239 results about "Transient signal" patented technology

The invention discloses a self-adaptive multi-dimensional adjustment metering box based on the Internet of Things, and relates to the technical field of intelligent monitoring of power equipment. The problem of measurement distortion caused by high fixed threshold false triggering rate, weak anti-aliasing capability and multi-source data asynchronization in a high-dynamic industrial scene in the prior art is solved. Multi-parameter acquisition is realized through the sensing acquisition module and the annular buffer area; a reconfigurable FIR filtering module is adopted to dynamically switch a low-pass / band elimination mode to suppress aliasing interference; noise features are extracted by combining spectral kurtosis analysis and concept drift detection, and a dynamic threshold value is generated by using exponentially weighted moving average and a sliding window standard deviation; a multi-channel time sequence is aligned through an IEEE 1588 protocol, and cloud parameter closed-loop optimization is realized based on extended Kalman filtering and a particle swarm algorithm; according to the method, the threshold fault tolerance, the high-frequency transient signal capturing precision and the multi-source heterogeneous data fusion reliability in a high-noise environment are remarkably improved.

The invention is suitable for the technical field of fault diagnosis, and provides a distributed power transmission line fault diagnosis method based on ground potential end transient information perception, and the method comprises the steps: collecting a broadband transient signal of each node ground potential end of a power transmission line; synchronously acquiring an electromagnetic transient signal and an environmental parameter through the composite sensing unit; performing dynamic filtering correction on the broadband transient signal according to a preset transmission line impedance matching model; a deep learning model is adopted to perform hybrid processing on the corrected broadband transient signals and electromagnetic transient signals, traveling wave head time-frequency features and power frequency abrupt change features are extracted, and fault probability and type classification results are output; verifying a fault positioning result through a multi-stage verification mechanism; the extracted characteristic quantity is preprocessed at the edge side of the detection terminal, and the processed data is transmitted to a master station system through an encryption protocol, so that the fault diagnosis accuracy of the distributed power transmission line is effectively improved.

The invention provides a transformer fault intelligent diagnosis method and system based on a deep convolutional network, and the method comprises the steps: capturing a transformer global transient signal through a heterogeneous physical field synchronous sampling array which is deployed in a spatial discretization manner; inputting three kinds of heterogeneous data streams of vibration spectrum, electromagnetic leakage wave and partial discharge pulse into a multi-scale feature decoupling program; forming an initial feature primitive set of fault diagnosis; inputting the generated vibration mode tensor, the electromagnetic eddy current characteristic spectrum and the discharge pulse characteristic cloud into a characteristic interaction contribution degree analysis program; generating a fault sensitive feature topological graph carrying a weight identifier; training a depth differentiable diagnosis decision tree by using the fault sensitive feature topological graph, and analyzing and positioning key diagnosis nodes; and millisecond-level synchronous output of fault types and positioning information is realized. The system comprises a multi-modal feature generation module, an adaptive fusion module and a lightweight diagnosis module. The invention provides a new-generation interpretable, low-delay and high-precision diagnosis normal form.

The invention provides a high-precision electric energy metering and fault diagnosis method adaptive to multiple working conditions, and the method comprises the following steps: carrying out the multi-rate synchronous sampling of an electric parameter through a double-AD parallel sampling architecture, collecting a fundamental wave voltage / current signal through a low-speed AD module at a sampling rate of 4kHz, capturing a high-frequency harmonic wave and a transient signal through a high-speed AD module at a sampling rate of 256kHz, and carrying out the fault diagnosis of the high-frequency harmonic wave and the transient signal; a sampling mode is dynamically switched according to the load harmonic content; a sliding window mechanism is adopted to carry out multiple times of repeated detection on the same electric parameter, five times of independent measurement are completed in a 200ms time window, an outlier is eliminated based on a Pauta criterion, then a weighted mean value of a signal-to-noise ratio is calculated, AD reference voltage is calibrated in real time through a PT100 temperature sensor, and the accuracy of the AD reference voltage is improved. Carrying out nonlinear dynamic compensation by combining a cubic spline interpolation method of a pre-stored load characteristic curve; and constructing a composite electric energy calculation model, summing the fundamental wave electric energy and the harmonic electric energy weighted according to the standard to generate a total electric energy value, and comparing a metering result with a theoretical value in real time by using a digital phase-locked loop.

The invention provides an intelligent fault diagnosis method and system for an intelligent pod. According to the method, a power line voltage fluctuation signal and a motor control line transient signal are synchronously collected, and multi-frequency-band feature extraction and transient energy density analysis are carried out respectively. Establishing a contrastive analysis mechanism of voltage fluctuation amplitude and transient amplitude based on the synchronous trigger point, calculating a time difference and generating a composite rule containing an amplitude threshold interval and a phase correlation value; a multi-dimensional space model fusing amplitude, energy and time difference is constructed, a fault prediction rule base is established, finally, an abnormal point distribution mode is recognized through feature vector matching degree, a fault type is accurately positioned, a diagnosis result is output, and fault diagnosis and positioning of the intelligent pod are achieved. According to the technical scheme provided by the invention, accurate fault positioning and diagnosis of the intelligent pod power supply and control system are realized through multi-dimensional signal feature fusion and an intelligent matching algorithm.

The invention discloses a distribution line fault detection and positioning method based on current signal analysis, and belongs to the technical field of power system fault diagnosis. The method comprises the steps of collecting three-phase and zero-sequence current data before and after a fault, calculating current variation, judging whether the fault is an interphase short-circuit fault, and determining a first fault interval by combining an impedance method; and performing wavelet packet transformation on the transient voltage signal to obtain an actual transient signal, injecting a test signal in the first fault interval, collecting a response signal, constructing an autoregression model, solving an inverse noise function, and performing denoising processing on the actual transient signal to obtain a target transient signal. Wavelet packet decomposition is carried out on the denoised target transient signals, energy distribution and signal-to-noise ratio information of all frequency bands are combined, a corrected positioning formula is introduced, and accurate positioning of a second fault interval of the fault point is achieved. The method effectively inhibits noise interference, improves the positioning precision, and is suitable for a real-time fault detection and positioning system in an intelligent power distribution network.

The invention provides an adaptive stochastic resonance weak fault detection method based on kurtosis optimization, and belongs to the technical field of fault detection, and the method comprises the steps: collecting and preprocessing a transient signal on a cable line; constructing a bistable stochastic resonance model; kurtosis of the output signal is used as a fitness function, and potential well parameters of the bistable stochastic resonance model are optimized based on a particle swarm optimization algorithm; and substituting the optimized potential well parameter into the bistable stochastic resonance model, and processing the preprocessed transient signal to obtain an enhanced output signal for fault detection. The method has the beneficial effects that the stochastic resonance system is constructed on the basis of the classical bistable model, the kurtosis of the output signal is used as the fitness function of the particle swarm optimization algorithm, the potential well parameters are adaptively optimized, the potential well parameters are driven to automatically converge to the optimal state, and the robustness of the system is improved. The high-impedance grounding fault traveling wave signal can be extracted and enhanced from a strong noise background, and the detection sensitivity and reliability are improved.

The invention relates to the technical field of discharge monitoring and early warning, in particular to a partial discharge real-time monitoring and early warning system based on multi-source data fusion, which is characterized in that decision tree classification is carried out on a mixed transient signal flow through a signal physical separation module, and pure electromagnetic wave speed type and mechanical wave speed type signal sources are separated; the feature marking module extracts feature points with a unified time reference and marks physical attribute categories; the double-path credibility fusion module executes time difference logic verification on the two types of feature points, a credible discharge event is marked only when an electromagnetic wave verification result and a mechanical wave verification result are logic values 1 at the same time, event confidence parameters are generated through dynamic confidence fusion, and physical interlocking verification and uniqueness locking are achieved; the anti-interference response module dynamically adjusts a response threshold value based on the event confidence parameter, and only triggers a targeted response to the threat which is physically and essentially confirmed to be real discharge and has confidence reaching the standard; the problem of distinguishing a real discharge event from multi-source interference is solved, and the real discharge event is reliably identified.

The invention relates to the technical field of power distribution network fault positioning, in particular to a power distribution network fault traveling wave positioning method and system, and the method comprises the steps: synchronously collecting a transient signal through a distributed traveling wave sensing unit, and extracting an initial traveling wave arrival time; constructing an adaptive wave velocity model based on line physical parameters to calculate an equivalent propagation velocity; dynamically generating a traveling wave propagation tree according to the real-time switch state to determine an effective monitoring path; based on the propagation path and the actual arrival time, an optimization algorithm is adopted to determine a preliminary fault point; and calculating a consistency index of the polarity of the traveling waves of the adjacent nodes, starting a multi-hypothesis tracking mechanism in a preliminary point neighborhood when the consistency index is lower than a threshold value, performing confidence coefficient weighted evaluation by combining a traveling wave attenuation model, and outputting an accurate positioning result. According to the method, through cooperation of dynamic topology mapping, adaptive wave velocity correction and multi-source closed-loop verification, the precision, reliability and adaptive capability of fault positioning in a complex power distribution network environment are remarkably improved, and the operation and maintenance requirements of rapid self-healing are met.

The invention relates to the technical field of power distribution network fault detection, and discloses a power distribution network high-resistance grounding fault intelligent detection method, system and device and a medium, and the method comprises the steps: collecting a power distribution network line transient signal which comprises a transient zero-sequence voltage signal and a transient zero-sequence current signal; preprocessing the transient signal and extracting a multi-dimensional feature set for representing high-resistance grounding fault characteristics, wherein the multi-dimensional feature set comprises a transient energy feature, a wavelet entropy feature and a harmonic component feature; and inputting the extracted multi-dimensional feature set into a pre-trained machine learning model, and simultaneously outputting a judgment result of the high-resistance grounding fault and an estimated value of the grounding resistance through the machine learning model. According to the method, the transient energy, the multi-scale wavelet entropy, the odd harmonic amplitude and the phase, which are derived from different analysis domains and have complementary physical meanings, are deeply fused, so that the problem that a single feature is weak in characterization capability and easy to fail under the conditions of high noise and high resistance is effectively solved.

The invention provides a power distribution network fault section rapid calibration method based on a feeder terminal. According to the method, power distribution network line electrical quantity and distributed power supply output information are collected in real time through a feeder terminal and preprocessed, then fault transient signals are processed through complete set empirical mode decomposition of mean value self-adaptive noise, and intrinsic mode components are obtained to judge fault types. Thirdly, fault section preliminary positioning is carried out, and an approximate area is determined according to a positive sequence voltage vector for a symmetric short-circuit fault, a negative sequence voltage vector for an asymmetric short-circuit fault and a zero sequence current amplitude for a single-phase earth fault; then accurate calibration is carried out, a single-phase earth fault is judged through a filter and correlation coefficients, and short circuit and broken line faults are calibrated through a model or an intelligent algorithm in combination with line topology and electrical parameters. And finally, establishing a target function containing a penalty term, converting the target function into a Markov decision process, solving through an approximate dynamic programming algorithm, realizing rapid and accurate calibration of a fault section, and improving the power supply reliability of the power distribution network.

The invention relates to the field of machine learning, particularly discloses an artificial intelligence-based power system fault analysis and diagnosis method, and effectively solves the problem of information loss caused by neglecting a key waveform form in a transient signal in the prior art through a local feature extraction and serialization module. An original signal is converted into a local feature sequence with more characterization significance. Aiming at the averaging bottleneck of an existing model in an information aggregation stage, a traditional feature compression method is abandoned, and a sequence information aggregation and decision-making mechanism is provided. According to the mechanism, a context sensing sequence is regarded as a probability event, and modeling is carried out on the sequence from three orthogonal dimensions of a content center, time sequence dispersion and distribution uncertainty by calculating feature expectation, time sequence variance and information entropy of the context sensing sequence. The method can deeply insight and quantify the essential difference of different events in the time sequence dynamic evolution mode, thereby fundamentally solving the problem of misjudgment caused by feature confusion.

The invention discloses an ultratrace mercury isotope analysis method, and relates to the technical field of environmental analysis. The method comprises the following steps: purging a liquid sample to be detected by nitrogen in a stannous chloride atmosphere, and trapping and concentrating mercury into a gold trapping tube; pyrolyzing the pyrolytic gold trapping tube to release mercury vapor; pyrolytic mercury vapor and thallium isotope internal standard aerosol are mixed to serve as a sample injection sample, and the sample injection sample is introduced into a multi-receiver inductively coupled plasma mass spectrometry; acquiring a mercury instantaneous signal and a thallium isotope signal; and calculating the mercury isotope ratio by adopting least square linear regression fitting in combination with a thallium internal standard ratio correction algorithm. According to the method, the instantaneous mercury isotope signal is generated by online combination of gold trapping and multi-receiver inductively coupled plasma mass spectrometry, the instantaneous signal is strategically collected and processed, the determination concentration of isotope analysis can be greatly reduced, the sensitivity of the method is remarkably improved, and direct determination of mercury isotope composition in a water sample with the concentration of 10 ng L <-1 > is met.

The invention belongs to the technical field of cable fault positioning, and discloses a cable branch box fault early warning and positioning system based on online partial discharge monitoring. Comprising a topology building module which is used for building a topology model in a cable branch box and defining a signal propagation path rule; pre-calculating signal expectation characteristics from each topological node to all monitoring sensors to form a structured expectation library; the acquisition and analysis module is used for acquiring multi-source transient signals and performing feature extraction to obtain an information fusion matrix; the decoupling positioning module is used for performing analysis based on the information fusion matrix and the structured expectation library to obtain a fault positioning point; the evaluation and early warning module is used for extracting all transient events in a preset time period corresponding to the fault positioning point, forming a time sequence feature sequence, extracting and analyzing an evolution index of the time sequence feature sequence, obtaining a risk transition probability, dividing risk levels and carrying out corresponding early warning; according to the invention, non-confusion accurate positioning of the partial discharge source of a specific joint or a cable section in the branch box is realized.

According to the power distribution network fault protection and positioning method and system based on the fast switch, an edge calculation node adopts noise self-adaptive improved Morlet wavelet transform, and a current change rate and a voltage break variable are extracted from a transient signal; constructing a three-dimensional decision model taking the current change rate, the voltage break variable and the line characteristic impedance as variables; based on a three-dimensional decision-making model, when it is judged that an internal fault occurs by using the extracted current change rate and voltage break variable, the distributed detection unit sends the action state quantity of the equipment where the distributed detection unit is located to an edge computing node; after the edge computing node utilizes the action state matrix of the equipment, a state reasoning method based on a fuzzy Petri network is adopted to determine a fault section; verifying the fault section according to the matching degree of the fault current weighting and the characteristic impedance of the equipment in the fault section; after the fault section is verified, fault isolation is executed; and after fault isolation, fault positioning is optimized, and it is ensured that the cooperative breaking time sequence of the multi-stage switch is accurately matched with a fault diffusion path.

The invention relates to the technical field of battery science, in particular to a battery health real-time diagnosis method based on voltage internal resistance correlation analysis in the battery replacement process. The method comprises the following specific implementation steps: firstly, capturing a high-frequency transient signal at the moment of battery connection and disconnection, and carrying out real-time evaluation and error correction on the high-frequency transient signal based on a signal symmetry evaluation mechanism; thirdly, a battery health model is designed, the model generates a nonlinear internal resistance spectrum through an AI model part, and a health state physical model is used as priori knowledge for constraint; and the health state physical model outputs an initial health state according to the internal resistance spectrum adjustment parameters. And finally, training the battery health verification model by utilizing meta learning, outputting a health state predicted value by the model, and obtaining a fusion result of an error of the battery health verification model and an error of the health state predicted value through an error intelligent fusion method. And performing weighted average on the health state prediction value and the initial battery health state according to a fusion result to determine a final battery health state.

The invention provides a multi-physics field coupled transient signal sensing method and device, and the method comprises the steps: carrying out the measurement of an electric signal of a target multi-physics field through a plurality of preset sensors, and carrying out the amplification and digital processing of the electric signal obtained from the target multi-physics field, and obtaining an output signal; performing signal amplitude detection on the output signal, calculating current bias according to the deviation between the amplitude of the output signal and a target amplitude, and adjusting a bias magnetic field according to the current bias; performing time relative synchronization on the sensors to obtain time alignment data; and analyzing the characteristics of the output signal in real time, and carrying out global fault positioning, fault type judgment or transient process tracking on time data and the characteristics of the output signal through a preset analysis program. According to the invention, high-frequency transient signals can be captured; and high-precision monitoring, feature extraction and real-time analysis of transient signals on the low-voltage power distribution side are realized without depending on a synchronous calibration method of a high-precision time service module and relative time.

The invention discloses a power distribution network traveling wave fault positioning method and a related device, and relates to the technical field of power distribution networks, and the method comprises the steps: collecting a transient signal after a fault at a preset key monitoring point of a power distribution network, extracting an initial traveling wave head from the transient signal, and measuring the moment when the initial traveling wave head reaches other preset monitoring points in the power distribution network, obtaining traveling wave arrival time between the key monitoring point and each monitoring point; the time difference between the traveling wave arrival time is used as the actually measured traveling wave arrival time difference to construct a fault positioning optimization model with the purpose of minimizing the error sum of squares of the theoretical traveling wave arrival time difference and the actually measured traveling wave arrival time difference; and solving the fault positioning optimization model through a pre-constructed chaos particle swarm optimization algorithm to obtain a traveling wave fault positioning result of the power distribution network. The method solves the problems that the positioning effect is not ideal due to the fact that the prior art is prone to falling into local optimum and is poor in adaptability to a complex topological structure, and the convergence speed cannot meet the requirement for rapid power supply recovery.

The invention relates to the technical field of aero-engine state monitoring and fault diagnosis, in particular to a fault monitoring method and device for an aero-engine and a storage medium. The method comprises the steps that vibration signals of all parts in the working process of the aero-engine are obtained; dividing the vibration signal into a steady-state signal, a periodic impact signal or an unsteady-state transient signal based on the short-time energy and the zero-crossing rate; when the vibration signal is a steady-state signal, determining whether a fault exists or not by adopting a time domain feature extraction and classification model; when the vibration signal is a periodic impact signal, Fourier transform and envelope spectrum analysis are adopted to determine whether a fault exists; and when the vibration signal is an unstable transient signal, determining whether a fault exists or not by adopting time-frequency analysis. Compared with a single analysis algorithm, the method has the advantages that different algorithms are matched for fault monitoring based on the characteristics of the vibration signals, the monitoring accuracy is improved, and the false alarm rate is reduced.

The invention discloses an ultra-long distance cable fault monitoring method and system, and belongs to the field of power system safety monitoring, and the method comprises the steps: obtaining a partial discharge high-frequency transient signal, a hyperspectral remote sensing image and atmospheric parameter data of a cable, carrying out the phase-space reconstruction of the partial discharge high-frequency transient signal, and constructing a chaotic feature; and performing atmospheric correction on the hyperspectral image based on the atmospheric parameters, generating a surface reflectance image, performing spectral unmixing on the surface reflectance image, and extracting a spectral fingerprint. The method comprises the following steps: performing space-time alignment on a partial discharge high-frequency transient signal and a hyperspectral remote sensing image to generate a space-time mapping index, and according to the space-time mapping index, pairing a chaotic feature with a spectral fingerprint to form a space-time feature pair; and predicting the fault probability of each position point of the cable in a future time range through the cross-modal fusion model, and judging a latent fault according to a threshold value. According to the invention, the problem of inaccurate monitoring caused by weak signals and index lag in the prior art can be solved.

The invention provides an online power supply switch mutual inductor fault detection method and system, a medium and a product, and relates to the technical field of online monitoring and fault diagnosis of power equipment, and the method comprises the steps: generating a synchronous triggering instruction at the zero crossing point moment of the power frequency current of a mutual inductor; in response to the instruction, injecting an excitation pulse into a secondary circuit of the mutual inductor and synchronously acquiring a feedback voltage to obtain a mixed transient signal; extracting a pure response waveform only containing a high-frequency component from the mixed transient signal to carry out multi-resolution time-frequency transformation, and calculating and outputting a multi-dimensional feature vector representing a winding state; inputting the multi-dimensional feature vector into a pre-trained neural network model for classification operation to obtain a fault classification probability of the current transformer state; and comparing the fault classification probability with a preset confidence threshold to generate a fault diagnosis result of the mutual inductor. By implementing the method, the recognition sensitivity of the system on early weak faults and high-frequency distribution parameter changes of the mutual inductor can be improved.

The invention provides a dynamic control system for coordinating streptomyces secondary metabolite production and application of the dynamic control system. Firstly, a general artificial promoter PQS (trigger) responding to a quorum sensing system of various streptomycetes is designed, then a stabilizer is constructed based on a bistable gene circuit, instantaneous signal output is converted into stable signal output, dependence of an inducer is successfully eliminated, finally, an orthogonal amplifier is developed based on a streptomycete rare extracellular function sigma factor, and finally, the specific function of the quorum sensing system of various streptomycetes is achieved. And different promoter element libraries containing amplifiers with response from weak to strong and with strength span of about 535 times are constructed. An amplifier and a CRISPRi system are combined to construct a turner, and gene expression inhibition with the strength span of about 200 times is achieved. The combination of the amplifier, the promoter library and the turner is called a multi-effect device, and abundant elements are provided for fine target gene expression. The combination of the trigger, the stabilizer and the multiple-effect device is called as a streptomycete multipath manual control system (SMARTS), and control tools of streptomycete are enriched.

The invention discloses a power distribution network self-healing strategy generation method and system which are used for solving the technical problem that when an existing power distribution network self-healing strategy generation method faces a fault, simple label matching is carried out only according to a fault type and a line basic structure, and the rapid power restoration quality of a power distribution network is poor. The method comprises the following steps: acquiring surface temperature distribution data of power distribution network equipment and abrupt change high-frequency transient signals of power distribution line nodes; constructing a dynamic topological graph of the power distribution network, and constructing a dynamic three-dimensional matrix according to the dynamic topological graph of the power distribution network, the surface temperature distribution data of the power distribution network equipment and the abrupt change high-frequency transient signals of the power distribution line nodes; generating a plurality of priority self-healing strategies according to a preset basic strategy library, the power distribution network optimization function and the power distribution network constraint conditions; and based on a fuzzy analytic hierarchy process, screening each priority self-healing strategy by adopting a power distribution network optimization function according to the dynamic three-dimensional matrix, and determining a target power distribution network self-healing strategy.

The invention discloses a seismic wave velocity change analysis method under a hydraulic fracturing condition, and belongs to the technical field of seismic wave propagation analysis. The method comprises the following steps: S1, acquiring data by using a plurality of seismic stations and removing transient signals by using a long and short window method; s2, calculating a cross-correlation function between the two stations; s3, extracting a tail wave signal and estimating relative velocity change by using mobile window cross-spectrum analysis; s4, extracting a group velocity frequency dispersion curve based on a cross-correlation function, and constructing a two-dimensional velocity model before and after fracturing; and S5, generating a speed image through inversion, and calculating a difference image to reveal speed change characteristics. According to the method, the tail wave sensitivity and the moving window cross-spectrum analysis are combined, the speed change and the anisotropy characteristics of the speed change are accurately quantified, the method is suitable for the complex geological environment, and a new interpretation framework is provided for the influence of hydraulic fracturing on the underground structure.

The invention discloses a power transmission line fault positioning method, device and equipment and a computer readable storage medium, and the method comprises the steps: collecting an electrical transient signal when a power transmission line breaks down, and carrying out the preprocessing of the electrical transient signal, and obtaining a standardized signal; performing multi-scale decomposition on the standardized signal, extracting energy features of each sub-band, and constructing a time-frequency-energy three-dimensional feature vector representing a fault; constructing a gating circulation unit network model, and performing global optimization on key hyper-parameters of the GRU network model by adopting a sparrow search algorithm SSA; and processing the time-frequency-energy three-dimensional feature vector by using a GRU network model subjected to SSA optimization so as to position the fault of the power transmission line. According to the method, the problems of low positioning precision and poor adaptability caused by noise interference, model mismatch and manual parameter adjustment limitation in a complex power grid environment can be solved, and millisecond-level, high-precision and high-robustness precise positioning of the power transmission line fault is realized.

The invention discloses a transient signal detection and sorting method based on time-frequency image deep learning, and relates to the technical field of signal sorting. Based on a hypergraph model and core polymorphic sites, the hypergraph model is divided into a plurality of independent substructures by analyzing the distribution characteristics of hyperedge dynamic weights, independent modal analysis is performed on each substructure, and a transient signal detection and sorting result is obtained. Extracting a time-frequency distortion coefficient matrix and an interference response mode matrix; inputting the multi-modal data into a multi-modal network to generate a homogeneous transformation matrix; and based on the homogeneous transformation matrix, deriving attitude error parameters and translation error parameters, feeding back the attitude error parameters and the translation error parameters to a feature input layer and a decision layer of the multi-modal deep learning detection and sorting model, completing dynamic correction of model parameters, and detecting and sorting real-time signals again after model correction. According to the sorting method, the detection rate and sorting accuracy of transient signals are remarkably improved in real-time processing, and meanwhile, the false alarm and leak detection risks are reduced.

The invention discloses a fault diagnosis method and system for hydraulic element general equipment, and relates to the technical field of hydraulic element fault diagnos.The method comprises the steps that control channels of the general equipment are recognized based on hydraulic elements, transient signals are collected in all the control channels in real time, preset interference constraints are introduced, and fault diagnosis is conducted on the general equipment; generating an interference factor through a high-entropy random disturbance source, performing disturbance analysis on the amplitude, the frequency and the waveform state of the transient signal, and generating an interference identifier; a health state vector is formed in combination with the operation working state, the deviation degree of the health state vector and the health baseline characteristic spectrum is compared, if the deviation degree is smaller than a standard deviation threshold value, a state evaluation strategy is executed in the first diagnosis channel, and a health stability trend is recognized; if the deviation degree is greater than or equal to the standard deviation threshold value, executing a fault diagnosis strategy in the second diagnosis channel, and identifying a fault sign; according to the invention, efficient division of labor of health assessment and fault diagnosis is realized, and the effect of accurately identifying fault signs is achieved.

The invention discloses an ultrafast spectrum transient signal acquisition device and method based on a high-speed signal acquisition card, and the device comprises an external clock which generates a modulation synchronization signal, and inputs the modulation synchronization signal to an optical modulation device and a high-speed data acquisition card to achieve synchronization; an optical modulation device which receives the modulation synchronization signal, changes the frequency of the input optical pulse to be the same as the synchronization signal, and generates a modulation optical pulse; the photoelectric detector generates an analog electric signal based on the modulated detection light; the high-speed data acquisition card generates a digital electric signal based on the analog electric signal; and the data processing center is used for receiving the digital electric signal and carrying out modulation processing based on a time integral window to obtain an ultrafast spectrum transient signal acquisition result. The high-speed acquisition card is adopted to directly record the voltage value of the detector, so that waiting time is avoided; the voltage values and the difference value in the pump on state and the pump off state are obtained through the time integral window, the light intensity of the pump off and the light intensity difference brought by modulation are collected at the same time, and the collection efficiency is improved.

The invention relates to the technical field of audio signal processing, and discloses a wind-noise-resistant self-adaptive volume adjustment method and system for a riding earphone, and the method comprises the following steps: S1, collecting environment sound and a source audio signal in parallel; s2, analyzing environment sound, and determining macroscopic adjustment intensity; s3, analyzing the source audio in parallel, and identifying a content type and a transient signal of the source audio; s4, based on the psychological acoustic model, generating a frequency-related target method tone quality compensation gain; s5, performing dynamic smoothing and frequency weighting on the target gain according to the content and the transient characteristics to generate an application gain; s6, fusing the macroscopic adjustment intensity and the application gain, and synthesizing a final gain parameter; and S7, applying the final gain to the source audio, and outputting the compensated audio. According to the method, the psychological acoustic model and audio content parallel analysis are fused, frequency-level accurate compensation is realized, the problems of traditional degradation and sudden hearing sense change are solved, and the audio definition and comfort are improved.

The invention relates to the technical field of electrical variable measurement, in particular to an analysis system for generator parallel operation impact data, which comprises a transient signal acquisition module for synchronously monitoring an ampere value of an impact current and a volt value of an impact voltage during generator parallel operation, obtaining an instantaneous reading of a transient electrical signal, and acquiring a transient signal; and the reading is converted into a digital sequence, and a multi-channel transient electrical signal value is recorded to form an original electrical measurement data set. According to the method, the ampere value of the impulse current and the volt value of the impulse voltage are synchronously monitored in the generator parallel operation period, the instantaneous reading of the transient electric signal is obtained and converted into the digital sequence, a high-precision data foundation is laid for subsequent analysis, and the analysis time period of the impulse event is further set based on the original electrical measurement data set; and carrying out synchronous alignment and effective data segment interception on the current and voltage numerical value sequences according to timestamps, and constructing a structured time domain impact data sequence group.