9 results about "Stability diagram" patented technology

The invention relates to a bridge modal parameter automatic identification method and system considering multi-channel information, and the method comprises the following steps: S1, directly analyzing a multi-channel monitoring signal through COV-SSI, generating a stability diagram, and automatically extracting a stability axis in combination with a DBSCAN clustering algorithm, thereby achieving the automatic identification of a modal frequency; s2, performing signal decomposition on the multi-channel monitoring data by adopting MvFIF to generate an intrinsic mode function (IMF) group with a mode alignment characteristic; s3, the instantaneous frequency and bandwidth of the IMF in the step S2 are calculated through HHT, IMF components containing target modal frequency are screened out, and linear reconstruction is carried out; s4, taking the multi-channel monitoring data reconstructed in the step S3 as system input of a COV-SSI algorithm, calculating a system matrix and an output matrix, and calculating a modal damping ratio by utilizing eigenvalue decomposition; according to the method, the spatial-temporal correlation among multi-channel data can be considered, and synchronous decomposition of multi-channel monitoring data is realized; and automatic and accurate identification of the structural modal parameters under different noise levels can be realized.

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 present invention relates to the technical field of wheel-rail tribology and discloses in particular a prediction method for rolling contact fatigue damage of wheel / rail materials based on various evaluation indicators. This method comprises the following steps: performing a wheel-rail rolling contact fatigue simulation test based on a wheel-rail stability diagram to determine the damage state and the stability limit curve equation of wheel / rail materials with different shear yield strengths under different contact parameters; calculating the importance and relative weighting of various evaluation indicators based on roughness quantity theory; and creating an integrated evaluation indicator cloud model to calculate the association of the attribute values ​​of the indicators to be evaluated with different damage states.Evaluating and predicting a rolling contact fatigue damage condition using the maximum value rule; performing a check against the attribute value of the integrated evaluation indicator, and determining the prediction results of the rolling contact fatigue damage condition for selected wheel and rail material under the conditions to be predicted. The method according to the present invention relies solely on existing technical data, which can significantly reduce the subjectivity of the damage condition prediction process.

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.

The invention provides a method and a system for judging a circumferential crack initiation condition of a wheel tread of a railway wagon. The method comprises the following steps of: establishing a railway wagon dynamic model; based on the action relation between the circumferential cracks and the shear stress borne by the wheels, the bogie working condition with the suspension failure is set in the railway wagon dynamic model; on the basis of the railway wagon dynamics model, statistics is conducted on the dynamics simulation result of each wheel of each bogie working condition, the bogie working condition when transverse force and transverse creep force borne by the wheels are maximum is obtained, and the bogie load working condition when circumferential cracks are generated is judged; based on the preliminarily judged bogie load working condition when the circumferential crack is generated, the maximum Hertz contact stress borne by the wheel is calculated through the Hertz contact theory; based on a dynamic simulation result, the maximum Hertz contact stress borne by the wheel and the shear yield limit of a wheel material, a wheel stability diagram is drawn by combining a stability theory; and judging the mechanism of circumferential crack initiation by analyzing a wheel stability diagram.

The invention discloses a model training method, a modal parameter identification method, a model training device, a modal parameter identification device, an electronic device and a nonvolatile computer readable storage medium. The model training method comprises the steps that a plurality of stability diagram samples are obtained, each stability diagram sample comprises a real mode and a false mode, the frequencies of stable points forming the real modes are the same, and the frequencies of at least part of the stable points and / or unstable points forming the false modes are different; and training to obtain a modal parameter identification model based on the plurality of stability map samples. It can be understood that in the training process, the training model can learn based on the real modalities in the multiple stability diagrams, and the real modalities of the multiple stability diagrams are different, so that the modal parameter recognition model obtained after training has high real modal recognition ability, and the modal parameter recognition efficiency is improved after the modal parameter recognition model is put into use. The modal parameter identification model can rapidly and accurately identify modal parameters.