20 results about "Higher-order statistics" patented technology

The application discloses a power supply equipment state monitoring system and method based on AI power spectrum, and relates to the technical field of electrical intelligent sensing; the method comprises the following steps: obtaining original analog signals of the power supply equipment through real-time synchronous acquisition, and preprocessing the original analog signals to obtain digital waveform data frames; performing time domain feature calculation on the digital waveform data frames to obtain a time domain feature set; performing frequency spectrum feature extraction on the digital waveform data frames to obtain a frequency domain feature set; performing high-order statistical analysis and nonlinear signal processing on the digital waveform data frames to obtain a nonlinear feature vector; based on the frequency domain feature set, the nonlinear feature vector and the time domain feature set, combining a pre-trained artificial intelligence model, outputting a fault type identifier and a corresponding fault probability, and calculating an abnormal score based on the fault type identifier and the corresponding fault probability; the application can effectively identify the camouflage fault of the power supply equipment and reduce the cumulative risk of potential faults.

The application discloses a power transformer winding fault detection method based on vibration spectrum analysis and relates to the technical field of power equipment detection, which comprises the following steps: collecting original vibration acceleration signals of a transformer and preprocessing the original vibration acceleration signals to form a signal frame sequence; applying an improved Hilbert-Huang transform to each signal frame to obtain a set of intrinsic mode functions, calculating a component instantaneous frequency sequence and extracting a frequency band with an energy proportion exceeding a preset threshold as a candidate fault characteristic frequency band; extracting a subband signal of each frequency band and calculating a high-order statistical quantity feature matrix; loading a health state reference feature matrix matched with a transformer model, capacity and voltage grade; comparing the two in an element-by-element manner to identify an abnormal feature submatrix; spatially clustering the abnormal feature submatrix to form a suspected fault mode cluster; and outputting a specific winding fault mode diagnosis result. The method can accurately extract fault characteristics and improve the accuracy and reliability of fault detection.

The application relates to the computer technical field, in particular to a traditional Chinese medicine processing parameter optimization method and system based on small sample transfer learning, which comprises the following steps: collecting multi-source domain historical data and target domain small sample data, extracting high-order statistics and calculating initial distribution distance; constructing an adaptive distribution difference measurement network, compensating density estimation deviation by dynamically adjusting kernel bandwidth; designing a negative transfer risk dynamic monitoring mechanism to monitor gradient direction consistency; establishing a collaborative failure detection model by using a bidirectional gate recurrent network to identify positive feedback amplification trend; and dynamically screening source domains and redistributing weights according to risk scores. The technical scheme can effectively inhibit negative transfer and collaborative failure of process parameters in the traditional Chinese medicine processing process, and realize stable optimization of traditional Chinese medicine processing process parameters under small samples.

This invention discloses a curvature-guided distillation method and system for synthetic image datasets, belonging to the field of image processing technology. To address the technical problems of existing technologies, such as large variance in higher-order statistical estimation under subsampling conditions, uncontrollable computational overhead, and insufficient robustness of synthetic samples, this invention preprocesses real samples and extracts their features and kernel functions along with those of the synthetic samples to be optimized; calculates the sampling weights of the real samples using a sampler; calculates the first-order loss based on the weights and features, and extracts Hessian second-order information to obtain the curvature loss term; synchronously updates the parameters of the synthetic samples and sampler using the first-order loss and the curvature loss term to generate a parent set; finally, the parent set is cropped and corrected. This invention can stabilize higher-order statistical estimation and reduce gradient variance, improve the structural fidelity and generalization ability of synthetic samples, and achieve single distillation adaptability to high-quality datasets of various sizes.

This invention relates to the field of industrial energy management technology and discloses an AI-driven large-scale model-based power consumption tracking and energy-saving control system. The method includes: acquiring multi-cycle power consumption sampling data from industrial equipment and performing phase normalization processing; extracting high-order statistical features at the same phase points to generate cross-cycle energy consumption feature vectors; calculating trend slope and phase synchronization to filter abnormal phase points; encoding abnormal information and inputting it into a large-scale model to generate semantic embedding vectors; using an attention mechanism to calculate semantic distance distribution and generate energy consumption anomaly feature labels; and generating cause diagnosis and energy-saving control instructions through large-scale model-driven inference. This invention achieves an automated closed loop from energy consumption tracking to energy-saving control.

The invention relates to the technical field of metal component nondestructive testing, and discloses a metal component magnetic coercive force damage assessment system and method.The metal component magnetic coercive force damage assessment method comprises the steps that repeated standardized magnetization cycle collection and synchronous average processing are carried out on all measuring points, and high-signal-to-noise-ratio average hysteresis loop data are generated; extracting high-order statistical characteristics of a hysteresis loop local interval, and generating a local skewness value sequence, a local kurtosis value sequence and an inter-cycle micro variation value sequence; a multi-channel high-order statistical feature space field is generated through Kriging interpolation; performing time sequence difference and gradient vector field calculation on the multi-moment multi-channel high-order statistical feature space field; performing DBSCAN density clustering on the cross-channel fusion gradient vector, identifying an early damage active evolution leading edge and extracting leading edge features; and outputting a magnetic coercive force damage evaluation report of the metal component.

The application discloses a kind of portable three-phase power quality field diagnosis method, the application is first with 0.1~1Hz low-speed acquisition voltage, current signal of the three-phase alternating current loop to be measured, synchronous acquisition ambient temperature and complete phase shift compensation;Current waveform high-order statistical feature is extracted again to realize load type identification, corresponding power quality evaluation threshold is matched and core index is calculated, index is judged as power quality disturbance when threshold, generate local diagnosis prompt and trigger 3.2~12.8kHz high-speed sampling.High-speed sampling stage real-time detects communication link state, link is normal then real-time upload data, interrupt then data is cached to local storage, link recovers after breakpoint and completes supplement transmission;Diagnosis whole process carries out FIFO management to local storage capacity, and high-speed sampling data is preferentially retained.The application realizes local intelligent diagnosis and adaptive sampling, solves the problem that traditional equipment is poor in portability, has no local diagnosis capability, data is easy to lose, sampling strategy is rigid.

This invention discloses a method for enhancing motor fault data based on a spectral normalization-conditional generative adversarial network (GAN) and high-order statistics for screening, belonging to the field of motor fault diagnosis and data augmentation. The method includes: first, constructing a filter incorporating three aspects: energy efficiency, physical stability, and fault saliency, and a generative adversarial network (GAN) incorporating spectral normalization; second, generating initial fault signal samples using the generator and filtering them through the filter; inputting the filtered fault signal samples and collected real fault signal data into a discriminator for discrimination; and updating the GAN parameters based on the discriminator's discrimination results to obtain a trained generator; finally, using the filter and the trained generator, specifying the fault type, yields new fault signal samples corresponding to that fault type. This invention effectively enhances the diversity of data samples, providing a reference and approach for the practical deployment and optimization of deep learning models in industry.

This invention relates to the field of on-orbit telemetry data analysis, and particularly to a method for in-depth analysis of on-orbit data fault diagnosis results based on high-order statistics. First, historical fault diagnosis conclusion data is segmented by a preset time length, and the types, durations, and switching processes of the diagnosis conclusions are statistically analyzed to form high-order statistical information. Second, different conclusion types are labeled with color bands of different lengths according to their duration, and these bands are then stitched together according to the switching process of the diagnosis conclusion types to form a changing color band diagram. Next, based on manually labeled fault tags, a diagnostic model is trained using a deep learning algorithm. Finally, the statistical analysis and color band diagram drawing process are repeated for new data, and the trained diagnostic model outputs abnormal diagnosis conclusions. This invention can effectively filter out false alarms, uncover deep fault patterns, significantly reduce the burden of manual analysis, and achieve keen early warning of early fault symptoms, thus improving the automation level and reliability of on-orbit data fault diagnosis.

The application relates to the technical field of laser cutting and discloses a steel plate laser cutting machine fault detection method and system, the method comprising the following steps: collecting a driving power current signal in real time and filtering out a direct current component to obtain an alternating current sequence; separating a multi-scale subband component containing Gaussian noise by using wavelet transform; performing empirical mode decomposition on the subband component to extract an intrinsic mode function component set; calculating the kurtosis value, kurtosis change rate and phase coupling index based on bispectrum analysis of the component to construct a high-order statistical feature vector; classifying the feature vector by using a support vector machine to output an operation state category label; combining the label risk value and the kurtosis evolution trend slope to generate a fault early warning grade value; and verifying early fault states by using a historical state sliding window sequence and a fault confirmation confidence degree, so that the method can solve the problem of low precision of nonlinear fault feature extraction in a complex noise interference background existing in the prior art.

This invention discloses a native digital human that continuously grows alongside a real individual and its construction method, comprising: establishing a mapping relationship between a target activity task and multiple predetermined human functional indicators; collecting measurement values ​​and actual performance values ​​of the target individual over multiple time periods to form an individual functional dataset; extracting high-order statistical values ​​from a homogeneous database that are in the top P percentile of a reference group as a top-tier benchmark; generating a first difference between the predicted performance value and the actual performance value, and a second difference between the individual measurement value and the top-tier benchmark; generating intervention target values ​​based on the first and second differences and encoding them into a structured training scheme; continuously injecting new periodic measurement values ​​generated after the real individual executes the training scheme into the dataset and triggering iterative updates, so that the native digital human evolves synchronously with the training results. This invention can construct a native digital human that continuously grows alongside a real individual, enabling lossless experiments in the virtual world and supporting team collaborative simulations.

This invention discloses a method and system for controlling the wire winding of a power pole frame based on real-time tension feedback. Specifically, it relates to the field of wire winding tension control technology in the manufacturing of prestressed concrete power poles, and is used to solve the problems of tension feedback distortion and uneven prestressing caused by the dynamic changes in the friction coefficient between the steel wire and the frame in existing technologies. By acquiring the steel wire tension feedback value and the axial position information of the winding head in real time, the system judges control anomalies based on tension deviations. When an anomaly occurs, it continuously calculates the higher-order statistics of the tension value and identifies the directional change of the friction state based on its evolution trajectory. It correlates this evolution feature with the axial position to determine the spatial propagation characteristics of the friction state change, evaluates the degree of tension transmission distortion based on this feature, and finally dynamically compensates and adjusts the tension control system parameters according to the degree of distortion. This method can effectively identify and compensate for tension distortion caused by time-varying friction, and improve the uniformity and accuracy of prestressing application.

This invention discloses a method and system for controlling the wire winding of a power pole frame based on real-time tension feedback. Specifically, it relates to the field of wire winding tension control technology in the manufacturing of prestressed concrete power poles, and is used to solve the problems of tension feedback distortion and uneven prestressing caused by the dynamic changes in the friction coefficient between the steel wire and the frame in existing technologies. By acquiring the steel wire tension feedback value and the axial position information of the winding head in real time, the system judges control anomalies based on tension deviations. When an anomaly occurs, it continuously calculates the higher-order statistics of the tension value and identifies the directional change of the friction state based on its evolution trajectory. It correlates this evolution feature with the axial position to determine the spatial propagation characteristics of the friction state change, evaluates the degree of tension transmission distortion based on this feature, and finally dynamically compensates and adjusts the tension control system parameters according to the degree of distortion. This method can effectively identify and compensate for tension distortion caused by time-varying friction, and improve the uniformity and accuracy of prestressing application.

This invention discloses an automated testing method based on knowledge base management. The method first acquires telemetry data streams from the target device in real time and calculates the gradient of its higher-order statistical moments to detect phase transition characteristics of the system transitioning from a steady state to an unstable state. When a phase transition characteristic is detected, the posterior probability of a specific failure mode is updated based on a Bayesian network risk model. If the posterior probability exceeds a dynamic risk threshold, relevant diagnostic test scripts are automatically retrieved and matched from an intelligently managed knowledge base. This knowledge base uses natural language processing and graph neural network technology to intelligently classify and associate historical test problems. Finally, the diagnostic scripts are injected into the target device for execution to proactively verify potential faults, and the knowledge base and risk model are updated based on the execution results. This invention achieves a shift from passive response to proactive prediction and diagnosis, significantly improving fault predictability, testing efficiency, and system reliability.

The invention discloses a coal field earthquake interpretation multi-mode hidden geological disaster information fusion and dynamic modeling method. The method comprises the following steps: acquiring multi-source heterogeneous geological data of a target coal field area; according to the method, the multi-modal feature tensor fusion model is constructed, so that the problem of multiplicity of solutions of a single data source is effectively solved, and the integrity and consistency of hidden geological disaster features are remarkably improved; conventional seismic attributes are extracted, high-order statistical characteristics of a logging curve and geologic body structure parameters are introduced, lithologic sudden change and tectonic deformation are described from multiple dimensions, and the recognition capacity of a micro-scale hidden disaster body is enhanced; a hybrid neural network combining CNN and LSTM is adopted, historical stratum stress field data and geologic features are fused, a space-time prediction module is constructed, the limitation of traditional static snapshot type interpretation is broken through, a geologic structure stability probability body of a future time sequence can be output, and dynamic prediction of a disaster evolution trend is achieved.

This invention discloses a method and system for simulating stationary non-Gaussian wind fields based on random waves, relating to the field of non-Gaussian wind field simulation in structural wind engineering. It includes: acquiring the autopower spectrum and coherence function of the target wind speed field to construct the target non-Gaussian wavenumber-frequency spectrum; discretizing the wavenumber and frequency, and generating a bottom-level stationary Gaussian random field by combining random wave spectrum representation and two-dimensional fast Fourier transform; reconstructing the target non-Gaussian marginal probability density function and cumulative distribution function based on finite-order statistical moments using the maximum entropy method; embedding a piecewise Hermite polynomial model to construct a piecewise translation mapping function from Gaussian to non-Gaussian; inversely calculating the bottom-level Gaussian correlation structure and determining its spectral characteristics based on the correlation distortion relationship; and applying the piecewise translation mapping function to obtain stationary non-Gaussian wind field samples. This invention eliminates the need to decompose high-dimensional matrices, balancing computational efficiency and numerical stability, characterizing the high-order statistical properties and tail probabilities of strong non-Gaussian wind fields, and ensuring the spatiotemporal correlation of the wind field.

This invention discloses a generative statistical semantic guidance processing method for time series classification. Addressing the issues of attention diffusion in long sequences and overfitting in small samples, this method projects multivariate time series to generate morphological token sequences. It extracts high-order statistical features such as skewness and kurtosis from the tokens to construct statistical semantic cue vectors, calculates importance scores based on these vectors, and performs Top-K soft sparse reweighting. Simultaneously, redundant tokens are adaptively compressed into global context features. Finally, the fused features are input into a multi-scale conditional expert routing module for dynamic calculation, and a large temporal model is introduced during training for manifold distillation. This invention effectively eliminates computational redundancy while preserving global coherence without loss, significantly improving classification accuracy and robustness in small sample and noisy environments.

The application relates to the technical field of metal component nondestructive testing, and discloses a metal component magnetic coercivity damage evaluation system and method, wherein the metal component magnetic coercivity damage evaluation method comprises the following steps: performing repeated standardization magnetization cycle collection and synchronous average processing on each measuring point to generate high signal-to-noise ratio average magnetic hysteresis loop data; extracting high-order statistical features of a local interval of the magnetic hysteresis loop to generate a local skewness value sequence, a local kurtosis value sequence and a cycle interval micro-variability value sequence; generating a multi-channel high-order statistical feature space field through Kriging interpolation; performing time sequence difference and gradient vector field calculation on the multi-time and multi-channel high-order statistical feature space field; performing DBSCAN density clustering on the cross-channel fusion gradient vector to identify an early damage active evolution front and extract front features; and outputting a metal component magnetic coercivity damage evaluation report.

The application belongs to the technical field of intelligent fault diagnosis of mechanical equipment, and discloses a mechanical equipment domain generalization fault diagnosis method based on intra-class similarity spectrum. The method breaks through the limitation of traditional distribution alignment method for eliminating the difference between domains, and extracts inherent intra-class structure invariance from a micro level. The technical scheme is as follows: the geometric similarity of the same samples in a single source domain is extracted, and the geometric similarity is encoded into a high-order statistical similarity spectrum to offset the numerical drift caused by domain offset, and a six-layer fully connected network is used to map the high-order statistical similarity spectrum to a fault category. The application does not depend on prior knowledge of the target domain and does not need to be trained in an adversarial manner, thereby ensuring high diagnostic accuracy, reducing computational delay, and improving the generalization ability and practicality of the model in resource-limited industrial edge scenarios.

The present application relates to the technical field of bearing fault diagnosis, and particularly relates to a bearing fault migration diagnosis method based on high-order statistical difference. The method comprises the following steps: collecting vibration signals of a rolling bearing under a source domain working condition as a source domain data set; performing sliding window processing to obtain a plurality of sample segments; a one-dimensional convolutional neural network extracts deep feature representation of the sample segments; a maximum mean r-order difference measure MMRD is calculated in a reproducing kernel Hilbert space; a fault diagnosis model and a feature extractor are trained based on a joint loss function to obtain a fault diagnosis model with domain-invariant characteristics. The present application can improve the feature distribution alignment capability in cross-condition mechanical fault migration diagnosis, thereby improving the fault recognition accuracy and stability of the model under different working conditions.