48 results about "Discrete time domain" patented technology

A method for extracting a wear particle feature signal based on segmentation entropy is provided, including obtaining a raw signal to be processed by performing real-time data acquisition using a lubricating oil wear particle monitoring system; obtaining a preprocessed signal by performing low-pass filtering and harmonic interference suppression on the raw signal to be processed; dividing the preprocessed signal into a plurality of time domain sequence segments with a sliding window; calculating segmentation entropy corresponding to each time domain sequence segment, normalizing a segmentation entropy set to obtain normalized segmentation entropy; obtaining an adaptive threshold through curve fitting based on empirical cumulative distribution of normalized segmentation entropy, obtaining a plurality of non-zero discrete time domain signal segments by segmenting the preprocessed signal by the adaptive threshold; and obtaining final extraction results of the wear particle feature signal by excluding residual noise interference through target signal feature recognition indices.

The invention belongs to the technical field of energy system scheduling, and particularly discloses a multi-time-scale dynamic optimization scheduling method and system for an integrated energy system, and the method comprises the steps: collecting the operation data and topological parameters of each energy device in the integrated energy system, constructing an initial energy flow model, and mapping the initial energy flow model to a discrete time domain from a Laplacian domain, establishing a linear dynamic energy flow model; calculating the inertia time of the system according to the normalized step response of the heat supply network and the natural gas network, and constructing a multi-time scale scheduling framework with double-layer intra-day rolling optimization according to the inertia time; and setting different optimization objective functions in three scheduling levels, namely a day-ahead scheduling level, an intra-day upper layer scheduling level and an intra-day lower layer scheduling level, so as to realize dynamic optimization of the running state and load adjustment of the coupling equipment. The problems of time scale separation, discontinuous state, poor adaptability of heterogeneous network inertia difference and the like in an existing scheduling method are solved, and the scheduling stability and the operation economy of the comprehensive energy system under the high permeability of renewable energy sources are improved.

The invention discloses a harmonic and inter-harmonic parameter detection method and device based on a self-multiplication convolution window algorithm, and belongs to the field of signal detection, and the method comprises the steps: obtaining a discrete time domain signal, and carrying out the windowing and mapping of the time domain signal based on the self-multiplication convolution window algorithm, and obtaining a frequency domain signal; calling a quantum particle swarm algorithm to estimate the frequency domain signal to obtain an estimation parameter matched with a preset frequency spectrum; and performing nonlinear error compensation on the estimation parameter by using a preset space-time attention residual network to obtain harmonic and inter-harmonic parameters. According to the method, signal leakage can be suppressed by windowing the signal, the energy of the main lobe of the frequency spectrum is more concentrated, the frequency domain signal is estimated by using the quantum particle swarm algorithm, the evaluated frequency spectrum meeting the actual demand can be quickly screened, and finally, the frequency spectrum is compensated, so that the error can be further reduced, and the detection precision can be further improved.

The invention discloses a lithium iron phosphate battery state-of-charge estimation method, which comprises the following steps: establishing a first-order RC equivalent circuit model of a lithium iron phosphate battery, performing pull-type transformation on the first-order RC equivalent circuit model to obtain a complex frequency domain equation, bilinearly changing the complex frequency domain equation to a z plane to obtain a system function expression, and estimating the state-of-charge of the lithium iron phosphate battery according to the system function expression. Performing pull-type inverse transformation on the system function expression to a discrete time domain; identifying the parameters of the lithium iron phosphate battery by using an AFFRLS algorithm to obtain model parameter values; constructing a space state equation set of the first-order RC equivalent circuit model, estimating the state of charge of the lithium iron phosphate battery by using an EKF algorithm, and defining an SOC estimation error function; optimizing the process noise covariance matrix Q and the measurement noise covariance matrix R of the EKF by using a simulated annealing algorithm to obtain a final estimated SOC; the SOC estimation precision and stability of the lithium iron phosphate battery are improved through the simulated annealing algorithm.

The invention relates to the technical field of electromagnetic scattering, in particular to a frequency domain response calculation method and device of an electromagnetic field, equipment and a storage medium. Performing recursive transformation on the time domain basic system equation according to the discrete time domain set and a backward Euler method to obtain a time domain transformation equation and an algebraic matrix equation; carrying out back substitution solving operation on the algebraic matrix equation according to a time domain stepping method and a time domain transformation equation; performing full-time-domain response calculation on the time-domain finite element solution based on a time-domain finite element method to obtain a time-domain base solution; calculating the time domain base solution according to a preset linear superposition principle to obtain a frequency domain response; a continuous problem is dispersed through a backward Euler method, rapid back substitution solution is carried out after factorization for one time, and redundant calculation is reduced; based on a linear superposition principle, phase weighting is carried out to synthesize a time domain base solution, frequency domain response of any frequency is rapidly obtained, frequency-by-frequency repeated calculation is avoided, efficiency is improved, and the method is suitable for broadband high-precision electromagnetic field analysis and has remarkable application value.

The invention discloses a power distribution network multi-type flexible resource low-carbon scheduling method based on carbon emission flow, and relates to the technical field of power system low-carbon scheduling, and the method comprises the steps: constructing a scheduling time period set of a discrete time domain, and building an equivalent lossless network model; power flow calculation is carried out, and a node-branch carbon flow mapping relation is established; according to the carbon emission intensity of the unit, the carbon flow rate and node carbon emission flow of each branch are calculated; performing differential approximate calculation on the node injection power to obtain a carbon marginal coefficient of each node; generating a time-varying carbon price signal of each node in combination with an external carbon price reference; constructing a weighted combination objective function based on the node time-varying carbon price signal; and solving a weighted combination objective function and constraint conditions by adopting a distributed multi-agent collaborative optimization strategy. According to the invention, collaborative scheduling of multiple types of flexible resources under a low-carbon target is realized, and the carbon emission reduction effect is improved.

The invention belongs to the related technical field of ocean monitoring, and discloses a wave spectrum analysis method and system based on a laser radar. The method comprises the following steps: obtaining a plurality of groups of concentric circles in obtained wave field point cloud data; fitting discrete time-domain signals of the wave height and the derivative of the wave height corresponding to the concentric circles along with time change, and calculating a discrete value of the amplitude of the wave spectrum along with frequency change; calculating unknown parameters in the directional spread function; the discrete value of the amplitude of the wave spectrum along with the frequency change is multiplied by the spread function to obtain the discrete value of the direction spectrum in the frequency and direction, namely the analysis of the wave spectrum is realized. According to the invention, the problem of realizing wave field spectrum and direction spectrum analysis by using point cloud data in wave field analysis is solved.

The invention provides a multi-converter system small interference stability analysis method based on a discrete state space model, and the method comprises the steps: firstly building a whole multi-converter system to carry out a small interference model based on a discrete state space principle by using the advantages of discrete domain modeling, and more specifically, carrying out the small interference stability analysis of the multi-converter system; a multi-converter system is divided into two parts, namely a converter network and a connection network, so that subsequent targeted analysis on converters is facilitated; then, on the basis of the network discrete state space models on the two sides, a discrete time domain model and a discrete frequency domain model of the whole system are constructed through different matrix transformation relations; and finally, a parameter-modal sensitivity calculation method is further introduced in combination with a system discrete domain modal identification result, and the method realizes stability analysis and quantitative calculation by using a chain rule. Example analysis shows that in a multi-converter grid-connected scene, the method can realize rapid and effective evaluation and optimization of the small interference stability by the key control parameters of the system, and provides a clear parameter adjustment direction.

A system, method, and computer program product for monitoring a wind turbine (10) are provided. The system receives a signal (426) indicating pitch forces being applied to blades (20) of the wind turbine (10). The signal (426) is sampled to generate a discrete time-domain signal (426) comprising a plurality of pitch force samples. The samples are selected for analysis using a sampling window (326) that excludes samples obtained under operating conditions determined to be detrimental to obtaining good data. The selected samples are processed to generate a spectral density (150) of the signal (426), and the frequency content of the spectral density (150) is analyzed to determine a condition of one or more components of the wind turbine (10). If the analysis indicates that a component of the wind turbine (10) requires attention, the system generates an alarm.

The application relates to the field of circuit state space equation solving technology in the real-time simulation field, and discloses a state space equation discretization method for capacitor and inductor element characteristics, which comprises the following steps: A) establishing a state space differential equation of a circuit; B) establishing a characteristic equation of a capacitor and an inductor element, determining a numerical integration method used by each capacitor and inductor element in the circuit, and obtaining a discrete time domain characteristic equation by using the numerical integration method; C) combining the discrete time domain characteristic equation of step B) and the state space differential equation of step A) to obtain a state space discrete equation of the circuit; and D) cyclic simulation. The state space equation discretization method for capacitor and inductor element characteristics improves the speed and precision of real-time simulation of a circuit system by independently discretizing the characteristic equations of each capacitor and inductor element and optimizing the discretization process of the state space equation.

The invention provides a wind turbine generator rotating shaft fault monitoring method and system based on pulse amplitude modulation. The method comprises the steps that pulse amplitude modulation signals of a rotating shaft are collected through a pulse amplitude modulation measuring device installed on a target shaft section of the rotating shaft of a measured wind turbine generator; performing amplitude demodulation on the pulse amplitude modulation signal to obtain an eccentric compound vibration component of the rotating shaft; carrying out discretization processing to obtain a composite vibration component sequence of a discrete time domain, then carrying out Fourier transform on the sequence of the discrete time domain to obtain phase differences, and extracting vibration amplitudes and amplitude characteristic parameters of each vibration component under different frequencies; and monitoring a misalignment abnormal state of the rotating shaft according to the vibration amplitudes, the amplitude characteristic parameters and the phase differences of the vibration components under different frequencies. According to the technical scheme, signals are collected through the pulse amplitude modulation measuring device, and high-precision monitoring and accurate fault type identification of the misalignment of the rotating shaft of the wind turbine generator are realized in combination with amplitude demodulation and frequency domain analysis.

The present application belongs to the field of software analysis, and specifically discloses a Bayesian verification method, system and device for reliability of discrete software, the method comprising: obtaining a discrete data set for reliability growth testing of discrete software; determining a reliability growth model of discrete software by combining the data set and a non-homogeneous Poisson process in a discrete time domain; estimating the probability of discrete software successfully executing a preset specified task again based on the reliability growth model; setting the prior distribution of reliability to a beta distribution, and estimating the parameters of the beta distribution based on the probability of successfully executing a preset specified task and the maximum entropy method; determining the posterior distribution of reliability based on the prior distribution and Bayes' theorem; determining the number of test cases required for reliability verification of discrete software under a preset tolerable number of failures based on the posterior distribution; and performing reliability verification based on the number of test cases and the tolerable number of failures. Through the present application, an effective reliability verification scheme for discrete software is proposed.

The invention discloses an unmanned aerial vehicle anti-interference inhabitation trajectory planning method and system, a medium, equipment and a product in the technical field of unmanned aerial vehicle inspection. The method comprises the steps of performing real-time positioning on an unmanned aerial vehicle through a sensor loaded on the unmanned aerial vehicle and an environmental map constructed by SLAM, and generating an unmanned aerial vehicle reference path by using a mixed A * optimization algorithm based on node expansion; according to the obtained environmental influence data, performing environmental influence prediction by using the trained LSTM model to obtain an environmental influence prediction sequence; and according to the current position of the unmanned aerial vehicle, the current speed of the unmanned aerial vehicle, the environmental influence prediction sequence and the reference path of the unmanned aerial vehicle, constructing a discrete time domain flight state equation, and generating a flight path of the unmanned aerial vehicle based on an improved model prediction control framework MPC. The method is high in real-time performance, high in anti-interference capability and stable in tracking precision, and ensures that the unmanned aerial vehicle completes a precise, safe and efficient inspection task in an environment influenced by the environment.

The invention discloses a small-signal frequency domain impedance rapid modeling method for a grid-connected system of a network construction type energy storage device. The method comprises the following steps: obtaining topology and parameters of the grid-connected system of the network construction type energy storage device; constructing a time domain state space model of the network construction type energy storage equipment, wherein the time domain state space model comprises an equipment internal circuit structure parameter model and a control loop signal model; constructing a time domain state space model of the basic element of the power system; linearizing the time domain state space model of the networking type energy storage equipment and the time domain state space model of the basic element of the power system to obtain a small signal model, and further obtaining a port discrete time domain Norton equivalent model; and performing z change on the port discrete time domain Norton equivalent model to obtain a port discrete frequency domain impedance model, and performing mapping from a discrete domain to a continuous domain on the port discrete frequency domain impedance model to obtain a port continuous frequency domain impedance model. According to the invention, through a multi-mapping impedance analysis method, the complex power system modeling process is simplified, and the modeling efficiency is improved.

The application discloses a disturbance-resistant multi-agent discrete-continuous hybrid specified-time state estimation method, and belongs to the field of distributed state estimation of multi-agent systems, and comprises the following steps: S1, considering the state dynamics of the multi-agent system and the influence of external disturbance, a dynamic equation of the multi-agent system is constructed; S2, based on the dynamic equation of the multi-agent system, an augmented state estimation error equation and an extended state observer of the multi-agent system are constructed; S3, an estimation error dynamic equation in a discrete time domain is constructed, and the convergence of the augmented state estimation error is analyzed; S4, a discrete sampling interval is designed, so that the augmented state estimation error converges to zero within a specified time. The above-mentioned disturbance-resistant multi-agent discrete-continuous hybrid specified-time state estimation method can effectively cope with external disturbance, fuse the advantages of discrete and continuous time, be faster, more accurate and robust, and ensure that the state estimation of the multi-agent system is efficient and reliable.

The invention provides a reference voltage bias generation circuit for a discrete time domain, which comprises a transistor voltage difference generation module and a switched capacitor adder module which are electrically connected to generate bias voltage, and the transistor voltage difference generation module comprises a dynamic element matching module I to output two pairs of voltage difference circuits; the switched capacitor adder module comprises a switched capacitor adder for outputting bias voltage and a second dynamic element matching module, and the second dynamic element matching module comprises a plurality of clock switch control sampling structures, is connected to the switched capacitor adder and is connected with the two pairs of voltage difference circuits. Adding and subtracting operation of base-emitter voltage is achieved through the switched capacitor adder, reference voltage with small temperature influence is output, and the negative influence of process mismatch on reference voltage precision is reduced through the three-group dynamic element matching technology.

The invention discloses a two-dimensional wave field reconstruction method, device and terminal based on spatial sparse sampling, and the method comprises the steps: obtaining an original two-dimensional time domain signal corresponding to an original signal, carrying out the compression sampling of the original two-dimensional time domain signal, and determining a two-dimensional discrete time domain signal; fourier transform is carried out on the two-dimensional discrete time domain signal, and a two-dimensional discrete frequency domain signal is determined; solving a two-dimensional wave number signal according to the two-dimensional discrete frequency domain signal based on a compressed sensing theory; and performing inverse Fourier transform on the two-dimensional wave number signal, determining a reconstructed two-dimensional time domain signal, and determining a reconstructed signal corresponding to the original signal according to the reconstructed two-dimensional time domain signal. According to the invention, the two-dimensional wave number signal is reconstructed according to the two-dimensional discrete frequency domain signal obtained by sampling through compressed sensing, and the reconstructed signal corresponding to the original signal is obtained according to the two-dimensional wave number signal, so that the problem that the two-dimensional wave field data is sampled by adopting Nyquist in the prior art can be effectively solved; the data redundancy is high; and the transmission pressure is large.

The invention belongs to the technical field of fuel cells, and discloses a direct identification method for equivalent circuit model parameters of a hydrogen fuel cell. The method comprises the following steps: firstly, establishing an R-(RC)-(RC)-(RC) equivalent circuit model and a frequency domain transfer function of the R-(RC)-(RC) equivalent circuit model according to response characteristics of an internal electrochemical reaction of a fuel cell in different frequency bands of an impedance spectrum; secondly, the continuous transfer function is converted into an input and output equation of a discrete time domain through variable substitution and bilinear transformation, and an identifiable mathematical model of the parameters to be identified is formed; then, a recursive least square method containing a forgetting factor is adopted, and the coefficient of the mathematical model is identified online in real time; and finally, reversely resolving all impedance parameters of the equivalent circuit based on an identification result, and drawing an electrochemical impedance spectrum curve according to the impedance parameters. According to the invention, the online rapid identification of the impedance parameter can be realized only by using the output voltage and current data during the normal operation of the fuel cell without injecting any external excitation signal.

The present invention discloses a forward synthesis method for a digital predistortion nonlinear model derived from a circuit description, the main steps of which include establishing a frequency band continuous time domain expression, converting the frequency band expression to a baseband expression, and converting the baseband continuous time domain expression to a baseband discrete time domain expression. The present invention can forward synthesize a corresponding digital predistortion nonlinear model from an existing radio frequency power amplifier system equivalent circuit model, providing an accurate model structure and initial parameter values ​​for the establishment of a radio frequency power amplifier model in power amplifier nonlinear predistortion technology. The present invention can fully utilize the prior knowledge of the radio frequency power amplifier system to obtain a nonlinear model structure that best conforms to the physical essence, can directly obtain the minimum memory depth and nonlinear order, and can be integrated into radio frequency power amplifier circuit simulation software.

The application belongs to the technical field of marine monitoring, and discloses a wave spectrum analysis method and system based on a laser radar.The method comprises the following steps: obtaining a plurality of concentric circles in the obtained wave field point cloud data; matching the discrete time domain signals of the wave height corresponding to the concentric circles and the derivative of the wave height with respect to time, and calculating the discrete values of the amplitude of the wave spectrum with respect to frequency; calculating unknown parameters in a directional spread function; and multiplying the discrete values of the amplitude of the wave spectrum with respect to frequency and the spread function to obtain the discrete values of the directional spectrum in frequency and direction, i.e., realizing the analysis of the wave spectrum. Through the application, the problem of realizing the analysis of the wave field spectrum and the directional spectrum by using point cloud data in the analysis of the wave field is solved.

This invention provides a method and apparatus for extracting the time-frequency characteristics of combined impact and corrugation defects in railway tracks. The method includes: determining a window length limit based on the discrete-time domain signal; using a third-order B-spline function as the window function for the Short-Time Fourier Transform (STFT) based on the discrete-time domain signal to determine the initial STFT time-frequency diagram and the initial Rayleigh entropy; utilizing the recursive property of the third-order B-spline function as the window function to determine the STFT result sequence and the Rayleigh entropy sequence of equal-length window sequences; determining the time-frequency transformation result and the optimal Rayleigh entropy based on the window length limit, the initial STFT time-frequency diagram, the initial Rayleigh entropy, the STFT result sequence, and the Rayleigh entropy sequence; and determining the time-frequency characteristics of the impact signal and the corrugation signal based on the time-frequency transformation result and the optimal Rayleigh entropy. This invention can simultaneously, quickly, and effectively extract the time-frequency characteristics of the impact component and the corrugation component in railway track defects and express them on a time-frequency diagram.

The invention discloses a frequency correction algorithm for a non-stationary signal, which is characterized in that a signal chain is specially designed, an original target non-stationary signal is divided into two paths after passing through a signal conditioning circuit, one path of signal x1 (t) is directly input to an analog-to-digital converter, and a discrete time domain signal x (n) is obtained after sampling by the analog-to-digital converter; the other path of signal passes through a waveform shaping circuit, a non-stationary signal is converted into a unipolar square wave digital signal x2 (t), the signal enters a digital processor with pulse capture and timer technical functions as a digital signal, and the digital processor can obtain a frequency value fc of the signal x2 (t) after passing through the waveform shaping circuit by adopting an equal-precision frequency measurement method; fc is defined as coarse frequency. Under the condition that all original time domain information of the signal is reserved, the judgment coefficient gamma is calculated by dividing frequency spectrum and power spectrum intervals, and the corresponding frequency correction formula is selected according to the value range of the judgment coefficient gamma, so that the accurate frequency correction of the non-stationary signal is realized, and the data measurement accuracy is improved.

The invention discloses a methanol reforming fuel cell modeling and parameter identification method and application, and the method comprises the following steps: using combustion chamber temperature, reforming chamber inlet temperature and outlet temperature as state variables, defining a state vector, using combustion chamber methanol or tail gas feed, air feed and reforming chamber methanol feed as input, defining an input vector, and using combustion chamber methanol or tail gas feed, air feed and reforming chamber methanol feed as state variables; establishing a discrete time domain state space model; s2, disassembling the discrete time domain state space model into a scalar equation; s3, collecting experimental data, wherein the experimental data comprises any one or more of combustion chamber temperature, reforming chamber inlet and outlet temperature, combustion chamber methanol or tail gas feeding, air feeding and reforming chamber methanol feeding; and S4, constructing an error objective function and performing parameter identification by adopting a particle swarm optimization algorithm to obtain a model for simulation and control. According to the method, the dynamic process of preheating and power generation stages can be effectively reflected, and support is provided for system simulation, control and operation optimization.

The invention discloses a micro-motion frequency extraction method based on a micro-Doppler time-frequency contour and a power sequence, and belongs to the field of laser application optics and signal processing. The method comprises the following steps: acquiring a micro-Doppler time-frequency spectrum of a nutation cone; based on a nutation cone dynamic model and a micro-Doppler frequency analysis formula of vertex and bottom surface edge scattering points, judging the formation condition of upper and lower outer envelope contour lines of a time-frequency spectrum; if the contour line is completely composed of bottom surface edge scattering points, extracting an upper outer envelope contour line and a lower outer envelope contour line of the time-frequency spectrum, adding and averaging the upper outer envelope contour line and the lower outer envelope contour line, and performing FFT (Fast Fourier Transform) on an average curve to obtain four frequency spectrums of precession frequency, swinging frequency, swinging frequency + precession frequency and swinging frequency-precession frequency of the nutation cone; and if the contour line is formed by alternating peaks and bottom surface edge scattering points, calculating the power of each frame of discrete time domain intermediate frequency signal of the time-frequency spectrum, obtaining a sequence of power changing along with time, and performing FFT (Fast Fourier Transform) on the sequence to obtain the four frequency spectrums as well. The method is used for precession and swing frequency extraction.

The invention discloses a near-zero refractive index metamaterial design method based on a neural network, and the method comprises the following steps: constructing a Fourier transform neural network, carrying out the fast Fourier transform of a discrete time domain signal sequence through the Fourier transform neural network, obtaining a frequency spectrum sequence of a signal, removing a high-frequency component, and obtaining simplified data, finally, structure parameters are speculated from the simplified data through a neural network, and a result is verified. The discrete S parameter curve data, obtained through simulation, of the metamaterial unit with the near-zero refractive index serves as the discrete time domain signal sequence, and the Fourier transform neural network is trained; and translation of a near-zero refractive index point can be realized by using the trained Fourier transform neural network.

The present application relates to a kind of CZT frequency estimation methods, comprising the following steps: S1: the signal to be estimated is sampled, and the maximum spectral line value of the FFT spectrum function is extracted from the frequency index of the FFT spectrum function obtained by the N-point fast Fourier transform of the discrete time domain signal after sampling;S2: the frequency interval of the CZT transform is determined according to the value of the frequency index, and the maximum spectral line value of the CZT spectrum function is extracted from the CZT spectrum function obtained by the discrete time domain signal and the left and right two spectral line values of the CZT spectrum function;S3: the relationship parameter between the maximum spectral line value and the left and right two spectral line values is calculated;S4: error is calculated from the relationship parameter;S5: the estimated frequency of the signal to be estimated is calculated according to the relationship parameter and error.

The invention discloses an underwater rotating machinery multi-component vibration signal decoupling method based on order analysis. The method comprises the following steps: firstly, acquiring a discrete time domain vibration signal of the underwater rotating machine, wherein the discrete time domain vibration signal is formed by superposing multiple harmonic components; converting the discrete time domain vibration signal into an angle domain signal, executing discrete Fourier transform, and extracting an amplitude spectrum of each order component; on the basis, a multi-peak detection algorithm is adopted to carry out spectrum peak identification on the order amplitude spectrum, and a main order component order set is screened out; and finally, extracting a time domain waveform corresponding to the main order component through an order decoupling method. According to the method, the multi-component vibration signals in the underwater rotating machinery can be effectively decoupled, and a quantitative basis is provided for excitation source positioning, excitation source characteristic analysis or fault diagnosis.

The embodiment of the application discloses a gain self-adjusting discrete current regulation method of a permanent magnet synchronous motor, relates to the technical field of motor control, and can reduce the calculation amount of high-performance vector control of the permanent magnet synchronous motor while guaranteeing robustness. In the application, a discrete time domain permanent magnet synchronous motor complex vector mathematical model is established, a complex vector current regulator is designed through zero-pole configuration and internal model principle, and a multi-gain parameter online identification and self-adjusting method is provided. The current control method can realize high-performance vector control of the permanent magnet synchronous motor, and has the advantages of small calculation amount, strong robustness and the like.

The application discloses a kind of underwater rotating machinery multi-component vibration signal decoupling methods based on order analysis.Method includes: first, the discrete time domain vibration signal of underwater rotating machinery is obtained by superimposing multiple harmonic components;Then the discrete time domain vibration signal is transformed into angular domain signal, performs discrete Fourier transform, and extracts the amplitude spectrum of each order component;On this basis, the order amplitude spectrum is identified by using multi-peak detection algorithm, and the main order component order set is screened out;Finally, the time domain waveform corresponding to the main order component is extracted by order decoupling method.The application can effectively decouple the multi-component vibration signal in underwater rotating machinery, and provide quantitative basis for locating excitation source, analyzing excitation source characteristics or fault diagnosis.

The present invention discloses a discrete-time integration method for nonlinear structures based on the update of a particle swarm optimization model. In the first step, the parameters of the particle swarm optimization integration algorithm are set. In the second step, according to the discrete-time domain formula of the structural dynamics equation, the initial parameters and initial conditions of the structure, the particle swarm optimization algorithm is used to obtain the optimal integration parameters. In the third step, the obtained optimal integration parameters are substituted into the iteration matrix, and the discrete-time domain motion equation is iteratively solved until the preset time condition is reached, and the change range of the structural characteristic parameters is output. N points with equidistant distribution are taken within the change range of the structural characteristic parameters, and according to the structural characteristic parameters corresponding to each point, particle swarm optimization is carried out to obtain the corresponding N groups of optimal integration parameters. In the fourth step, the motion equation is iteratively solved, and in each time step, the optimal integration parameters in the corresponding interval are selected according to the value of the structural characteristic parameters for calculation until the preset time limit is reached, and the structural response time history curve is output.