277 results about "Continuous wavelet" patented technology

A method for locating a high-impedance ground fault of a smart distribution network with topology change adaptation includes: acquiring a fault traveling wave sample within a specified time window after a fault occurs, and performing continuous wavelet transform on the fault traveling wave sample to obtain traveling wave full waveform feature information; establishing a graph structure of a power distribution network, obtaining a corresponding adjacency matrix, and obtaining node position and structure encoding information in the graph structure through graph random walk and graph Laplace transform; concatenating the node position, the structure encoding information and the traveling wave full waveform feature information to obtain a node feature, and inputting the node feature and an edge feature into the graph structure to establish a graph sample data set; constructing and training a Graph Transformer model; and calling the trained Graph Transformer model to locate a fault in to-be-detected sample data.

The invention relates to the technical field of intelligent manufacturing equipment fault diagnosis, and particularly discloses a Meta-Transform driven multi-working-condition equipment bearing fault diagnosis method and system. The method aims at bearing fatigue damage risks caused by dynamic adjustment of technological parameters of a numerical control machine tool in the aerospace manufacturing process and challenges such as feature distribution offset and fault sample scarcity caused by variable working conditions. The diagnosis system is constructed through three core modules. The method comprises the following steps: firstly, reconstructing an original bearing signal into a multi-scale time-frequency feature space by adopting continuous wavelet transform; then designing a causal Transform architecture with a strict lower triangle attention mask, and realizing feature extraction and classification according to a physical causal law of fault propagation; and finally, integrating the mechanisms into a model-independent element learning framework, and realizing cross-working-condition rapid self-adaption through a self-adaption gradient pruning strategy. The bearing fault diagnosis accuracy under the condition of few samples is improved, the interpretability and generalization ability of the model are enhanced, and the industrial application practicability of bearing fault diagnosis is improved.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a power cable electrical performance detection method and system, and the method comprises the steps: synchronously collecting a broadband electromagnetic signal, a mechanical vibration signal and a temperature signal at a cable monitoring point; calculating a wavelet coherence coefficient between the signals through continuous wavelet transform, and constructing a multi-modal coupling tensor fusing amplitude and cross-modal time-frequency correlation characteristics; performing time slicing and high-order singular value decomposition on the tensor to obtain a time-varying core tensor sequence, mapping the time-varying core tensor sequence into a high-dimensional manifold curve, and generating a system state fingerprint by calculating local curvature distribution and topology invariants of the curve; inputting the fingerprints into a pre-trained defect prediction model, and directly outputting defect inoculation probability and evolution stage judgment; according to the method, the limitation that early weak defect detection is not sensitive in a traditional method is broken through, and early warning and accurate diagnosis of cable insulation latent defects are achieved.

The invention relates to the technical field of power equipment monitoring, in particular to a transformer fault diagnosis method, system and device based on multi-modal deep learning and a storage medium. Obtaining the volume fraction of gas dissolved in oil of the transformer, the local discharge capacity, the sleeve dielectric loss factor, the vibration data and the infrared image data; continuous wavelet transform processing based on integrated gradient is carried out on the vibration data, and key fault frequency bands are dynamically screened to generate a time-frequency map; inputting the numeric data into a stack-type denoising auto-encoder network to extract depth features; respectively inputting the infrared image and the radio frequency map into a double-branch convolution encoder for early feature fusion, and extracting map depth features through a stack type convolution auto-encoder network; and based on the Dempster-Shafer evidence theory, carrying out conflict resolution and evidence synthesis on the fault probability distribution output by the two types of modes, and outputting a diagnosis result.

The invention belongs to the technical field of bearing fault diagnosis, and discloses a CNN-MFKAN-based bearing fault diagnosis method and system, and the method comprises the steps: obtaining a bearing fault signal, dividing data through a sliding window, and generating a two-dimensional time-frequency image through continuous wavelet transform, and storing the two-dimensional time-frequency image; constructing a bearing fault diagnosis model in combination with CNN and MFKAN; dividing the data into a training set, a verification set and a test set; inputting the training set into a bearing fault diagnosis model for training; optimizing the model parameters and judging whether convergence occurs or not, if not, returning to the training set, and if yes, completing training and storing the optimal model parameters; and calling the optimal model parameter to carry out bearing fault judgment to obtain a fault classification result. According to the bearing fault diagnosis model, the MFKAN module is innovatively designed, the extraction capability of the model for different frequencies and different scale features is effectively enhanced, and the recognition precision and robustness of bearing faults under complex working conditions are remarkably improved.

The invention discloses a fault diagnosis method and device, computer equipment and a computer readable storage medium, relates to the technical field of power systems and automation thereof, and solves the problem of fault misjudgment caused by important fault characterization of a method which is easy to lose when single modal information is used for fault diagnosis at present. The method comprises the following steps: converting a bus voltage signal into a two-dimensional time-frequency diagram based on a continuous wavelet transform model, and converting the bus voltage signal into a one-dimensional frequency spectrum sequence based on a fast Fourier transform model; performing feature extraction on the two-dimensional time-frequency graph based on a time-frequency image feature extraction model to obtain a first feature vector, and performing feature extraction on the one-dimensional frequency spectrum sequence based on a frequency spectrum sequence feature extraction model to obtain a second feature vector; performing feature fusion on the first feature vector and the second feature vector based on a feature fusion model, and obtaining a category probability vector by using a fault diagnosis model based on the fused feature vector; and determining the highest probability value in the category probability vector, and taking the corresponding fault type as a target fault type.

The invention relates to a flying dust monitoring data processing and classifying method based on multi-source sensing fusion, and the method specifically comprises the following steps: firstly, deploying multi-source flying dust monitoring sensor nodes in a target region to collect data, carrying out the marking, and generating a data set; performing continuous wavelet transform on the acquired data, extracting a wavelet energy spectrum and a Shannon entropy, and splicing to obtain an enhanced feature tensor; secondly, through a two-stage fusion and coding strategy, frequency band energy features are extracted through wavelet packet decomposition, multi-channel cross-correlation, statistical moment and ratio features are calculated to form time sequence mode coding features, and multi-source heterogeneous feature fusion is achieved in combination with a local time sequence feature matrix; then constructing a deep learning model containing a multi-scale time sequence feature extraction and dynamic fusion module, and inputting a fusion feature matrix for training; and finally, inputting the preprocessed new monitoring data into the trained model, and outputting a dust source and pollution level classification result. The dust monitoring data classification accuracy and the dust source identification precision can be effectively improved.

The invention discloses a soil heavy metal inversion method and system integrating satellite remote sensing and near-end sensing, and the method comprises the steps: collecting a soil sample, and measuring the soil heavy metal content and a soil visible light-near infrared spectrum; obtaining a time sequence multispectral image of a research area, calculating a spectral index, and selecting and screening bare soil pixels through a threshold value to obtain a bare soil image; performing spectrum correction on the bare soil image; obtaining a joint dictionary and a sparse coefficient through sparse representation and dictionary learning, and reconstructing a hyperspectral image of the bare soil image; converting the one-dimensional spectral data into a two-dimensional spectrogram by using continuous wavelet transform, extracting spectral features in combination with a 2D-CNN algorithm, and constructing a soil heavy metal inversion model; and using the trained inversion model to predict the soil heavy metal content of the research area based on the reconstructed hyperspectral image. According to the method, satellite remote sensing and near-end sensing are integrated to obtain a large-scale accurate soil heavy metal content distribution map, deep features are extracted in combination with a 2D-CNN algorithm, and the inversion model precision and model efficiency are improved.

The invention discloses an intelligent pipe network leakage active prediction and early warning system based on multi-technology fusion, and the system comprises a multi-source heterogeneous data fusion collection module, a spatial-temporal feature depth extraction module, a degradation trend prediction and residual life evaluation module, and a multi-stage early warning and decision generation module. The multi-source heterogeneous data fusion acquisition module acquires and fuses ultrasonic guided wave signals, pressure flow time sequence data and environmental factor data; the spatial-temporal feature depth extraction module extracts spatial-temporal fusion features through continuous wavelet transform and a CNN-LSTM hybrid network; the degradation trend prediction and residual life evaluation module determines a degradation level, predicts residual life and quantifies a pipe explosion risk probability; the multi-stage early warning and decision generation module generates graded early warning signals and maintenance strategy suggestions, the technology crossing from post-event detection to pre-event prediction is realized, and the scientificity and refinement level of operation and maintenance management of a pipe network are effectively improved.

The invention relates to a mechanical fault diagnosis method and system based on deep learning. The method comprises the following steps: converting a multi-source time domain signal into a time frequency image through continuous wavelet transform, extracting features by using a primary feature encoder, and extracting cross-source common features through adversarial training of a shared feature discriminator; therefore, a gated multi-scale encoder is guided to enhance common feature expression, and deep fusion of multi-source features is realized through a cross multi-head attention network. Global average pooling and maximum pooling are synchronously carried out on the fused features to give consideration to overall and local information, and a comprehensive feature vector is formed; and finally, by means of a double-branch diagnosis network, the training loss of the multi-class network is dynamically weighted according to the prior probability output by the binary network, so that multi-source information is effectively fused under the condition of data imbalance, and the accuracy and robustness of fault classification are remarkably improved.

The invention relates to the field of fault diagnosis methods, in particular to a fault diagnosis method based on multi-modal deep learning, and the method comprises the specific steps: S1, carrying out the processing of an original vibration signal of a bearing through continuous wavelet transform, and converting the original vibration signal into a time-frequency image; s2, constructing a multi-scale Mamba network model, and capturing a long-term dependency relationship in the time sequence data; s3, constructing a high-efficiency network model for extracting time-frequency features; s4, introducing a cross attention mechanism, and dynamically splicing the two modal features; and S5, verifying by using an MEF-Net model, and comparing the performance of the MEF-Net model with other reference models, thereby solving the problems that the integrity, accuracy and reliability of data are influenced due to the introduction of noise in the data acquisition process at the present stage, and the accuracy, accuracy and reliability of the data are influenced due to the inaccuracy, gradient disappearance, overfitting and the like of the data. Therefore, the problems of poor model training effect and low accuracy are solved.

The invention discloses a PVC film coating uniformity detection method based on spectral analysis, and relates to the technical field of optical detection, and the method comprises the following steps: S001, adopting a controllable incident illumination system with a multi-angle rotation function to carry out continuous angle scanning on the surface of a PVC film, collecting reflection spectrum intensity data in a corresponding wave band range according to different incident angles, and generating a spectrum data matrix containing interference characteristics; and S002, carrying out continuous wavelet transform processing on the spectral data matrix, carrying out multi-scale local decomposition on a spectral response signal, extracting and separating a periodic interference characteristic signal, and obtaining a purified spectral matrix after interference reduction. According to the method, through multi-angle spectrum collection and interference feature stripping, accurate identification of a coating thickness abnormal area and combination of strength indexes, spraying parameters are regulated and controlled, a detection-compensation-reinspection closed-loop mechanism is constructed, interference misjudgment is effectively avoided, and the accuracy and regulation and control efficiency of uniformity detection of the PVC film coating are improved.

The invention relates to the technical field of acoustic emission damage detection, in particular to an acoustic emission source positioning method based on lightweight convolution and an attention mechanism, according to the method, a four-channel continuous wavelet image (CWT) is used as input, a DSConv module carries out integral solution on a standard convolution into channel-by-channel convolution and point-by-point convolution, and the network parameter quantity and the calculation overhead are effectively reduced; the attention module adaptively adjusts feature weights through channel and space attention combination, highlights key time frequency features and inhibits redundant information; the four-branch features integrate multi-channel complementary information through adaptive weighted fusion, so that the signal characterization capability is improved; gradient propagation is improved through residual connection, the deep feature learning efficiency is enhanced, and therefore light-weight and high-precision sound emission source positioning is achieved.

The invention relates to the technical field of mechanical fault diagnosis, and provides a fault diagnosis and classification method for a bearing of an aluminum alloy impeller die-casting ladling machine, which comprises the following steps: acquiring a vibration signal of the bearing of the ladling machine, carrying out continuous wavelet transform on the vibration signal, generating a time-frequency diagram, and carrying out adaptive grid segmentation on the time-frequency diagram. Grid granularity is adjusted according to the local change rate of the time-frequency graph, multi-scale nodes are generated, edge connection is generated for the multi-scale nodes based on a K-nearest neighbor algorithm, and a multi-scale graph structure is constructed; performing unsupervised feature extraction on the multi-scale image structure, including: performing data enhancement on the multi-scale image structure through edge deletion and feature mask to generate an enhanced view; a graph attention network encoder is used for encoding the enhanced view, graph-level embedding is generated, and graph-level embedding is optimized by comparing a loss function; and based on the optimized graph-level embedding, performing fault classification by using a classifier constructed by a graph attention network and a multi-layer perceptron, and outputting a fault category.

The invention provides a picoampere-level precision data acquisition method and system for a large model special for the corrosion industry, and relates to the technical field of corrosion, and the method comprises the steps: collecting electrochemical impedance spectroscopy data and corrosion environment parameters, obtaining a time-frequency domain characteristic pattern through continuous wavelet transform, determining corrosion characteristic indexes of a target frequency interval based on a self-adaptive segmentation algorithm, and obtaining a picoampere-level precision model. And constructing a causal reasoning decision tree, and updating the correlation strength of the corrosion knowledge graph by using a Bayesian network. The micro corrosion signal can be accurately captured, the corrosion mechanism evolution path can be predicted, and the corrosion monitoring precision and prediction accuracy can be improved.

The invention provides a vibration event classification method and system based on distributed optical fiber sensing, and relates to the technical field of structural health monitoring, and the method comprises the steps: collecting a vibration signal, and carrying out the de-noising processing, and obtaining a de-noised vibration signal; statistical features are extracted; performing continuous wavelet transform and short-time Fourier transform on the denoised vibration signals, and performing fusion based on an entropy weight mechanism to obtain a preliminary fusion feature map; image features are extracted by using Grubrum angle difference field coding; convolution is carried out on the preliminary fusion feature map to extract local features, and the local features are fused with the Grubrum angle difference field image features to obtain fused image features; performing depth separable convolution on the fused image features, and extracting image depth features; introducing multi-scale expansion convolution based on the features to obtain sequence features; and finally, performing multi-modal fusion on the statistical features, the image deep features and the sequence features to obtain final fusion features, and outputting vibration event categories through a full connection layer and a Softmax classifier.

The invention discloses a seismic wave velocity model inversion method based on time-space coupling deep learning, and the method achieves a time-space coupling mechanism through a Fourier neural operator framework of a wavelet transform-attention mechanism, and supports the reconstruction of a spatial two-dimensional velocity model from a time sequence track of multiple seismic sources. Firstly, the local time-frequency representation of each seismic time-domain trajectory is extracted by adopting continuous wavelet transform, and then a wavelet time-frequency spectrogram is coded through a convolution feature extractor. Then, information from multiple sources is fused at each receiver point location through a multi-head attention mechanism, enhancing correlation between multiple sources. And finally, projecting the fused features into a two-dimensional Fourier neural operator to efficiently learn space mapping and reconstruct a bottom-layer velocity model based on sound velocity. According to the method, the time sequence characteristic and the spatial distribution characteristic of the seismic channel are fully utilized, the precision and robustness of an inversion result are improved, and a more reliable speed model is provided for geological structure interpretation and acoustic imaging.

The invention relates to the technical field of audio signal processing and biomedical signal analysis, and still has a further optimized space for the recognition of anti-noise requirements, signal individual differences and complex pathological modes in a noise environment. The invention provides a double-path CNN heart sound classification method based on time-frequency and double-spectrum fusion features, and the method comprises the steps: carrying out the preprocessing of an original heart sound signal of a data set which is classified into a normal heart sound and an abnormal heart sound, and obtaining a to-be-recognized heart sound signal; based on dynamic continuous wavelet transform, adaptively selecting parameters to extract time-frequency characteristics, introducing bispectrum analysis, capturing nonlinear characteristics, generating a dual-channel characteristic pattern, and efficiently storing the dual-channel characteristic pattern in an HDF5 format; and based on a designed double-path convolutional neural network structure, respectively processing the extracted time-frequency and double-spectrum features, performing classification after fusion, and training a model in combination with category weighted loss and an optimization strategy to obtain a heart sound classification result. The heart sound recognition accuracy can be improved.

The invention relates to an unbalanced bearing data fault diagnosis method based on an improved condition Wasserstein generative adversarial network. The problem of bearing data imbalance under a small sample condition is effectively relieved. According to the technical scheme, the method comprises the following steps: firstly, processing a bearing vibration signal by adopting a sliding window overlapping sampling strategy, and converting the bearing vibration signal into a time-frequency image by applying continuous wavelet transform so as to enhance fault feature representation; secondly, an improved condition Wasserstein generative adversarial network is used for carrying out data enhancement, the network measures sample distribution difference through a Wasserstein distance, introduces gradient penalty and L1 loss, combines spectrum normalization and a self-attention mechanism, stabilizes a training process and prevents gradient explosion, and meanwhile, generates a specific category of high-quality samples through condition information; and finally, carrying out accurate diagnosis by adopting a convolutional neural network equipped with a global attention mechanism. According to the method, the common collapse and instability problems of the generative adversarial network can be avoided, and the precise diagnosis of the rolling bearing can still be realized under the conditions of small samples and unbalanced data.

The invention relates to the field of fan spindle state monitoring, and aims at synchronously acquiring sound and vibration signals through multiple channels, realizing nanosecond time alignment by adopting a precision time protocol, and performing denoising and normalization preprocessing on the signals to improve data integration and signal fidelity. Furthermore, short-time Fourier transform and continuous wavelet transform are combined to extract multi-scale time-frequency features, and a high-dimensional combined feature vector is generated in combination with cross-correlation analysis. And mapping the feature vectors to a low-dimensional manifold space through a local linear embedding algorithm, and constructing a dynamic mode reference template. Indexes such as curvature, track length and direction entropy are monitored in real time, whether the spindle has an abnormal evolution trend or not is judged through a self-adaptive curvature threshold, and abnormity judgment is achieved in combination with track backtracking verification. According to the scheme, the abnormal starting boundary of the spindle state can be caught in a refined mode, and the early warning and operation and maintenance response capacity of fan operation is effectively improved.

The invention relates to the technical field of computers, and discloses a power battery SOH prediction method based on big data. The method comprises the following steps: acquiring voltage, current and temperature time sequence data in a battery charging process; intercepting a constant current charging segment and generating a two-dimensional time sequence spectrogram through continuous wavelet transform; constructing a multi-task attention deep convolutional network, performing decoupling quantization on three attenuation mechanisms of lithium ion precipitation, active substance loss and internal resistance increase, and outputting quantization vectors of the attenuation mechanisms; and finally, calculating an SOH value through a comprehensive evaluation model with physical significance. According to the method, the unification of end-to-end self-learning, strong noise immunity and high interpretability is realized, and the SOH prediction precision and practicability are improved.

The invention discloses an electric power aerial patrol operation data mining method, and belongs to the technical field of intelligent patrol. Coordinate values and temperature data of target equipment are synchronously collected through an infrared thermal imager, a visible light camera and a vibration sensor carried by power equipment, infrared coordinates and visible light coordinates are projected to the same geographic coordinate system through affine transformation, space registration is achieved, continuous wavelet transformation is conducted on collected vibration signals, and the temperature of the target equipment is obtained. The method comprises the following steps: extracting vibration feature vectors including global vibration intensity, wavelet energy spectrum and wavelet entropy, calculating the weight of each feature through image quality, carrying out weighted fusion to calculate a state index representing the health state of equipment, judging the state index according to a dynamic threshold value, judging that the equipment is abnormal if the state index exceeds the threshold value, and judging that the equipment is abnormal if the state index does not exceed the threshold value. And a feedback mechanism is triggered to collect data again to monitor the trend, otherwise, the data is stored for subsequent analysis. According to the invention, the precision and reliability of abnormal state identification in electric power aerial patrol operation and the robustness to a complex field environment are significantly improved.

The invention discloses a bearing fault diagnosis method based on wavelet time-frequency coding and a convolution visual converter. The method comprises the following steps: collecting vibration signals of a bearing under different working conditions; performing continuous wavelet transform on the one-dimensional vibration signal to generate a two-dimensional time-frequency diagram with high time-frequency resolution; carrying out coding and image enhancement on the time-frequency graph through an enhanced color mapping mode; inputting the time-frequency graph into a convolution visual converter model, and realizing local and global combined extraction of vibration features through convolution projection, a self-attention mechanism and a multi-stage feature fusion structure; the features are classified to identify a state of health or type of fault of the bearing. According to the method, the diagnosis precision and robustness in a complex working condition and high-noise environment are effectively improved, and the method is suitable for state monitoring and early fault early warning of various types of rotary mechanical equipment.

The invention discloses a uterine electromyographic signal classification method based on deep learning. The method comprises the following steps: 1, acquiring uterine electromyographic signals by using a multi-channel electrode, and carrying out filtering and noise reduction on the uterine electromyographic signals; 2, segmenting the signal by adopting a sliding window; 3, performing continuous wavelet transform on each window signal to obtain a two-dimensional time-frequency graph, and extracting the normalized peak amplitude of a specific frequency band of the whole section of signal as an auxiliary feature; 4, constructing a deep learning classification model combining cost-sensitive learning and Focal Loss, and training the deep learning classification model; and 5, applying the trained model to unknown user signals, summarizing window-level prediction results, and obtaining a final classification result through user-level post-processing. According to the method, the multi-scale time-frequency characteristics and auxiliary discrimination information of the uterine electromyographic signals can be fully excavated, so that the classification accuracy of the uterine electromyographic signals is improved.

The invention belongs to the field of building environment control, and provides a thermal comfort dynamic adaptive control method based on multi-modal wave analysis and AI driving. According to the method, environmental parameters, user physiological signals (HRV) and subjective thermal comfort data are synchronously collected in real time, time-frequency characteristics of the physiological signals are extracted through continuous Morlet wavelet transform, high-order dynamic characteristics are constructed, the thermal comfort state is predicted in combination with deep learning, a deep reinforcement learning strategy is optimized through an NSGA-II algorithm, a multi-target optimal strategy set is generated, and the optimal thermal comfort state is obtained. And finally, a multi-arm bandit model with Bayesian inference is utilized to realize strategy online adaptive updating. By applying the method, the thermal comfort prediction precision can be expected to be greater than or equal to 90%, the net energy conservation is greater than or equal to 32%, the limitation of the existing thermal comfort regulation and control method in a dynamic situation is effectively solved, and the dynamic balance of personalized thermal comfort and system energy conservation is realized.

The present application relates to the technical field of acoustic emission damage detection, in particular to an acoustic emission source positioning method based on light-weight convolution and attention mechanism, wherein a four-channel continuous wavelet image (CWT) is taken as input, a DSConv module decomposes standard convolution into channel-by-channel convolution and point-by-point convolution, effectively reducing network parameter quantity and calculation overhead; an attention module adaptively adjusts feature weight through joint channel and spatial attention, highlights key time-frequency features and suppresses redundant information; four-branch features integrate multi-channel complementary information through adaptive weighted fusion, improving signal representation capability; residual connection improves gradient propagation and enhances deep feature learning efficiency, thereby realizing light-weight, high-precision acoustic emission source positioning.

The invention belongs to the related technical field of fault diagnosis, and discloses a fault diagnosis sample generation method, application and equipment based on a physical constraint adaptive migration network, and the method comprises the steps: (1) converting original vibration signals of reference equipment and monitoring equipment into a time sequence spectrogram through continuous wavelet transform; (2) processing the time sequence spectrogram by adopting a spectrum feature encoder of the physical constraint adaptive migration network to extract multi-scale spectrum features; wherein a loss function of the physical constraint adaptive migration network comprises content loss, style loss and frequency band energy loss; (3) processing the multi-scale spectrum features by adopting the adaptive style normalization module so as to fuse the fault content features of the reference equipment and the machine style features of the monitoring equipment; and (4) processing the fused features by adopting a feature reconstruction decoder so as to reconstruct and generate a synthetic sample. According to the invention, the problem of insufficient data authenticity and physical consistency is solved.

The invention belongs to the field of data processing, and particularly relates to a three-dimensional roughness evaluation method for a rock mass structural plane, which comprises the following steps of: performing multi-scale decomposition on point cloud data by utilizing two-dimensional continuous wavelet transform, and introducing scale confidence to correct the unreliability of small-scale analysis; the method comprises the following steps: automatically identifying valley point scales in a double logarithm correction scale roughness spectrum, and separating macroscopic undulations and microcosmic rough bodies representing roughness types; according to the method, the total roughness and the wave roughness energy ratio are combined, a double-threshold classification system is established, rock mass structural surfaces are divided into four types, and therefore more accurate and more physical mechanical response prediction and parameter bases are provided for stability analysis and support design of geotechnical engineering.

The invention relates to the technical field of equipment lubricating oil monitoring, in particular to a wear debris electrostatic monitoring method and system based on combination of characteristic mode decomposition and time-frequency images, and an electrostatic sensor monitoring system comprises a sensor shell, an annular copper probe, a polyether-ether-ketone pipeline, a shielding cover, a signal processing circuit and other modules. An insulating pipe in the sensor is a polyether-ether-ketone pipeline, lubricating oil passes through the inside of the polyether-ether-ketone pipeline, and a red copper probe of the sensor is attached to the outer surface of the polyether-ether-ketone pipeline. The method comprises the following steps of: firstly, constructing a signal enhancement model based on characteristic modal decomposition and a kurtosis measurement criterion, and screening out a key modal component with high sparsity for reconstruction by calculating a Gini coefficient of each modal component; further enhancing the signal pulse of the wear debris through modal component optimization reconstruction and a sparse representation model; and finally, identifying the wear debris impact signal based on the continuous wavelet transform time-frequency image to realize real-time monitoring of the wear debris.

The invention discloses a sleep staging method based on multi-view gating interactive attention fusion. The sleep staging method comprises the steps that a single-channel electroencephalogram signal is preprocessed; an original electroencephalogram sequence and a time-frequency graph obtained through continuous wavelet transform are generated and serve as multi-view-angle input; time sequence features are extracted from the original electroencephalogram sequence through a feature extraction module, and time-frequency features are extracted from the time-frequency graph; fusing the time sequence features and the time frequency features through a feature fusion module, including respectively applying convolution attention to highlight internal key features at an electroencephalogram view angle and a time frequency graph view angle, and performing interaction between view angles through cross attention; convolutional attention output and cross attention output are adaptively fused through a hierarchical expert hybrid mechanism; and outputting a sleep stage classification result through the time convolution network. According to the invention, more comprehensive feature representation is realized.