2418 results about "Time frequency domain" patented technology

The invention discloses a ring main unit monitoring method and system based on the Internet of Things, and the method comprises the steps: building a multi-source data collection network of a ring main unit, extracting the multi-dimensional time-frequency domain features of a signal through an edge computing device, generating a multi-source feature data set, and constructing a dynamic digital twinborn model, real-time mapping of the running state of the ring main unit is achieved through a physical-virtual synchronization algorithm, and twin data volumes are output; according to the node influence coefficient of the twin data volume, analyzing a topological structure of a ring network system by adopting a graph neural network, identifying a key fault propagation path and generating a priority monitoring node list; based on the priority monitoring node list and the twin data volume, dynamically generating an adaptive monitoring strategy through a reinforcement learning algorithm, executing the adaptive monitoring strategy and collecting feedback data in real time, and completing closed-loop iteration of the monitoring strategy through an online learning algorithm; through closed-loop control of multi-source perception-digital twinning-intelligent decision, high efficiency, intelligence and reliability of ring main unit monitoring are realized.

The invention relates to the technical field of automation, in particular to an automatic monitoring and optimizing system for a fine chemical production process, which comprises the following steps of: extracting time-frequency domain fusion characteristic quantity of stirring torque power time sequence fluctuation data in real time through an incidence matrix construction module, dynamically inverting a thixotropic index by combining a deep neural network model, and optimizing the stirring torque power time sequence fluctuation data; the problem that a traditional method is difficult to perceive material rheological characteristics in real time is solved. The dynamic coupling analysis module analyzes the material viscosity change rate based on the thixotropic index, fuzzy PID control is adopted to generate a stirring speed adjusting instruction dynamically matched with the viscosity and a jacket temperature compensation value, and the defects of uneven mixing and local overheating caused by lagging adjustment of process parameters are overcome; the multi-target collaborative optimization module locks the mass optimization weight in the viscosity sudden change stage, rapidly stabilizes the reaction condition through a feed-forward compensation algorithm, dynamically balances the stirring power consumption and the heat transfer efficiency based on Pareto frontier search in the steady state stage, and solves the conflict between the mass and the energy efficiency target.

The present application provides a method for pipeline defect detection using the magnetostrictive characteristics of ultrasonic guided waves, which relates to the technical field of pipeline defect detection, including: obtaining the geometric and material parameters of multi-layer heterogeneous pipelines, and establishing a mathematical model of the pipeline; according to the mathematical model, calculating the dispersion curve and modal characteristics of ultrasonic guided waves in the pipeline; selecting a suitable excitation frequency and guided wave mode, and optimizing the detection parameters of ultrasonic guided waves; presetting defects of different types and sizes in the pipeline, and analyzing the influence of defects on guided wave propagation characteristics through numerical simulation; building a multi-layer heterogeneous pipeline ultrasonic guided wave detection experimental system, collecting guided wave signals under different defect conditions, and extracting time-frequency domain characteristic parameters. The present invention can realize the effective detection and evaluation of internal defects of multi-layer heterogeneous pipelines at different temperatures, and provides an important technical means for ensuring the safe operation of pipelines.

The invention relates to a multi-microphone array beam forming signal enhancement method and device, and the method comprises the steps: obtaining a multi-channel time domain signal collected by a multi-microphone array, and carrying out the time-frequency conversion, and obtaining a plurality of time-frequency domain signals; acquiring a spatial distribution vector of the multi-microphone array, and performing deformation compensation processing on the complex number of time-frequency domain signals to obtain sound field consistency signals; performing sound source direction feature extraction on the sound field consistency signal, and performing complex time-space attention beam forming network processing to obtain a beam forming signal; performing beam direction and gain parameter optimization on the beam forming signal through a preset adversarial environment simulator to obtain a dynamic frequency band enhanced signal; and performing inverse time-frequency conversion on the dynamic frequency band enhanced signal, and performing low-delay optimization to obtain a target quality voice output signal. According to the invention, signal deformation compensation can be carried out more accurately.

The invention provides a high-temperature chemical pipeline defect detection system and method based on magnetostrictive ultrasonic guided waves, and relates to the technical field of high-temperature chemical pipeline defect detection.The method comprises the steps that magnetostrictive excitation signals are applied, the propagation process of the guided waves in a pipeline is calculated through numerical simulation, and the amplitude and phase information of guided wave signals is obtained; performing time-frequency domain analysis on the guided wave signal, extracting group velocities and phase velocities of guided waves in different modes, determining frequency dispersion characteristics of the guided waves, and evaluating the influence of a high-temperature environment on guided wave propagation; processing the received guided wave signal by adopting a modal decomposition and reconstruction algorithm to obtain a corrected guided wave signal; and carrying out feature extraction and parameter estimation on the corrected guided wave signal to obtain the position, size and type information of the pipeline defect. According to the invention, the problem of multilayer heterogeneous pipeline guided wave detection in a high-temperature environment is solved, the accuracy and reliability of defect detection are improved, and an effective method is provided for pipeline health monitoring and safety assessment.

The invention relates to the technical field of vehicle motors, in particular to a vehicle motor stator and rotor life prediction method and system, and the method comprises the steps: obtaining temperature distribution data, vibration spectrum data and current harmonic characteristic data during the operation of a stator and a rotor; performing time-frequency domain fusion analysis based on the temperature distribution data and the vibration spectrum data to obtain thermal coupling degradation characteristics, and obtaining electromagnetic performance degradation characteristics based on the current harmonic characteristic data; training to obtain a life prediction model based on a mapping relation between historical degradation characteristics and a failure threshold value, inputting the thermal coupling degradation characteristics and the electromagnetic performance degradation characteristics into the life prediction model, and outputting a residual life prediction value of the stator and the rotor; and dynamically adjusting operation parameters of the motor according to the residual life prediction value. According to the method, the problems that the life prediction precision is low and the operation strategy cannot be dynamically optimized due to the fact that the collaborative degradation effect of the stator and the rotor of the vehicle motor under the multi-physical field coupling effect is difficult to quantify in real time are effectively solved.

The invention relates to the technical field of wireless signal detection, and discloses a wireless anti-interference signal detection method, device and equipment and a storage medium, and the method comprises the steps: collecting a standardized interference feature vector through a multi-scene wireless signal receiving device; performing interference feature enhancement processing on the standardized interference feature vector to obtain an interference feature sample set; constructing a dual-array cooperative detection system comprising a passive detection array and an active detection array, and performing real-time monitoring to obtain dual-path interference signal monitoring data; performing spatial domain and time-frequency domain dual feature extraction and fusion processing on the dual-path interference signal monitoring data to obtain a comprehensive interference feature; the receiver filtering parameter matrix and the detection threshold vector of the double-array cooperative detection system are dynamically adjusted according to the comprehensive interference characteristics, the method can adapt to changes of different interference environments in a self-adaptive mode, comprehensive coverage of communication spectrums is achieved, and the wireless anti-interference monitoring range and the spectrum sensing capacity are greatly expanded.

The invention relates to a railway traction substation state monitoring method and system, equipment and a medium. The monitoring method comprises the following steps: acquiring real-time monitoring data of the equipment in a railway traction substation; performing data preprocessing on the real-time monitoring data to obtain a preprocessed monitoring data set, performing protocol identification, and converting heterogeneous data in the monitoring data set into structured data according to a preset protocol template library; based on the structured data, time-frequency domain characteristic parameters of the equipment are extracted, and a multi-dimensional characteristic matrix is constructed; inputting the multi-dimensional feature matrix into a pre-trained hybrid diagnosis model, and generating an equipment health degree score and a fault probability value; according to the health degree score and the fault probability value, generating an early warning instruction in combination with a dynamic threshold algorithm; and generating a priority maintenance strategy through a maintenance strategy optimization model based on the early warning instruction and the equipment maintenance resource constraint condition. According to the invention, accurate perception and intelligent decision making of the equipment state are realized in a multi-source heterogeneous data environment.

The embodiment of the invention provides a humanoid robot self-adaptive control method and system, electronic equipment and a storage medium, and belongs to the technical field of robot intelligent control. The method comprises the steps that electroencephalogram signals and electromyographic signals of a target object are collected; determining a first time-frequency domain feature of the electroencephalogram signal according to the electroencephalogram signal, and determining a second time-frequency domain feature of a muscle activation state according to the electromyographic signal; fusing the first time-frequency domain feature and the second time-frequency domain feature by adopting a dynamic weighting algorithm to obtain a multi-modal feature; inputting the multi-modal features into an action intention recognition model to obtain a predicted action intention; and determining a robot control instruction according to the predicted action intention and the current state of the robot so as to control the humanoid robot. The precision of self-adaptive control of the humanoid robot can be improved.

The invention provides a traveling wave cable measurement sensing method and system based on a time-frequency domain combined reflection principle, and relates to the technical field of measurement, and the method comprises the steps: arranging a high-frequency signal transmitting device and a signal receiving device at one end of a traveling wave cable; establishing a time domain reflection function and a frequency domain reflection function of the traveling wave cable; constructing a time-frequency domain joint reflection model; collecting a reflected signal of the traveling wave cable through the signal receiving device to obtain time domain data and frequency domain data of the reflected signal; carrying out conjoint analysis on the time domain data and the frequency domain data by utilizing a time-frequency domain joint reflection model, and extracting characteristic parameters of a reflected signal; calculating impedance change and signal propagation characteristics along the traveling wave cable based on the characteristic parameters in combination with a time-frequency domain joint reflection model; through analysis of impedance change and signal propagation characteristics, intrusion detection, leakage positioning and fault diagnosis along the traveling wave cable are realized.

The present application relates to a switch cabinet discharge fault detection method, system, equipment and medium, belonging to the technical field of power equipment fault detection, the fault detection method includes: receiving real-time monitoring data of the switch cabinet to be monitored and aligning the timestamp; performing dynamic amplitude compensation on the electric field signal data based on the aligned current humidity data, generating the corrected electric field signal data and performing time-frequency domain analysis, extracting the discharge pulse density and high-frequency energy proportion; performing frequency filtering and waveform analysis on the vibration signal, calculating the impact intensity factor and the vibration energy entropy value; making abnormal judgments on the discharge pulse density and the impact intensity factor, and marking the abnormal discharge according to the abnormal judgment results; based on the synchronous change correlation of the high-frequency energy proportion and the impact intensity factor and the vibration energy entropy value, verifying the abnormal discharge marking result, and obtaining the discharge fault result. The present application can realize efficient detection and accurate diagnosis of switch cabinet discharge faults.

The invention discloses an intelligent fault detection system and method for a wind driven generator, and relates to the technical field of deep learning, and the method comprises the steps: collecting multi-dimensional data, and carrying out the preprocessing; extracting multi-dimensional features from the preprocessed multi-dimensional data, fusing the multi-dimensional features through a graph neural network and short-time Fourier transform, and generating a time-frequency domain signal; key features are extracted from the time-frequency domain signals, and fault features of the operation state of the wind driven generator are detected through a support vector machine model; based on the fault features, a fusion neural network model is constructed, fault prediction is generated, a diagnosis report is output, and a maintenance suggestion is provided; according to the method, the neural network model is fused, the convolutional neural network and the long-short-term memory network are combined, the spatial features and the time sequence data are extracted, and multi-dimensional characterization of the fault features of the wind driven generator is achieved.

The invention discloses a GIS partial discharge intelligent diagnosis system and method based on one-dimensional ultrahigh frequency signal analysis, and relates to the technical field of power electrical equipment intelligent monitoring, and the system comprises a signal collection and preprocessing module which is used for collecting ultrahigh frequency signals of GIS equipment and obtaining preprocessed signal data through a dynamic threshold algorithm; the discharge initial judgment module is used for performing multi-dimensional sequential judgment to eliminate interference discharge data so as to obtain effective discharge signal data; the feature extraction module is used for performing time domain kurtosis and pulse width analysis, frequency domain energy distribution analysis and time-frequency domain wavelet entropy calculation based on the multi-dimensional features of GIS partial discharge, and generating an optimized feature subset; and the type identification module is used for identifying the partial discharge type by using the integrated learning model to obtain a diagnosis result. According to the invention, the problem of unstable recognition accuracy caused by insufficient signal preprocessing, single feature representation and single classification algorithm in the prior art is solved.

The invention discloses a deep well rock burst early warning system and method based on multi-dimensional monitoring, and belongs to the technical field of deep well rock burst early warning. According to the method, multi-dimensional data such as stress, strain and microseism are collected through a monitoring network, and an aligned multi-source data set is obtained through space-time registration; after dynamic noise suppression processing matched with physical characteristics is adopted, strong correlation characteristics are screened through mutual information entropy; frequency domain, time domain and time-frequency domain features are extracted through principal component extraction and phase-space reconstruction, and a multi-dimensional state space data set is formed; and inputting the prediction model to obtain a danger level and trigger a corresponding early warning signal, and finally dynamically adjusting the monitoring network layout and prevention and control measures based on the early warning signal. According to the method, the early warning accuracy and real-time performance are improved, and effective technical support is provided for deep well rock burst prevention and control.

The invention discloses a bearing fault diagnosis method based on fusion of an improved capsule network and zero sample learning, and relates to the technical field of state monitoring and fault diagnosis of electromechanical equipment, and the method comprises the following steps: collecting a multi-mode signal during the operation of a bearing, employing an improved wavelet threshold denoising algorithm for the multi-mode signal to eliminate environmental noise, and then employing a zero sample learning algorithm for the multi-mode signal; according to the method, the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm are adopted to extract the time-frequency domain mixed features as sample data, and the GAN is combined to expand the bearing sample data, so that the data dependence of traditional deep learning is broken through, the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm, and the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm. Small sample data learning is realized, and by training a pyramid capsule network and optimizing a cross entropy loss function and combining cross-modal joint optimization and a zero sample inference engine, the diagnosis accuracy of known faults is greatly improved, and unknown fault types can be effectively inferred.

The invention relates to an audio noise reduction method, device and system based on deep learning, and the method comprises the steps: obtaining an input audio signal with noise, and carrying out the multi-scale time-frequency decomposition, and obtaining a mixed time-frequency feature and a noise fingerprint spectrum; performing parameter parallel processing on the noise fingerprint spectrum through a preset dynamic kernel generation network, and performing preliminary noise reduction processing on the mixed time-frequency characteristics to obtain noise-reduced mixed data; performing dual-path processing structure construction on the noise reduction mixed data to obtain amplitude optimization data and phase optimization data; performing dynamic time-frequency domain cross fusion on the amplitude optimization data and the phase optimization data to obtain fused audio data; and carrying out differentiable acoustic equation constraint adversarial training on the fused audio data, and carrying out inverse time-frequency transformation processing to obtain a target noise-reduced audio signal. According to the invention, the overall efficiency and effect of audio signal processing can be effectively improved.

The invention discloses a photoelectric event identification optimization method and system for a single photoelectron detector, and the method comprises the steps: collecting environment noise data, carrying out the modeling of environment noise, and generating a noise processing model; real-time operation monitoring data of the target single photoelectron detector are acquired, noise processing is carried out, and time-frequency domain waveform decoupling is carried out after processing is completed; performing pulse accumulation detection through a time-frequency domain decoupling result, marking an accumulation event, and separating accumulated pulses by using a pre-trained pulse separation network to obtain pulse separation information; generating a floating judgment boundary parameter according to the pulse separation information, and carrying out photoelectric event judgment to obtain a photoelectric event judgment list; and obtaining a historical photoelectric event discrimination list of the target single photoelectron detector, extracting an abnormal discrimination event, analyzing whether maintenance is needed, generating a maintenance self-inspection report, and pushing the maintenance self-inspection report. The detector keeps high robustness and anti-interference capability in the whole life cycle, and the identification error rate improvement caused by aging change is avoided.

The invention belongs to the technical field of intelligent equipment fault diagnosis, and particularly relates to a motor fault real-time diagnosis method and system based on LSTM and random forest. The method comprises the steps that a vibration signal, a current signal and a temperature signal of a motor are collected and preprocessed; performing feature engineering, extracting time domain, frequency domain, time-frequency domain and cross-modal correlation features, and determining an optimal static feature subset through a hybrid screening strategy; constructing a hybrid fault prediction model comprising a random forest model and an LSTM sequential network model; fusing prediction results of the two models by adopting a dynamic credibility weighted fusion mechanism; and performing real-time decision and hierarchical feedback control based on a fusion result. According to the method, advantages of multi-source information and the model are fused, the classification precision of the lag type is remarkably improved, real-time fault diagnosis and active protection are realized, the equipment maintenance cost is reduced, and the method is suitable for motor health management of intelligent agricultural equipment such as mowers.

The invention discloses a coal and rock mass fracture instability multivariate signal fusion analysis method, and belongs to coal and rock mass analysis. The method comprises the following steps: carrying out time-frequency domain processing on an acoustic emission signal to obtain an accumulated ringing characteristic value # imgabs0 # and an accumulated b characteristic value # imgabs1 #; a main control frequency characteristic value # imgabs2 # and an amplitude characteristic value # imgabs3 # are accumulated; a spatial characteristic value # imgabs4 # of acoustic emission; fitting a fitting function # imgabs7 # from the characteristic value # imgabs5 # to the characteristic value # imgabs6 # respectively; according to the fitting function # imgabs8 #, calculating a correlation coefficient matrix R between the characteristic values # imgabs9 # to # imgabs10 #; according to the matrix R, determining a weight coefficient # imgabs11 # of each characteristic value; a first-order derivative function # imgabs13 # and a second-order derivative function # imgabs14 # of the fitting function # imgabs12 # are obtained, and according to the first-order derivative function # imgabs15 # and the second-order derivative function # imgabs16 #, a demarcation time point # imgabs17 # in the coal and rock mass damage process is determined; and the boundary time point # imgabs19 # is corrected by using the weight coefficient # imgabs18 #, and a corrected boundary time point # imgabs20 # is obtained. In order to solve the problems that an existing coal and rock mass fracture instability judgment index is single, and coal and rock mass fracture instability early warning is low in single index precision, the early warning precision is improved through multi-element signal fusion.

The invention relates to the technical field of circuit breakers, in particular to a circuit breaker fault diagnosis method and system for switching an alternating current filter in an extra-high voltage converter station, and the method comprises the following steps: S1, synchronously collecting multiple parameters: synchronously collecting vibration acceleration signals, opening and closing coil current waveforms, arc light intensity and environment temperature and humidity parameters; s2, time sequence feature extraction: performing time-frequency domain decomposition on the vibration acceleration signal to obtain a mechanical action time sequence spectrum; s3, operation mode classification: determining the operation mode of the circuit breaker; s4, three-dimensional fault matrix construction: generating a three-dimensional fault matrix including spatial distribution characteristics; s5, dynamic threshold analysis: generating a diagnosis threshold interval dynamically adjusted along with the environmental parameters; and S6, comprehensive diagnosis output: outputting a mechanical jam grade, a contact ablation degree and an insulation deterioration early warning signal. According to the invention, the mechanical, electromagnetic and insulation characteristics of the circuit breaker can be reflected more comprehensively, and reliable guarantee is provided for safe and stable operation of a high-voltage power grid.

The invention discloses a wind turbine generator data analysis and fault diagnosis method and system based on big data and artificial intelligence. According to the method, a blade image, a vibration signal, audio data and operation parameters are synchronously acquired through an unmanned aerial vehicle multi-mode sensor and a ground monitoring system, and a multi-source heterogeneous data set is constructed; after the data is classified and preprocessed, image features, vibration time-frequency domain features and operation parameter key value pairs are extracted respectively; dimensionality reduction is carried out by using an auto-encoder, feature-level space-time alignment is realized through an improved DTW algorithm, and a multi-dimensional fault feature matrix is generated; a hierarchical diagnosis model including a GRU auto-encoder, an MLP network and an attention mechanism CNN is constructed, and training is carried out by taking minimization of sub-model deviation as an optimization target; and finally, fusing multi-source features to realize fault classification, and generating a visual diagnosis report. According to the method, efficient fusion and accurate diagnosis of multi-source heterogeneous data are realized, and the accuracy and the real-time performance of fault detection of the wind turbine generator are remarkably improved.

The invention discloses a method for identifying a multi-model fusion signal modulation mode based on a time-frequency diagram, and belongs to the field of signal processing. Preprocessing the original signal to obtain a signal sample; constructing a data set by using the normalized signal samples; constructing a fusion model; training a modulation identification module in the fusion model; performing modulation mode identification on an input unknown signal by using the fusion model to obtain a preliminary identification result; and performing comprehensive judgment on the preliminary recognition result of the fusion model by using a comprehensive judgment device to obtain a final recognition result. According to the invention, by combining a plurality of time-frequency analysis methods, the time-frequency domain feature information of the signal is fully extracted; the reliability and robustness of a final decision are improved by adopting a multi-model fusion framework, and the modulation recognition performance of the system under the condition of a low signal-to-noise ratio is improved.

The invention discloses a direct-current arc fault diagnosis method and device with adaptive capacity. The method comprises the following steps: 1) collecting a current alternating-current component when a current direct-current system normally works; 2) extracting time-frequency domain characteristics of a current AC component, wherein the time-frequency domain characteristics comprise a current peak-to-peak value, a standard deviation and an amplitude reconstruction harmonic power sum; 3) performing multi-time-frequency-domain feature fusion based on mahalanobis distance to obtain arc detection features; 4) calculating an arc fault judgment threshold value based on a box plot method; 5) collecting a current AC component in real time to calculate a Mahalanobis distance, comparing the Mahalanobis distance with an arc fault determination threshold, and recording an arc event; the method has the advantages that the system adaptability is high, the detection rate is high, the false alarm rate is low, and the method can be widely applied to high-voltage and low-voltage direct-current power distribution systems such as airplanes, vehicles and ships.

The invention discloses a life prediction method based on health index construction and neural network fusion, and belongs to the technical field of equipment state monitoring and predictive maintenance. According to the method, through multi-source degradation feature extraction, common dynamic principal component analysis (CDPCA) dimensionality reduction, health index construction and normalization, deep learning multi-model modeling, integrated learning fusion and Bayesian optimization hyper-parameter optimization, online health assessment and residual life prediction of the equipment part degradation process are realized. Specifically, the method comprises the following steps: firstly, extracting time domain, frequency domain and time-frequency domain features from a sensor acquisition signal, and performing dimension reduction through CDPCA to obtain effective degradation characterization; then, weighting the main features to construct a health index (HI) curve, optimizing the weight through a genetic algorithm, and then performing normalization; a plurality of neural network models such as CNN, Bi-GRU, Bi-RNN, Bi-LSTM and SRNN are constructed based on the normalized HI sequence, and degradation trend modeling is realized; inputting the output results of the neural networks into an integrated learning module for fusion optimization; and finally, carrying out automatic optimization on the key hyper-parameters of the model by utilizing Bayesian optimization. In the equipment operation process, a normalized HI curve can be calculated in real time and input into the fusion model, and the residual life estimation value of the part is dynamically output. According to the method, high-precision, high-robustness and online life prediction can be provided under complex working conditions, the safety and reliability of equipment operation and maintenance are improved, and the method has wide engineering application value.

The invention relates to the technical field of chip testing, and discloses a chip power supply noise influence testing method and device. The method comprises the following steps: carrying out multi-dimensional data acquisition on a chip power supply by utilizing a multi-mode sensing array to obtain original sensing data, and carrying out time synchronization calibration and signal preprocessing to obtain high-quality multi-dimensional data; carrying out time-frequency domain decomposition and correlation calculation on the processed data, and carrying out time delay and position calculation to determine noise source distribution; noise source distribution is mapped to a chip layout, multi-physical field calculation is carried out, a coupling propagation matrix is generated, and a noise propagation characteristic spectrum is obtained through characteristic value decomposition and path calculation; and designing a test condition based on the propagation characteristic spectrum, performing a chip function module performance test, establishing a response relationship between noise and function performance, determining a function failure noise critical value and a tolerance boundary, and forming a complete power supply noise influence test result. According to the method, the accurate positioning of the chip power supply noise is realized, and the evaluation accuracy of the power supply noise influence is improved.

The invention relates to the technical field of voice processing, can be applied to service system platforms of medical health, financial science and technology, communication and the like, and discloses an audio compression and reconstruction method, device, equipment and medium. Residual vector quantization and inverse quantization are carried out to generate basic spectrum features; and compensating a high-frequency component by adopting a spectrum expansion network, predicting a time domain error in combination with a long-short-term memory network, generating a time domain residual compensation signal, and superposing the time domain residual compensation signal with the reconstructed time domain audio signal to obtain a target audio signal. Through residual vector quantization and neural network modeling, the audio reconstruction quality under low-bit-rate compression is improved, spectrum detail reservation and time domain error compensation are optimized, meanwhile, the calculation complexity is reduced, and the method is suitable for high-sampling-rate and resource-limited scenes.

The invention relates to the field of equipment diagnosis, and particularly discloses an equipment predictive maintenance method based on deep learning, and the method comprises the steps: carrying out the time domain amplitude normalization and frequency domain weighted normalization of training data, and carrying out the dual-channel feature fusion, so as to obtain a fusion feature vector; performing feature extraction on the fused feature vector by using multiple groups of self-adaptive wavelet kernels to obtain a self-adaptive time-frequency feature vector; constructing a weight population through statistical characteristics, and screening the optimal initial weight of the initial diagnosis model; carrying out multi-scale depth feature calculation, time-frequency domain attention feature fusion, fault prototype comparative learning and a pre-constructed total loss function on the adaptive time-frequency feature vector, and carrying out iterative updating on the initial diagnosis model to obtain a diagnosis model; and the equipment is diagnosed through the diagnosis model. Multi-scale feature fusion and a double-path attention mechanism can cooperatively capture short-time impact and a long-period mode, and the limitation of a traditional method in diversified fault scenes is overcome.

The invention discloses a GNSS deception signal detection method based on a time-frequency domain feature fusion deep learning model, and relates to the technical field of intelligent detection. The method comprises the following steps: acquiring original GNSS signal data, preprocessing the data, and dividing the data into a training set and a test set; in the model training stage, a pre-training lightweight deep learning model is utilized to extract time domain convolution features and multi-resolution frequency domain features, and weighted correction and fusion among the features are realized through a bidirectional attention mechanism; and carrying out iterative training on the model based on the fusion feature vector, and finally carrying out deception signal detection on a test set by utilizing the trained model. According to the invention, through a time-frequency domain feature deep interaction and model training mechanism, the deception signal is detected through the model, and the detection precision of the GNSS deception signal in a complex scene and the anti-interference capability of the model are significantly improved.

The invention belongs to the field of machining, and relates to a machining process quality online monitoring and feedback adjusting system and method. The system comprises a multi-modal data acquisition module used for acquiring a mechanical vibration signal and a high-frequency micro-damage signal; the feature preprocessing and cross-modal fusion module is used for carrying out time domain, frequency domain and time-frequency domain feature extraction on the collected mechanical vibration signals and high-frequency micro-damage signals, carrying out edge, texture and shape feature extraction on workpiece machining surface image signals, and introducing a cross-modal attention mechanism to achieve weighted fusion of heterogeneous signals. And finally generating a fusion feature vector with high discrimination. The machine tool / cutter state and workpiece quality intelligent analysis module is used for constructing an intelligent analysis model for the equipment operation state and the workpiece machining quality; inputting the fusion feature vector into an intelligent analysis model to obtain a quality evaluation result; and the quality judgment and feedback adjustment module is used for adopting a dynamic threshold adjustment mechanism to realize adaptive comparison with a process standard according to a quality evaluation result.

The invention discloses a wind turbine generator anti-impact noise fault identification method based on feature embedding deep learning, and the method comprises the steps: carrying out the feature mode decomposition of an original vibration signal of a wind turbine generator, screening out an optimal mode component, and converting a time domain signal of the optimal mode component into an envelope spectrum; using the minimum envelope entropy as a fitness function, and using a sparrow search algorithm to globally optimize the filter length and the decomposition modal number of characteristic modal decomposition; and extracting time-frequency domain features, constructing a multi-dimensional time-frequency domain feature vector, inputting the multi-dimensional time-frequency domain feature vector into the combined fault recognition model, and outputting a fault classification result. According to the method, on the basis of an FMD and SSA-MEE joint optimization framework, the sensitivity limitation of a traditional envelope demodulation method on impact noise is broken through, modal aliasing is restrained, and noise robustness is enhanced. According to the method, the vibration signals are subjected to characteristic mode decomposition, the influence of early impact noise is inhibited, a CNN-GRU-Attention fault recognition model is provided, and the accuracy of fault recognition is greatly improved.