability thermal distribution diagram of an abnormal risk propagation path in combination with airspace node communication parameters and a target position, and locates a risk influence area. And the intelligent disposal strategy generation module configures parameters according to the graph, starts high-frequency monitoring for a high-probability area, and performs communication disturbance test on an adjacent target.

The invention provides a hydro meteorology early warning method for an offshore oil and gas platform, and belongs to the technical field of offshore hydro meteorology. Extreme weather events are identified by adopting minimum probability abnormal event identification vectors to match abnormal characteristic parameters, and abnormal signal characteristic parameters are input into an ocean dynamics prediction model to calculate real-time sea condition parameters; calling a multi-temporal-spatial-scale early warning fusion matrix to combine with a wavelet decomposition technology and a recurrent neural network to realize multi-scale information integration, analyzing an environmental parameter change trend through a sea condition jump identification model and triggering an emergency response, dynamically adjusting system parameters according to a stability evaluation index vector, and optimizing prediction precision by adopting an early warning residual value compensation matrix. And finally, multi-level early warning information is generated and a real-time early warning notification is sent to an operator, so that the technical problem of insufficient early warning precision of an offshore oil and gas platform hydro meteorology early warning system in multi-spatio-temporal scale data fusion processing is solved.

The invention provides a full-focusing ultrasonic imaging method for multi-modal damage of a composite material, and the method comprises the steps: calculating the correlation of the reflection intensity of a sound wave signal at different moments and spatial positions through employing an established quantitative corresponding relation, recognizing a close contact region and a loose separation region between a bonding layer and a substrate, and carrying out the recognition of the close contact region and the loose separation region. Judging the continuity distribution condition of the interface contact state; according to the continuous distribution condition of the interface contact state, key feature points of dynamic damage evolution are extracted, a time sequence analysis method is adopted to track the feature points to adapt to damage features of dynamic changes, and prediction data of the damage expansion trend are obtained; and according to the dynamic representation result of the interface state, fusing the obtained multi-period signal characteristic data, carrying out quantitative calculation on the area change of the debonding region, judging the residual distribution state of the bonding strength, and obtaining a comprehensive evaluation report of the damage degree of the bonding interface.

The invention discloses a multi-probe on-line real-time film thickness measuring method and a multi-probe on-line real-time film thickness measuring device. In combination with the multi-wavelength analysis capability and multi-channel parallel signal characteristics of the spectrum detection unit, different plane positions are adopted to correspond to different wavelengths or central wavelengths, the transmissivity of each wavelength is obtained, and the thickness of the corresponding position is obtained respectively; the precision and the accuracy of obtaining the thickness by a single wavelength are improved through a multi-mode cooperative signal analysis algorithm, so that the thicknesses of a plurality of different positions of the same thin film are accurately obtained at the same time through one spectrum detection unit, or the thicknesses of a plurality of different thin films are obtained; according to the invention, the problems of large absorption, transparent or non-transparent substrate compatibility, quick response, multi-branch and on-line real-time measurement of the film are successfully solved; the device and the method can be integrated in film growth equipment to carry out rapid and high-precision measurement on the film, so that closed-loop control on film production is realized, the iteration speed is increased, the yield of high-quality film production is greatly increased, and the film production cost is saved.

The invention relates to the cross technical field of signal processing and artificial intelligence, in particular to a multi-source noise removal method and system based on DAE, and the method comprises the steps: 1, carrying out the data collection and feature extraction of multi-source noise and pure signals; step 2, constructing a de-noising recognition knowledge base based on feature analysis; step 3, constructing a deep denoising auto-encoder model based on a knowledge base; step 4, hierarchical training and optimization guided by a mixed loss function of the deep denoising model; step 5, denoising processing of a target signal and output evaluation based on a discrimination model; according to the invention, noise data in multiple fields such as electromagnetism, remote sensing and biological signals are integrated, a dynamic mixing strategy and a data enhancement technology are adopted, a training set which highly simulates a real environment is constructed, and a unique cross-scene adaptation module can perform adaptive adjustment according to signal characteristics of different application scenes; the problem that a traditional method is poor in scene adaptability is solved.

The invention discloses a wide-frequency-domain weak signal data acquisition method, system, equipment and medium, and relates to the technical field of power system monitoring and fault diagnosis, and the method comprises the steps: collecting an input signal, analyzing the signal characteristics in real time, obtaining the frequency composition and amplitude change information of the signal, and dynamically adjusting the sampling rate according to the signal characteristics. Performing noise reduction processing on the collected signals, eliminating noise interference and retaining effective signal components, performing time alignment processing on the data to form a data set with a unified time reference, extracting multi-category features based on the data set, performing fusion judgment, identifying whether the system is in a fault state, and when it is judged that a fault occurs, judging whether the system is in a fault state or not; if yes, fault analysis and positioning are executed, and an analysis result is generated and output. According to the method, fast Fourier transform analysis is carried out on the signals, the dominant frequency and harmonic components can be accurately recognized, the energy ratio can be calculated, and a reliable data basis is provided for signal feature extraction and fault diagnosis.

The disclosure generally relates to a method, system and apparatus to improve a user's understanding of speech in real-time conversations by processing the audio through a neural network contained in a hearing device. The hearing device may be a headphone or hearing aid. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; a neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and a digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

The invention relates to the technical field of ultrasonic signal processing, in particular to a wavelet and MAD adaptive threshold combined laser ultrasonic signal denoising method. The method comprises the following steps: firstly, preprocessing an acquired trigger channel signal and an ultrasonic channel signal, determining a signal starting point through differential positioning, and intercepting an effective signal segment; carrying out multilayer wavelet decomposition on the effective signal segment to obtain a wavelet coefficient of each layer; extracting a detail coefficient of the highest decomposition layer, and adaptively estimating a noise standard deviation based on a median absolute deviation criterion; calculating an adaptive threshold according to the noise standard deviation and the signal length, and processing each layer of wavelet coefficient by adopting a hard threshold function; and finally, carrying out wavelet inverse transformation reconstruction to obtain a denoised signal. According to the method, prior noise information is not needed, the noise level can be adaptively estimated, the optimal threshold value can be determined, the signal features are reserved while noise is effectively suppressed, and the signal-to-noise ratio is remarkably improved.

The invention discloses a convolutional neural network information processing method for heart function dynamic monitoring, and belongs to the technical field of medical detection and monitoring. Comprising the following steps that multi-mode electrocardiosignal data and current physiological state parameters of a patient are obtained, and initial configuration of the intelligent heart function monitoring device is obtained; determining the signal quality grade of each signal channel, and generating a dynamic filtering adjustment strategy of the multi-modal signal acquisition module; configuring a feature extraction strategy of a double-branch convolutional neural network module based on the signal features of the target analysis signal segment and the physiological state parameters; performing multi-scale time sequence feature extraction and frequency domain autonomic nerve regulation feature extraction on the target analysis signal segment by using a double-branch convolutional neural network module according to a feature extraction strategy; executing arrhythmia classification, heart rate variability parameter quantification, heart function evaluation grade and graded early warning tasks, and outputting multi-stage intelligent early warning information from normal monitoring, potential risk and abnormal early warning to an emergency state.

The invention discloses a non-convex contiguous group sparse variational filtering hyper-parameter adaptive calculation method, which comprises the following steps of: selecting a reference signal according to ultrasonic signal characteristics, and determining the size of a group sparse group through a specific formula in combination with a sampling frequency and a probe frequency; after interference removal is carried out on the ultrasonic signal, a scattering noise standard deviation is calculated based on the reference signal, and a regularization parameter is obtained in combination with a formula; calculating a penalty factor; when the detection sound path distance is less than 1000mm, adopting global filtering; when the sound path distance is detected to be larger than 1000 mm, local filtering is adopted, a punishment intensity reference value is estimated based on the noise level, the reference value is corrected in combination with the signal-to-noise ratio, and then a formula is substituted to calculate a punishment factor coefficient and a final punishment factor; in the iteration process, the relative variable quantity of the cost function is used as a convergence criterion, iteration is stopped when the variable quantity is smaller than a preset threshold value, and efficient and stable filtering is ensured; the method completely meets the engineering application requirements of industrial ultrasonic detection.

The invention discloses an acoustic emission signal noise reduction and feature extraction method and system suitable for a deep roadway, and relates to the technical field of safety monitoring of deep mineral resource mining, and the method comprises the specific steps: arranging an acoustic emission sensor array along the deep roadway, collecting multi-source data, converting the multi-source data into digital signals, and storing the digital signals; identifying an interference type through a wavelet packet decomposition and environment correction algorithm; self-adaptive noise reduction is carried out by using a complexity sensitive penalty algorithm; time domain and frequency domain features are extracted, coupling parameters are calculated, and time domain and frequency domain features are obtained through Hilbert-Huang transform; and finally, through principal component analysis dimensionality reduction and mutual information entropy screening, constructing a feature vector and transmitting the feature vector to a safety early warning system. According to the invention, the processing precision and reliability of the acoustic emission signal are improved through the multi-source signal acquisition module, the interference identification module and the adaptive noise reduction module; signal characteristics are comprehensively described through algorithm optimization, key characteristics are output through characteristic optimization, real-time monitoring and early warning are achieved through a dynamic damage vector algorithm, and deep roadway construction safety is guaranteed.

The invention belongs to the technical field of sewage treatment and membrane separation, and provides an in-situ nondestructive membrane pollution detection system and method based on an ultrasonic transmission signal, and the system comprises an ultrasonic excitation module which is used for generating a sine wave excitation signal which penetrates through a membrane body to excite the ultrasonic transmission signal for pollution detection; the signal processing module is used for continuously collecting and preprocessing ultrasonic transmission signals of the clean membrane body and the membrane bodies with different pollution degrees, extracting ultrasonic transmission signal characteristics under different pollution degrees, and establishing a pollution thickness-signal characteristic mapping model; wherein the ultrasonic transmission signal features comprise a time-frequency domain energy gravity center feature and a frequency attenuation spectrum slope feature; and the pollution detection module is used for judging the pollution stage of the membrane body to be detected based on the pollution thickness-signal feature mapping model, and completing in-situ nondestructive membrane pollution detection. According to the technical scheme, the sludge pollution state in the ceramic membrane and flat membrane filtering process can be monitored in real time, and early warning and pollution dynamic tracking are achieved.

The invention relates to the technical field of electron microscope sample pretreatment, and discloses a critical point drying control method and device based on online near-infrared monitoring, and the method comprises the steps: obtaining infrared spectrum data, and unmixing the spectrum data to obtain a feature data set of a solvent; outputting a to-be-corrected signal according to the feature data set, correcting the to-be-corrected signal with preset data, and determining a signal feature of the solvent; carrying out dynamic correction on the signal characteristics and a preset characteristic matrix, outputting concentration time sequence data of the solvent, carrying out steady state judgment by combining a preset convergence threshold value, and determining a critical state of the solvent; acquiring control strategy data according to the critical state, analyzing the control strategy data into execution response characteristics, performing multi-channel mapping and correction processing, and outputting a process control signal; and performing control gain correction on the process control signal to finish closed-loop feedback control of the final drying process. The method is based on multi-solvent spectrum sensing and concentration dynamic analysis, and self-adaptive closed-loop control of the critical point drying process is achieved.

The invention provides an accurate calculation method and system for the axial pretightening force of an ultrasonic bolt, and the method combines a high-order statistic theory with a nonlinear filtering technology, and solves the problem that the measurement precision of a conventional method is insufficient under the conditions of strong noise and nonlinearity. Particle filtering and Kalman filtering are combined, so that the problem that a traditional Kalman filtering method easily causes sensitivity of an initial value of filtering divergence is solved; a double-Gaussian attenuation model is adopted to fit an echo envelope, the problem that a single Gaussian or single index model cannot adapt to envelope distortion caused by stress change is solved, and a nonlinear state space model is established to adapt to nonlinear echo signal characteristics under dynamic stress; gaussian noise is effectively suppressed by using a third-order cumulant cross-correlation algorithm, the time difference resolution in a low signal-to-noise ratio environment is remarkably improved, and the Gaussian noise and asymmetric interference are effectively suppressed.

The invention provides a mobile terminal self-adaptive reminding method and system in a severe cold environment. The method comprises the following steps: acquiring interference signal characteristics, severe cold environment characteristics and operation data; obtaining a communication strategy according to the severe cold environment characteristics and the interference signal characteristics; integrating the communication strategy and the operation limited type to configure a reminding strategy; the configuration communication module reconstructs a communication link; and according to a reminding module configured according to the reminding strategy, self-adaptive reminding is realized, and the linkage equipment is utilized to assist in judging whether the user receives message reminding or not. Through linkage design of the mobile terminal and the linkage equipment, fluctuation problems of signal attenuation, clutter interference and the like in a severe cold environment are continuously adapted, sensing obstruction of cold-proof equipment is effectively broken through, link interruption is avoided, key information transmission is guaranteed, and the service life of the cold-proof equipment is prolonged. The balance between the accuracy and reliability of the reminding function of the mobile terminal in the severe cold environment and the cruising ability of the equipment is realized, and the practical performance of the mobile terminal in the severe cold environment and the use experience of a user are remarkably improved.

The invention relates to the technical field of encryption communication, in particular to an encryption method and system for an intelligent digital encryption radio station, and the method comprises the following steps: storing communication node information to an encryption database based on maritime communication environment signal characteristic parameters, extracting communication link quality indexes, recording multi-path interference, and screening a high-frequency band; and detecting the identity and authority change of a communication operator, and analyzing the communication use trend and frequency change to obtain a communication use trend analysis conclusion. Through dynamic monitoring of signal characteristics and self-adaptive optimization of a network structure, real-time reconstruction of a communication path and interference area avoidance are realized, stable transmission in a marine environment is guaranteed, frequency and power are dynamically adjusted in combination with link quality and interference analysis, the coverage rate and reliability are improved, and the method is suitable for large-scale popularization and application. Abnormal identification and risk early warning are realized through comprehensive judgment of communication behaviors and authority changes, the adaptability and protection capability of the system are enhanced, and the stability, security and resource utilization efficiency of a communication network are improved.

The invention belongs to the technical field of geophysical exploration, and particularly discloses a differential transient electromagnetic method device and an unexploded ordnance detection method thereof.The differential transient electromagnetic method device is characterized in that a transmitter injects emission current into an emission loop, and the emission loop generates a primary field excitation signal; the underground anomalous body target generates a secondary field excitation signal under the action of the primary field excitation signal; the receiving coil assembly is based on the structure of a differential coil, synchronously counteracts a primary field excitation signal and a uniform background signal in an induced voltage, and expands a local asymmetric differential signal generated by an underground anomalous body target; and the receiver carries out time sequence analysis on the local asymmetric differential signals and extracts differential signal features so as to realize positioning and identification of an underground anomalous body target. The device is suitable for detection of shallow-buried small bodies such as UXO, the overall arrangement range of the device is small, and rapid arrangement and mobile observation in the field are facilitated.

The invention relates to a magnetotelluric noise suppression method based on ResAtt-CAE, and the method comprises the steps: generating a noise-free signal, adding a noise type into the noise-free signal to construct a noisy signal, and enabling the noisy signal to be matched with the corresponding noise-free signal to form a training set and a test set; designing a ResAtt-CAE model architecture, and realizing cross-level feature fusion by adopting a symmetric structure and jump connection; the ResAtt-CAE model architecture is trained and optimized through the training set and the test set, and the magnetotelluric noise is suppressed through the optimized ResAtt-CAE model architecture. The method is high in noise suppression capability: ResAtt-CAE captures cross-time scale signal features through multi-scale residual learning, enhances effective signal weights in combination with a time domain attention mechanism, and has a remarkable suppression effect on common magnetotelluric noise (square waves, harmonic waves, pulses, triangular waves and the like).

The invention discloses an AI-based cooperative communication method and system for dynamic spectrum allocation and interference elimination, and the method comprises the following steps: 1, collecting historical data of power equipment, including power line environment data collected by a sensor and power line data collected by a dual-mode communication module; 2, marking the historical data, and inputting the marked historical data into the AI model for training; 3, predicting communication performance through the trained AI model, and dynamically adjusting spectrum allocation according to a prediction result; and step 4, the AI model analyzes interference signal characteristics in a prediction result, and interference is eliminated for different interference types by adopting a matching algorithm. According to the invention, the AI technology is utilized to dynamically optimize the power and frequency spectrum selection of the HPLC and the HRF under the condition of meeting the upper limit of equipment energy consumption and the upper limit of transmission power of the dual-mode communication module, and the interference in a complex power line environment is eliminated, so that the optimal communication performance is ensured.

The present invention relates to the technical field of fault diagnosis, and provides a fault diagnosis method for a wind-turbine drive train based on time-frequency plane expectation maximization. The method comprises the following steps: configuring a cylindrical MEMS acceleration sensor on a drive chain of a wind turbine, and acquiring a vibration signal of the drive chain of the wind turbine via the cylindrical MEMS acceleration sensor; adopting a spectrogram-zero-based unsupervised classification method, acquiring a vibration signal and a rotational speed signal of the wind turbine during operation, generating a time-frequency representation of the vibration signal by using a short-time Fourier transform, and extracting spectrogram zeros and performing unsupervised classification to implement denoising processing of the vibration signal; on the basis of a multi-component signal estimation method under time-frequency plane expectation maximization, accurately estimating an instantaneous frequency and an instantaneous amplitude of a multi-component signal on a time-domain plane of the denoised signal; and performing order spectrum analysis to identify vibration signal characteristics of the drive chain of the wind turbine, so as to achieve fault diagnosis of the drive chain of the wind turbine under variable rotational speeds.

The invention relates to a liquid crystal phased array dynamic star finding control method based on holographic phase coding. The method comprises the following steps: acquiring an expected beam pointing angle of a target satellite relative to an antenna array; generating a two-dimensional phase matrix suitable for a liquid crystal phased array based on the expected beam directional angle; performing temperature compensation on the two-dimensional phase matrix, converting the two-dimensional phase matrix into a digital driving level, and loading the digital driving level to a liquid crystal driving circuit; a radio frequency beam pointing to the expected beam pointing angle is formed through radiation of the liquid crystal phased array antenna panel; and beam pointing errors are detected in real time according to downlink signal characteristics and are compensated. The liquid crystal phased array dynamic star finding control method based on holographic phase coding shows remarkable technical progress and practical advantages in multiple aspects; according to the method, a high-performance and high-reliability dynamic beam pointing system is constructed through key technologies such as holographic phase coding, real-time temperature compensation, high-speed calculation and closed-loop feedback, and the method is particularly suitable for high-dynamic scenes such as high-speed mobile carriers and low-orbit satellite communication.

The invention relates to an intelligent identification and conversion control method for a charging interface of a multi-standard charging pile in the field of new energy automobiles, and the method comprises the steps: carrying out the mode matching analysis of interface physical feature data and communication signal feature data, and obtaining a preliminary classification result of the type of a charging interface device; according to the loading result of the communication configuration parameters, obtaining a confirmation feedback signal of successful protocol switching; judging whether the load demand data conforms to a rated output power range of a charging interface device or not; according to the authorization instruction, current intensity data including a peak value and an average level and voltage level data including a fluctuation amplitude in the charging process are monitored in real time; if the current intensity data or the voltage level data exceed a preset safety threshold value, power supply connection is disconnected, and a circuit path is cut off; and generating a power-off record log containing abnormal fluctuation data and a power-off moment according to a result of disconnecting the power supply connection.

The invention discloses a membrane bioreactor membrane pollution prediction method and system, and relates to the technical field of pollution detection. The method comprises the following steps: setting a frequency detection strategy, and connecting a membrane bioreactor to obtain a membrane feedback signal based on the frequency detection strategy; performing feedback signal fluctuation time sequence characteristic analysis based on the membrane feedback signal to obtain a feedback signal characteristic; the feedback signal features are combined with reactor operation parameters to be imported into a mechanism hybrid prediction model, mechanism constraint is carried out through a mechanism constraint layer according to the feedback signal features, data analysis is carried out on time series data features on the basis of the mechanism constraint through a data driving prediction layer, and a membrane state prediction result is obtained; and performing membrane pollution state analysis and progress prediction according to the membrane state prediction result, and generating a membrane pollution prediction data chain. The technical problems that in the prior art, membrane pollution prediction depends on an empirical threshold, so that recognition lags and precision is insufficient are solved, and the technical effect of improving membrane pollution prediction accuracy and real-time performance is achieved.

The invention provides an intelligent motion evaluation and damage early warning method and system based on multi-source biological signal fusion, and is applied to the technical field of medical data processing. The method comprises the following steps: acquiring a multi-modal biological signal acquired based on a multi-source sensing node; carrying out self-adaptive preprocessing on the multi-modal biological signals, eliminating motion artifacts by adopting an improved spectral subtraction formula, realizing multi-rate synchronization through a Lagrange kernel, triggering node vibration by virtue of signal quality evaluation to prompt electrode reattachment, and generating preprocessed signal features; and constructing cross-modal fusion features by using multi-scale cavity convolution and the like, and outputting results such as a motion mode and the like. A target depth model is trained in combination with signal optimization and data enhancement and the like, a motion pattern classification result, joint load estimation data and a damage risk scoring result are processed based on the target depth model, damage early warning information is generated, and accurate and dynamic motion evaluation and damage early warning are achieved.

The invention relates to the technical field of fault diagnosis, in particular to an intelligent street lamp fault diagnosis and maintenance system, which comprises a signal collaborative acquisition module, a frequency domain conversion module, a fault identification module, a coupling verification module and a maintenance scheduling module. According to the invention, through a multi-source data synchronous acquisition and time sequence alignment technology, the influence of external environment interference on monitoring data is eliminated, and the stability and accuracy of photoelectric signals are ensured. Stable signal features are extracted through frequency domain conversion and feature screening, accurate fault recognition is achieved based on frequency components and similarity calculation, multi-dimensional verification is conducted in combination with instantaneous power comparison and communication delay detection, the accuracy and reliability of fault judgment are improved, therefore, misjudgment and missed judgment are avoided, maintenance scheduling is optimized, and the fault diagnosis efficiency is improved. The fault response efficiency and the overall stability of the street lamp system are improved, maintenance lag and resource waste caused by inaccurate fault diagnosis are effectively reduced, and the operation reliability and the maintenance response speed of the street lamp are improved.

Methods for tracking a signal origin by a spectrum analysis and management device are disclosed. Signal characteristics of other known emitters are used for obtaining a position of an emitter of a signal of interest. In one embodiment, frequency difference of arrival technique is implemented. In another embodiment, time difference of arrival technique is implemented.

The invention discloses an eye pattern generation method based on channel simulation, a storage medium and electronic equipment, and the method comprises the steps: carrying out the channel simulation according to a target channel parameter, a signal source parameter and an initial code pattern sequence, and generating jitter-free initial eye pattern data; obtaining a deterministic jitter parameter and a random jitter parameter, and generating a deterministic jitter time sequence based on the obtained deterministic jitter parameter and the initial code pattern sequence; generating a random jitter time sequence based on the obtained random jitter parameter; synthesizing the deterministic jitter time sequence and the random jitter time sequence to generate a total jitter time sequence offset sequence; and modulating time sequence information in the initial eye pattern data by using the total jitter time sequence offset sequence to generate a target eye pattern. Simulation of deterministic jitter and random jitter is added in the channel simulation process, and the consistency of the simulation result and the signal characteristic of a real communication scene is greatly improved.

The invention discloses an intelligent monitoring method, device and equipment for partial discharge of power equipment and a medium, and the method comprises the steps: obtaining the real-time circuit information of a target circuit, so as to determine a target point location; acquiring a partial discharge signal of the target point location, and inputting the partial discharge signal into three analysis channels in parallel so as to respectively extract a time domain feature, a frequency domain feature and a spatial feature of the partial discharge signal; judging whether a partial discharge fault exists or not according to the discharge signal characteristics through a preset first neural network model; if the partial discharge fault exists, a first early warning signal is sent out; and through a second neural network model, according to the final PRPS map, outputting a partial discharge type of the target point location, and sending out a second early warning signal including the partial discharge type. The accuracy of partial discharge monitoring of the power equipment can be improved.

Systems and methods for monitoring structural integrity are provided. Acoustic sensors are disposed in relation to an area of interest of a structural member to detect an acoustic emission and output an acoustic emission signal. The signal is collected, and a computing device evaluates acoustic emission signal characteristics and predicts whether a crack is present. The acoustic emission signal characteristics are used to detect and predict aspects associated with formation and growth of cracks at various locations of the structural member.

The invention provides a crimping quality online detection method and system based on acoustic emission signal analysis, relates to the technical field of industrial detection, and solves the technical problems that an existing detection method is not sensitive to early defects and is insufficient in adaptability under complex working conditions. The method comprises the following steps: applying mechanical excitation to the crimping structure, exciting the crimping structure to generate vibration response, and synchronously collecting a vibration response signal and an acoustic emission signal; wherein the mechanical excitation represents sweep frequency excitation or pulse excitation generated by a piezoelectric vibration exciter or an impact hammer so as to excite a plurality of co-frequency offset vibration modes of the crimping structure; calculating the resonant frequency of the crimping structure based on the vibration response signal; calculating frequency offset characteristic parameters according to the resonant frequency based on an inversion method of a physical model; calculating acoustic emission signal characteristic parameters based on the acoustic emission signals; and identifying the type and severity of the crimping defect by utilizing a D-S evidence theory and combining the frequency offset characteristic parameters and the acoustic emission signal characteristic parameters.