227 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.

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 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 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.

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.

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.

The invention provides a GIS tank induced voltage and current feature identification method, which comprises the following steps: performing electrical performance detection on a current transformer and a voltage transformer in GIS equipment, and acquiring induced current and induced voltage in real time; performing data analysis on the induced current and the induced voltage, extracting features, and obtaining an induced voltage and current waveform; the method comprises the following steps: acquiring data acquired by a current transformer and a voltage transformer in a GIS tank body in real time, performing time domain and frequency domain analysis on induced current and induced voltage in real time, performing feature extraction on the voltage and current waveforms, and generating corresponding measures. And the fault type of the corresponding waveform is matched and searched, and finally a specific fault event is analyzed, so that the fault condition and the fault position can be known at the first time when the GIS tank body has a fault, the equipment at the fault position is maintained, and the stability and the safety of the whole power system are ensured.

The invention discloses a fluid pipeline system diaphragm valve fault self-adaptive identification method, relates to the technical field of diaphragm valves, and solves the problems that signal characteristics are not subjected to fine analysis and are easily interfered by fluctuation of normal working conditions. The method comprises the following steps: acquiring a to-be-determined wave band, comparing the to-be-determined wave band with a preset standard waveform, identifying a to-be-verified amplitude associated with a corresponding processing process based on a corresponding movement comparison processing process, carrying out abnormity determination on a diaphragm valve based on a specific numerical value of the to-be-verified amplitude, and confirming an abnormal diaphragm valve. The specific accuracy of amplitude verification in the subsequent movement process is facilitated, the abnormal influence caused by signal fluctuation can be effectively reduced, and the specific accuracy in the valve abnormality determination process is improved.

The invention discloses a seismic wave line analysis processing method based on a deep learning technology, and the method comprises the following steps: 1, inputting seismic data, and carrying out the normalization processing, so as to eliminate the magnitude difference between different seismic events, and make preparations for the input of a subsequent deep learning model; step 2, feature extraction and analysis are performed on the normalized seismic data by using a deep learning model for training a large number of seismic waveform data sets so as to realize classification and identification of different types of seismic events; step 3, associating the extracted features with a known earthquake directory event to determine detailed information of an earthquake, such as an epicentral position, an earthquake magnitude and the like; and step 4, based on the output of the deep learning model, classifying and identifying seismic events, and the method solves the problems that a traditional seismic waveform analysis method depends on expert experience and a signal processing technology, but the methods are often low in efficiency and difficult to process a large-scale data set.

The invention discloses a power grid fault situation sensing method, device and equipment, a medium and a product, and relates to the field of power systems. The method comprises the steps of obtaining a waveform signal measured by a waveform measurement unit at a power grid node; the waveform signal is converted into a decoupled modal waveform signal through Clark transformation; extracting fault features from the decoupled modal waveform signal by using a time-frequency waveform analysis algorithm based on a moving window; the fault features comprise complex frequency and residual errors; constructing a grid perception learning model based on autoregressive moving average graph convolution; the grid sensing learning model represents a topological structure and fault features of a power grid as a graph structure, the fault features serve as graph signals of nodes in the graph structure, and fault classification and positioning are carried out by aggregating and learning the graph signals of different nodes in the graph structure; and according to the topological structure of the power grid and the extracted fault features, classifying and positioning faults by using a grid perception learning model. According to the invention, the accuracy of fault classification and positioning can be improved.

A LIDAR apparatus disclosed in an embodiment may include: a data collection unit that collects raw data; a preprocessing unit that removes noise from the collected data; a histogram circuit that converts an output of the pre-processing unit into a histogram; a buffer that buffers an output of the pre-processing unit; a sub-histogram extraction unit that receives the converted histogram data and the buffered data, and detects at least one peak value from the histogram; and a waveform analyzer that generates a target waveform based on a correlation between the at least one peak data and the buffered data.

The invention belongs to the technical field of power distribution network electric power intelligent monitoring, and discloses a power distribution network electric power supply and distribution intelligent monitoring system which comprises the steps of collecting electric power data and state data, and performing data reinforcement on the electric power data to obtain reinforced electric power data; performing waveform analysis on the enhanced power data to obtain a load curve graph, performing state influence on the load curve graph to obtain a load correction graph, and performing feature extraction on the load correction graph to obtain load features; predicting load characteristics based on the constructed fault prediction model to obtain a fault message; load prediction is carried out on the load correction graph to obtain a future load demand, a demand topological graph is constructed based on the future load demand, fault cutting is carried out on the demand topological graph to obtain a local demand graph, power optimization scheduling is carried out on the local demand graph to obtain an optimal scheduling scheme, and the stability of the power supply and distribution system is greatly improved.

The invention discloses an unmanned aerial vehicle cluster collaborative formation control method and system. The method comprises the following steps: acquiring multi-source sensor data in real time, fusing the multi-source sensor data by using an extended Kalman filtering algorithm, and performing state estimation when part of sensor data is missing to obtain state information; based on the state information, interference is detected by monitoring related parameters of communication signals and sensor data, the interference type is identified through spectrum and waveform analysis, and the intensity is evaluated; determining interfered unmanned aerial vehicles and adjacent unmanned aerial vehicles according to a communication neighborhood, performing local formation adjustment on the interfered unmanned aerial vehicles and the adjacent unmanned aerial vehicles by using a distributed control algorithm, and estimating a speed optimization algorithm of the adjacent unmanned aerial vehicles by using Kalman filtering under incomplete information to realize unmanned aerial vehicle cluster collaborative formation control; and establishing an early warning model based on the distance and the speed, and realizing collision avoidance by adopting an optimized artificial potential field method. The anti-interference capability of the unmanned aerial vehicle cluster is improved, the collision probability is reduced, and the formation stability is enhanced.