425 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 electrical fault detection, in particular to a photovoltaic inverter fault detection system based on an electric signal, and the system comprises a signal collection module which continuously collects the output electric signal of an inverter, records the amplitude, frequency and phase of the electric signal, and generates an original signal record; and based on the original signal record, screening the electric signal, eliminating noise and non-characteristic data, and generating screened signal data. According to the method, the time frequency characteristics of the signals are extracted in real time through continuous wavelet transform, and the sensitivity and accuracy of fault detection are improved. According to the detailed feature recognition method, even tiny abnormal waveforms or frequency deviations can be captured, and therefore early warning of potential faults is achieved. Meanwhile, the fault detection threshold value is dynamically adjusted through the environmental parameters, flexible response is allowed according to the actual operation environment change, the false alarm and missing alarm conditions are reduced, and the adaptability and the reliability are improved.

The invention discloses a mechanical rotating part fault diagnosis method and device, equipment and a medium, and relates to the technical field of fault diagnosis, and the method comprises the steps: monitoring and obtaining the current operation data of a mechanical rotating part, and carrying out the Fourier transform and continuous wavelet transform of the current operation data, obtaining a target frequency domain image and a target time frequency image under the same pixel size; inputting the target frequency domain image and the target time frequency image into a pre-training fault diagnosis model, so that a trained one-dimensional convolutional network and a trained two-dimensional convolutional network of the model carry out feature extraction and fusion on the target frequency domain image and the target time frequency image respectively, and carrying out fault classification prediction on fused image features. Outputting probability distribution results of the current operation data relative to various standard fault data sets; and taking the standard fault category corresponding to the maximum probability value in the probability distribution result as a predicted fault diagnosis result. And through similarity calculation, the fault type of the mechanical rotating part is accurately judged.

The invention discloses an on-load tap-changer fault diagnosis method based on lightweight YOLO11, and is used for solving the problem of low mechanical fault diagnosis efficiency of a traditional on-load tap-changer. Comprising the following steps of: converting vibration signals in a motor starting stage and a change-over switch action stage into high-resolution time-frequency images through continuous wavelet transform (CWT) so as to comprehensively reflect time and frequency characteristics of the signals; starNet is adopted to replace a YOLO model backbone network, and the feature extraction capability is enhanced while the network compactness is kept through star operation; and designing an efficient joint convolution detection head EJCD, and realizing model lightweight and multi-scale target detection through grouping normalization and convolution parameter sharing. According to the invention, through combination of the time-frequency image representation capability of continuous wavelet transform and an efficient target detection framework of deep learning, the limitation of a traditional method in non-stationary signal processing is overcome, and the diagnosis efficiency and the model performance are significantly improved.

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 relates to the technical field of belt conveyor carrier roller fault monitoring, and discloses a belt conveyor carrier roller fault distributed optical fiber monitoring system which comprises a double-light-source DAS system, optical fiber information acquisition, filtering preprocessing, time domain analysis, time-frequency domain analysis, phase analysis, multi-mode feature fusion, fault classification and fault position and grade output. By building a dual-light-source polarization diversity DAS system and combining an adaptive filtering preprocessing technology, high signal-to-noise ratio acquisition and noise reduction processing of vibration signals are realized, dual-light-source orthogonal polarization state transmission effectively suppresses polarization fading, wavelet packet denoising and variable-step LMS filtering collaboratively filter environmental noise and pulse interference, and the noise reduction performance of the vibration signals is improved. Meanwhile, the application of the dynamic weighted root-mean-square value and the continuous wavelet transform ensures the accurate capture of the time domain energy characteristic and the frequency domain time-varying characteristic, and lays a reliable foundation for the multi-modal characteristic fusion.

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 plunger pump fault diagnosis method and device, a medium and equipment. The method comprises the following steps: acquiring a pressure signal and an original vibration signal of the plunger pump; acquiring data in different states; obtaining frequency domain information based on continuous wavelet transform; performing multi-source data fusion; acquiring a global dependency relationship between the input and the output based on an encoder; and the fault of the plunger pump is diagnosed. By introducing a multi-source data fusion method, time domain and frequency domain signals collected by different types of sensors are comprehensively processed, so that information of different data sources is effectively integrated, and the accuracy and comprehensiveness of fault diagnosis are improved; important features in fault signals are automatically recognized and weighted through the Transform model, the extraction precision of fault features is improved, the model can effectively learn the fault features in a few-sample environment by optimizing a network structure and training parameters in the training process, and the diagnosis precision is 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 discloses a modulation signal identification method based on continuous wavelet transform and deep learning, and the method comprises the steps: firstly modulating a to-be-transmitted sequence at a transmitting end, obtaining a wireless continuous time signal at a receiving end, sampling the continuous time signal, carrying out the wavelet decomposition of the sampled data, and filtering out the high-frequency noise; carrying out reconstruction by using wavelet inverse transformation, and carrying out median filtering on the reconstructed signal to obtain a new data set; performing continuous wavelet transform on the new data set to obtain a time-frequency graph, and extracting feature information of a time domain and a frequency domain of the time-frequency graph to obtain a two-dimensional feature graph; constructing a convolutional neural network prediction algorithm model, and training the convolutional neural network prediction algorithm model by using the two-dimensional feature map extracted by continuous wavelet transform; and finally, classifying the modulation modes of the signals by using the trained convolutional neural network prediction algorithm model. According to the method, the modulation signal identification accuracy in a complex environment in reality is improved, the network parameter quantity is reduced, and the modulation signal identification efficiency is improved.

The invention relates to the technical field of wireless communication, and provides a wireless interference source automatic identification and classification system and method based on deep learning, and the method comprises the steps: 1, receiving an original wireless interference signal, and carrying out the denoising and filtering processing; signal energy is converted into a power spectrum and a time-frequency diagram through fast Fourier transform (FFT) and continuous wavelet transform (CWT); step 2, constructing a time-domain graph branch and a frequency-domain graph branch through a convolutional neural network CNN and a self-attention mechanism Transform architecture, and capturing frequency-domain features and time-frequency features of the interference signal at the same time; 3, fusing the frequency domain and time-frequency domain features through a multi-scale feature fusion network MT in combination with a convolutional neural network and a self-attention mechanism; and step 4, adopting a Softmax activation function to carry out classification identification on the fused features, and outputting the category of the wireless interference source. According to the invention, the wireless interference source can be efficiently, accurately and automatically identified and classified.

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 a partial discharge mode identification method and device, and relates to the field of power equipment fault diagnosis, and the method comprises the steps: collecting a partial discharge signal of high-voltage electrical equipment; preprocessing the collected data to obtain a single pulse signal; the method comprises the following steps of: generating a time-frequency graph of a single pulse on the basis of a continuous wavelet transform (CWT) method, and carrying out spatial feature extraction on the generated time-frequency graph by using a multi-scale convolutional neural network (MCNN); the features extracted by the MCNN are input into a bidirectional long short-term memory neural network (BiLSTM), and the time sequence characteristics of the partial discharge features are obtained; an attention mechanism is used to focus key segments of a splicing sequence output by the BiLSTM, global features are extracted, and the accuracy of pattern recognition is improved. According to the invention, mode identification can be carried out according to the collected partial discharge signal, the partial discharge type is judged, related personnel are helped to make a targeted maintenance plan, and long-term reliable operation of electrical equipment is guaranteed.

The invention provides a method for carrying out corn quality seed selection by using a hyperspectral imaging technology, and belongs to the technical field of corn quality seed selection. Corn kernels are subjected to spectrum scanning in a wavelength range of 400nm to 2500nm, spectrum data are preprocessed through a continuous wavelet transform algorithm, baseline drift and noise interference are removed, and a high-quality seed selection result is obtained. And accurately extracting spectral characteristic peaks of protein, starch, grease and moisture. And establishing a characteristic peak distribution matrix to record peak intensity distribution, and calculating a characteristic peak intensity weight coefficient and a position offset. And constructing a characteristic peak quality incidence matrix, establishing a numerical mapping relationship between the spectral characteristics and the quality parameters, and obtaining a peak width parameter and a spectral noise level. The spectral quality fusion function is adopted to process the multi-dimensional characteristic parameters, and the comprehensive quality evaluation index and the single quality evaluation index are calculated, so that the technical problem that the multi-dimensional quality characteristics of the corn kernels cannot be accurately identified and accurately graded is solved.

The invention relates to the technical field of fetal heart monitoring, in particular to a fetal heart monitoring analysis method, monitoring equipment and a storage medium. The fetal heart monitoring analysis method comprises the following steps: acquiring a heart rate signal and clinical table data of a fetus; performing continuous wavelet transform on the heart rate signal to obtain a time-frequency domain image, and inputting the time-frequency domain image into a ResNet50 network and a Mangban module to obtain image time sequence characteristics; inputting the clinical table data into a multi-scale text encoder, obtaining dependency relationships of the clinical table data in time sequence data of different scales, and fusing the dependency relationships to obtain time sequence dependency features; fusing the time sequence features and the time sequence dependence features of the image by adopting a cross-modal attention mechanism to obtain fused features; the fusion features are input into a classification module, an anoxia risk assessment result is obtained, and a joint loss function of the classification module comprises intra-modal alignment loss, inter-modal alignment loss and masking language modeling loss. According to the invention, the accuracy and reliability of diagnosis are improved.

The invention relates to the technical field of charging infrastructure management, and particularly discloses a charging pile appointment scheduling and path planning cooperation system and a charging pile appointment scheduling and path planning cooperation method. According to the method, continuous wavelet transform and fast Fourier transform are adopted to extract a charging rate fluctuation characteristic value and a power grid power supply stability characteristic value for quantitatively evaluating the service stability of a charging pile and the power supply reliability of a regional power grid, and the system fuses the characteristic values into a comprehensive characteristic vector as the input of a gradient boosting tree model so as to obtain a gradient boosting tree model; and training the prediction model to output a charging planning score so as to judge whether the user can smoothly complete the charging task, and if a prediction result does not meet a set threshold value, dynamically adjusting a reservation scheduling strategy and re-planning an optimal charging path.

The invention discloses an intelligent track fault diagnosis method and system based on an improved CWT-CNN, and the method comprises the steps: decomposing a steel rail vibration signal according to continuous wavelet transform, obtaining target steel rail vibration sub-signals containing fault features, and calculating the wavelet entropy of each target steel rail vibration sub-signal; reconstructing the wavelet entropy according to an entropy dissimilation reconstruction comparison strategy to obtain a target wavelet entropy; inputting the steel rail vibration signal into a preset compression excitation resonance network, outputting by the compression excitation resonance network to obtain feature information at the same latitude as the target wavelet entropy, and fusing the feature information with the target wavelet entropy to obtain a fused feature signal; and performing iterative training on the optimized convolutional neural network according to the fused feature signal and the fault tag corresponding to the fused feature signal to obtain a track fault diagnosis model. The track fault type identification accuracy can be improved, and the real-time diagnosis of the track fault is realized.

The invention discloses an electro-hydrogen system safety domain dynamic regulation and control method based on digital twinning-reinforcement learning, and belongs to the field of power grid safety operation, and the method comprises the following steps: S1, constructing a multi-energy flow coupling model comprising new energy power generation, an electrolytic cell and a hydrogen storage tank; s2, decomposing a new energy original output sequence by adopting a method of combining continuous wavelet transform and an autoregressive moving average model to form time-frequency characteristics; s3, state space modeling considering space-time coupling; s4, generating a dynamic security domain based on digital twinning; s5, constructing a multi-target reward function, and solving by taking the dynamic security domain as a constraint; and S6, performing closed-loop verification and model evolution. By adopting the electric hydrogen system safety domain dynamic regulation and control method based on digital twinborn-reinforcement learning, safety domain dynamic adaptation and intelligent control of the electric hydrogen system under new energy fluctuation are realized, and the operation safety and economy of the system are remarkably improved.

The invention discloses a harmonic reducer fault diagnosis method based on multi-source heterogeneous data fusion, and relates to the field of collaborative robot joint fault diagnosis. In order to solve the problem that an existing harmonic reducer fault diagnosis method is low in diagnosis rate, the invention designs a harmonic reducer fault diagnosis method based on multi-source heterogeneous data fusion, and the method comprises the steps: collecting the vibration, torque, temperature and other multi-sensor original data of a harmonic reducer in different fault modes; one-dimensional time-domain signals of a plurality of sensors are converted into two-dimensional time-frequency images through continuous wavelet transform, a fault feature time-frequency graph data set of the harmonic reducer is constructed, feature selection and noise suppression are performed through a residual shrinkage unit in a DRSN-SE algorithm, the feature representation capability is enhanced by using an SE-Net attention mechanism, and the fault feature time-frequency graph data set of the harmonic reducer is constructed. And fusing the features of the multi-source heterogeneous data to obtain final fused features, and outputting a fault diagnosis result through a classification head. According to the invention, the problem of poor reliability of single fault data can be effectively solved, and high-precision fault diagnosis and classification identification of the harmonic reducer are realized.

The invention belongs to the technical field of side slope micro-seismic signal analysis and identification, and relates to an automatic side slope rolling stone micro-seismic signal detection method, which comprises the following steps: acquiring signals by using a node seismograph, and establishing a database; acquiring a time-frequency diagram by adopting continuous wavelet transform; building a convolutional neural network introducing a double-attention mechanism, and obtaining a network weight parameter with a loss value small enough by training a loss value reduction function; carrying out abnormal signal pickup on the data detected in real time by using a de-noised MER-AIC method; and taking a pickup signal as the input of the convolutional neural network after continuous wavelet transform, and giving an identification result by a trained model to realize automatic detection. According to the method, abnormal signals can be fully automatically and accurately picked up and side slope rolling stone micro-seismic signals can be detected in data which are collected in real time in a rock side slope and mixed with various kinds of noise, intelligence and automation of real-time detection work of rolling stone events are achieved, dependence on manpower is reduced, and the recognition efficiency is improved.

The invention discloses a parallel double-path bearing fault diagnosis method and system based on deep learning, and the method comprises the steps: collecting a fault vibration signal of a rolling bearing, and carrying out the feature engineering processing of the fault vibration signal; the feature engineering processing comprises variational mode decomposition and continuous wavelet transform, and intrinsic mode functions of different fault types and corresponding wavelet time-frequency diagrams are generated. Feature data obtained through processing serve as input of a CNN branch in the two-way model, and the other branch adopts an original signal to be input to an MLP branch. And finally, forming a fault diagnosis result by combining the outputs of the two branches through the full connection layer. The method has the characteristics of low sample demand, high diagnosis accuracy and strong robustness, is suitable for bearing fault detection under the condition of limited data samples, and remarkably improves the efficiency and accuracy of fault diagnosis.

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 belongs to the technical field of underwater acoustic communication signal and digital signal processing. The invention provides a multi-domain feature fusion underwater acoustic communication modulation mode autonomous discrimination method. According to the embodiment of the invention, the data is preprocessed, and the orthogonal component and the in-phase component of the communication signal are extracted and input into a time domain network branch. And the frequency domain waveform obtained after Fourier transform is carried out on the signal is input into a frequency domain network branch. And inputting a time-frequency diagram obtained by performing continuous wavelet transform on the signal into a time-frequency diagram network branch. And carrying out correlation calculation on the time domain network branch, the frequency domain network branch and the continuous wavelet transform time-frequency graph network branch, and carrying out weighted fusion on the spliced network branches to obtain features. And after feature fusion is completed, the modulation modes of the underwater acoustic communication signals are classified through a full connection layer and a softmax layer. Feature information of a time domain, a frequency domain and a wavelet packet time-frequency graph is used as neural network input, feature results are fused flexibly, and robustness of underwater acoustic communication signal modulation mode autonomous recognition is improved.

The invention provides a bearing fault diagnosis method and system, and relates to the technical field of bearing fault detection, and the method comprises the steps: collecting bearing vibration data; the bearing vibration signals are converted into a wavelet two-dimensional time-frequency image, an angle field two-dimensional time-frequency image and a transfer field two-dimensional time-frequency image through a continuous wavelet transform algorithm, a Grubrum angle field coding algorithm and a Markov transfer field algorithm; fusing the wavelet two-dimensional time-frequency image, the angular field two-dimensional time-frequency image and the transfer field two-dimensional time-frequency image to generate a fused time-frequency image; constructing a bearing fault detection model with a Swinin-T channel, a mixed attention channel and a feature fusion module; inputting the bearing vibration data as a training set into a bearing fault detection model until the predicted loss value of the bearing fault detection model is within a preset predicted loss value interval; acquiring a vibration signal of the to-be-detected bearing; and inputting a vibration signal of the bearing to be detected into the trained bearing fault detection model, and outputting a predicted bearing fault type.

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.