286 results about "Waveform analysis" patented technology

The present application relates to a switch cabinet discharge fault detection method, system, equipment and medium, belonging to the technical field of power equipment fault detection, the fault detection method includes: receiving real-time monitoring data of the switch cabinet to be monitored and aligning the timestamp; performing dynamic amplitude compensation on the electric field signal data based on the aligned current humidity data, generating the corrected electric field signal data and performing time-frequency domain analysis, extracting the discharge pulse density and high-frequency energy proportion; performing frequency filtering and waveform analysis on the vibration signal, calculating the impact intensity factor and the vibration energy entropy value; making abnormal judgments on the discharge pulse density and the impact intensity factor, and marking the abnormal discharge according to the abnormal judgment results; based on the synchronous change correlation of the high-frequency energy proportion and the impact intensity factor and the vibration energy entropy value, verifying the abnormal discharge marking result, and obtaining the discharge fault result. The present application can realize efficient detection and accurate diagnosis of switch cabinet discharge faults.

The invention discloses a 35kV wind power plant current collection line traveling wave fault early warning and positioning method and system, and relates to the technical field of traveling wave fault detection, and the method comprises the steps: obtaining a fault traveling wave signal in a detection region of a wind power plant current collection line; preprocessing the fault traveling wave signal to obtain a preprocessed target fault traveling wave signal; performing waveform analysis on the target fault traveling wave signal, and determining feature information of the target fault traveling wave signal; determining a fault type of the detection area based on the feature information; obtaining a topological structure of the detection area; determining a fault position in the detection area based on the topological structure and the feature information; and based on the fault type and the fault position, performing fault early warning on the wind power plant current collection line, and generating a fault early warning scheme. According to the invention, the accuracy of wind power plant current collection line fault positioning can be effectively improved.

The invention relates to the technical field of industrial equipment diagnosis, in particular to an AI-driven industrial equipment fault analysis method and system, and solves the problems of insufficient multi-source data fusion, weak working condition adaptability and inaccurate fault positioning in the prior art. According to the method, high-frequency time sequence signals are synchronously collected through a vibration sensor, a temperature sensor and the like, working condition parameters are marked, a dynamic confidence coefficient weight is generated based on a working condition matching historical fault library, and cross-modal features are extracted through a space-time convolutional network after multi-source data are fused. A fault mode separation module is used for decoupling the fault contribution degree of mechanical transmission, electric power and heat dissipation systems, precise diagnosis is achieved in combination with frequency domain fingerprints, time domain waveform analysis and cross decision verification, finally, the remaining life is predicted through a long-short-term memory network and a Weibull model, and the system integrates multi-source sensing, dynamic fusion and intelligent diagnosis units. The fault early warning accuracy and the equipment maintenance scientificity under complex working conditions are improved, and a solution is provided for intelligent diagnosis of industrial equipment.

The invention relates to a system and a method for testing the conductivity of an engine wire harness, and relates to the field of intelligent detection, a programmable switch matrix is formed by acquiring a wire harness layout diagram and arranging matrix relays, and after logic self-inspection is executed, an optimal conduction path is searched based on a hierarchical partition model and a DFS algorithm; according to the method, the path image sets of the partitions are overlapped and mapped to identify abnormal wire harness sections, and finally, abnormity is determined through analog signal injection and waveform analysis and physical repair guidance is performed, so that the technical problems of low test efficiency, poor flexibility and difficulty in adapting to complex variable wire harness layout conductivity test are solved, and the purposes of improving the test flexibility and efficiency and reducing the test cost are achieved. And a fault point can be accurately positioned.

The invention relates to the technical field of ultra-far offshore data transmission, and discloses an ultra-far offshore distance real-time data stable transmission method and system. The method comprises the following steps: acquiring target transmission link data, and extracting dynamic fluctuation characteristics containing signal strength distribution information and a delay jitter relationship from the target transmission link data; calling the trained channel estimation network to carry out signal positioning processing on transmission link data, and generating a signal transmission path set containing a channel type identifier and phase code mapping relation; based on the set, calling a waveform analysis network to perform multi-modal feature fusion on the signal intensity distribution information to obtain a fusion result containing time-frequency domain features and modulation mode features; and inputting a fusion result into a transmission strategy model to perform dynamic path selection, and outputting a real-time data transmission element containing a control instruction generated based on a signal transmission sequence and a phase self-calibration parameter. According to the method, stable transmission of real-time data under the ultra-far offshore distance can be realized.

The invention discloses a steam turbine safe operation health assessment method and system, and belongs to the technical field of steam turbine monitoring. The method comprises the following steps: acquiring running data of a steam turbine in real time through a multi-source sensor network deployed on key components of the steam turbine; the method comprises the following steps: preprocessing original operation data of a steam turbine by adopting edge computing equipment to generate a time domain waveform analysis diagram or a time sequence analysis diagram; transmitting the preprocessed structured feature data to an operation and maintenance management platform, inputting a fault diagnosis model for analysis, pre-training through a historical fault case library, and aligning feature distribution of the historical case library and current equipment data through a domain adaptive algorithm in transfer learning; and inputting a diagnosis result into a self-adaptive dynamic evaluation model based on a reinforcement learning engine, dynamically adjusting a model weight and an alarm threshold according to real-time working condition parameters, outputting a health score and a maintenance decision, and forming a closed-loop process of perception-edge processing-cloud diagnosis-dynamic optimization-decision output.

The invention discloses an integrated safety monitoring control method and system for a jet flow axial flow fan, and relates to the field of fan monitoring. The method comprises the steps that fan vibration data of a current fan are collected in real time through a vibration sensor, loosening state data of fan hardware are detected in real time through a displacement sensor, and motor temperature data are collected in real time through a temperature sensor; waveform analysis is carried out on the vibration data to obtain abnormal vibration characteristics, spectral analysis is carried out on the loosening state data to obtain a specific loosening part, and analysis is carried out on the motor temperature data to obtain a temperature abnormal part; carrying out correlation analysis on the abnormal vibration characteristic, the specific loose part and the temperature abnormal part to obtain an overall fault state of the fan; feature matching is conducted on the overall fault state of the draught fan and a preset fault feature library, and when the feature matching degree exceeds a preset matching threshold value, the fault type and the fault danger level of the current draught fan are determined. By implementing the method, the accuracy of fault diagnosis can be improved.

The invention relates to the technical field of submarine cable fault positioning, in particular to a submarine cable fault positioning system based on underwater beacons. The method comprises the steps that an underwater acoustic beacon array unit monitors and collects submarine cable fault sound wave signals in real time based on underwater acoustic beacons; the fault signal processing analysis unit extracts submarine cable fault sound wave signal parameters based on a non-point source space sound source waveform analysis model, and generates multi-parameter fault sound source feature vectors; the sound source positioning analysis algorithm unit performs three-dimensional inversion positioning based on the multi-parameter fault sound source feature vector and the arrival time difference of the submarine cable fault sound wave signal to the underwater acoustic beacon, and obtains the spatial position information of the submarine cable fault point; and the data communication alarm management unit sends the spatial position information of the submarine cable fault point to a shore-based operation and maintenance system. The submarine cable fault positioning method is used for realizing a submarine cable fault positioning technology for quickly and accurately positioning and identifying spatial extension characteristics in a complex submarine environment.

The invention provides an operation optimization method and system based on electric power data conjoint analysis, and the method comprises the steps: obtaining synchronous sampling data and power grid topological parameters of a wide-area electric power system; performing equipment state evaluation, broadband resonance traceability and harmonic responsibility quantification on the synchronous sampling data and the power grid topological parameters by using a conjoint analysis engine, and generating a power grid control strategy; and carrying out operation optimization on the power equipment in the wide-area power system based on the power grid control strategy. According to the method, conjoint analysis is carried out on equipment state evaluation, broadband resonance traceability and harmonic responsibility quantification of the wide-area power system, traditional static spectrum analysis is upgraded to dynamic behavior perception, the power grid analysis depth and accuracy can be improved, the high-frequency harmonic analysis requirement can be met through broadband resonance traceability, and the method is suitable for large-scale popularization and application. Intelligent analysis can be realized for different application scenes, the waveform analysis capability and the frequency response range are improved, and the analysis effect is improved.

The invention relates to the technical field of scene supervision, in particular to an AI-based emergency rescue scene intelligent supervision system and method, and the system comprises a scene data collection module, a data feature analysis module, an intelligent decision support module and a rescue action execution module. According to the method, the dynamic change features in the rescue scene are accurately recognized by performing detailed color depth analysis, edge detection, waveform analysis and tone recognition on the collected images and audios, the recognition of key moment changes is more accurate through advanced data processing, and the construction of key morphological feature data is more comprehensive; through three-dimensional space mapping and analysis of time sequence information, accurate assessment of an accident range and rapid determination of resource demands can be realized, configuration and response time of rescue resources are greatly optimized, comprehensive application of technical means is realized, the rescue efficiency is remarkably improved, the risk of life and property loss is reduced, and the method is suitable for popularization and application. And the safety and the accuracy of rescue operation are improved.

A system comprises a signal generator circuit configured to generate a modulated radio frequency (RF) signal based on a predefined waveform. An output signal path is connected to the signal generator circuit and is connectable to a device under test. A first signal path is connected to the signal generator circuit so as to receive a reference signal corresponding to the modulated RF signal. A second signal path is connectable to the device under test so as to receive a measurement signal from the device under test. A analysis signal path is connected to the first and second signal paths for merging the reference signal and the measurement signal into an analysis signal. A signal analysis circuit is configured to separate the reference signal from the measurement signal for analyzing the analysis signal based on the predefined waveform.

A device estimation system (1) includes a packet analysis model learning apparatus (20) for generating a packet analysis model (200), a waveform analysis model learning apparatus (30) for generating a waveform analysis model (300), a traffic information collection apparatus (50) for collecting packets of a target device (5), and a device estimation apparatus (10) for extracting a statistical value and a keyword from a packet, generating candidates for a manufacturer and a functional category of the target device (5) using the packet analysis model (200), generating traffic waveform information from the packet, narrowing down the candidates using the waveform analysis model (300), specifying the manufacturer and functional category of the target device (5), and transmitting the same to the analysis model management apparatus (40).

The invention discloses a rectifier bridge arm fault identification method and system circuit based on multi-characteristic current waveform analysis, and relates to the technical field of rectifier bridge arm fault identification, and the method comprises the steps: collecting a current signal of an AC input side of a rectifier bridge through a current sensor, and obtaining an original current time domain waveform after filtering conditioning; performing period division on the original current time domain waveform based on a voltage synchronization signal, and outputting a positive half-cycle conduction interval and a negative half-cycle conduction interval in each power frequency period; in each power frequency period, performing square integral operation on the current waveforms of the positive half-cycle conduction interval and the negative half-cycle conduction interval to obtain a positive half-cycle energy value and a negative half-cycle energy value, and calculating an energy unbalance degree; and windowing fast Fourier transform is carried out on the original current time domain waveform, amplitudes and phases of third, fifth and seventh characteristic harmonics are extracted to form a harmonic characteristic vector, the vector is matched with a pre-stored bridge arm fault harmonic fingerprint template, and the matching degree of each fault type is obtained.

A waveform analytical device 4 which analyzes a target waveform which is a chromatogram or an optical spectrum includes a waveform division unit 54 configured to divide the target waveform into a plurality of partial waveforms, a determination unit 55 configured to determine whether each of the plurality of partial waveforms of the target waveform is a peak portion using a learned model created by machine learning using a plurality of sets of a plurality of partial waveforms created by dividing a reference waveform having a peak portion whose position is known, and a classification unit 56 configured to classify the target waveform into a peak region where the peak portion continues and a non-peak region other than the peak region based on a determination result from the determination unit.

The invention discloses a multispectral and hyperspectral laser radar weak multi-echo signal extraction method. According to the method, a B-spline model is adopted to establish models for transmitted pulses and received echoes. Compared with a traditional Gaussian model and a B-spline model, the method has local control characteristics, is more suitable for modeling and analysis of complex waveforms, improves the adaptability to diversified signals, remarkably reduces the probability that noise is misjudged as echo signals by conducting preliminary screening and positioning on received echoes of all channels, and improves the accuracy of noise detection. Therefore, error accumulation is effectively reduced. The process not only improves the accuracy of signal identification, but also greatly reduces the complexity of subsequent data processing, reduces the calculation burden of the system, and lays a solid foundation for refined waveform analysis.

The invention provides a construction method of a non-coupling blasting explosion load model and related equipment, and the method comprises the steps: describing non-coupling blasting explosion loads at different time stages through piecewise functions, and building a non-coupling blasting explosion load theoretical model; a pressure-time curve is collected through a non-coupling bursting pressure test, waveform analysis is carried out, and mechanical parameters are obtained; calibrating various parameters in the non-coupling blasting load theoretical model through mechanical parameters; various parameters in the non-coupling blasting explosion load theoretical model are input into a calculation program for calculation, and a non-coupling blasting explosion load model is obtained; the model provided by the invention can more accurately describe the action characteristics of the non-coupling blasting load, the peak values and the loading rates of the blasting shock waves and the blasting gas are directly controlled by respectively inputting the peak values and the rising time, so that the dynamic response of the structure under different peak values and loading rates can be conveniently researched, and the model has the advantages of simple model, high reliability and the like. The input parameters are few, and the applicability is wide.

The invention discloses an electric energy quality comprehensive monitoring method and system based on real-time waveform analysis. The method comprises the following steps: S1, acquiring an analog voltage signal acquired by a mutual inductor; s2, preprocessing to obtain a voltage waveform data segment; s3, performing multi-dimensional feature extraction to obtain a voltage waveform multi-dimensional feature vector; s4, extracting a voltage RMS effective value, a total harmonic distortion rate and wavelet packet energy; s5, inputting the voltage RMS effective value, the total harmonic distortion rate and the wavelet packet energy into a grading event pre-detection model to obtain an event feature subset coding vector; and S6, enabling the event feature subset coding vector to pass through an intelligent event fine classification model to obtain an electric energy event accurate classification result. According to the electric energy quality comprehensive monitoring method and system based on real-time waveform analysis disclosed by the invention, the accuracy and efficiency of electric energy quality event monitoring are greatly improved.

The invention discloses a chip functional security vulnerability detection method and system, and relates to the technical field of chip security vulnerability detection, and the method comprises the steps: collecting chip data, and carrying out the preliminary waveform analysis of the collected data; performing energy consumption feature analysis, preliminarily identifying vulnerabilities, and verifying functionality and task integrity; and carrying out vulnerability analysis, and carrying out processing decision. According to the chip functional security vulnerability detection method provided by the invention, energy consumption feature analysis and functional verification are combined, a multi-dimensional vulnerability detection method is provided, and two types of problems of side channel attacks and functional vulnerabilities can be covered. Through optimization of an orthogonal polynomial filter and an anomaly detection algorithm, the accuracy of vulnerability detection is remarkably improved; through an intelligent repair mechanism, dynamic repair and security startup enhancement of vulnerabilities are realized, and the method has better effects in the aspects of detection coverage, real-time performance and repair efficiency.

The invention provides a correction system and method for power frequency waveform distortion of a potential indicator, and the system comprises a waveform collection unit which is used for carrying out the real-time collection of a waveform signal outputted by the potential indicator through a preset sensor; the waveform analysis module is used for carrying out spectrum analysis and waveform overall form analysis on the collected signals, determining the component, amplitude and frequency of harmonic waves and determining the characteristic change of the waveform; the correction processing module is used for determining a corresponding waveform distortion feature according to the waveform feature change and calling a corresponding correction strategy to correct the waveform; the correction strategy at least comprises harmonic elimination and waveform reconstruction; and the nuclear phase module is used for performing nuclear phase operation by using the corrected waveform, and obtaining an accurate nuclear phase result through a preset phase comparison algorithm. According to the invention, the problem of inaccurate nuclear phase data caused by waveform distortion of the potential indicator is effectively solved, and the accuracy and reliability of nuclear phase operation of a power system are improved.

The invention discloses an interference pulse suppression technology, and aims to provide a multi-type interference pulse suppression method based on signal waveform analysis, and the key point of the technical scheme is that the method comprises the steps: S1, building a partial discharge test system, and collecting and measuring partial discharge waveforms of different defect types; s2, collecting a partial discharge waveform of the to-be-detected cable; s3, preprocessing the collected partial discharge pulses of different defect types and the partial discharge waveform of the cable to be detected, analyzing the pulse waveforms corresponding to different defects and noise sources, and using an envelope line and a sine function to construct a mathematical model of partial discharge signals to extract pulse waveform characteristics; and S4, drawing a feature distribution two-dimensional diagram (beta-alpha and Ap-At) and a three-dimensional diagram (f-beta-alpha and f-Ap-At) through the extracted feature parameters, establishing an interference pulse and defect knowledge base, and realizing effective separation of partial discharge signals and interference pulses. The method is suitable for the technical field of interference pulse suppression.

According to the laser full-waveform distance measuring device and method based on correlated double sampling, a high-transmissivity low-reflection spectroscope is adopted to complete light splitting of laser emitted by a laser emitting module, more than 99% of energy of the emitted laser is transmitted to form emitted light which is emitted to a target, and less than 1% of energy is reflected to form reference light which is emitted to the target. The reflected reference light is received by the laser receiving module, and the reflected light after the emitted light is irradiated to the target and the reference light are received by the same laser receiving module. The laser receiving module converts received reference light and emitted light into electric signals and outputs the electric signals to the control and processing module for processing, the control and processing module performs waveform calculation on the electric signals converted from the reference light and the received light, and due to the homology of the emitted light and the returned light, distance measurement errors caused by inconsistent light sources in the distance measurement process are eliminated, and the distance measurement accuracy is improved. Meanwhile, sampling noise of the laser receiving module can be suppressed through correlated double sampling of the emitted light and the received light, so that the sampling signal-to-noise ratio is improved, and the ranging precision is improved.

The invention provides a source network operation risk monitoring and early warning method and system, and relates to the technical field of power grids, and the method comprises the steps: obtaining the line information of a source network, determining a signal peak value, carrying out the waveform analysis, selecting a time domain feature through correlation analysis, converting a time domain signal into a frequency domain signal through Fourier transform, and extracting the frequency domain feature through spectrum analysis; integrating the time domain features and the frequency domain features to obtain feature input, adding the feature input into a random forest model for prediction, synthesizing prediction output of each tree to obtain a comprehensive prediction result, and determining a decision boundary through a sequence minimum optimization algorithm by taking maximization of a boundary interval as a target; and based on the decision boundary, constructing a comprehensive objective function, and processing fault data through a least square method to minimize an error and obtain a fault probability.

In general, in one aspect, embodiments relate to a method and / or system that includes disposing a cement bond logging tool into a wellbore. The method and / or system comprising: a cement bond logging tool comprising: two or more transmitters configured to transmit an acoustic wave from cement bond logging tool; and an array of receivers configured to receive a refracted waveform; and an information handling system configured to process the refracted waveform from the array of receivers into a cement bond property log.

According to the method for testing the interface in the server provided by the embodiment of the invention, the to-be-tested interface is acquired to obtain a plurality of pin signals, the voltage information of the to-be-tested interface is determined according to the plurality of pin signals, and the handshake information in the test request is analyzed to obtain the waveform information; and determining a target protocol matched with the to-be-tested interface according to the waveform information, the plurality of pin signals and the voltage information, determining a target test module from a plurality of preset test modules according to the target protocol, and testing the to-be-tested interface by using the target test module. According to the embodiment of the invention, the protocol type corresponding to the to-be-tested interface is determined by analyzing the signal, the voltage and the waveform of the to-be-tested interface of the server, and the test is carried out through the target test module, so that the test requirements of various different types of interfaces in the server can be met without concerning the line sequence access of the test interface; and the test cost and the test difficulty of the traditional board card test are reduced.

The invention relates to the technical field of safety monitoring, and particularly discloses a storage tank safety monitoring method based on the Internet of Things, and the method comprises the steps: synchronously collecting a high-frequency leakage acoustic emission signal and a reference environment noise signal through a piezoelectric acoustic emission sensor disposed on the outer wall of a storage tank and an industrial microphone disposed in a storage tank region; after time delay alignment is carried out on a reference environment noise signal and a high-frequency leakage acoustic emission signal, self-adaptive filtering processing is carried out, so that environment noise interference in the high-frequency leakage acoustic emission signal is filtered out. And then, further introducing a deep learning algorithm to carry out time domain waveform analysis and adaptive content perception enhancement processing on the denoised high-frequency leakage acoustic emission signal so as to enhance key acoustic emission characteristics in the signal, thereby realizing intelligent identification of the storage tank leakage on the basis. According to the method, the accuracy and sensitivity of storage tank leakage monitoring can be effectively improved, the influence of noise interference on an industrial site on storage tank leakage identification is overcome, and early warning of tiny leakage of the storage tank is achieved.

The invention discloses a hump humping vehicle weight measuring method and device based on a strain type weighing sensor, and the method and the device are corresponding schemes, in the scheme, the strain type weighing sensor is used for replacing a plug pin type piezomagnetic weighing sensor, and a signal isolation and acquisition circuit is used for rapidly reading the voltage of the sensor; on one hand, the measurement precision of the weighing equipment can be improved by utilizing the strong calculation and storage capability of the data processing device, and on the other hand, the weight information is obtained through waveform analysis, and a large amount of waveform data is stored in a quite long time. And a reliable basis is provided for equipment maintenance. Meanwhile, weight waveform analysis can be carried out according to the collected data, data abnormity alarm and equipment maintenance guidance are achieved, and the problem that existing maintenance difficulty is large is better solved.

The invention provides a frequency converter embedded current waveform analysis method and system and a medium, and relates to the technical field of current waveform analysis, and the method comprises the steps: obtaining a sampling circuit configured in a current path of a frequency converter; calculating the required total gain based on the original output voltage signal of the sampling resistor and the required input voltage of the analog-to-digital converter; the signal quality of the original output voltage signal is evaluated, the required total gain is dynamically distributed to an instrument amplifier and a programmable gain amplifier, the original output voltage signal is amplified and then passes through an analog-to-digital converter, and current waveform data is obtained for current anomaly analysis; when the signal quality is lower than a first threshold value, the gain share of the instrument amplifier is increased preferentially; when the signal quality is higher than a second threshold, the gain share of the programmable gain amplifier is increased preferentially. According to the invention, the technical problem of low accuracy of current waveform analysis in the prior art can be solved, and the technical effect of improving the accuracy of waveform analysis is achieved.

A method to automatically optimize waveform captures from an electrical system includes capturing at least one energy-related waveform using at least one Intelligent Electronic Device (IED) in the electrical system. The at least one captured energy-related waveform is analyzed to determine if the at least one captured energy-related waveform is capable of being compressed, while maintaining relevant attributes for characterization, analysis and / or other use. In response to determining the at least one captured energy-related waveform is capable of being compressed, while maintaining relevant attributes for characterization, analysis, and / or use, the at least one captured energy-related waveform may be compressed using at least one compression technique to generate at least one compressed energy-related waveform. One or more actions may be taken based on or using the at least one compressed energy-related waveform.

The invention discloses a motor noise detection method and system based on waveform analysis, and relates to the related field of motor fault diagnosis, and the method comprises the steps: carrying out the time domain collection of an original noise signal during the operation of a motor, obtaining an original waveform signal, carrying out the Fourier transform of the original waveform signal, extracting the spectrum characteristics, and obtaining the spectrum energy distribution; performing threshold segmentation on the frequency spectrum energy distribution, screening out motors which meet a preset quality standard but have preset abnormal indications, and obtaining critical qualified motors; extracting an original waveform signal of the critically qualified motor, and extracting a transient impact component from the original waveform signal to obtain a transient feature; and matching the transient feature with an early fault mode in a pre-constructed fault feature library, and outputting a potential fault detection result. The technical problem that the current qualified motor with potential faults cannot be identified in the existing motor noise detection is solved, and the technical effect of accurately identifying the qualified motor with the potential faults is achieved.

The invention provides a blood pressure measurement system and method for identifying oscillatory wave characteristics by using an elastic template method, and relates to the technical field of wearable medical health monitoring, the blood pressure measurement system comprises a pressure control module, a data preprocessing module, a data quality evaluation module and a waveform analysis and matching module; the method comprises the following steps: acquiring cuff pressure control and an original oscillation wave signal of a to-be-identified sample, and preprocessing the acquired original oscillation wave signal; then carrying out quality evaluation on the preprocessed oscillatory waves, wherein the quality evaluation comprises data quality evaluation of the oscillatory waves and data quality evaluation related to inflation and deflation blood pressure measurement; and finally, performing blood pressure estimation based on a dynamic time warping (DTW) algorithm, fusing an amplitude coefficient method in an oscillography, oscillatory waves and changes of characteristic wave bands of the oscillatory waves, and estimating the blood pressure based on a waveform template matching method.