8 results about "Random subspace method" patented technology

The invention discloses a high arch dam operation modal parameter automatic identification method and system based on discharge excitation. The method comprises the following steps: 1) obtaining a vibration displacement response signal: determining a penalty factor and an optimal decomposition layer number based on an adaptive multivariate variational mode decomposition algorithm to obtain an optimal IMF component of each sensor channel signal, and performing IMF component screening and signal reconstruction through a frequency domain cross correlation coefficient to realize adaptive noise reduction of the signal; 2) establishing a Monte Carlo three-dimensional stability diagram based on a random subspace recognition algorithm driven by a covariance matrix; and 3) modal parameter automatic identification based on an intelligent clustering algorithm. According to the method, the noise is suppressed by automatically optimizing the modal component reconstruction signal of the multi-sensor vibration signal; a Monde-Carlo three-dimensional stability diagram is established in combination with a Monde-Carlo theory and a covariance driven random subspace method to determine a model order, and automatic interpretation of the stability diagram is realized by applying improved fuzzy clustering, so that operation modal parameters of the high arch dam are accurately identified.

The invention discloses a mode identification method based on an unsupervised optimization covariance random subspace method. The method comprises the following steps: arranging a vibration sensor on a to-be-detected structure with noise interference or a weak excitation mode to obtain a structure dynamic response; constructing a Hankel matrix, calculating a covariance matrix Ri, and constructing a Toeplitz matrix based on the Ri; calculating an extended observable matrix Oi and an extended controllable matrix Gamma i based on the weighted Toeplitz matrix; defining the range of the row block number i and the model order N of the Toeplitz matrix; analyzing the sensitivity of the row block number i of the Toeplitz matrix and the model order N based on a parameter optimization index kP (i, N); the singular entropy increment of the weighted Toeplitz matrix T1i is calculated; calculating the singular entropy increment curvature of the weighted Toeplitz matrix T1i, and determining a critical model order Nc (i); calculating an accumulated parameter optimization index kappa P value from a minimum model order Nmin to a critical model order Nc (i) under the condition of different Toeplitz matrix row block numbers i; selecting a parameter combination {iopt, Nopt} corresponding to the minimum parameter as an optimal parameter; identifying a system matrix and determining modal parameters, and drawing an original stability diagram; and on the basis of DBSCAN clustering, automatically identifying each order of physical modality from candidate modalities containing noise interference. A corresponding system is also disclosed.

The application provides a cut learning system and a bandwidth-aware neural subspace compression method, device and equipment and medium thereof, and relates to the technical field of cut learning. The bandwidth-aware neural subspace compression method comprises: obtaining to-be-compressed data. According to the to-be-compressed data, a tensor singular value spectrum is estimated by an adaptive rank selection module based on a random subspace method, and is trimmed in combination with an energy coverage threshold, a bandwidth budget and a rank upper limit to obtain a compression rank. According to the to-be-compressed data, the compression rank and an error feedback item of a previous round of iteration, left and right factor matrices are alternately updated by an alternating orthogonal approximation module, and subspaces in row and column directions are orthogonalized to obtain row and column subspace orthogonal bases and a low-rank approximation. According to the to-be-compressed data and the low-rank approximation result, an error feedback item of the current round of iteration is obtained. The row and column subspace orthogonal bases are compressed data suitable for transmission.

The invention provides a method for identifying modal parameters of a supporting structure of a wind generating set, which comprises the following steps of: acquiring acceleration data of the supporting structure through a vibration data acquisition system, and identifying the modal parameters of the supporting structure by adopting a modal parameter identification algorithm based on a random subspace method, a clustering method and a modal shape denoising method. The modal parameters such as the vibration frequency, the damping ratio and the modal shape of the supporting structure are accurately extracted, and the health state evaluation and early warning of the supporting structure and the correction of a prediction model are realized through the constructed prediction model among the environment parameters, the operation parameters and the modal parameters and by utilizing an artificial intelligence edge calculation box. Finally, the operation strategy of the wind generating set and the frequency of the damping tuning vibration attenuation system are adjusted in time through the operation control system, and safe operation of the supporting structure is guaranteed.

The application relates to the field of non-contact monitoring of overhead transmission lines, and particularly provides a method and device for identifying the galloping mode of a transmission line based on magnetic field induction. The method comprises the following steps: obtaining the initial magnetic induction intensity of a magnetic dipole in each direction in a space coordinate system when a target overhead conductor gallops; wherein the initial magnetic induction intensity is measured by two magnetic induction sensors above the target overhead conductor; inversely calculating the conductor movement by using a nonlinear optimization method according to the magnetic induction intensity in each direction; wherein the conductor movement comprises the vertical displacement and the torsion angle of the target overhead conductor; and identifying the target mode of the target overhead conductor galloping by using a random subspace method based on the vertical displacement and the torsion angle. The technical scheme provided by the application can improve the accuracy of the galloping mode identification of the transmission line to a certain extent.

The invention discloses an automatic modal parameter identification method based on improved DBSCAN clustering. The method comprises the following steps: calculating a frequency domain average regularization power spectrum density ANPSD based on each test channel signal of a to-be-tested structure; automatic model order determination is carried out according to singular entropy increment and a corrected Akaike information criterion AICC, modal calculation is carried out based on a time domain covariance random subspace method to obtain an original stability diagram, and ANPSD of a frequency domain is superimposed and drawn; determining a minimum neighborhood sample number range according to the to-be-clustered modal feature dimensions and the total number of samples of the stability diagram; the modal distance of the kth neighbor of each modal pole under each minimum neighborhood sample number Minpt is calculated to form a k-dist graph, and a neighborhood radius value Eps under the corresponding k-dist graph is determined by using bisection K-means clustering, so that a {Minpt, Eps} parameter combination value range is formed; dBSCAN clusters under different {Mints, Eps} parameter combinations are calculated through traversal, and the parameter combination with the maximum cluster contour coefficient is automatically selected and substituted into the DBSCAN clusters to obtain a noise-rejected stability graph; on the basis of linear normalized modal energy and clustering cluster dimensions, performing bisection K-means clustering to screen clusters of candidate physical modals; evaluating modal splitting according to the modal confidence factor MAC and the modal overlap factor MOF; a representative modal of the damping ratio median index is extracted and modal parameters are identified. The invention further discloses a system, electronic equipment and a computer readable storage medium.

The application discloses a hybrid wind turbine tower modal test method and system, comprising the following steps: S1, obtaining target tower design parameters; S2, forming an overall and local test point array; S3, applying excitation in a manner of combining environmental excitation and artificial excitation and collecting response signals; S4, synchronously collecting acceleration, excitation force and environmental parameters through a multi-channel collector; S5, after data preprocessing, respectively adopting a random subspace method and a frequency response function modal analysis method to extract modal parameters, and obtaining final modal parameters through fusion correction; and S6, result verification and application, comparing the test results with a finite element model, correcting the model and outputting structure evaluation and optimization suggestions. The local modal of a cross rod, an inclined rod and a connecting node is captured, a weak link of the structure is effectively identified, the loading mode of environmental excitation+artificial excitation cooperation is adopted, and a modal confidence criterion is introduced for verification, so that the modal frequency test error is reduced.

The application discloses a high arch dam operation modal parameter automatic identification method and system based on outflow excitation, and the method comprises the following steps: 1) obtaining a vibration displacement response signal: determining a penalty factor and an optimal decomposition layer based on an adaptive multivariate variation modal decomposition algorithm, obtaining the best IMF component of each sensor channel signal, and performing IMF component screening and signal reconstruction through a frequency domain cross correlation coefficient to realize adaptive noise reduction of the signal; 2) establishing a Monte Carlo three-dimensional stability diagram based on a covariance matrix driven random subspace identification algorithm; and 3) automatically identifying modal parameters based on an intelligent clustering algorithm. The application automatically optimizes the modal component reconstruction signal of the multi-sensor vibration signal to suppress noise, combines the Monte Carlo theory and the covariance driven random subspace method to establish a Monte Carlo three-dimensional stability diagram to determine the model order, and applies the improved fuzzy clustering to realize automatic interpretation of the stability diagram, so that the high arch dam operation modal parameters can be accurately identified.