9 results about "Cross correlation matrix" patented technology

The application belongs to the technical field of transformer partial discharge identification, and discloses a transformer partial discharge intelligent identification method based on deep learning, which constructs an electroacoustic thermal three-mode acquisition architecture, captures 1ns level discharge pulses through a high-frequency current sensor, avoids sound field superposition interference by reasonably arranging an ultrasonic sensor on the oil tank wall, focuses on the easy discharge area to collect thermal signals through an infrared thermal imager, and synchronously obtains working condition parameters such as load rate and oil temperature; subsequently, the effective signal segments are retained through a multi-modal signal cross-correlation matrix, and the interference signals with large deviations are removed, then different networks are used to extract electroacoustic thermal characteristics, the coupling characteristics are calculated by combining the physical constraint layer weight and the cross-field interaction layer, and the key characteristics are retained; the feature overlap of air gap and surface discharge is effectively reduced, the influence of high-frequency interference and signal distortion is reduced, the partial discharge identification precision is improved, the samples are expanded and the fault and normal sample ratio is reasonable through time stretching and additive noise processing.

This invention relates to a method for diagnosing composite faults in rolling bearings based on fast eigenmode decomposition (EMD) in the field of rolling bearing fault diagnosis technology. The method includes the following steps: S1, Signal initialization and period estimation: Segmented narrowband filtering of the rolling bearing vibration acceleration signal is performed using a Hanning window; S2, Adaptive filter design: The original signal is filtered using the MOMEDA method to obtain multiple modal signals; S3, Correlation kurtosis calculation and mode selection: The correlation kurtosis value of each filtered signal in each frequency band is calculated, and a cross-correlation matrix between modes is constructed based on cross-correlation theory, retaining a preset number of optimal modes; S4, Fault diagnosis analysis: Hilbert envelope demodulation is performed on the retained optimal mode signals. This invention solves the mode breakage problem that may occur in existing technologies while saving significant computational costs and improving computational efficiency, demonstrating good practicality and engineering application value.

This invention discloses a method for detecting quality faults in a flotation process based on distributed dynamic graph embedding. Step 1: Collect process variables such as concentration, pH value, ore fineness, froth layer thickness, and maximum allowable density during the flotation production process as input variables, and the concentrate grade of the flotation process as the output quality indicator. Step 2: Based on the mutual information method, establish a cross-correlation matrix MI between process variables and quality variables, calculate the average threshold, and select key variables. Step 3: Based on the production process and field experience of the flotation process, divide the key variables into Q quality-related sub-blocks and B quality-unrelated sub-blocks. Step 4: Use the key variable selection and sub-block decomposition results to establish a distributed dynamic graph model to achieve quality-related fault detection. Step 5: Use a Bayesian fusion network to fuse the monitoring results for decision-making. Step 6: Determine whether the fault is quality-related based on the monitoring results.

The application provides a surface electromyogram signal decomposition method based on fast gradient kernel compensation, comprising the following steps: mathematically modeling a multi-channel surface electromyogram signal; extending the constructed multi-channel surface electromyogram signal model; constructing a convolution mixed model of the corresponding surface electromyogram signal based on the extended electromyogram signal, a firing time sequence and a noise sequence; constructing a cross-correlation matrix of the surface electromyogram signal and initializing an activity index; initializing a cross-correlation vector based on the activity index; obtaining a gradient function and a learning rate; introducing an exponential weighted moving average model, offline calculating and iteratively updating the cross-correlation vector; extracting the electromyogram signal in a sliding window and expanding it, calculating the cross-correlation matrix after expansion, and estimating the firing time sequence of each motor unit; recalculating the firing time sequence of each motor unit until all signal decompositions are completed. The application can improve the surface electromyogram signal decomposition efficiency and is suitable for real-time decomposition process.

This invention discloses a method for tracing the source of generated images based on spatial-frequency cross-domain second-order statistics. This method extracts spatial domain residual anomalous features and frequency domain anomalous features from the generated image, and constructs a cross-domain second-order statistical coupling relationship between them, forming a cross-domain statistical fingerprint for source tracing. The cross-domain statistical fingerprint matrix is ​​obtained by calculating the cross-domain cross-correlation matrix, and then normalized and compressed to obtain a cross-domain second-order statistical feature representation. Based on the similarity matching or distance measurement results between this feature representation and the statistical fingerprint templates of each source category, the source category or confidence score of the generated image is output. This invention can explicitly characterize the coupling relationship between spatial anomalies and frequency anomalies, and still has strong source discrimination ability and good interpretability and generalization performance even when the generation models are highly similar.

The application discloses a kind of multi-parameter lognormal random field automation generation method based on finite element grid, the method first reads finite element grid file, parses the unit topological relation of target analysis area and automatically extracts the geometric center point coordinate of entity unit;Obtain multidimensional uniform distribution sample, and convert into independent initial standard normal random matrix;Respectively construct the cross correlation matrix of each parameter space autocorrelation matrix and parameter;Multi-parameter lognormal random field is generated using nonlinear mapping equation;Finally, the random field data is mapped according to unit number and verified.Outputs.Through the combination of original finite element grid analysis and multiple correlation decoupling calculation, the method can efficiently and accurately generate the random field subject to complex correlation structure, effectively eliminate the error caused by traditional independent grid interpolation mapping, provide a fully automated pre-processing tool for large-scale random finite element simulation and engineering reliability assessment.

The present invention belongs to the technical field of hydraulic pump anomaly detection. Disclosed is a mining area load spectrum anomaly detection method based on canonical variate analysis. The method comprises: S1, performing data preprocessing to obtain standardized data, and in combination with operating conditions of an excavator, constructing a typical operating condition data set of a hydraulic pump; S2, constructing a historical vector and a future vector, and on the basis of the historical vector and the future vector, constructing a historical observation matrix and a future observation matrix; S3, constructing a Hankel matrix on the basis of an auto-correlation matrix and a cross-correlation matrix, decomposing the Hankel matrix, and determining a model order; S4, mapping original data into a canonical variate space and a residual space, and evaluating the total variation of canonical variates in a state space and the sum of squared variation errors in the residual space; and S5, determining an evaluation threshold value, and if a control limit is exceeded, determining that the hydraulic pump operates abnormally. In the present invention, pressure pulsation data of a hydraulic pump is used to perform anomaly detection on the basis of canonical variate analysis, and the method in the present invention is sensitive to the internal operating state of the pump, is not prone to the impact of an external environment, and enables early warning of faults in the hydraulic pump.

This invention relates to the field of data processing, specifically to an intelligent early warning method and system for sintering furnaces used in powder metallurgy. The method includes: collecting historical normal operation data and dividing it into multiple windows; extracting multi-dimensional features for each window, including a structural feature vector based on the singular values ​​of the autocorrelation matrix, a coupling feature vector based on the cross-correlation matrix, an inertial-coupling dominant feature, and an inertial-coupling consistency vector; using the structural feature vector as the first sub-vector and the remaining features as the second sub-vector, employing a cosine kernel and a radial basis function kernel respectively, and determining the weights of the two kernels to construct a combined kernel function to train a single-class support vector machine; collecting data in real time and extracting identical features, and substituting them into a decision function to determine whether an early warning is triggered. This invention, through multi-dimensional feature extraction and adaptive combined kernel functions, can effectively distinguish between normal fluctuations and abnormal precursors, improving the accuracy of early warnings for sintering furnace anomalies.

This invention provides a spectral analysis method for undersampled harmonic signals, comprising the following steps: acquiring multi-channel leaf tip timing signals; constructing multiple snapshot matrices based on the multi-channel leaf tip timing signals; calculating a cross-correlation matrix; calculating the following power expression of the cross-correlation matrix; calculating the steering vector; and obtaining the function beamforming output. The technical solution of this invention can be widely applied in the field of harmonic signal processing technology to solve the problem of processing undersampled harmonic signals.