5 results about "Stability diagram" patented technology

The application provides an offshore wind turbine structure modal parameter automatic identification method, comprising: power spectrum density analysis on measured structural dynamic response of an offshore wind turbine structure, identification of harmonic frequencies by using a long short-term memory (LSTM) deep learning network; obtaining the dynamic response of the offshore wind turbine structure only with environmental excitation by using a periodic sub-signal-Kalman filter; then obtaining a Monte Carlo stability diagram by using a Monte Carlo covariance-driven stochastic subspace identification method, and further identifying stable modes of the structure; finally, obtaining modal parameters of the offshore wind turbine structure by using a density-based noise application spatial clustering method. The application can effectively solve the challenge of harmonic excitation to modal parameter identification, improve the accuracy of structural modal parameters, and improve the structural health monitoring, state evaluation and damage identification capability of the offshore wind turbine, thereby supporting the development of offshore wind energy.

The application relates to a bridge modal parameter automatic identification method and system considering multi-channel information, which comprises the following steps: S1, generating a stability diagram by directly analyzing multi-channel monitoring signals by using COV-SSI, automatically extracting stable axes by combining a DBSCAN clustering algorithm, and realizing automatic identification of modal frequencies; S2, decomposing multi-channel monitoring data by using MvFIF to generate an intrinsic modal function (IMF) group with modal alignment characteristics; S3, calculating the instantaneous frequency and bandwidth of the IMF in step S2 by using HHT, screening out an IMF component containing a target modal frequency, and performing linear reconstruction; and S4, taking the multi-channel monitoring data after the reconstruction in step S3 as system input of the COV-SSI algorithm, calculating a system matrix and an output matrix, and calculating modal damping ratios by using eigenvalue decomposition; the method can consider the space-time correlation between multi-channel data, realize synchronous decomposition of multi-channel monitoring data, and realize automatic and accurate identification of structural modal parameters under different noise levels.

The invention discloses a steel cable fundamental frequency automatic identification method and device, a storage medium and computer equipment, and the method comprises the following steps: S0, data collection and preprocessing; s1, random subspace identification; s2, stability diagram and stability grading; s3, strip construction and same-order de-weighting are carried out; s4, strip consistency feature construction is carried out; s5, learning discrimination; and S6, scoring and multi-view integration. Under the conditions of low signal-to-noise ratio, colored excitation and multi-mode coupling, the fundamental frequency can still be recognized stably, the risk that a high order is misjudged as the fundamental frequency is remarkably reduced, strip consistency of a stability graph is structured through unified strip modeling and cleaning rules, and dependence on a manual threshold value is reduced.

A semiconductor device includes a layer of a semiconductor material in which is formed an active zone; a plurality of first gates forming a plurality of lines substantially parallel to each other and covering in part the active zone; a plurality of second gates forming a plurality of columns; at least one third gate, designated measurement gate, extending along an axis substantially parallel to the lines of the plurality of lines and in a direction opposite to the lines of the plurality of lines with respect to the active zone, and a first electrode and a second electrode situated on either side of the plurality of measurement gates in the active zone.

A high arch dam modal parameter automatic identification method based on strong earthquake monitoring data, comprising: based on high arch dam strong earthquake monitoring data, using random subspace identification method algorithm to identify high arch dam modal parameter, taking self-vibration frequency and modal confidence criterion as coordinates, and the maximum value of power spectrum of all measuring points as background curve to draw stability diagram; combining local outlier factor and kernel density estimation to identify abnormal poles in stability diagram, eliminating false modes, and then using sparrow search algorithm to search for the optimal combination of K-means clustering algorithm to obtain the initial clustering center of stability diagram; using K-means algorithm to cluster and analyze effective poles in stability diagram, and realizing automatic identification of modal parameters. The present application combines SSI, SSA, LOF, KDE and K-means clustering algorithm, can significantly suppress noise under low signal-to-noise ratio condition, eliminate false modes, and automatically and accurately identify high arch dam modal parameters without human intervention.