195 results about "Time frequency distribution" patented technology

The invention discloses a cable fault positioning method and system based on reflected wave decomposition and kurtosis sorting, and aims to solve the problem that a cable fault is difficult to detect under a low impedance change rate. The method comprises the steps of transmitting an incident excitation signal to a cable to obtain a first reflection signal in the cable; an improved VMD algorithm is adopted for decomposition to obtain K IMF components, and the IMF with the maximum kurtosis is selected as a second reflection signal; acquiring a time-frequency distribution curve of the second reflected signal by using short-time Fourier transform and frequency spectrum redistribution technologies; and constructing a two-dimensional energy-time curve by extracting an energy value and time under the frequency of the maximum energy amplitude point, and determining a fault position. The system is composed of an incident excitation module, a decomposition module, an STFT-RSP module and a fault positioning output module, and the positioning method is achieved. According to the method, the limitation of a traditional spread spectrum time domain reflection positioning method in cable fault detection is effectively overcome, and an efficient and accurate instructive technology is provided for cable fault positioning and detection.

The invention relates to a bridge structure vibration monitoring and analysis method based on artificial intelligence, and the method specifically comprises the following steps: setting an acceleration sensor network at a key part of a bridge, collecting and marking a vibration signal sample, and forming a data set; a high-resolution time-frequency matrix of sample adaptive optimal kernel time-frequency distribution is calculated, a damage sensitive resonance frequency band is positioned according to spectrum kurtosis, and the time-frequency matrix is enhanced by the resonance frequency band subjected to adaptive gain enhancement; dividing a matrix frequency axis multi-scale binary tree, calculating and normalizing sub-band average energy, quantifying energy distribution uniformity through information entropy, and splicing multi-scale entropy values into feature vectors; then constructing and training a bridge detection data model; and finally, preprocessing new data, inputting the preprocessed new data into the trained model, and automatically analyzing to obtain a bridge health monitoring result. Through time-frequency optimization, multi-scale entropy and deep learning technologies, the problems of large noise, difficult feature extraction and low automation and accuracy of traditional monitoring can be solved.

The invention discloses a multistage safety response control method of an industrial and commercial liquid cooling energy storage system, and relates to the technical field of electric energy storage systems, a sensor network collects an operation state data time sequence matrix, and abnormal data cleaning is carried out on the time sequence matrix; the method comprises the following steps: preprocessing a vibration signal of a cooling system, extracting characteristic data of abnormal vibration through Fourier transform and energy distribution analysis, and positioning a time-frequency distribution abnormal region; generating a time sequence multivariable data set according to the characteristic data of the abnormal vibration, determining a cause label according to the fused time sequence multivariable data set, obtaining the inherent frequency and the resonant frequency of the cooling system in combination with simulation analysis, and matching and determining the cause of the abnormal vibration; an LSTW neural network is used for predicting the abnormal resonance risk, and corresponding countermeasures are taken for the cooling network according to the prediction result; unnecessary vibration caused by error adjustment is avoided, the relation between cooling efficiency and safety is balanced, and unnecessary power loss is reduced.

The invention relates to the field of defect detection, and particularly discloses a cable defect positioning method which comprises the following steps: injecting a Gaussian envelope linear chirp signal into a to-be-detected cable, and obtaining a corresponding reflection signal; adding a generalized adjustment factor in the reflected signal to carry out generalized adjustment so as to obtain first time-frequency distribution after generalized adjustment; performing energy rearrangement on the first time-frequency distribution under the same frequency to obtain second time-frequency distribution after energy arrangement; determining a cross-correlation function of the incident signal and the second video distribution to obtain a defect positioning curve of the to-be-detected cable; and determining the defect position of the to-be-detected cable based on the defect positioning curve. Generalized adjustment and energy rearrangement are carried out on traditional S transformation, so that cross terms are inhibited, and meanwhile, the positioning and distinguishing capability of the method is improved; inverse transformation is carried out on time-frequency distribution in an effective frequency band range of the signal, and interference of noise on defect polarity discrimination is effectively reduced.

The invention relates to the technical field of laser radars, in particular to an obstacle detection method and system based on a frequency-modulated continuous wave laser radar, and the method comprises the steps: transmitting a chaotic frequency-modulated continuous wave signal to a detection region, and obtaining a reflection echo signal of a target object; performing decoupling calculation on the difference frequency electric signal to obtain a target time frequency distribution matrix; according to the target time-frequency distribution matrix, predicting the Doppler frequency shift amount of the target object at the next moment; based on the chaos frequency modulation adjustment signal, obtaining a distance measurement deviation compensation amount; the chaos frequency modulation adjustment signal is obtained by dynamically adjusting the chaos frequency modulation slope of the chaos frequency modulation continuous wave signal through the Doppler frequency shift amount; and compensating the difference frequency electric signal frequency value of the target object according to the distance measurement deviation compensation amount and the environmental noise data so as to solve obstacle distance speed detection information. According to the invention, the Doppler frequency shift amount is predicted in real time and the chaotic frequency modulation continuous wave signal parameters are dynamically adjusted, so that the accurate compensation of the distance measurement deviation is realized, and the accuracy of obstacle distance speed detection is improved.

The invention belongs to the technical field of gear health state evaluation, and discloses a vibration signal-based gear wear area monitoring method under a variable speed condition, which comprises the following steps of: acquiring a vibration signal of a gear, and calculating a time-frequency distribution signal of the vibration signal based on multiple synchronous extraction transformation; phase information is extracted from the time-frequency distribution signal, and then an angular domain signal is obtained through angular domain resampling; segmenting and aligning the angular domain signal according to the fault period of the gear to obtain a deterministic component related to gear wear; the deterministic component is optimized through a Savitzky-Golay filter, and then an order spectrum and a comprehensive order band energy ratio are calculated; according to the method, the accumulated comprehensive energy ratio index is used for evaluating or monitoring the wear area of the gear, so that the wear severity degree is quantitatively evaluated, the wear area of the gear can be accurately and quantitatively evaluated under the condition of variable rotating speed, and the accuracy and the reliability of the gear are improved. And accurate monitoring and evaluation of gear wear are realized.

The invention provides a method for reducing electrolyte impurities based on copper foil production equipment, which comprises the following steps: identifying a pressure fluctuation abnormal region from a pressure time-frequency distribution map, taking the pressure fluctuation abnormal region as the input of a pre-constructed high-deposition region distribution model, outputting to obtain the position distribution of the high-deposition region, and determining potential high-risk nodes of impurity deposition in a pipeline; according to the determined potential high-risk node of impurity deposition in the pipeline, a filtering device is deployed at the potential high-risk node of impurity deposition, impurity particle size separation is carried out through a multi-stage filtering structure arranged in the filtering device, separated impurities with different particle sizes are directionally collected, and impurity data are obtained.

The invention discloses a live detection method for insulation defects of high-voltage power equipment. The method comprises the following steps: firstly, synchronously arranging ultrahigh frequency sensors and acoustic emission sensors on a plurality of monitoring points on the surface of a gas insulated switchgear shell to form an array, and synchronously acquiring signals for filtering pretreatment; then, pulse events are extracted from the two types of signals respectively, and associated pulses in a time window are matched into matched pulse pairs representing the same discharge source; then, carrying out time-frequency transformation on the ultrahigh frequency and acoustic emission pulse waveform in each matched pulse pair, carrying out joint noise reduction by calculating a coherence coefficient between time-frequency distribution matrixes, and extracting a joint feature vector containing an energy ratio, a time parameter ratio and a frequency difference from the denoised time-frequency matrix; according to the invention, multi-source signals are fused, and high-sensitivity detection, high-precision positioning and high-accuracy identification of insulation defects are realized.

The invention provides a passive perception radar target classification method and system based on electromagnetic detection feature extraction, and the method comprises the steps: carrying out the multi-dimensional signal decomposition of an original scattered signal sequence through obtaining an electromagnetic radiation signal set of a target region; obtaining a time-frequency distribution feature set and a polarization response feature set corresponding to each signal acquisition period, extracting a target electromagnetic feature combination from the time-frequency distribution feature set and the polarization response feature set according to a preset electromagnetic feature association rule, generating a weight distribution parameter, and performing weighted fusion processing on the target electromagnetic feature combination to obtain a target electromagnetic feature combination; and the fusion feature vector set is input into an adaptive classification network, hierarchical classification operation is performed on the fusion feature vector set, the category identifier of the target scatterer and the corresponding classification confidence are output, and a radar detection optimization instruction is generated based on the category identifier and the corresponding classification confidence. According to the method, high-precision distinguishing of multiple types of scatterers is achieved under the passive sensing condition, and meanwhile the environmental adaptability and the calculation efficiency of the classification process are guaranteed.

The invention relates to the field of gear fault diagnosis, in particular to a rack rail train driving gear fault diagnosis method, system and device, and solves the problem that fault features cannot be effectively extracted from rack rail section vibration signals in the prior art. The method comprises the following steps: identifying a transition section vibration signal of a transition device; identifying a toothed rail section vibration signal; the rack rail section vibration signals are processed; and fault diagnosis is realized according to the meshing frequency and the sideband information. According to the time domain characteristics of the vibration signals, the vibration signals generated when the driving gear passes through the rack rail section can be accurately recognized from the vibration signals, and self-adaptive recognition of the vibration signals of the rack rail section is achieved. According to the method, noise interference in time-frequency distribution of toothed rail section vibration signals is removed, time-frequency aggregation of the time-frequency distribution is improved, fault features of the driving gear are extracted from the time-frequency distribution, fault diagnosis of the driving gear is achieved, health state monitoring of the toothed rail train driving gear is achieved, and train operation safety is guaranteed.

The invention discloses a dry-type reactor overheating fault detection method based on sound signal analysis, and the method comprises the steps: S1, forming a dry-type reactor overheating fault detection system through a voiceprint wave sensor, a main control box, and a 4G antenna, and obtaining a first sound signal source generated during the operation of a dry-type reactor; s2, processing the first sound signal source by adopting a time-frequency distribution signal reconstruction technology to obtain a second sound signal source; s3, performing local feature scale decomposition on the second sound signal source to obtain an overheat fault feature parameter of the second sound signal source; and S4, taking the characteristic parameters in the normal state and the overheating fault state as training data to train a diagnosis model, establishing a sound signal overheating fault diagnosis model, and carrying out overheating fault detection on the dry-type reactor. According to the invention, efficient monitoring and early warning of the overheating fault of the dry-type reactor are realized, and the safety and stability of a power system are enhanced.

The invention relates to an electric power quantity accumulated temperature effect duration prediction method and device and a medium, and the method comprises the following steps: obtaining original data, dividing the original data into a plurality of groups of data, and carrying out the wavelet transformation of temperature-sensitive load data and temperature data in a high-temperature time period in each group, so as to obtain time-frequency distribution data; performing causal inference by using a frequency domain causal analysis method and integrating the time-frequency distribution data to obtain a causal effect of the temperature on the temperature-sensitive power and electric quantity data, and determining occurrence time of an explicit causal relationship; daily load data, daily electric quantity data and daily temperature data of multiple consecutive days are collected as to-be-analyzed data; and predicting the duration of the accumulated temperature effect according to the data to be analyzed by using a high-order Granger causality method. Compared with the prior art, the method has the advantages of high accuracy, high interpretability and the like.

The invention discloses an electroencephalogram signal time-frequency domain denoising method based on a noise attention mechanism. The method comprises the following steps: performing time-frequency transformation on a noisy electroencephalogram signal to generate a plurality of time-frequency features; time domain and frequency domain noise masks are extracted through a dual-path neural network and fused; a two-stage complex convolutional network is constructed, a noise attention module of the two-stage complex convolutional network uses a feature splitting and space weight mechanism, time-frequency distribution of noise is positioned in the first stage, and clean signal retention and noise suppression are collaboratively optimized in the second stage; and finally, reconstructing the de-noised signal through inverse transformation. According to the method, time domain and frequency domain information of the electroencephalogram signals of the complex convolutional neural network is combined at the same time, artifacts in the electroencephalogram signals are more accurately separated and useful electroencephalogram signals are fully reserved on the basis of a noise attention mechanism of time-frequency domain segmentation, meanwhile, the interpretability of the model is improved, and the accuracy of the model is improved. The method solves the problem that the existing electroencephalogram signal denoising method does not consider time-frequency domain combined denoising. In addition, the invention further comprises a system, equipment and a medium which can store and operate the method.

The invention discloses a rotating machinery fault diagnosis method based on frequency domain adaptive Kaiser convolution window Fourier transform, which comprises the following steps: constructing a frequency domain adaptive Kaiser window function for an acquired discrete vibration signal containing multi-harmonic fault information, and reducing the influence of frequency aliasing in different frequency domain intervals; a frequency domain adaptive Kaiser convolution window model is established by applying self-convolution operation, and the influence of spectrum leakage and noise interference is reduced; calculating the Fourier transform of the signal based on an adaptive Kaiser convolution window, establishing a time-frequency (IKTF) model of an improved Kaiser window base, and carrying out model solution calculation to obtain the time-frequency distribution of the signal; and determining selection of frequency domain adaptive parameter values, and identifying fault characteristic frequency information in time-frequency distribution. According to the innovative method, the weak fault features of the rotating machinery can be extracted, and the influence of factors such as background noise, harmonic interference and random impact is effectively overcome, so that the accuracy of fault detection is improved.

The invention discloses an indoor multi-sound-source positioning method based on DOA estimation and DOA association, and the method comprises the steps: judging single-source time-frequency points through the correlation of time delay inequality vectors of adjacent time-frequency points between microphones, and constructing a variable-size elastic single-source region; applying the FSSZ distribution condition of the sound source in a reference array to all arrays by using the high correlation characteristic of the FSSZ time-frequency distribution of the same sound source at different microphone arrays in a frame; performing middle DOA estimation on the FSSZs of all the arrays in the whole observation time period by using a circular integral cross spectrum method, and constructing a sound source position histogram in combination with FSSZ aggregation degree weighting; 2D positions of the plurality of sound sources are estimated from the sound source position histogram using 2D-CFAR. According to the invention, under the conditions of indoor reverberation and noise, position estimation can be carried out on a plurality of sound sources with an unknown number, the detection performance under the conditions of positioning precision and missed detection is obviously superior to that of an existing method, and the defects of the prior art are overcome.

The invention relates to the technical field of mechanical fault diagnosis, in particular to a mechanical fault diagnosis method and system based on a pulse neural network. According to the invention, a fault diagnosis network is constructed, and the fault diagnosis network comprises a pulse time-frequency repair module, a pulse space-time attention module and a pulse residual network; wherein the pulse time-frequency repairing module comprises a time-frequency convolution layer and a pulse repairing sub-module; time-frequency distribution characteristics of the collected mechanical vibration signals are extracted by using a time-frequency convolution kernel, the time-frequency distribution characteristics are input into a pulse repairing sub-module, and a time-frequency characteristic group of a pulse residual network time step length number is output; inputting each time-frequency feature group into a pulse space-time attention module, and extracting key time-frequency features of each time-frequency feature group; and taking the key time-frequency characteristics of each time-frequency characteristic group as the input of each time step length of the pulse residual network, and outputting a prediction fault category label. According to the invention, the mechanical fault diagnosis precision is effectively improved.

The invention discloses a frequency modulation continuous wave radar multi-target dynamic separation and anti-interference method and system based on synchronous extraction transformation, and the method comprises the steps: S1, generating a linear frequency modulation continuous wave, and transmitting a high-frequency signal through a radio frequency front end; s2, receiving a target reflection signal, mixing the target reflection signal with the emission signal through a mixer, extracting an intermediate frequency beat signal, and performing denoising; s3, performing time-frequency decomposition on the signal by adopting synchronous extraction transformation to generate a high-resolution time-frequency distribution matrix; s4, separating overlapped targets based on the time-frequency matrix by adopting a density clustering algorithm, eliminating a distance-speed coupling error in combination with a dynamic decoupling compensation algorithm, and outputting a time-frequency track of an independent target; constructing a dynamic time-frequency domain mask filter to filter noise, and optimizing the detection sensitivity and performing false alarm control through an adaptive threshold detection mechanism; according to the method, dense targets are accurately separated through high-resolution time-frequency analysis, and the anti-interference capability and the dynamic target tracking precision are improved in combination with a dynamic Doppler decoupling algorithm.

The invention discloses a coal-fired power plant belt conveyor driving gear box bearing fault diagnosis method, which relates to the field of fault diagnosis and evaluation, and is used for extracting gear box bearing fault features based on an order tracking analysis method of tachometer-free instantaneous rotation frequency estimation. The method specifically comprises the following steps: S1, obtaining an instantaneous rotation frequency ridge line through the time frequency distribution of a vibration signal, carrying out the rotation frequency estimation of a rotating shaft, and improving the precision of the instantaneous rotation frequency ridge line through an improved multi-source ridge line fusion algorithm; and S2, extracting bearing fault feature order information by using a calculation order tracking COT algorithm, and adaptively processing a bearing fault diagnosis problem of the driving gearbox of the belt conveyor. According to the bearing fault diagnosis method for the rubber belt conveyor driving gearbox of the coal-fired power plant, the bearing inner ring fault is successfully diagnosed, and the feasibility of a bearing fault diagnosis strategy for the rubber belt conveyor driving gearbox is verified.

The present invention provides a life detection method and system based on radar signal processing, which relates to the technical field of radar signal processing. The method and system include collecting radar echo signals in a target area, controlling a phased array radar transmitting antenna array to transmit an orthogonal frequency division multiplexed dual-frequency continuous wave radar signal, inputting the processed radar echo signal into an orthogonal demodulation module for synchronous coherent demodulation to obtain a baseband signal; inputting a feature vector into a multi-task deep learning network with an integrated attention mechanism for feature fusion and target recognition to obtain a second processed signal; obtaining a target feature spectrum by calculating the time-frequency distribution of the second processed signal, judging whether there is a life form in the target area according to the time-varying characteristics of a respiratory frequency component and a heartbeat frequency component and the law of amplitude fluctuation; if it is determined that there is a life form in the target area, calculating the three-dimensional spatial coordinates of the life form through a multi-base station collaborative positioning algorithm, and outputting a detection result containing the three-dimensional spatial coordinates and the life form feature information.

The invention belongs to the technical field of rotating machinery signal processing, and discloses a multi-shafting rotating machinery rotating speed estimation method based on a displacement vibration signal, and the method comprises the steps: collecting the displacement vibration signal, and carrying out the short-time Fourier transform to obtain a time-frequency distribution matrix; screening out a safety harmonic set of each shafting based on the estimated rotating speed range; calculating a definition evaluation value of each safety harmonic wave and selecting an optimal harmonic wave as a target tracking harmonic wave; constructing a cost function fusing amplitude and frequency abrupt change penalty, and performing ridge line tracking in the local time-frequency matrix after noise reduction to obtain a preliminary estimation time-frequency curve; and constructing a demodulation phase function based on the curve, performing generalized demodulation and narrow-band filtering on an original signal, extracting a pure harmonic component, and finally reconstructing a final rotating speed curve of a corresponding shaft system through instantaneous phase differential. According to the method, high-precision and anti-interference decoupling and estimation of the rotating speed of each shafting under a strong interference background are realized.

The invention discloses a ship signal adaptive time-frequency analysis method based on instantaneous frequency direction self-adjustment. The ship signal adaptive time-frequency analysis method comprises the steps of collecting ship signals to be subjected to time-frequency analysis and performing short-time Fourier transform; estimating the instantaneous frequency of the ship signal based on short-time Fourier transform time-frequency distribution in combination with time-frequency narrow-band features; calculating the instantaneous frequency and the optimal window length after smoothing; segmenting the smoothed instantaneous frequency in time, calculating time-frequency domain direction pre-guidance of each time period, and constructing a Chirp atomic direction over-complete dictionary; segmenting the ship signal in time, and calculating the matching direction of the ship signal in each time period so as to calculate and improve Chirplet transformation time-frequency distribution; and calculating the optimal short-time Fourier transform in combination with the optimal window length, and correcting and improving Chirplet transform time-frequency distribution. According to the method, the robustness of short-time Fourier transform and the anisotropy of improved Chirplet transform are utilized at the same time, and time-frequency distribution with high robustness and high time-frequency resolution is obtained.

The invention provides an underwater acoustic signal identification method based on cross attention feature fusion, and belongs to the technical field of underwater acoustic signal processing. In the noise reduction module, a double-branch structure is used for processing amplitude and phase information respectively, an information interaction mode is adopted, information of the other path can be used as reference in the amplitude or phase processing process, and therefore the feature representation capacity is improved; the amplitude and phase information obtained by the noise reduction module is dynamically fused by using the feature fusion module, so that the problem that only the amplitude information is used and the phase feature is ignored is solved, and the robustness of the system is enhanced while the information loss is remarkably reduced; in the recognition module, a feature extraction module is designed, the time dimension and the frequency dimension are processed through TF-transform, and the time-frequency distribution characteristics of the voice are modeled by capturing the global dependency relation of the time dimension and the frequency dimension.

The invention provides a rotating speed extraction method, system and equipment based on time-frequency analysis and a medium. The method comprises the following steps: acquiring a signal x (t) to be analyzed; performing spectral analysis transformation on the to-be-analyzed signal x (t) to obtain a spectral analysis result; acquiring a three-dimensional time-frequency distribution diagram A (t) (f) of the to-be-analyzed signal x (t) according to the spectral analysis result; searching a peak frequency in the three-dimensional time-frequency distribution diagram A (t) (f) according to a preset extraction rotating speed frequency Nfn and a preset search range so as to obtain the rotating speed fn (t); according to the method, the rotating speed of the equipment can be accurately extracted according to the monitoring data under the condition of not depending on an external rotating speed sensor; the system has the same beneficial effects.

The invention relates to the technical field of signal analysis and processing, in particular to an FSK signal blind demodulation method and system in underwater acoustic communication signal analysis and processing, and the method comprises the steps: carrying out the coarse estimation of the bandwidth and carrier frequency of an underwater acoustic FSK signal; performing symbol rate estimation and timing synchronization based on an enhanced digital phase-locked loop; down-converting the received signal to zero intermediate frequency; carrying out discrete short-time Fourier transform to obtain a time-frequency domain distribution matrix of the signal; extracting time-frequency domain distribution in a time dimension by taking a symbol period as an interval, and taking out a frequency point with the strongest power at a corresponding moment to obtain a time-frequency distribution sequence of the signal; clustering the time-frequency distribution sequence by using K-means clustering, and judging the modulation order M of the FSK signal according to the contour value; and clustering the time-frequency distribution sequences into M classes, simultaneously obtaining the class to which the frequency corresponding to each sampling point in the sequences belongs, and judging the class as an M-system symbol until the blind demodulation is finished. According to the invention, blind demodulation of FSK signals with guard intervals and without guard intervals can be realized.

The invention discloses an ant colony optimization energy focusing cable insulation damage positioning method and device, and relates to the technical field of cable detection engineering, and the method comprises the steps: generating a Gaussian envelope linear frequency modulation chirp detection signal, injecting the detection signal into a to-be-detected cable, and collecting an incident signal and an echo signal containing a reflection component; constructing a parameterized time-frequency analysis kernel function family under a Cohen type time-frequency distribution framework, and searching and optimizing kernel parameters by using an ant colony optimization algorithm; and calculating the time frequency distribution and energy-time curve of the signal based on the energy focusing time frequency analysis kernel function obtained through optimization, extracting the arrival time corresponding to an energy peak value, and realizing cable insulation damage positioning according to the reflection time delay and the propagation wave velocity. According to the method, energy peak focusing and peak position stability are enhanced while cross terms are restrained, defect positioning precision and robustness are remarkably improved, and the method is suitable for engineering online detection and automatic implementation of cable insulation damage.

The invention provides a passive perception radar AI weak target detection method and system, and the method comprises the steps: obtaining a multi-dimensional electromagnetic signal data set of a target monitoring region, carrying out the signal feature extraction of the multi-dimensional electromagnetic signal data set, and obtaining the time-frequency distribution features and signal mode features corresponding to the multi-dimensional electromagnetic signal data set; based on a pre-trained weak target identification model, carrying out joint analysis processing on the time-frequency distribution characteristics and the signal mode characteristics, and generating weak target confidence parameters of potential weak target reflection signals; generating a weak target detection result of the target monitoring area according to a comparison result of the weak target confidence coefficient parameter and a preset confidence coefficient threshold value; and transmitting the weak target detection result to the target terminal device to trigger an alarm response operation. According to the method, the problem of characteristic loss caused by signal preprocessing and target detection module separation in a traditional method is solved, and the real-time performance of weak target detection and the system robustness are remarkably improved.

The invention discloses a wave head calibration method and system for fault traveling waves, a storage medium and electronic equipment, and the method comprises the steps: obtaining fault traveling wave data of a detection point, and obtaining time-frequency distribution based on the fault traveling wave data; acquiring kurtosis values of different frequencies based on the time-frequency distribution, and selecting the frequency corresponding to the maximum kurtosis value as a target frequency; and performing energy demodulation on a time domain signal corresponding to the target frequency in the time frequency distribution to obtain an energy spectrum, and selecting a moment corresponding to a point with the maximum energy amplitude in the energy spectrum as a wave head arrival moment. According to the method, the time-varying characteristics of different frequencies of the traveling wave can be accurately captured by using the GLCT, and the frequency component which is most easy to calibrate is selected by using the kurtosis value, so that the interference of background noise is effectively inhibited, and the overall efficiency and precision of the algorithm are improved; the TEO can be used for rapidly capturing transient abrupt change in signals, the detection sensitivity of energy abrupt change points is enhanced, the wave head time domain characteristics are effectively enhanced, and the method is easy and convenient to use and high in adaptability.

Example systems, methods, and apparatus are disclosed herein for a high-resolution time-frequency analysis for nonstationary signals. Time-frequency distributions (TFDs) are essential tools for analyzing nonstationary signals in various applications. The proposed technology, Zoom, a novel time-frequency distribution (TFD) designed to enhance the resolution and reduce cross-term interference. The performance of the proposed TFD is rigorously evaluated by studying its behavior across different smoothing values, allowing for a detailed analysis of its time-frequency localization capabilities. To quantify its effectiveness, the proposed technology computes several performance metrics, including time-frequency concentration and cross-term reduction.

The invention relates to a rotary machine composite fault detection method and system under a time-varying working condition, and belongs to the technical field of rotary machine fault diagnosis. The method comprises the steps of collecting a non-stationary vibration signal under a variable speed working condition, fusing a third-order Renyi entropy and kinematics prior to construct an adaptive time-frequency enhancement model, and outputting weighted time-frequency distribution; global optimization is carried out through a priori constraint Viterbi algorithm, and an instantaneous frequency ridge line is extracted under the condition of no rotating speed sensor; performing angular domain resampling by taking the ridge line as a phase reference, and converting a non-stationary signal into an angular domain stationary signal; constructing an extended difference mode decomposition convex optimization model, and iteratively solving an optimal difference spectrum; and determining a frequency spectrum segmentation threshold value based on self-adaptive change point analysis, performing decoupling separation and time domain reconstruction on the optimal difference spectrum, and completing composite fault diagnosis through feature comparison. The method does not need an external tachometer, can adaptively extract and separate the composite fault features under the variable speed working condition, is high in diagnosis accuracy, and is good in engineering application value.

The present invention relates to the field of electrical load prediction, and in particular to a method and system for predicting building electrical load based on deep learning. The method comprises the following steps: obtaining operating parameters of electrical equipment and building traffic flow monitoring parameters; identifying electrical equipment in the building based on sensors, and marking building electrical equipment nodes; reconstructing the spatial topological plane of building electrical equipment nodes, and constructing a spatial topological network of electrical equipment; performing multi-scale time-frequency decomposition on the operating parameters of electrical equipment, and generating multi-time-frequency frequency domain segments; performing frequency energy distribution analysis on the multi-time-frequency frequency domain segments, and generating time-frequency distribution features; performing time-series voltage feature extraction processing on the operating parameters of electrical equipment, and generating a time-series voltage feature sequence; performing multi-period trend evolution on the time-series voltage feature sequence based on the time-frequency distribution features, and generating multi-period load trend feature data. The present invention realizes high-precision and highly adaptive building electrical load prediction.