12 results about "Spectral centroid" patented technology

The application discloses an intelligent noise monitoring system, comprising: a distributed sampling probe array installed on the bottom of a vehicle, a carriage and a skirt plate; a damping protection unit; a data acquisition and preprocessing unit; a vehicle-mounted multi-source information fusion module; an intelligent analysis and processing unit containing feature extraction, deep learning classification, anomaly detection and comprehensive evaluation modules; a remote operation and maintenance and self-calibration unit; a data storage and communication unit, which collects multi-channel noise and synchronizes the vehicle state; extracts MFCC, short-time energy, spectral centroid and formant features; inputs the feature matrix into a CRNN model to identify noise event types; calculates an anomaly score using an isolation forest and LOF fusion model; and judges the anomaly in combination with the vehicle state, classification probability and anomaly score. The application solves the problems that the existing system cannot adapt to the subway environment, has low recognition accuracy and poor operation and maintenance efficiency, and realizes precise monitoring and intelligent operation and maintenance.

This invention relates to a method for identifying weathered rock strata interfaces and adaptively controlling excavation parameters based on vibration spectrum analysis, belonging to the field of intelligent excavator construction technology. It addresses the problems of relying on manual experience for soil-rock interface identification during weathered rock strata trench excavation, low efficiency of mechanical switching, and slope vibration risks. The method collects vibration acceleration signals using a triaxial accelerometer, extracts frequency domain feature vectors including the dominant frequency amplitude, high-frequency band energy proportion, spectral centroid, and kurtosis value, and inputs these vectors into a multi-class support vector machine model to output category labels. When the category label changes from "cohesive silt" to "strongly weathered gneiss," the input current to the proportional solenoid valve of the excavator's hydraulic main pump is reduced, and a breaker hammer warning signal is activated. When the category label is "moderately weathered gneiss with interbedded boulders" and the high-frequency band energy proportion and kurtosis value exceed the safe range, the pilot oil pressure of the breaker hammer is limited. This method enables real-time identification of the soil-rock interface and adaptive control of excavation parameters.

The application relates to the technical field of structural health monitoring and nondestructive testing, and provides an acoustic emission feature parameter extraction and monitoring method and system based on an optical fiber grating, which comprises the following steps: acquiring waveform flow data; performing digital band-pass filtering processing on the waveform flow to obtain preprocessed waveform flow; identifying and intercepting acoustic emission event waveforms from the preprocessed data flow based on threshold value, peak value definition time, hit definition time and hit lockout time parameters; calculating multidimensional feature parameters according to the acoustic emission event waveforms to obtain a feature parameter set; and structurally saving the acoustic emission event waveforms and the feature parameter set. The application weakens the influence of noise through band-pass filtering; solves the problem of repeated triggering through configurable peak value definition time, hit definition time and hit lockout time parameters; and describes the transient and non-stationary characteristics of the corrosion process through root mean square value, kurtosis, spectral centroid, wavelet entropy and peak frequency, so as to provide a high-quality data set for subsequent corrosion identification and damage assessment.

This invention relates to the field of industrial automation control technology, specifically disclosing a fault diagnosis method and system for control valves. The method includes: acquiring operating data of the control valve, including pressure data, temperature data, and vibration acoustic data; extracting features from the vibration acoustic data, obtaining features including root mean square value, peak value, peak factor, spectral centroid, energy ratio of a specific frequency band, and main direction of the sound source; wherein, based on a dynamic adaptive beamforming algorithm, spatial filtering and anti-interference mechanisms are introduced to calculate the main direction of the sound source; inputting a fault diagnosis model to calculate the fault type and fault probability; when a fault occurs, adjusting the valve according to the operating data. This invention can accurately identify various control valve faults, and the classification accuracy of cavitation faults reaches over 92%, making it particularly suitable for cavitation fault identification of electro-hydraulic linkage control valves under harsh operating conditions such as high pressure, large diameter, large pressure difference, and high precision regulation.

The application discloses a pump unit cavitation fault diagnosis method based on a multi-modal physical information neural network, comprising the following steps: calculating vibration logarithmic total energy and spectrum centroid based on vibration signal data, calculating current standard deviation based on current signal data, constructing and training a teacher model composed of three parallel independent sub-networks, constructing and training a student model, wherein the student model comprises a shared vibration encoder, a shared current encoder, a feature fusion layer, a fault classification head and a physical regression head, adopting a weighted sum of a classification loss, a physical consistency loss and a physical evolution trend constraint loss as a total loss function, training the student model through a knowledge distillation mode, and forcing the model to learn a feature manifold conforming to a cavitation physical evolution law. The application embeds a physical mechanism into a neural network through a PINN architecture, and solves the problems of lack of physical interpretability and weak generalization ability of a pure data-driven model.

This invention discloses a product design strategy generation method based on environmental feature extraction, belonging to the field of intelligent product design technology. The steps are as follows: constructing a multimodal dataset containing ambient sound, scene images, and odor data; extracting root mean square energy, zero-crossing rate, spectral centroid, and spectral flux features from the ambient sound; extracting explicit visual features from the scene images and implicit style features through an improved Swin-Transformer model; extracting intensity, pleasantness, and familiarity features from the odor data; performing semantic mapping through Mamdani fuzzy inference to generate a design guidance parameter vector, including sensory stimulation, morphological simplicity, emotional affinity, technological feel, interaction rhythm, stability, sense of security, and environmental integration; and generating a product design strategy based on this vector. This invention automatically transforms environmental perception into a product design strategy, improving the objectivity and efficiency of design decisions.

The application discloses a cross-domain migration method and device for intelligent diagnosis, and belongs to the technical field of signal processing and intelligent diagnosis. The cumulative energy frequency and the spectrum centroid distribution of a vibration signal are calculated, a unified sampling frequency is selected based on time-frequency feature difference, and feature set unification and standardization preprocessing are carried out; a subspace alignment algorithm with maximum mean difference constraint is adopted to map source domain and target domain features to a shared low-dimensional subspace, so that the distribution difference is reduced; a multi-task learning model with fusion credibility decision is further constructed, and through dynamic weight distribution and credibility weighted fusion, the final fault diagnosis of the target domain sample is realized. The application improves the accuracy and adaptability of fault diagnosis under cross-condition and cross-equipment conditions.

The application discloses a speech structure sensitive area constraint enhancement method and system for a coal mine scene, and the method comprises the following steps: obtaining an original amplitude spectrum and a high-order statistical quantity feature vector corresponding to a noisy speech signal, and performing splicing processing on the two to obtain a composite tensor; time dimension features and frequency dimension features are extracted based on the composite tensor, a preliminary enhanced amplitude spectrum is further obtained, and a joint feature tensor comprising a spectral centroid change rate, spectral flatness, an inter-frame correlation coefficient and a medium frequency energy proportion is constructed; a sensitive probability graph is constructed based on the joint feature tensor, which is isomorphic with the original amplitude spectrum; a suppression penalty term is constructed according to the sensitive probability graph, the preliminary enhanced amplitude spectrum and a pure speech signal amplitude spectrum; and a preset speech deep learning model is optimized and trained by using the suppression penalty term. The application can consider both the suppression of non-stationary transient noise and the preservation of the sensitive area of the speech structure, and solves the problem of the decline of speech quality and communication intelligibility under extreme working conditions.

The application relates to the field of device life prediction, and provides a contact wear intelligent evaluation method, system, device, product and medium, which comprises the following steps: measuring contact closing vibration data through a vibration sensor and measuring contact closing temperature data through a temperature sensor; performing self-calibration vibration data collection and frequency response function identification to obtain a frequency response function, and obtaining interface coupling characteristic parameters according to the frequency response function; compensating the contact closing vibration data, correcting the spectral centroid, and obtaining compensated vibration parameters; performing short-time Fourier transform and modulus conversion to obtain a two-dimensional sound spectrogram, constructing a convolutional neural network, inputting the two-dimensional sound spectrogram into the convolutional neural network, and obtaining a first wear amount; performing band-pass filtering to obtain high-frequency vibration energy, obtaining a second wear amount according to the high-frequency vibration energy, performing weighted summation to obtain a contact wear amount, evaluating contact residual life through the contact wear amount, and completing evaluation of the contact wear.

This invention relates to the field of physiological signal analysis technology, specifically to an automatic analysis method and system for electroskin activity (ESA) signals based on multi-domain features. The method includes the following steps: acquiring ESA signals and correcting abnormal fluctuations within a fixed time window to obtain stable and continuous time-series data; filtering and downsampling the corrected signals to characterize the time-domain stability; analyzing signal change trends using moving averages and extracting low-frequency evolution and differential changes to form a multi-component joint characterization; iteratively calculating the multi-component power distribution and spectral centroid features from the frequency domain; statistically judging the frequency domain consistency of the multi-components; and fusing time-domain stability information to form a multi-domain feature analysis result. In this invention, robust statistical correction of abnormal fluctuations and characterization of signal stability ensures that the ESA time series remains consistent under noise drift. The fusion of low-frequency and differential changes constructs a multi-component characterization, and combined with frequency domain consistency analysis, achieves time-frequency verification, improving the stability and comparability of the results.

This invention provides a scene-aware intelligent audio equalizer adaptive adjustment system, including an audio acquisition and preprocessing module, a feature extraction module, a scene classification module, and a dynamic equalizer configuration module. The audio acquisition and preprocessing module uses a microphone to sample and acquire raw audio data in real time, obtaining preprocessed data. The feature extraction module performs frequency domain transformation on the preprocessed data and calculates acoustic feature values, including Mel-frequency cepstral coefficients (MFCC), spectral centroid, and zero-crossing rate. The scene classification module inputs the acoustic feature values ​​into a lightweight decision tree model to achieve scene recognition and output scene labels. The dynamic equalizer configuration module enables seamless adjustment of audio effects. This system can improve the environmental adaptability, real-time performance, and power efficiency of audio processing, and can reduce computational overhead and energy consumption.

A method, system, storage medium, and program product for controlling the rhythmic flashing of an LED light strip are disclosed. The method involves: calculating the relative entropy values ​​between high-frequency, mid-frequency, and low-frequency bands; establishing an energy flow diagram and determining the dominant frequency band based on the topological structure of the energy flow in the diagram; constructing a phase tracking function and classifying the beat markers into hierarchical levels; establishing a recursive segmentation window, calculating the spectral centroid trajectory, and extracting the inflection point sequence; establishing the correspondence between polar coordinate angles and LED light strip color parameters, and the correspondence between polar radius and brightness parameters; designing the spatial propagation mode of the LED light strip based on the topological structure of the energy flow diagram, ensuring that the luminous efficacy along the propagation direction of the LED light strip aligns with the energy flow direction between frequency bands, and adjusting the propagation speed according to the energy flow intensity; and generating a driving control sequence for the LED light strip based on a gradient function. The method also captures the emotional progression and tension changes in music and accurately controls the rhythmic flashing of the LED light strip based on these changes.