295 results about "Frequency analysis" patented technology

The invention relates to the technical field of prediction algorithms, and discloses a high-precision active power filtering and prediction algorithm for a three-phase ammeter, which comprises the following steps: acquiring original signals of three-phase voltage and current for baseline correction, performing time-frequency analysis on non-stationary harmonic waves and noise of the original signals, and dynamically adjusting filtering parameters; voltage and current phase alignment is carried out through FIR phase shift and zero crossing point detection, a phase-locked loop is constructed by using GRU to track the frequency of a power grid, the sampling frequency is dynamically adjusted, and frequency mutation is detected; iapFFT transformation is optimized to suppress spectrum leakage, a phase error is corrected through a deep learning network, and weak harmonic detection is enhanced by using an attention mechanism; classifying harmonic waves, and adaptively calculating total active power; dimensionality reduction is carried out on historical power and environmental parameters through an auto-encoder, and future active power is predicted; low-power-consumption hardware is adapted, and training efficiency and data security are optimized; three-phase signals are processed in parallel, and FPGA storage and FFT / FIR cores are optimized for filtering processing; and dynamically adjusting parameters of the phase-locked loop.

The invention provides a large-scale equipment early fault detection system and method based on energy flow time-frequency analysis. The large-scale equipment early fault detection system based on energy flow time-frequency analysis comprises a multi-sensor data acquisition and preprocessing module, an energy flow time-frequency feature extraction module, a deep transfer learning fault identification module, a data enhancement and dynamic balance module and a multi-scale energy flow modeling module. According to the energy flow time-frequency analysis-based large-scale equipment early-stage fault detection system and method provided by the invention, in order to solve the problems of poor time-frequency feature extraction adaptability, insufficient noise robustness and weak cross-domain migration capability in a variable working condition scene in the prior art, through collaborative optimization of multi-modal data fusion, adaptive time-frequency analysis and dynamic migration learning, the time-frequency feature extraction adaptability is improved; and the fault detection precision and generalization capability under the complex working condition are obviously improved.

The invention belongs to the field of artificial intelligence, particularly relates to a method for predicting the residual life of a key part of a coal mill, and aims to solve the problems of low prediction precision and poor extrapolation caused by working condition disturbance interference and inaccurate degradation characterization in a traditional method. The method comprises the following steps: collecting vibration, temperature, current and acoustic emission multi-source synchronous data; constructing a dynamic working condition decoupling model of fusion of the variational auto-encoder and the attention mechanism, and separating degradation sensitive components; and a high-fidelity degradation index sequence is generated through fusion of time-frequency analysis and a gating circulation unit. According to the method, multi-modal sensing and physical priori knowledge are fused, the monotonicity of a degradation index is remarkably improved to 98% or above, the extrapolation error of an extreme working condition is reduced by 40%, edge-cloud collaborative deployment and digital twinborn visualization are supported, multi-component collaborative early warning is realized, and the early warning amount of non-planned shutdown is improved from 7 days to 21 days or above; and a high-precision, strong-robustness and landing life prediction solution is provided for intelligent operation and maintenance.

The invention discloses a multi-factor coupling dynamic error compensation method, system and device and a storage medium, and the method comprises the steps: obtaining an original signal sequence, and carrying out the time-frequency analysis of the original signal sequence, and obtaining a time-frequency matrix; performing feature extraction on the time-frequency matrix to obtain feature information; according to the feature information, calculating a Lagrange interpolation reference node of a corresponding time point, and carrying out signal reconstruction to obtain a synchronous sampling sequence; performing harmonic analysis on the synchronous sampling sequence to obtain harmonic parameters; the dynamic compensation amount is calculated in combination with the characteristic information and the harmonic parameters, the electric energy metering value is corrected, and the real-time performance and accuracy of electric energy measurement are effectively improved.

The invention relates to the field of charging pile use, and discloses a charging pile use frequency analysis method based on charging frequency data, which comprises the following steps: acquiring charging frequency original data of a plurality of charging piles at different time periods and different dates, performing structured processing in combination with a time label, and constructing a charging behavior data set containing a time dimension label; introducing a sliding window algorithm and a periodic spectrum analysis method, and performing frequency feature extraction on the charging behavior data set; using a density peak clustering algorithm to classify the use frequency matrixes of different charging piles under the same time scale, and identifying use preferences and active features of the charging piles under various typical time scenes; based on a lightweight time sequence neural network structure, a frequency fluctuation sensing mechanism is introduced; and predicting the use condition of the charging pile in a future time period based on the trained frequency model in combination with regression prediction and an anomaly detection algorithm. The method has the advantage of improving operation scheduling.

The invention relates to the technical field of harmonic intelligent positioning, and discloses a coupling harmonic intelligent positioning method and system based on multi-modal fusion, and the method comprises the steps: obtaining the voltage amplitude time sequence data of each observation point, constructing a frequency distribution characteristic curve, and extracting a frequency center and a broadening parameter; parameter updating is realized in combination with priori information of power grid impedance, and an adaptive frequency analysis window is constructed on this basis to extract energy distribution characteristic quantity; the reliability weight of an observation point and the time sequence weight of harmonic source equipment are further introduced, and the dynamic characteristics of the space credibility and the operation state are comprehensively reflected; and finally, taking the two types of weights as weighting factors, carrying out weighted calculation on the harmonic propagation model deviation of the candidate nodes to obtain a comprehensive residual value, and positioning the node with the minimum comprehensive residual value as a coupling harmonic source node, thereby remarkably improving the accuracy of harmonic source positioning in a complex power grid environment.

The invention discloses a time-frequency analysis method and system for non-stationary signals of periodic pulses, and the method comprises the following steps: S1, carrying out the preprocessing of a vibration signal through a rapid CMSP method, and obtaining a periodic pulse component in the vibration signal; s2, carrying out iterative estimation on group delay by utilizing fixed point iteration; s3, utilizing iteration group delay estimation, and redistributing time-frequency coefficients of the STFT through time rearrangement multiple times of synchronous extrusion transformation; s4, performing integration on a time rearrangement multiple-time synchronous extrusion transformation result along a time direction so as to reconstruct the signal; according to the method, the problem that STFT time-frequency representation is fuzzy is solved through a fixed point iterative algorithm, meanwhile, signal reconstruction can be achieved, synchronous extrusion operation is carried out through iterative group delay estimation, an estimated value is closer to real group delay, divergent time-frequency coefficients in STFT after FC processing are redistributed, and the estimation accuracy is improved. Therefore, the energy concentration degree of time-frequency representation is improved.

The invention relates to the technical field of biological radar signal processing, in particular to an FMCW radar-based heart beat interval estimation method, which comprises the following steps of: S1, converting a chest vibration echo phase time sequence obtained by irradiating a chest area of a monitored object by an FMCW radar into an acceleration time sequence by using a second-order time derivative; s2, dynamically mapping the center frequency and the wavelet order in a preset frequency analysis interval, constructing a self-adaptive wavelet dictionary, then executing multi-order wavelet time domain convolution operation on the acceleration time sequence by using the dictionary, and aggregating time-frequency energy distribution to generate a time-frequency energy diagram; and S3, performing a deconvolution operation reconstruction strategy based on an energy-guided multi-stage time-frequency feature screening technology and wavelet function conjugation, generating an approximate time domain signal of heart beat vibration, and completing heart beat information inversion. According to the method, the accuracy and robustness of IBI extraction can be effectively improved under the condition of low signal-to-noise ratio, so that stable monitoring of light and moderate HRV (heart rate variability) is supported.

According to the hardware mold machining control method and system, energy of flutter characteristic frequency components is obtained by collecting cutting torque signals and conducting frequency analysis, the flutter risk is evaluated by dynamically setting an energy threshold value based on the overhanging length of a tool, the feeding rate and the rotating speed of a main shaft are adjusted when the response risk exists, and the machining accuracy of the hardware mold is improved. Therefore, fluttering in the machining process can be effectively recognized and inhibited, and the method has the advantages that fluttering in the machining process can be effectively recognized and inhibited, the machining quality is improved, and the service life of a tool is prolonged.

The invention discloses a line fault detection method and device based on morphological wavelets, relates to the technical field of line detection, and solves the problem that in the prior art, when a single-phase grounding fault occurs in a small-current grounding system, the fault current is small, the characteristics are not obvious, and when line fault detection is carried out by using time domain, frequency domain and time frequency analysis methods, the fault current is not obvious. And the technical problems of low fault line selection accuracy and poor reliability exist. The method comprises the following steps: acquiring zero-sequence current signals in a period before and after a line fault occurs; pre-processing the zero sequence current signal; decomposing the preprocessed zero sequence current signal by using a morphological Haar wavelet; calculating modulus maxima of the plurality of lines according to the decomposition result; correcting the modulus maxima of the plurality of lines based on the line length; screening fault lines according to the corrected modulus maxima; the accuracy and reliability of single-phase earth fault line selection can be improved.

The invention provides an interest point recommendation method based on a dynamic hierarchical attention fusion space-time network, and the method comprises the following steps: 1, obtaining user historical sign-in and interest point feature data, and carrying out the preprocessing of the user data; 2, learning robust spatio-temporal information representation of the interest points through an adaptive spatio-temporal double-graph convolutional network module; 3, constructing a'interest point-functional unit-regional unit 'multi-level semantic hierarchical structure, and learning hierarchical semantic information representation of interest points by using a bidirectional hierarchical graph attention network module; 4, after fusing the two interest point representations, coding a user track and inputting the user track into an attention-frequency fusion Transform module, so as to obtain a multi-level semantic hierarchical structure; the module separates long and short term dynamic signals in a user behavior sequence through frequency analysis and dynamically fuses the long and short term dynamic signals with a self-attention mechanism to predict a next point of interest; and 5, training by adopting a combined loss function. According to the method, the complex characteristics of the user and the POI are effectively modeled, and the accuracy and the capability of capturing the dynamic preference of the user are improved.

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 belongs to the field of non-intrusive power load monitoring, and discloses a non-intrusive power load identification method, which comprises the following steps of: constructing a load identification framework utilizing frequency domain characteristics, and aims to solve the problem that transient characteristics cannot be effectively applied in non-linear load identification in non-intrusive load monitoring. A current feature extraction technology based on fast Fourier transform and Hilbert-Huang transform and a classification method based on an extreme learning machine as a main body are used for training and testing a public data set to verify that the method is used for extracting transient load features and identifying nonlinear loads.

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 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 belongs to the technical field of nuclear equipment monitoring, and aims to solve the problems of spatial resolution reduction caused by a multi-source acoustic signal aliasing effect in a complex industrial environment and feature extraction difficulty caused by industrial noise and fault sound source spectrum overlapping. According to the method, spatial decoupling is carried out, sparse Bayesian learning and minimum variance undistorted response beam forming joint optimization is carried out, a sound source orientation is preliminarily estimated, synchronous extrusion wavelet transform and NMF joint time-frequency analysis is carried out, and a spatial anomaly acoustic diagnosis result is obtained. Sound source transient characteristics and energy evolution laws are extracted through time-frequency conjoint analysis, a multi-dimensional characteristic space is constructed, the number of fault types is determined for fault type classification, and three-dimensional coordinate mapping and error feedback are displayed; the system comprises an airspace decoupling module, a time-frequency decomposition module, a feature fusion module and a dynamic imaging module. According to the invention, the sound source positioning precision can be realized, and the resolution limitation of traditional beam forming is broken through.

The invention discloses a damage quantitative evaluation method based on an intelligent agent model and knocking scanning, and aims to realize accurate recognition and quantitative evaluation of beam structure damage through the agent model. The evaluation method comprises the following steps: firstly, according to a theoretical relational expression of a beam structure, selecting a cross-sectional inertia moment, a structure length, a damage position and a detection vehicle equivalent acceleration as input random variables, and taking a bending stiffness ratio of a damage position as a damage quantitative index, constructing and optimizing an agent model; establishing a nonlinear mapping relation between input and output; secondly, applying excitation by utilizing an intelligent knocking scanning detection vehicle, acquiring an acceleration signal, processing the signal by adopting a time-frequency analysis technology, and extracting feature information related to structural damage; and finally, predicting a damage quantitative index according to the agent model in combination with the identified damage position and feature data, and verifying and outputting a result.

The invention discloses a time-frequency analysis method based on local entropy optimization synchronous demodulation reassignment transformation, relates to the technical field of signal processing, and solves the problem that a traditional time-frequency analysis method is not easy to carry out when complex non-stable vibration signals generated by rotating machinery and the like are processed. The problems of difficulty in balancing time and frequency resolution, easiness in energy diffusion, difficulty in multi-component signal separation and the like exist; according to the method, zero padding is carried out on the two ends of a target signal, a discrete Gaussian window is constructed to generate a sliding windowing signal matrix, a projection angle sequence is generated in an angle space, a time axis and a frequency axis are discretized, a demodulation function containing undetermined parameters is constructed to generate a complex number time-frequency representation matrix, the Renyi entropy value of time-frequency sub-blocks is calculated to determine an optimal demodulation parameter, and the optimal demodulation parameter is obtained. And setting a frequency difference threshold to construct a frequency redistribution operator to generate an optimized time-frequency matrix. The method is mainly used for time-frequency analysis of complex non-stationary vibration signals in the fields of rotating machinery and the like, and high-precision frequency modulation feature extraction and enhancement processing are achieved.

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.

To predict whether there is no sound in a predetermined space based upon whether onomatopoeia indicative of silence that a human perceives in a quiet environment is generated.SOLUTION: A determination device comprises: an input part which inputs predetermined sound data generated by converting a predetermined sound into numeric data through frequency analysis to a determination model outputting whether a specific frequency of 1,800 Hz or more to 2,200 Hz or less indicative of induced otoacoustic emissions is included in addition to the frequency of sound data once the sound data is input; and a control part which displays, on a display unit, the generation of onomatopoeia indicative of silence that a human perceives in a quiet environment when the determination model receiving the predetermined sound data generates an output showing that the specified frequency is detected, but displays, on the display unit, no generation of onomatopoeia when the determination model generates an output showing no detection of the specific frequency.SELECTED DRAWING: Figure 1

The invention relates to the technical field of jitter analysis, in particular to a high-speed oscilloscope eye diagram real-time jitter detection method and system, and the method comprises the following steps: carrying out high-speed collection waveform zero-cross detection to obtain a timestamp, calculating offset grouping to obtain a cross-period edge time sequence, merging a jump cluster according to a sequence hierarchy cluster, calculating offset, obtaining a drift trend vector, and calculating the jitter of the drift trend vector; and based on the trend vector distribution compensation weight, weighting and correcting the time coordinate, calling a corrected coordinate detection period boundary to calculate a difference value exceeding a threshold value, triggering correction to obtain a period correction rear edge coordinate, and extracting a jitter frequency amplitude according to corrected coordinate Fourier analysis to generate an eye pattern jitter detection result. Phase correlation is established based on a cross-period sequence, an analyzable offset mode is formed through a clustering identification distribution structure, accumulated deviation is suppressed through a drift trend and weight correction, an abnormal period is calibrated through period dynamic discrimination, and the relation between a jitter structure and amplitude is separated through frequency analysis, so that the overall stability and integrity are enhanced.

The application discloses a flood scene division method and system based on flood characteristics and belongs to the technical field of flood control. The existing scheme needs to set a flood peak threshold and other limit conditions artificially. Due to the subjectivity and non-uniformity of the standard, the flood scene extraction process is unreasonable. The flood scene division method based on flood characteristics constructs a flood characteristic simulation model, an event screening model, an event merging model and a scene division model, performs frequency analysis on flow data, obtains a flood peak threshold and is used for representing flood characteristics. According to the flood peak threshold, a plurality of flow data points higher than the flood peak threshold are screened out, and then the flow data points are marked as events with potential flood risks, so that the flood scene can be accurately screened, the flood peak threshold and other limit conditions need not be set artificially, the subjectivity and non-uniformity of the threshold set artificially are effectively avoided, and therefore the identification efficiency of flood events can be effectively improved.

The invention relates to the field of numerical control machining data processing, in particular to a multi-axis linkage numerical control lathe machining data processing method and system. Comprising the following steps: acquiring vibration information, performing time-frequency analysis, and extracting energy change and instantaneous peak value characteristics in a preset high-frequency band; the features are compared with a preset judgment basis, an early sign of flutter is recognized, and when flutter is recognized, a machining parameter adjusting instruction for the rotating speed of the main shaft, the feeding speed and / or the cutting depth is generated; and the adjusted vibration information is obtained for time-frequency analysis, the flutter suppression effect is evaluated, machining parameters are further adjusted according to the flutter suppression effect, and / or an early warning or shutdown alarm is given out, so that closed-loop active suppression control over microcosmic cutting flutter is formed. The problems that due to the fact that a multi-axis linkage numerical control lathe cannot capture key and instantaneous high-frequency vibration mode information in a data collection and analysis frame, tool abrasion prediction is not accurate, the machining quality is reduced, the workpiece rejection rate is increased, and the production efficiency is reduced are solved.

This invention discloses a multi-scale time-frequency analysis method and system for ocean current meter signal processing, relating to the field of marine information processing. The method includes: S1: acquiring raw data and performing physical benchmark correction to obtain three-dimensional current velocity data and basic mass labels; S2: extracting first and second features from the three-dimensional current velocity data through spatiotemporal decoupling; S3: fusing and separating the three-dimensional current velocity data based on the first and second features to obtain an optimized set of flow field parameters, and generating the uncertainty of each parameter by combining the basic mass labels and error source information; S4: performing comprehensive quality assessment and grading based on the first and second features and the uncertainty, outputting a data product with mass labels and uncertainty information. By extracting spatiotemporal dual-dimensional features and using them to guide signal fusion and quality assessment, highly robust processing of ocean current data and end-to-end quality traceability are achieved, significantly improving the reliability of the data product.

This invention discloses an inversion method for S-wave velocity structure based on dense micromotion observations, belonging to the field of geophysical exploration and engineering geological exploration technology. The method includes: acquiring micromotion data; performing segmented preprocessing and cross-correlation calculations; constructing a phase consistency metric function based on the instantaneous phase information of the cross-correlation function for each time period; generating adaptive weighting coefficients; and then constructing an empirical Green's function through weighted superposition. Subsequently, time-frequency analysis is performed to obtain a time-frequency energy map; preliminary extraction of candidate dispersion velocities for each frequency is performed, and their comprehensive confidence level is calculated; after verification, a reliable dispersion curve is obtained. Based on the dispersion curve, a model parameter vector is established, and a joint objective function is constructed. A differential evolution algorithm is used for parallel global optimization inversion to obtain a one-dimensional S-wave velocity structure model below each station pair. The one-dimensional models are interpolated and fused to generate a two-dimensional S-wave velocity structure profile. This invention has the advantages of high inversion stability, high computational efficiency, and a high degree of automation.

The invention relates to the technical field of roadbed engineering monitoring, in particular to a roadbed engineering settlement deformation monitoring device and method, and the device comprises an optical fiber sensor network, a data collection module, a signal processing module, a model matching module, a frequency analysis module, a data fusion module, an early warning trigger module and a prediction report module. The optical fiber sensor network is deployed in a roadbed to collect an optical signal delay sequence, fluctuation characteristics are extracted through data collection and signal processing, settlement acceleration risks are judged through model matching, the speed trend is determined through frequency analysis, historical and real-time data are integrated through data fusion, an alarm is triggered when an early warning module exceeds a threshold value, and a risk assessment report is generated through a prediction module. According to the device, roadbed three-dimensional global monitoring and full-process automation are achieved, monitoring blind areas are eliminated, early warning lag is shortened, and accurate support is provided for roadbed stability operation and maintenance.

The invention discloses a sparse inversion time-frequency analysis method based on a semi-norm, and the method comprises the steps: designing a window function for original seismic data, so as to reduce the influence of a window truncation effect on time-frequency analysis; constructing a Fourier forward matrix and a windowed Fourier matrix according to the size of the window function and the size of the frequency spectrum; in-window seismic data extraction is carried out on sample points of single-channel seismic data, and a semi-norm constrained time-frequency spectrum sparse inversion objective function is constructed; performing time-frequency spectrum coefficient inversion on the time-frequency spectrum sparse inversion target function to obtain a sparse inversion time-frequency spectrum result of the seismic data in the window; and sliding the sample points of the seismic data to repeat the process, and performing time-frequency spectrum sparse inversion on all the seismic data to obtain a final time-frequency analysis result. According to the method, the time-frequency spectrum precision and the time-frequency focusing property of the seismic signal can be effectively improved, and a time-frequency analysis result with higher precision is provided for subsequent reservoir identification and oil and gas prediction work.

The invention discloses an SAR intermittent sampling forwarding interference suppression method and system based on pulse compression and time-frequency analysis, and a storage medium, and the method comprises the following steps: carrying out the range pulse compression of SAR echoes containing interference pulse by pulse, so as to accurately obtain an interference-containing pulse sequence; and then, carrying out short-time Fourier transform on the pulse with interference after pulse compression and the nearest pulse without interference, and carrying out superposition and differential operation of two different dimensions to realize time-frequency domain positioning of the 2D-ISRJ. And designing a time-frequency domain filter, and carrying out time-frequency domain wave trapping on the positioned 2D-ISRJ. And finally, recovering the missing part of the signal after wave trapping by using the time-frequency data containing the interference pulse and the nearest non-interference pulse after pulse compression. According to the invention, the interference detection precision of typical two-dimensional intermittent sampling forwarding interference forwarding types can be relatively high under the condition of relatively weak interference, and the suppression method not only can achieve a relatively good suppression effect, but also can better protect useful signals.

Embodiments of the present disclosure provide a centrifugal compressor fault diagnosis method, device and equipment. The method comprises: acquiring a current vibration signal of a centrifugal compressor to be diagnosed; extracting a current fault characteristic parameter of the current vibration signal based on a linear transformation time-frequency analysis method; acquiring a pre-stored fault characteristic sample library; and performing analogy analysis on the current fault characteristic parameter and the fault characteristic sample library to determine a fault type of the centrifugal compressor. In this way, the current fault characteristic parameter and the fault characteristic sample library can be automatically subjected to analogy analysis, so as to accurately and quickly locate the fault type of the centrifugal compressor. Since this fault analysis method does not require model training, the fault analysis result is not affected by the model precision, and the algorithm is simple to use, so the identification accuracy of the fault type of the centrifugal compressor can be ensured.

The invention discloses a method and a system for evaluating influence of discharged water temperature of a reservoir based on frequency analysis, and relates to the technical field of water conservancy projects, a distribution interval covering variation of natural water temperature is constructed based on a natural water temperature time sequence of many years in ten days, and the historical variation range of water temperature of a dam site and the occurrence probability of each temperature section are reflected more truly; by matching the discharged water temperature of the reservoir with the natural water temperature distribution interval in ten days, whether the discharged water temperature falls into the natural water temperature distribution interval or not is judged, so that whether the discharged water temperature deviates or falls into the natural water temperature variable amplitude after the reservoir operates or not is recognized; and whether the antenna belongs to a high frequency band or a low frequency band can be further judged. By means of the method, whether the discharged water temperature falls into the natural water temperature interval range or not can be recognized, whether the discharged water temperature belongs to a common or rare water temperature value in the natural water temperature state or not can be judged in a quantified mode, and therefore quantitative evaluation on the influence intensity of the discharged water temperature after the reservoir operates is achieved.