207 results about "Hankel matrix" patented technology

The invention discloses a network construction energy storage locating and sizing method for supporting transient stability of a high-proportion new energy power grid, and the method comprises the steps: obtaining power grid measurement data, extracting spatial-temporal features through segmented Hankel matrix recursive decomposition and dynamic weighted graph decomposition, and constructing a feature tensor through multi-scale tensor fusion; transient response features are analyzed based on hierarchical sparse principal component mapping, stability indexes are constructed through multi-scale coupling evaluation, and node scores are obtained through adaptive weight fusion; candidate nodes are screened through affinity propagation clustering, the supporting capacity is evaluated through multi-time-scale influence propagation, and capacity configuration is determined through fractional programming constraint optimization; real-time evaluation is realized based on recursive state estimation, a response strategy is generated through multi-time scale hierarchical decision, and cooperative control is realized by adopting distributed consistency optimization. The transient stability of the high-proportion new energy power grid is effectively improved.

The invention discloses a multi-modal fusion lithium iron phosphate battery thermal runaway early warning method and system, and the method comprises the steps: collecting multi-source heterogeneous data, such as temperature, voltage, gas concentration and shell strain pressure, in real time through deploying a heterogeneous sensor network, and carrying out the noise reduction and time sequence feature extraction through employing a sub-linear time low-rank approximation algorithm of a Hankel matrix; constructing a cross-modal feature association network by applying a secondary time algorithm of a maximum weight sparse subgraph problem, inputting a fused feature vector into a Bayesian network health degree evaluation model for probabilistic reasoning calculation to obtain a battery health degree score and a thermal runaway risk level, generating a graded early warning signal through multi-level early warning threshold comparison, and performing early warning on the battery health degree score and the thermal runaway risk level. And corresponding prevention and control suggestions are matched. The method solves the technical problems that single physical quantity monitoring is difficult to comprehensively reflect the complex change in the battery and the response delay of a centralized processing architecture causes the early warning lag, and achieves the timely capture and accurate early warning of the early weak characteristics of thermal runaway.

The invention provides a bearing fault diagnosis method based on a one-dimensional local binary pattern and a Hankel matrix. The bearing fault diagnosis method comprises the following steps: acquiring a discrete vibration signal; performing first-order differential operation on the discrete vibration signal to obtain a differential signal; performing inherent time scale decomposition on the differential signal to obtain an inherent rotation component signal; performing quantization and signal reconstruction on each inherent rotation component signal by taking a root mean square as a quantization criterion of a one-dimensional local binary mode method to obtain a decimal feature signal; constructing a Hankel matrix of the decimal characteristic signal and performing signal reconstruction according to a covariance matrix of the Hankel matrix; performing spectral analysis on the reconstructed signal, calculating the fault characteristic frequency of the bearing, and then judging the state and the fault type of the bearing through a frequency component obtained through spectral analysis and the fault characteristic frequency of the bearing obtained through calculation. According to the bearing fault diagnosis method, noise can be effectively suppressed, the bearing fault feature information can be effectively extracted, and the bearing state and the fault type can be accurately identified.

The invention relates to the technical field of fault detection and positioning, in particular to a power transmission line fault fusion distance measurement method and device, and the method comprises the steps: collecting a current sequence composed of current traveling wave signals at the moment when a fault occurs on a power transmission line; clustering local elements in the current sequence to obtain fault subsequences; calculating a trend fluctuation index of each fault subsequence; obtaining a peak drop difference and a peak interval difference; calculating a periodic confusion coefficient of the current sequence; the main component proportion and the difference index of the singular value sequence of the current sequence are obtained; obtaining a signal purity index of the current sequence based on the correlation between each row of data in the Hankel matrix, the periodic chaos coefficient, the main component proportion and the difference index; and carrying out distance measurement on power transmission line faults based on the signal purity index. According to the invention, the precision of fault distance measurement is improved.

The invention discloses a singular spectrum modal synthesis algorithm for separating unsteady state components in a spectrum aliasing environment, which comprises the following steps of: reconstructing all elements in an original signal x into a high-rank Hankel matrix X, performing singular value decomposition on the high-rank Hankel matrix X to obtain a left matrix, a singular value matrix and a right matrix, and aiming at the left matrix, the singular value matrix and the right matrix, calculating a singular value of the left matrix, the singular value matrix and the right matrix; according to the singular spectrum modal comprehensive algorithm, fast singular spectrum decomposition is carried out, m basic components yi are divided into # imgabs0 # groups, yi divided into the same group is summed, and an intrinsic mode function component of # imgabs1 # original signals x. According to the singular spectrum modal comprehensive algorithm, fast Fourier transform is utilized for fast singular spectrum decomposition, a large amount of matrix calculation in singular spectrum analysis is replaced, the calculation time is greatly shortened, and the calculation efficiency is improved. While the calculation speed is ensured, non-steady-state intrinsic mode function components overlapped in a frequency domain can be separated, and non-steady-state components in voice and mechanical vibration signals can be separated.

The invention discloses a sea surface weak target detection method and system based on sea clutter suppression and a storage medium, relates to the field of sea surface target detection, and aims to solve the problems that an existing sea surface target detection method is difficult to effectively suppress sea clutters and is difficult to recognize sea surface weak targets under complex sea conditions. Comprising the following steps: step 1, carrying out wavelet transform on a target echo signal of a radar, and changing the resolution of time and frequency through scale scaling to obtain time-frequency domain components under different scales; step 2, estimating clutters of a distance unit to be detected by using the clutters of the adjacent distance units according to the correlation between the clutters of the adjacent distance units of the sea surface and between multiple observation values of echoes of the same distance resolution unit; step 3, constructing a Hankel matrix for the echo signal, performing singular value decomposition, and eliminating a large singular value corresponding to the sea clutter according to the characteristic that the sea clutter energy in the same Chirp signal is greater than the signal energy to obtain a new Hankel matrix; and 4, echo signal reconstruction is carried out, and a sea surface target is detected.

The invention relates to a multi-parameter estimation algorithm combining a disturbance quantification model and a rooting method in interference measurement. In order to solve the problems that in the prior art, the disturbance degree of a sequence is difficult to quantify, and harmonic key parameters are influenced by spectrum leakage, the invention provides the following innovative scheme: firstly, a sequence multi-classification measurement entropy quantification model is constructed, and quantitative evaluation of the signal noise degree is realized; secondly, through a Hankel matrix construction and rooting parameter estimation method, realizing accurate estimation of the harmonic frequency of the real-value signal; meanwhile, an amplitude correction algorithm based on window function characteristics is designed, and the signal sampling condition is effectively judged; and finally, through matrix weighting operation of a phase demodulation function, efficient reconstruction of an optical surface shape is realized, the problems of spectrum leakage and disturbance quantification in a traditional method are solved, and the method has relatively high measurement precision.

The invention discloses a PRFI suppression method based on a Hankel structure and truncation nuclear norm regularization. The method comprises the following steps: acquiring an SAR echo signal interfered by a PRFI signal; the SAR echo signals are constructed into a Hankel matrix, and the Hankel matrix comprises a low-rank PRFI signal interference component matrix and a sparse SAR useful signal component matrix; constructing a low-rank sparse decomposition model based on a Hankel matrix, wherein an objective function of the low-rank sparse decomposition model comprises a truncation nuclear norm regular term used for constraining a PRFI signal component and a sparse regular term used for constraining an SAR useful signal component; performing iterative solution on the low-rank sparse decomposition model by adopting an alternating direction multiplier method to obtain a PRFI signal interference component matrix and an SAR useful signal component matrix after separation; and carrying out inverse transformation on the SAR useful signal component matrix to obtain an SAR useful signal from which the PRFI signal is removed. According to the method, the loss of the SAR useful signal can be reduced while the PRFI signal interference is efficiently suppressed, so that the fidelity of the SAR useful signal is improved.

The invention relates to the field of seismic data reconstruction, in particular to a seismic data low-rank reconstruction method fusing double-domain transformation, and the method comprises the steps: obtaining original incomplete seismic data, and initializing to-be-reconstructed seismic data and other model variables and parameters under an alternating direction multiplier method frame; performing fractional order gradient transformation on seismic data to be reconstructed along different directions, and mapping to obtain fractional order gradient domain data; performing space-time Hankel tensor expansion transformation on each piece of fractional order gradient domain data, and converting the data into a space-time Hankel matrix; performing low-rank constraint on each space-time Hankel matrix by adopting a Schatten norm to obtain a seismic data low-rank reconstruction model fused with double-domain transformation; and carrying out iterative solution on the model by using an ADMM framework until the model is stable, and obtaining a final seismic data reconstruction result. The method provided by the invention can effectively reconstruct the seismic data with the missing trace, and has a reconstruction effect with strong robustness and relatively good precision for incomplete seismic data with different missing degrees.

The invention discloses a wind turbine generator blade vibration mode feature recognition method, which mainly comprises the following steps of: arranging three-axis piezoelectric acceleration sensors at equal intervals from a blade root to a maximum chord length position, and adaptively selecting an optimal channel as a target input vibration signal according to envelope dispersion and a period proportion; introducing a vibration signal denoising method based on regenerative phase shift sine-assisted empirical mode decomposition, and constructing a Fisher ratio-based multi-dimensional fusion index to remove a noise component; estimating a system order range according to a singular entropy jump value theory, and designing modal similarity and a confidence index to accurately estimate a real order of a blade system; introducing three types of constraints of structure maintenance, modal sparsity and energy smoothness to jointly optimize a low-rank approximation strategy so as to realize optimal reconstruction of the Hankel matrix; constructing a fitness function selected by a clustering center by combining the point set density of the sample and Euclidean distance information, and optimizing a modal extraction result by adopting inter-class dispersivity and an intra-class sample number; according to the method, the environmental noise can be effectively removed, the system order can be accurately determined, and finally the modal parameters of the system can be accurately identified.

The invention discloses a transient voltage instability identification method based on a Koopman operator, and belongs to the field of power system operation and maintenance. The method comprises the following steps: firstly, on the basis of a Koopman operator theory, extracting a leading Koopman operator in voltage response after large disturbance through a Hankel matrix combined with an extended dynamic mode decomposition (Hankel-EDMD) method; secondly, a dominant Koopman operator is adopted to carry out continuation on a voltage response dominant component, and an enough voltage dominant mode time sequence is obtained; on the basis, a transient voltage stability discrimination model of a Koopman operator coupled with a Lyapunov exponent is provided, due to the dimension expansion effect of a Hankel matrix on measured data, the timeliness of the method is good, and therefore the method has high practical application value.

The invention discloses a hydroelectric generating set vibration signal noise reduction method based on SW-EEMD (Single Wave-Ensemble Empirical Mode Decomposition) and Hansel-SVD (Hanker-Singular Value Decomposition). The method comprises the following steps: processing an original noisy signal by adopting an SW-EEMD method; screening effective IMF components through correlation coefficients; respectively constructing Hankel matrixes for the screened IMF components and residual components; respectively carrying out singular value decomposition on the constructed Hankel matrix; carrying out soft threshold processing on the obtained singular value; the method aims at restraining the end effect of EEMD, noise in the vibration signals can be effectively filtered out, signal features are enhanced, more real and effective signal components can be obtained easily, and more reliable data are provided for state operation and maintenance of the hydroelectric generating set.

The invention discloses a data-driven multi-rate ore grinding loop control method and system, and the method comprises the steps: collecting the historical data of an ore grinding loop control system at different time scales, and estimating the future trajectory of the control system according to the characteristics of the historical data; the method comprises the following steps: predicting future data by taking known historical data of a set moment as a reference, rolling the set moment as the reference forwards along a time domain, constructing time-varying Hankel matrixes of historical output, historical input, future output and future input, and predicting future output data according to historical data characteristics; and a correction item is introduced for controller correction, the corrected frequency of the electric vibration feeder and the corrected opening degree of the water supplementing valve act on an ore grinding loop control system, and the ore grinding loop ore feeding amount, the water feeding amount and the set value are tracked through the predicted value. According to the control method, the control strategy is adjusted in real time in a data driving mode, and the control precision is improved so as to adapt to the time-varying characteristic and the multi-rate characteristic of the ore grinding loop.

The invention provides a distributed millimeter wave radar network-oriented DOA estimation method, a medium and equipment, and belongs to the technical field of radar signal processing. According to the method, after a large-aperture MIMO radar system is formed based on a plurality of small millimeter wave radars, a two-dimensional single-snapshot sparse array signal model is constructed by using a steering vector of a two-dimensional MIMO equivalent virtual plane array, and data interpolation and extrapolation processing are performed on aperture missing existing in a sparse array by constructing a block Hankel matrix and applying a matrix completion technology. On the basis, a fast two-dimensional angle estimation algorithm based on phase coherence factor weighting is adopted, and efficient joint estimation of the azimuth angle and the pitch angle of the target is achieved. The method can be flexibly applied to different types of distributed millimeter wave radar networks, effectively overcomes the interference effect of DOA estimation sidelobes and grating lobes in the prior art, and improves the signal-to-noise ratio level of array response.

The invention relates to the technical field of electrostatic dust collection and discloses an accurate frequency adjustment method for an intelligent frequency modulation high-voltage power supply of an electrostatic dust collector. The method comprises the steps of collecting voltage signals with frequency fluctuation in operation of a high-voltage power supply to form a voltage sequence; dividing period segments by using wave crest distribution in the voltage sequence, and clustering to obtain adjustment subsequences; obtaining a trend fluctuation index based on local signal change trend fluctuation and signal differences of the adjustment subsequences, and obtaining an amplitude drop difference and an amplitude interval difference according to an amplitude change trend difference and an amplitude bit sequence change trend difference; obtaining a period stability coefficient by combining the difference, the average condition of the trend fluctuation index and the average condition of the sub-sequence similarity; constructing a Hankel matrix and performing singular value decomposition, and obtaining a main component proportion and a difference index based on a singular value condition; and according to the Hankel matrix line data correlation, the period stability coefficient, the main component proportion and the difference index, obtaining a signal stability index, and according to the signal stability index, accurately adjusting the high-voltage power supply frequency.

The invention discloses a lithology identification-oriented while-drilling signal optimization reconstruction method, which is used for processing a vibration signal when a drill bit drills a rock sample, and belongs to the technical field of geological drilling, and comprises the following steps of: acquiring the vibration signal; performing Fourier transform to obtain a frequency spectrum; adaptively generating a frequency band boundary on the frequency spectrum; and constructing an initial matrix A and a Hankel matrix, performing singular value decomposition on the Hankel matrix, screening singular values, and sequentially reconstructing the Hankel matrix, the initial matrix and the signal. And a multi-domain feature fusion vector F is extracted based on the reconstructed signal. According to the method, the empirical wavelet transform method is improved, the problem of inaccurate frequency band division in empirical wavelet transform is avoided, the intrinsic mode function is accurately extracted, the function is ensured to better capture the local features of the signal at different frequencies, and the reconstructed signal has stronger feature retention capability and anti-noise performance; and the F can comprehensively capture time domain, frequency domain and entropy domain information of the signal, so that efficient characterization of the vibration signal is realized.

The invention belongs to the technical field of partial discharge detection, particularly relates to a partial discharge signal denoising method based on SVDTQWT, and aims to effectively remove periodic narrow-band interference and white noise in partial discharge signals. Comprising the following steps: performing Fourier transform on a noisy partial discharge signal to obtain a frequency spectrum, determining the number of periodic narrowband interferences through singular value decomposition, constructing a Hankel matrix to eliminate the periodic narrowband interferences, and obtaining a preliminary de-noised signal; and decomposing the preliminarily denoised signal by adopting adjustable quality factor wavelet transform to obtain a plurality of sub-bands. And dividing the plurality of sub-bands into high-frequency sub-bands and low-frequency sub-bands through sample entropy. Wherein the sample entropy indicates measurement of the complexity of the time series. And de-noising the high-frequency sub-band by using a group sparse total variation de-noising algorithm, de-noising the low-frequency sub-band by using an improved wavelet threshold de-noising algorithm, and reconstructing by using adjustable quality factor wavelet transform to obtain a pure partial discharge signal.

The invention discloses a mining area load spectrum anomaly detection method based on standard variable analysis, and belongs to the technical field of hydraulic pump anomaly detection.The mining area load spectrum anomaly detection method comprises the steps that S1, standardized data are obtained through data preprocessing, and a typical working condition data set of a hydraulic pump is constructed in combination with working conditions of an excavator; s2, constructing a historical vector and a future vector, and constructing a historical observation matrix and a future observation matrix according to the historical vector and the future vector; s3, constructing a Hankel matrix according to the autocorrelation matrix and the cross-correlation matrix, decomposing the Hankel matrix and determining a model order; s4, mapping the original data to a standard variable space and a residual space, and respectively evaluating the total variable quantity of the standard variable in the state space and the sum of squares of change errors in the residual space; and S5, determining an evaluation threshold value, and if the evaluation threshold value exceeds the control line, judging that the hydraulic pump operates abnormally. The method is based on standard variable analysis, adopts the pressure pulsation data of the hydraulic pump to perform anomaly detection, is sensitive to the internal running state of the pump, is not easily influenced by the external environment, and can perform early warning on faults of the hydraulic pump.

The invention discloses a noise reduction and rub-impact fault identification method based on information entropy and eigenvalue decomposition, and relates to the technical field of rotating machinery fault diagnosis, and the method comprises the following steps: obtaining an original discrete vibration signal through a sensor and a data collection card; according to the original discrete vibration signal, constructing a Hankel matrix, and through eigenvalue decomposition, obtaining an eigenvector of the Hankel matrix and an eigenvalue matrix; performing signal reconstruction according to different feature values in the feature value matrix and feature vectors corresponding to the feature values to obtain a reconstructed signal set; and selecting the reconstructed signal with the minimum information entropy in the reconstructed signal set as an optimal reconstructed signal, performing spectrum analysis on the optimal reconstructed signal, and judging the rub-impact fault according to the frequency component. Rub-impact fault recognition is realized through the optimal reconstruction signal determined by the feature value and the feature vector corresponding to the minimum information entropy, the influence of noise interference is reduced, the fault feature information is relatively prominent, and the fault recognition result is more accurate.

The present invention discloses a passive sensing device and method based on an intelligent reflective surface, wherein the device comprises an intelligent reflective surface and a sensor. The method comprises the following steps: the electromagnetic wave emitted by the wireless communication access point is reflected by the target after propagating through the free space, and the reflected signal is reflected twice when passing through the intelligent reflective surface. The control of the secondary reflection process is achieved by adjusting the relevant parameters of the intelligent reflective surface, and the sensor receives the secondary reflection signal, and uses the signal to achieve the perception of the space target; through the interference removal method based on the atomic norm, the interference signal can be suppressed, and the effective target echo signal can be extracted; finally, the receiving signal matrix is ​​constructed based on the Hankel matrix, and the perception of the space target is achieved based on the MUSIC method. The present invention improves the target perception performance of the communication perception integrated system, while reducing the overall system cost.

The invention relates to a damage prediction method and device for a tunnel lining structure, equipment, a medium and a program product. The method comprises the following steps: acquiring a strain monitoring data set of a plurality of lining structure monitoring points of a tunnel; the strain detection data set is composed of strain monitoring sub-data sets corresponding to load action times of each lining structure monitoring point at a plurality of historical moments; constructing a Hankel matrix of each lining structure monitoring point under the action of each load based on the strain monitoring sub-data set, so as to obtain the strain modal frequency of each lining structure monitoring point; determining a damage early warning index of each lining structure monitoring point based on the strain modal frequency; training and verifying a pre-constructed tunnel lining damage prediction model based on the plurality of damage early warning indexes to obtain a trained tunnel lining damage prediction model; and inputting the damage early-warning index set to be subjected to damage prediction into the trained tunnel lining damage prediction model to obtain an output damage prediction result, namely a predicted damage early-warning index.

The invention discloses a nonlinear multi-scale dynamic system control mechanism inference method based on a Koopman operator theory, and the method comprises the steps: building an order reduction mechanism model of a dynamic system from observed time series data based on the Koopman operator theory; firstly, dynamic sampling is carried out on observation time sequence data based on a self-adaptive sampling strategy designed by the method, the sampling rate is adjusted according to data changes, and information of different time scales is effectively separated. Thirdly, performing local data smoothing and numerical differentiation on the discrete sampling points by applying cubic B-spline interpolation; on the basis, a Block-Hankel matrix is innovatively constructed by using the nonlinear modeling capability of a Koopman operator theory so as to capture the multivariate dynamic characteristics of the system; and a main dynamic mode is extracted by using a kernel singular value decomposition technology, and system identification is expanded to a nonlinear power system. And finally, the SINDy algorithm is used for identifying the reduced-order model, and accurate modeling of the complex nonlinear multi-scale system is realized. According to the method, the advantages of Koopman embedding and sparse regression are fused on the basis of the multi-scale data processing capability of adaptive sampling, so that the method has relatively high robustness and universality, and has theoretical and practical significance for mechanism modeling of a nonlinear and multi-scale system.

The invention discloses a distributed traffic time sequence anomaly detection method for preventing data pollution, which comprises the following steps of: acquiring time sequence data of different characteristics of same equipment to be detected, performing adaptive multi-step coarse-grained denoising processing on the data, constructing the time sequence data into a Hankel matrix, and performing data processing on the Hankel matrix; dimensionality reduction and time sequence reconstruction are carried out according to a singular value decomposition result; multi-dimensional vector representation of each device is obtained by using a statistical method, the similarity between every two feature vectors is calculated by using cosine similarity, and a graph structure is constructed according to a similarity calculation result; and predicting and scoring based on the graph attention. According to the method, the relationship between the equipment and the variables can be fully mined on the premise of eliminating data pollution, so that more accurate behavior prediction is carried out.

The invention discloses a high-resolution tomographic SAR four-dimensional imaging method for space-time baseline decoupling processing, and the method comprises the following steps: 1, reconstructing original satellite data into uniform baseline observation data, and solving the noise-free estimation of the uniform baseline observation data through the low-rank characteristic of a Hankel matrix; step 2, inverting elevation information based on noise-free estimation, taking the elevation information as priori, constructing a deformation spectrum estimation dictionary through space-time decoupling, and performing deformation information estimation; and step 3, matching the estimated elevation and deformation quantity by using a maximum likelihood method so as to realize four-dimensional imaging. According to the method, the algorithm complexity of four-dimensional imaging is reduced exponentially while high-precision imaging is realized, and differential tomography SAR four-dimensional imaging result verification is carried out based on domestic land exploration No.1 satellite data.

The invention is based on a method for data-driven predictive control of a heat pump system (10), wherein at least one future input trajectory (28) of the heat pump system (10) and in particular at least one future output trajectory (38) of the heat pump system (10) is determined at least as a function of system data (30) of the heat pump system (10) measured at an earlier point in time, using a defined target function, which is in particular minimized, wherein the system data (30) comprise at least input measurement data (12) and output measurement data (14) of the heat pump system (10), wherein for the data-driven predictive control of the heat pump system (10), a control equation filled with the system data (30) is solved by means of a computing unit (22), wherein the computing unit (22, 22') uses the control equation to calculate the trajectories (28,38) of the heat pump system (10) are determined from a matrix-vector multiplication of at least one measurement data matrix with a decision variable vector, and wherein the measurement data matrix is ​​formed from at least two Hankel matrices written one above the other, each comprising only one measurement data type of the system data (30). , It is proposed that, in order to take into account a lower operating limit of the heat pump system (10), at least several entries of the future input trajectory (28) in the control equation are each assigned a binary variable, in particular by multiplication, wherein in particular the entries of the future input trajectory (28) each correspond to predicted time steps of a predicted input for the heat pump system (10).

The invention relates to a design method of a subspace predictive controller with a variable window length, and relates to the technical field of industrial process control, and the method comprises the steps: constructing a control right matrix; acquiring operation data at the current moment; inputting a control power matrix according to the operation data type to obtain the operation data after power adjustment; constructing a Hankel matrix according to the operation data after weight adjustment and the length value of the current rolling window; the historical operation data and the input data at the current moment are substituted into the subspace prediction model to obtain an output optimal prediction value; solving a difference between the output optimal predicted value and the current output value through an error calculation algorithm to obtain a correction deviation; respectively judging the window correction deviation with an upper limit deviation reference and a lower limit deviation reference, and adjusting the length value of the corresponding rolling window; and waiting for new operation data and returning the new operation data to the operation data after weight adjustment. The method has the effect of enabling the obtained model to accurately reflect the current system characteristics.

The invention provides a bearing fault identification method based on SVD and variance, and the method comprises the following steps: S1, carrying out the decomposition of a collected vibration signal through a signal decomposition algorithm, and obtaining a plurality of component signals; s2, constructing a Hankel matrix of each component signal; s3, performing singular value decomposition on the constructed Hankel matrix; s4, determining the effective length / optimal scale of the variance sequence by calculating the signal evaluation index of the new signal; s5, according to the effective length / optimal scale of the obtained variance sequence, constructing an optimal Hankel matrix for each component signal in sequence by taking variance as a criterion; and S6, reconstructing each component signal based on the constructed optimal Hankel matrix, and carrying out spectrum analysis on each noise reduction signal obtained after reconstruction to carry out fault identification on the bearing. Noise in the vibration signals can be reduced, fault information is highlighted, effective detection of the bearing state and accurate judgment of the composite fault type can be achieved, and the method has excellent engineering application value.

The invention provides a low-rank matrix analysis seismic data adaptive noise reduction method, which comprises the steps of collecting seismic data of a preset area, and establishing a three-dimensional seismic data volume according to the seismic data; the three-dimensional seismic data volume is a three-dimensional matrix of a space-time dimension; converting the three-dimensional matrix from a time domain to a matrix of a corresponding frequency domain; constructing Hankel matrixes line by line for the matrix of the frequency domain under a preset frequency to obtain the Hankel matrix of each line; building a block Hankel matrix by using each row of Hankel matrix; performing singular value decomposition on the block Hankel matrix to obtain a plurality of singular values; performing singular value truncation on the block Hankel matrix to obtain a frequency domain matrix after noise reduction; and converting the matrix of the frequency domain after noise reduction into a matrix of a time domain after noise reduction. According to the method, random noise in the seismic data can be effectively removed, and adaptive noise reduction of the seismic data is realized.

The invention provides a water chilling unit load prediction method based on CVA and genetic optimization ELM fusion, and belongs to the technical field of water chilling unit load prediction. The technical problem of excessive refrigeration or frequent start and stop is solved. According to the technical scheme, the method comprises the following steps that S1, collected open source water chilling unit data are divided into past data and future data; s2, constructing a Hankel matrix; s3, performing singular value decomposition on the Hankel matrix; s4, taking the standard variable matrix Xest as input data of a subsequent model; s5, initializing network parameters of the extreme learning machine; s6, calculating a hidden layer output matrix and a weight value matrix; and S7, respectively inputting the test data into the CVA-GA-ELM load prediction model and the PCA-BP load prediction model. The method has the beneficial effects that the adverse effects of high-dimensional and dynamic characteristics of data are reduced, the stability of the model is enhanced, and the accuracy of load prediction is improved.

The application discloses a kind of high-resolution tomography SAR four-dimensional imaging methods of space-time baseline decoupling processing, which comprises the following steps: step 1, reconstruct original satellite data into uniform baseline observation data, and utilize the low rank characteristic of Hankel matrix, solve its noiseless estimation;Step 2, elevation information is inverted based on noiseless estimation, and elevation information is used as priori, deformation spectrum estimation dictionary is constructed by space-time decoupling, and deformation information estimation is carried out;Step 3, the elevation and deformation variable estimated are matched using maximum likelihood method to realize four-dimensional imaging.The method realizes high-precision imaging while exponentially reducing the algorithm complexity of four-dimensional imaging, and the differential tomography SAR four-dimensional imaging result verification is carried out based on domestic land exploration No.1 satellite data.