813 results about "White noise" patented technology

The invention discloses a variable working condition migration bearing fault diagnosis method based on a ViTCapsNet network in a noise environment, and the method comprises the steps: firstly obtaining vibration signal data of a rolling bearing in different fault states under different loads, and adding Gaussian white noises of different degrees; performing continuous wavelet transform on the data added with the noise to obtain a two-dimensional wavelet image, and dividing the two-dimensional wavelet image into a training set and a test set; a ViT Encoder model is established, position information capable of being trained is added by using an Embedded module, position features are embedded into the model, and global information in the features is extracted through a multi-head self-attention mechanism in the Encoder module; constructing a capsule network which comprises a primary capsule layer and a digital capsule layer, further extracting spatial information in the features, and performing accurate fault diagnosis; and finally, performing visual interpretation on the features processed by the Encoder module by using Grad-CAM, and completing efficient migration fault diagnosis on a test data set.

The invention discloses a global ultra-precision strain measurement method, and relates to the technical field of strain measurement. The method comprises the following steps: continuously shooting moving images of a target area by using a high-frame-rate high-definition camera, and recording accurate time; and carrying out image correction, feature extraction and tracking and data normalization. And after Gaussian white noise is added, singular value decomposition and reconstruction are carried out. And a Lucas-Kanade optical flow method is adopted to calculate the displacement of the feature points in the reconstruction matrix. And a displacement smoothing stage: smoothing the displacement data by using an LOESS method. And carrying out global strain calculation by adopting a window sliding method, and calculating the strain through the spatial derivative of the displacement field. According to the method, the quantization effect can be relieved, the continuity of displacement calculation is improved, low-energy noise and irrelevant features are removed, main motion information is reserved, meanwhile, smoothness is enhanced, local features are captured in a high-precision mode, and measurement sensitivity is improved. The method is suitable for strain measurement of large-scale structures such as bridges and buildings.

The invention discloses an edge-based open vocabulary segmentation model target identification method. The method comprises the steps of obtaining a target image; initializing Gaussian white noise as a noise contour mask pattern of a target; inputting the target image and the noise contour mask image into an open vocabulary segmentation model to execute the following operations: extracting multi-scale features of the target from the target image and the noise contour mask image; encoding the noise contour mask pattern to obtain encoding features; the multi-scale features are used as condition features to be fused with the coding features, and fusion features are obtained; predicting a de-noised contour mask graph of the target according to the fusion features; and according to the de-noised contour mask graph and the multi-scale features, a single full connection layer is used to predict classification and positioning of the target. According to the method, target identification based on the open vocabulary segmentation model can be effectively and quickly completed.

The invention relates to the technical field of high-voltage switch cabinet partial discharge detection, and discloses a high-voltage switch cabinet partial discharge signal denoising method based on singular value decomposition and particle swarm optimization wavelet decomposition, and the method comprises the steps: firstly analyzing the characteristic parameters of a narrowband interference signal in a partial discharge signal, and extracting a partial discharge signal without narrowband interference; secondly, for the problem of white noise existing in the partial discharge signals, wavelet decomposition is carried out on the partial discharge signals after narrowband interference is eliminated, the partial discharge signals are converted into sub-band signals of different scales and frequencies, wavelet inverse transformation reconstruction is carried out after threshold processing, and denoised signals are obtained; and finally, optimizing the selection of threshold related parameters by using a particle swarm algorithm, solving the number of particles and a population optimal value by using a fitness function, and continuously iterating to finally obtain the optimal threshold related parameters. According to the method, the denoising effect is improved, and the method can be popularized to denoising of partial discharge signals of other devices in combination with performance characteristic parameters of different devices.

The invention discloses a GNSS real-time deception jamming detection method and system based on deep learning, and belongs to the field of satellite navigation signal processing, and the method comprises the steps: collecting Doppler frequency shift signals of a GNSS receiver in real time, and obtaining an initial training data set; an LSTM prediction model with an incremental learning mechanism is constructed, the LSTM prediction model comprises a double-layer LSTM network and a full connection layer, a gating mechanism is introduced into the double-layer LSTM network, and the initial training data set is adopted to add Gaussian white noise to the prediction model for model training; performing sliding window processing on the initial training data set, and extracting a multi-dimensional feature vector; inputting the trained prediction model for prediction to obtain a model prediction value; calculating a residual error between a model predicted value and a real value, calculating a standard deviation of the residual error after eliminating a preheating period, and dynamically setting a threshold value based on the standard deviation of the residual error; and when the absolute value of the residual exceeds a threshold value and the sampling point processed by the sliding window is located in a preset interference time window, judging that a signal interference event occurs.

The invention provides a high-voltage circuit breaker voiceprint denoising method based on data enhancement and a storage medium, and the method comprises the steps: processing a collected original voiceprint data sequence, extracting stable and effective Mel-frequency cepstrum coefficient features, and constructing a two-dimensional feature matrix; a parallel mixed data enhancement strategy is adopted to generate a positive sample pair, and an encoder is trained in combination with a contrast learning mechanism, so that the representation robustness of the model under different voiceprint change conditions is improved. The method comprises the following steps: decomposing an original signal containing noise fringes into a plurality of modal components by using variational modal decomposition, extracting low-frequency effective components, introducing Gaussian white noise, constructing a corrosion target signal as a decoder training target, learning through a denoising automatic encoder, and finally outputting a denoised voiceprint feature signal. The method has the advantages of high robustness, high noise suppression capability, excellent feature expression capability and the like, is suitable for the field of online monitoring and intelligent diagnosis of the state of high-voltage circuit breaker equipment, and has good application prospect and engineering value.

A distribution network fault positioning method based on particle filter algorithm multi-information fusion comprises the following steps: extracting transient information, and extracting a transient zero-sequence current high-frequency component by using discrete wavelet transform; calculating a similar entropy and a skewness coefficient of a transient zero-sequence current high-frequency component of each measurement point to obtain a respective corresponding initial fault section position; fusing the two preliminary fault section positions by using a particle filtering algorithm to obtain a fault section position; determining head and tail fault steady-state zero-sequence voltage and power of the fault section; and cutting a fault section, obtaining the zero-sequence voltage and power of a next cutting point by using a particle filter algorithm, carrying out iterative cutting until a threshold condition is met, and outputting a fault position. According to the method, transient zero-sequence current high-frequency components and steady-state zero-sequence voltage power serve as fault characteristic quantities, and the fault positioning function under the conditions of different fault positions, fault closing angles and white noise interference of the active distribution network is achieved.

1. Name of the product of this design: Graphical user interface for audio playback settings of electronic equipment. 2. Purpose of the product of this design: an electronic device. 3. The design highlights of this design product are the interface content of the graphical user interface in the electronic device. 4. The picture or photo that best illustrates the key points of the design: main view. 5. Purpose of the graphical user interface: The graphical user interface is used by the user to set the audio playback function. 6. Human-computer interaction mode of the graphical user interface: The main view is the home page interface of the audio white noise setting. The user triggers the "preset" option icon in the interface, and the interface switches to the interface change state diagram. The user can choose to set the volume of different white noise options in the interface. “XXX” in the interface represents text and / or numbers and / or letters and / or symbols.

The invention discloses a park heterogeneous load prediction method based on EEMD and Kmeans adaptive optimization. The method comprises the following steps: carrying out missing value filling, abnormal value elimination and feature coding on park historical load data and external influence factors; dividing the park load into a plurality of typical scenes through a Kmeans clustering algorithm based on the similarity of load fluctuation characteristics; eEMD decomposition is carried out on the load sequence corresponding to each typical scene, Gaussian white noise is added to suppress modal aliasing, multi-frequency components are extracted and fused with external variables, and a prediction input data set is constructed; a prediction model is selected for different scenes, and adaptive adjustment and optimization are carried out through a Bayesian optimization algorithm; and carrying out dynamic weight fusion on the prediction result of each scene, dynamically adjusting the weight according to the historical load proportion of the scene, and outputting an overall load prediction value of the park.

The present invention provides a method for preprocessing vibration signals of a hydropower unit. The present invention proposes five strategies to improve the optimization accuracy of a dung beetle algorithm so that it has good robustness. The improved dung beetle algorithm MSIDBO jumps out of the local optimum through PWLCM chaotic mapping, nonlinear boundary convergence factor, rolling ball behavior integrating dynamic golden sine, sparrow alert mechanism and parameter adaptive adjustment to improve the accuracy of the optimization problem. With the minimum envelope entropy as the fitness function, MSIDBO is used to optimize parameters such as the white noise amplitude weight Nstd and the number of noise additions NE of ICEEMDAN, and the optimized ICEEMDAN is used to perform modal decomposition of the vibration signal of the hydropower unit. The MSIDBO algorithm has a faster convergence speed, higher solution accuracy and better applicability than the standard dung beetle algorithm. Through simulation signal analysis, it is concluded that the decomposition accuracy of the optimized ICEEMDAN is higher than that of the conventional decomposition algorithm, which improves the accuracy of the preprocessing of the vibration signal of the hydropower unit and provides a basis for the intelligent vibration monitoring and diagnosis of the hydropower unit.

The invention relates to the technical field of ground wire damage detection, and provides a ground wire damage detection method and system based on magnetostrictive guided waves, and the method comprises the following steps: generating a white noise signal through a magnetostrictive transducer to excite a ground wire, and collecting a guided wave response signal of the guided waves; identifying the guided wave response signal through a spectrum analysis model to obtain the inherent frequency characteristic of the ground wire, and obtaining the optimal excitation frequency according to the inherent frequency characteristic and the amplitude frequency mapping relation; adjusting the excitation frequency of the white noise signal according to the optimal excitation frequency to obtain an optimized guided wave signal; identifying the optimized guided wave signal through a defect detection and tension monitoring model to obtain a defect tension detection result; and based on the defect tension detection result, judging the damage level of the ground wire, and generating a detection report. According to the invention, defect detection and tension abnormity monitoring are carried out at the same time, the reliability of ground wire detection is improved, and an effective monitoring means is provided for safe operation of the ground wire.

The invention discloses an OTFS channel estimation method based on a SwinTrans-CNN model, and belongs to the technical field of channel estimation. Comprising the following steps: determining channel basic parameters according to a specific channel scene, and designing a TDL channel model; designing a time delay-Doppler domain signal of a transmitting end by using an embedded pilot frequency method; carrying out two-dimensional cyclic convolution and Gaussian white noise processing on the time delay-Doppler domain signal obtained by sending; a SwinTranss-CNN (Convolutional Neural Network) denoising model is constructed; and carrying out channel state estimation by utilizing the trained SwinTranss-CNN denoising model. According to the invention, by adopting a method of combining an embedded pilot scheme and a SwinTrans-CNN model, the problems of presetting pilot and guard symbols at a sending end and channel estimation by a receiving end are converted into the denoising problem of a time delay-Doppler domain two-dimensional image, the problem of too high bit error rate in a low signal-to-noise ratio scene is solved, and the accuracy of channel estimation is improved.

The invention provides an unmanned aerial vehicle radio frequency signal identification method and a related device, and relates to the technical field of deep learning. Signal data are loaded from an unmanned aerial vehicle radio frequency signal data set, and the data set comprises different unmanned aerial vehicle types, detection distances and signal fragment labels; dividing the signal data into a plurality of sections of continuous sampling point sequences which are not overlapped with one another; adding Gaussian additive white noise with different powers into the signal data, and constructing a training data set comprising a plurality of signal-to-noise ratios; performing short-time Fourier transform on the signal data to generate a time-frequency graph; performing principal component analysis and dimension reduction on the time-frequency graph, and extracting key time-frequency features; performing hyper-parameter optimization on the deep neural network model by adopting a k-fold cross validation strategy; and the optimized deep neural network model is used to classify and identify the time-frequency graph. The technical effect of accurately identifying the radio frequency signal of the unmanned aerial vehicle under different signal-to-noise ratio conditions is realized.

The invention discloses a time domain channel estimation method and device based on a multi-antenna diversity technology, and the method comprises the steps: mapping a bit stream into a symbol stream to construct a transmitting signal matrix, carrying out the STBC coding, inserting a time domain pilot signal, obtaining a matrix after insertion, selecting a vector in the matrix after insertion, and carrying out the time domain channel estimation. The method comprises the steps of obtaining a frequency domain receiving signal by combining with relevant parameters of Gaussian white noise and a Rayleigh fading channel, then performing matrix transformation to obtain an STBC decoding matrix, introducing a weight matrix to update an established MIMO-OFDM system input and output model to obtain a time domain channel estimation value, and finally performing FFT transformation on the time domain channel estimation value to obtain a frequency domain channel matrix estimation value. And substituting the frequency domain channel matrix estimation value into the STBC decoding matrix and then carrying out ML signal detection to obtain a final estimation value. A channel estimation algorithm is introduced to improve the estimation of a dynamic channel, a space-time block coding technology is combined to improve the demodulation signal precision of a MIMO system, and the full-state channel of the MIMO system is estimated.

The invention relates to the technical field of signal processing, and discloses a power equipment partial discharge signal denoising method and system, and the method comprises the steps: S1, generating an exponential decay type pulse signal simulating partial discharge, and superposing white noise and narrow-band interference into the exponential decay type pulse signal to generate a noisy signal; s2, decomposing the noisy signal into a plurality of intrinsic mode components through an ensemble empirical mode decomposition method, calculating a correlation coefficient and a kurtosis index of each intrinsic mode component, and identifying a noise component and a signal component based on the correlation coefficient and the kurtosis index; s3, wavelet threshold denoising is carried out on the noise components, and a wavelet threshold is dynamically adjusted for each noise component through a particle swarm optimization algorithm; and S4, reconstructing the de-noised noise component and the reserved signal component into a final de-noised signal, and evaluating the de-noising performance based on the signal-to-noise ratio, the root mean square error, the peak signal-to-noise ratio and the correlation coefficient.

The invention relates to the technical field of rolling bearing fault diagnosis, in particular to a rolling bearing fault diagnosis method based on data fusion and a neural network, and the method comprises the steps: S1, collecting vibration data of a plurality of sensors, superposing Gaussian white noise and pulse noise, and simulating a strong noise industrial environment; s2, extracting time domain and frequency domain features of vibration signals of each sensor, and constructing a preliminary feature matrix; s3, performing noise reduction processing and depth feature extraction on the feature matrix by using SDAE, fusing to generate a bearing data set, and dividing a training set and a test set; and S4, inputting the training set into an Adam optimization-based fractional order GRU model for training, and verifying the trained model by using the test set. According to the rolling bearing fault diagnosis method, the precision and robustness of rolling bearing fault diagnosis in a strong noise environment are remarkably improved by fusing the multi-source sensing data and the fractional order neural network model.

The invention relates to a building structure health monitoring method and system based on machine learning, and the method specifically comprises the following steps: firstly, building a target building three-dimensional numerical model through finite element simulation, generating a simulation signal, injecting Gaussian white noise, and adjusting model parameters to form a data set with health category labels; performing data enhancement by combining adaptive wavelet denoising with dynamic normalization, and extracting and enhancing high-resolution time-frequency features through adaptive window short-time Fourier transform and adaptive frequency band enhancement; then, a neural network model fusing structure physical prior guidance and multi-scale space-time interaction is constructed, a feature matrix is modulated, fused and coded to obtain a refined feature vector, and damage state probability distribution output is achieved; and training is carried out by using a feature consistency and prediction smoothness regularization term constraint model, and finally, the trained model is deployed, so that building structure health state evaluation and safety early warning are realized, the monitoring accuracy and reliability are improved, and effective technical support is provided for building safety guarantee.

The invention belongs to the technical field of DC micro-grid power spring cooperative control, and particularly relates to a multi-DC power spring control method based on distributed Kalman filtering consistency, and the method comprises the following steps: constructing a system state relation model based on a multi-DC power spring main circuit topology; establishing a multi-direct-current power spring system state equation; enabling external random interference and measurement errors to be equivalent to process noise and observation noise of the system respectively, and adding the process noise and the observation noise into an input end and an output end of the system to obtain a new state after filtering; replacing a previous non-interference state with a new state, and further solving a distributed Kalman filter to carry out state prediction and state updating on consistency control; performing fusion state estimation on the dynamic consistency control by using the corrected Kalman filtering; and iteratively executing state prediction and updating. According to the method, the stability of the system is ensured, the voltage reference value of each section of direct-current bus can still be accurately obtained under the interference of white noise, and the robustness is relatively high.

The invention discloses a method for detecting broadband transmission characteristics of a capacitor voltage transformer (CVT), which comprises the following steps of: inputting a uniform white noise signal into a test system without a tested CVT, and calculating a transfer function H (f) of the test system and a corresponding compensation function A (f); a reference test voltage is constructed by superimposing multiple harmonic waves on the fundamental wave voltage, a reference spectrum Y (f) is obtained through Fourier transform, and a driving spectrum X (f) and a driving signal x (t) are obtained by combining the compensation function A (f); a driving signal x (t) is applied to a test system provided with a tested CVT, a test voltage ui (t) applied between a primary side medium voltage end of the tested CVT and the ground is collected, a response signal measurement spectrum Ui (f) of the test system is obtained through Fourier transform, and then the driving signal x (t) is corrected according to an error spectrum of the response signal measurement spectrum Ui (f) and a reference spectrum Y (f), so that the voltage signal ui (t) meets design requirements; and collecting the test voltage uo (t) output by the secondary side of the tested CVT at the moment, and calculating the transmission characteristic parameters of the tested CVT by combining the test voltage ui (t) and the test current ii (t) of the medium-voltage end of the tested CVT at the moment.

The invention relates to the technical field of short-term power load prediction, in particular to a short-term power load prediction method combining improved empirical mode decomposition and a bidirectional long short-term memory network, which comprises the following steps: preprocessing historical load data; decomposing the load data into a plurality of intrinsic mode functions by adopting a complete adaptive noise integrated empirical mode decomposition algorithm, and adaptively adding Gaussian white noise to suppress a mode aliasing problem; constructing a prediction model based on a bidirectional long-short-term memory network and a self-attention mechanism for each component; extracting features by an input layer through a sliding window; the training layer adopts a bidirectional long short-term memory network to extract time sequence hidden features, and a key time point is focused through weighting of a self-attention mechanism; the output layer generates a component prediction value; and superposing all component prediction results, and reconstructing to obtain an accurate load prediction value of the to-be-predicted day. According to the method, the prediction precision and stability are effectively improved by improving signal decomposition and deep learning model fusion.

The invention relates to an audio control method and device, electronic equipment and a vehicle. The audio control method comprises the following steps: acquiring multi-modal data; performing feature analysis on the multi-modal data to obtain an analysis processing result, and constructing a user portrait based on the analysis processing result; generating audio white noise corresponding to the user portrait based on the user portrait; according to the embodiment of the invention, the multi-modal data can be acquired, the personalized characteristics of the user can be determined by analyzing the multi-modal data from multiple dimensions, the accuracy of user portrait construction is improved, on the basis, the demands of the user are deeply understood based on the user portrait, and the user experience is improved. According to the method, the audio white noise suitable for the user is generated, and audio control is performed through the audio white noise, so that the in-vehicle environment is improved to meet different requirements of the user, and the user experience is improved.

According to the method and device for predicting the blasting vibration waveform, the computer storage medium and the terminal, the random vibration prediction model constructed by modulating the white noise filtering model is used for testing each blast hole to obtain the single-hole waveform of the blast hole, the rock mass characteristics and the charging condition characteristics can be reflected, and the blasting vibration waveform prediction accuracy is improved. The accuracy of the underground blasting vibration prediction result is ensured, the single-hole blasting vibration waveform of related parameters determined according to the single-hole waveform is used as the superposition wavelet, the superposition calculation of the porous blasting vibration is completed, the method can be suitable for different types of porous blasting vibration waveforms, and under the condition that the applicability of the underground blasting vibration prediction result is ensured, the accuracy of the underground blasting vibration prediction result is ensured. A prediction model capable of reflecting porous blasting vibration waveforms such as rock mass characteristics and charging conditions is established, a tool is provided for urban underground space blasting construction, and technical support is provided for urban underground space construction.

The invention discloses a structural damage detection method based on a bridge digital twinning group, and the method comprises the steps: building a general damage detection model through a multi-dimensional digital twinning technology: building a reference bridge model through Abaqus, and generating a digital twinning group containing a single / multiple damage scene; training the ethnic group by improving a WGAN-GP architecture, outputting a marked damage image by a generator, introducing a multi-scale attention module into a discriminator, and constructing an ethnic group characteristic spectrum (EFM) matrix through characteristic distillation; then collecting feature parameters to train a CNN model, and fusing features through SENet channel attention; through Gaussian white noise verification and n-fold cross optimization, the F1 score of the model is greater than or equal to 0.89 in a 40% noise environment, and the average accuracy rate reaches 95% or above. According to the technology, the limitation of single structure detection is broken through, the model generalization ability is improved by 300% and the detection efficiency is improved by 50% through a dynamic twin-driven convolutional network self-evolution mechanism, and complex scenes with + / -20% size change and 0-100% damage degree can be accurately identified; and an efficient anti-interference solution is provided for health monitoring of structures such as bridges, high-rise buildings and the like.

A HHT-based voltage quality disturbance detection method, including: obtaining frequency spectrum information of an original voltage signal, and determining whether the original voltage signal is a closely spaced mode signal; based on the frequency spectrum information, performing a singular value decomposition on the original voltage signal and performing a reconstruction to obtain a reconstructed voltage signal with an interference signal removed; if the original voltage signal is the closely spaced mode signal, performing a frequency modulation on the reconstructed voltage signal to obtain a frequency-modulated signal; adding white noise to the reconstructed voltage signal or the frequency-modulated signal, and then performing an empirical mode decomposition; and performing a Hilbert transform on each intrinsic mode function obtained by the empirical mode decomposition to obtain an amplitude and frequency information of a corresponding intrinsic mode function.

The invention relates to an intelligent hypnosis method and system based on a brain-computer interface and a storage medium, and belongs to the technical field of human-computer interaction and artificial intelligence. The method comprises the following steps: acquiring an EEG signal of a user in real time through EEG acquisition equipment, and extracting a brain wave segment power value as state input through preprocessing; a deep reinforcement learning model (such as DRQN) selects music type actions (such as classical music and white noise) based on an epsilon-greedy strategy; calculating a reward value (maximizing delta wave increment and inhibiting beta wave) according to the electroencephalogram state change after playing, and optimizing model parameters by adopting Q-Learning; and dynamically adjusting the strategy through iterative interaction until the user reaches a preset sleep target. The system comprises an electroencephalogram acquisition module, a preprocessing module, a reinforcement learning module and a music control module, and realizes closed-loop regulation and control. The method has the advantages of high personalization, high hypnosis efficiency (induction to sleep in 1-7 minutes), flexible adaptation to different users and self-evolution optimization capability, and effectively improves the sleep induction effect.

The invention provides a rolling bearing fault diagnosis method based on deep learning, and relates to the technical field of mechanical fault diagnosis. According to the method, global optimization is carried out on a parameter combination in an ICEEMDAN decomposition process through a CPO optimization algorithm, the optimal amplitude of white noise and the number of times of adding the white noise are determined, an intrinsic mode function is screened and denoised in combination with a correlation coefficient, reconstruction of a vibration signal of a rolling bearing is completed, and the vibration signal of the rolling bearing is reconstructed. The problem of mode aliasing is solved while the reliability and the stability of a vibration signal of the rolling bearing are improved; a K nearest neighbor method is used to construct a graph model, an edge connection relation between nodes can be effectively established, so that a foundation is laid for subsequent deep analysis and processing of vibration signals based on a graph structure, a graph attention neural network is input, topological structure information of the nodes and edges is fully utilized, extracted node features are classified and identified, and the accuracy of vibration analysis is improved. Therefore, fault diagnosis of the rolling bearing is realized.

The invention provides a power load prediction method and system based on adaptive modal decomposition, and the method comprises the steps: obtaining an original load sequence and a multi-modal data set based on time sequence distribution, and the multi-modal data set comprises meteorological data, equipment state data, and economic and social activity data; copying the original load sequence into a plurality of copies, and adding adaptive white noise to each original load sequence to obtain a plurality of noise-added sequences; performing modal decomposition, component screening and modal reconstruction on each noise adding sequence, and performing superposition to obtain a de-noising load sequence; performing feature screening based on causal driving on the de-noising load sequence and the multi-modal data set to obtain a feature vector; and inputting the feature vector into a preset load prediction model, so that the load prediction model outputs a corresponding load prediction result based on an attention mechanism and a preset physical rule constraint, and the accuracy of power load prediction in a complex scene is improved.

The invention relates to the technical field of geodetic survey and structural health monitoring, and discloses a GNSS-RTK coordinate domain error correction method, which comprises the following steps: acquiring GNSS-RTK dynamic observation data to form a mixed signal time sequence; executing an improved adaptive noise complete empirical mode decomposition algorithm on the sequence to obtain an intrinsic mode function component; identifying and eliminating high-frequency components representing Gaussian white noise based on an energy coefficient, and reconstructing residual components into a signal sequence after primary noise reduction; inputting the noise reduction sequence into a rapid independent component analysis model for blind source separation; and finally, carrying out sorting and phase and amplitude uncertainty correction on the separated independent components, and outputting a multi-path error model and a structure dynamic deformation signal. According to the method, the problem of blind source separation failure or low precision caused by strong noise covering source signal statistical characteristics is solved through a strategy of first noise reduction and then separation, and an effective physical signal can be accurately extracted from a strong noise background.

The invention relates to a switch cabinet partial discharge signal positioning method based on a time difference positioning method, and belongs to the technical field of electrical engineering and signal processing. Aiming at the problem that the ultrasonic partial discharge signal has white noise, an improved wavelet threshold algorithm is adopted to construct a threshold function, and a continuous self-adaptive wavelet threshold denoising algorithm is adopted to carry out denoising processing on the partial discharge signal, so that the problems of excessive denoising and incomplete denoising existing in a traditional denoising algorithm are solved, effective separation of the signal and the noise is realized, and the denoising efficiency is improved. And the performance of the denoising algorithm is improved. In order to obtain a time delay value between each path of signals, a generalized cross-correlation time delay estimation algorithm based on PHAT-SCOT joint weighting is used for substituting a time delay estimation value into a switch cabinet partial discharge positioning equation set, and an optimal equation is obtained. According to the partial discharge optimal solution algorithm based on DE-PSO, the optimization equation is solved, the spatial coordinate position generating the partial discharge signal is positioned, the defects of parameter setting and threshold selection of the optimization algorithm are overcome, and the positioning precision of the partial discharge signal is improved.

The invention relates to a cable partial discharge defect identification method based on a support vector machine, and belongs to the technical field of electrics. The problems that an existing method is low in recognition rate and prone to interference are solved. The method comprises the following steps: firstly, carrying out interference suppression on an acquired partial discharge high-frequency signal, wherein the interference suppression comprises narrowband interference suppression by subspace reconstruction and frequency slice wavelet transform white noise suppression based on ensemble empirical mode decomposition; then, carrying out support vector multi-classification by utilizing an optimized support vector data description algorithm, and constructing a plurality of classifiers; and finally, calculating the generalized distance from a test sample to each classifier, and making a decision, thereby achieving the recognition of the partial discharge defect. According to the method, the partial discharge defect recognition rate is effectively improved, the anti-interference capability is high, and a reliable technical means is provided for power cable insulation state detection.