141 results about "Cross correlation analysis" patented technology

The invention relates to a data processing method, device and equipment based on an artificial intelligence technology and a data model. The data processing method comprises the following steps: acquiring historical process parameter data of original production equipment and real-time process parameter data of novel equipment; constructing an association network model reflecting the mutual influence strength of the parameters; carrying out partial correlation analysis and cross correlation analysis on the real-time process parameters of the novel equipment and the historical process parameters of the original production equipment; performing incremental updating on the process parameter prediction model; integrating the real-time process parameter characteristic representation of the novel equipment into the association network model; applying the optimal process parameter configuration set to an actual production process; and adaptability knowledge introduced by novel equipment is continuously accumulated and perfected. According to the method, a set of dynamically optimized closed-loop control mechanism is formed through continuous learning and knowledge accumulation, technological parameter changes caused by introduction of novel equipment can be effectively coped with, and continuous optimization and quality improvement of the production process are achieved.

The invention relates to an intelligent microphone pickup and speech enhancement method, system and device, and the method comprises the steps: obtaining an audio signal collected by a dual-silicon microphone array, and carrying out the cross-correlation analysis of the audio signal, and obtaining a corresponding human voice signal correlation feature; performing sound source positioning analysis on the audio signal based on the human voice signal correlation feature to obtain a corresponding target human voice signal; performing frequency characteristic analysis on the target human voice signal, and performing segmented dynamic gain processing on the signal according to a preset frequency band range to obtain a corresponding voice enhancement signal; performing noise component adaptive filtering processing on the target human voice signal to obtain a corresponding noise suppression parameter; and inputting the speech enhancement signal and the noise suppression parameter into a preset echo cancellation model for joint optimization to obtain a corresponding output human voice signal. According to the invention, the coupling problem of multiple acoustic interferences can be effectively solved.

The invention provides a multi-source data fusion modeling method and system in an aeration process, and is applied to the field of intelligent aeration control in sewage treatment. The method comprises the steps that multi-source time sequence data such as dissolved oxygen, turbidity, flow, temperature, power and pool bottom pressure pulsation signals are collected, dissolved oxygen response lag is calculated through cross-correlation analysis with power change as the reference, time sequence alignment is carried out, and a dissolved oxygen reference interval is predicted by utilizing calibration data in combination with a physical constraint LSTM model; performing spectral analysis on the pressure pulsation signal to extract a gas-liquid coupling characteristic value, and generating a cooperative regulation instruction of the frequency of the blower and the rotating speed of the stirrer based on the information; by means of the scheme, control oscillation caused by lag of the dissolved oxygen sensor can be effectively overcome, online monitoring of bubble form distribution is achieved, the gas-liquid mass transfer efficiency is improved, invalid aeration is avoided, and system energy consumption is remarkably reduced on the premise that stable effluent quality is guaranteed.

The invention relates to the technical field of voiceprint recognition, in particular to a photovoltaic equipment fault detection method and system based on voiceprint recognition, and the method comprises the following steps: collecting the sound of the surrounding environment of a current photovoltaic power station, and building scene associated equipment spectrum peak features; according to the method, classification of environment sound is introduced in the sound acquisition stage, and scene association is performed on the operation sound of the photovoltaic equipment and the surrounding background sound in combination with Mel-frequency cepstrum coefficient extraction and acoustic scene recognition, so that a more targeted reference framework is provided for subsequent analysis. Modeling of multi-source sound features is achieved by synchronously collecting operation sound of an inverter and a cooling fan in equipment and extracting spectrum peak frequency points and amplitudes. In the signal processing process, multiple sub-bands are divided on the basis of scene correlation spectrum peak frequency points, so that each sub-band is aligned with the acoustic characteristics of equipment, the structural precision of short-time energy envelope extraction is improved, and it is ensured that the cross-correlation analysis result is more reliable.

The invention discloses a power distribution network fault positioning method and device based on a satellite time service inverter, and belongs to the technical field of power system fault diagnosis, and the method comprises the steps: obtaining a time service signal of a satellite navigation system, decoding the time service signal to generate a unified space-time reference, distributing the unified space-time reference to a plurality of inverter nodes in a power distribution network, and forming a cooperative measurement network; a coding sequence current signal is injected into a power distribution network line through a master control inverter node; synchronously acquiring response voltage traveling wave data by each inverter node, performing cross-correlation analysis on the response voltage traveling wave data and the coded sequence current signal, and extracting propagation time delay of each inverter node; and correcting the reference line wave velocity by using the current line parameters and the current meteorological data, and calculating fault point position data in combination with propagation time delay. According to the method, an inverter cooperative measurement network is constructed by using satellite time service, a coding sequence current signal is actively injected, and a real-time wave velocity is corrected by combining an intelligent model, so that rapid, accurate and high-reliability positioning of a power distribution network fault can be realized.

The invention provides a blast furnace carbon emission anomaly prediction method based on digital twinning, relates to the technical field of blast furnace carbon emission, and provides a blast furnace top abnormal carbon emission detection method based on deep learning and mechanism fusion. Firstly, key features of carbon traces in the blast furnace production process are collected, and gas components at the top end of a blast furnace and peripheral production data are collected through a sensor. Feature data which cannot be acquired by some sensors in real time are iteratively solved through a Navier-Stokes NS equation. Then, cross-correlation analysis is carried out to find the hysteresis relation of carbon traces in production, and a carbon emission hysteresis relation model is established; abnormal operating conditions are detected by a residual neural network. And finally, testing and evaluating the performance of the model by using actual production data. The result shows that the provided method can accurately detect the abnormal carbon emission at the top end of the blast furnace, early warning can be given out in time, and environmental pollution and energy waste caused by the abnormal carbon emission are effectively reduced.

The invention relates to the technical field of safety monitoring, and provides an oil and gas pipeline leakage monitoring method and system, a medium and equipment, and the method comprises the steps: obtaining a leakage vibration signal of each measurement point of an oil and gas pipeline, and extracting a time domain feature, a frequency domain feature, a spatial-temporal feature and an initial distance from a leakage point to a starting end of an optical fiber; performing cross-correlation analysis on the leakage vibration signals of the adjacent measuring points to obtain a propagation time difference and a cross-correlation coefficient, and calculating a correction distance from the leakage point to the starting end of the optical fiber; if the leakage point is in the spiral section, calibrating the correction distance to obtain a calibration distance; calculating the difference value between the initial distance from the leakage point to the optical fiber starting end and the corrected distance to obtain a preliminary positioning error value; and based on the time domain feature, the frequency domain feature and the spatial-temporal feature, in combination with a cross correlation coefficient, a calibration distance, an amplitude enhancement coefficient and a preliminary positioning error value, obtaining a leakage medium type through a medium identification model. The method gives consideration to the positioning efficiency and precision, and greatly improves the accuracy and robustness of a leakage medium type recognition result.

The invention relates to the technical field of rock mechanical tests and safety monitoring, and discloses a rock fracture prediction method based on acoustic emission and surface strain cooperative monitoring, and the method comprises the steps: building a monitoring system comprising acoustic emission and an industrial camera array, and building a unified timestamp of each subsystem through a GPS time service module; through load triggering logic, a synchronous trigger is utilized to synchronously acquire an acoustic emission signal and a sample surface image sequence, and evolution characteristics of three-dimensional coordinates of an acoustic emission source and surface full-field strain data are solved. And on the basis, calculating a main strain field variation coefficient, and performing cross-correlation analysis on the resampled energy rate and strain rate. And finally, according to multi-parameter coupling criteria of acoustic emission time sequence parameters, positioning events and surface strain, identifying rock fracture precursor types, and predicting fracture moments and areas by combining a Voight model inverse velocity method and a seismic source projection technology.

A method for measuring coating thickness, roughness and interface stiffness by using an ultrasonic phase derivative spectrum, the method belonging to the technical field of ultrasonic non-destructive testing. In the method, an ultrasonic testing system comprising a coating sample, an ultrasonic flat probe, an ultrasonic flaw detector, an XYZ three-dimensional stepping apparatus and a computer is used. In the method, a phase screen approximation spring model of ultrasonic waves acting on a rough and weakly bonded interface is provided, an ultrasonic phase derivative spectrum of ultrasonic waves being vertically incident on a three-medium two-interface structure consisting of a coupling medium, a coating and a substrate is derived, high-sensitivity ranges of the ultrasonic phase derivative spectrum in respect of coating thickness, roughness and interface stiffness are identified by means of sensitivity analysis, and deviations introduced by initial phases of reference signals and detection signals are eliminated by means of cross-correlation analysis, thereby implementing simultaneous quantitative characterization of coating thickness, roughness and interface stiffness. The method overcomes the dependence of conventional ultrasonic amplitude spectrums or phase spectrums on high-quality reference signals, and solves the problem of it being difficult to use existing non-destructive testing methods to simultaneously measure roughness and interface stiffness.

The invention relates to the technical field of hydraulic engineering safety monitoring, and discloses a dam safety studying and judging method based on monitoring data multi-physics field simulation, which comprises the following steps: S1, multi-source data acquisition and time-space alignment; s2, dynamically updating the numerical model; s3, safety evaluation and early warning decision making; s4, performing multi-source risk coupling analysis; and S5, issuing the early warning information in a multi-mode manner. According to the method, time-space reference unification of multi-source monitoring data is achieved through feature point matching and sliding window cross-correlation analysis, a self-adaptive Kalman filtering algorithm is adopted to dynamically invert permeability coefficients and elastic modulus parameters, and boundary conditions of a finite element model are adjusted in combination with real-time water level changes; the dynamic simulation precision of a seepage field-displacement field-stress field coupling model is improved, the static evaluation limitation of a fixed threshold value method is broken through through a three-dimensional time-varying safety envelope surface and a Bayesian network grading early warning decision tree, and the risk prediction capability in the flood routing process is enhanced through multi-parameter joint probabilistic reasoning.

The invention discloses a pressure reconstruction method and system based on error correction and an adaptive grid, and a storage medium. The system comprises a PIV experiment test subsystem, a processing module (comprising a PIV post-processing unit and a pressure reconstruction unit) and the like. The method comprises the following steps: firstly, controlling a laser emitting sheet to irradiate tracer particles in a water tank through a synchronizer, and simultaneously triggering a high-speed camera to capture a particle image sequence at a microsecond-to-millisecond-level time interval; the PIV post-processing unit performs cross-correlation analysis on the image to generate a gridding velocity field; a pressure reconstruction unit solves a pressure Poisson equation based on the velocity field to obtain a preliminary pressure field, then iteratively corrects an error through an error correction mechanism and gradient correlation optimization, and triggers self-adaptive mesh refinement / coarsening according to a pressure / velocity gradient amplitude; and finally, outputting a high-precision pressure field on the optimized grid. The method can be applied to flow field tests and hydrodynamic performance analysis in ship and ocean engineering, especially pressure field reconstruction in a complex flow field, and has a wide application prospect.

The invention relates to the technical field of power system court management, and particularly discloses a court identification and user binding method based on a special transformer acquisition terminal, which comprises the following steps: separating harmonic interference generated by start and stop of an electric welding machine through a kurtosis index and time-frequency energy entropy fusion detection method, and extracting a fundamental voltage and a pure power signal; constructing a dynamic time bending model containing phase consistency and gradient feature constraint based on the fundamental wave voltage, and calculating voltage track similarity to judge electrical coupling; for the users with electrical coupling, dynamic weight power cross-correlation analysis is fused to generate a comprehensive decision value, and a space-time diagram neural network is utilized to update a transformer area topology structure online in combination with a historical topology change event, so that the dynamic determination of the transformer area to which the users belong is realized; through multi-dimensional feature fusion and anti-interference algorithm design, the problems of phase deviation and waveform distortion caused by electric welding machine harmonic waves are reduced, and the transformer area identification accuracy and real-time performance are improved.

The invention relates to an inter-beam time synchronization measurement device and method after high-power laser beam splitting. The device comprises a triple frequency simulation light source, a first photoelectric tube, a second photoelectric tube, a high-speed oscilloscope and a data processing module. The frequency tripling simulation light source provides frequency tripling light signals of all light paths to be measured, the first photoelectric tube receives the light signals to be measured and outputs electric signals to be measured, and the second photoelectric tube receives time reference light signals and outputs time reference electric signals. The high-speed oscilloscope receives the electric signal to be measured and the time reference electric signal and outputs waveform sequence data to the data processing module, and the module calculates the time interval delta t between the waveform sequence data. According to the method, triple frequency simulation light is divided into a plurality of laser beams through a beam splitting module, and each laser beam reaches a target spot through an independent terminal transmission module. A first photoelectric tube is arranged at a target spot to measure the arrival time of each laser beam, and a second photoelectric tube is arranged in a terminal transmission module of a certain split laser beam to serve as a time reference. A light beam to be measured is selected through the shading assembly, and the time interval between each laser pulse and the time reference pulse is calculated by using a cross-correlation analysis method. According to the invention, high-precision measurement of time synchronization between high-power laser beam splitting is realized, and main laser emission or additional devices are not needed.

The invention relates to the technical field of biomedical signal processing, and particularly discloses a fetal growth and development detection method and system based on intellectualization, and the method comprises the steps: obtaining a mixed physiological signal and a fetal movement signal, obtaining a fetal movement interference coefficient and an interference peak period through cross-correlation analysis, constructing an artifact monitoring vector, and predicting a signal deterioration probability; artifact separation is carried out; distinguishing physiological and non-physiological types of the artifact signal, and extracting a signal degradation event; and if the degradation event exists, analyzing a conduction relationship between the event and equipment sampling parameters through a rule engine, generating a main conduction path, constructing an artifact strategy mapping model, and outputting a parameter correction rule. The system comprises a fetal movement analysis module, an artifact separation module, a degradation analysis module and an equipment correction module. According to the method, the multi-dimensional feature analysis and the intelligent model are combined, so that the fetal physiological signal purity and the anomaly detection accuracy can be improved.

The invention relates to the technical field of power equipment monitoring and fault diagnosis, and discloses a power transformer fault early warning method and system. The method comprises the following steps: acquiring a load current sequence, an environment temperature sequence and a top oil temperature sequence of a transformer; calculating an initial actually measured temperature rise based on the top oil temperature and the environment temperature, and determining thermal response lag time through cross-correlation analysis; carrying out translation correction on the load current according to the lag time, and constructing a time-aligned excitation input set and an actually measured temperature rise sequence based on a common effective time window; substituting the excitation input set into a thermal circuit model based on a thermal balance differential equation to calculate a theoretical reference temperature rise, calculating a residual error in combination with an actually measured temperature rise sequence, performing moving average filtering, and constructing a fault feature vector; and inputting the fault feature vector into a fault diagnosis classification model to determine a fault type and severity, and generating a grading early warning instruction according to the fault type and severity. According to the method, thermal hysteresis interference can be eliminated, and accurate quantitative early warning of the latent thermal fault of the transformer under the dynamic working condition is realized.

The invention provides automatic particle image velocity measurement equipment and a control method. The automatic particle image velocity measurement equipment comprises a three-axis lead screw open T-shaped sliding table, a four-axis lead screw closed gantry sliding table, a machine vision system, a PIV system, a controller and an automatic equipment control system, the machine vision system collects an image of the experimental model, the controller compares the image of the experimental model with a database, when the matching rate is larger than or equal to a preset percentage, feature matching is completed, a measurement result and a decision instruction are generated, and the automatic equipment control system controls the sliding table to move according to a given motion path. And meanwhile, the high-speed camera is controlled to trigger double-frame exposure according to given exposure parameters, and real-time cross-correlation analysis is carried out to obtain velocity field data, so that automatic PIV measurement is realized. According to the method, the problems of long time consumption, low efficiency and high operation repeatability caused by frequent manual calibration of PIV equipment in the experiment process are solved, a large amount of manpower and time cost is saved, and the research speed is increased.

The invention relates to the technical field of optical fiber acoustic sensing, in particular to a method for monitoring invasion and damage along a coal conveying belt based on distributed optical fiber acoustic sensing. The method comprises the following steps of: acquiring acoustic signals when a coal conveying belt normally runs and is static, and establishing a reference model library of normal acoustic characteristics of each road section; a continuous online data acquisition module is utilized to receive and store optical fiber acoustic signals in real time according to a preset sampling rate and a time window, coherent noise reduction processing is performed on the received optical fiber acoustic signals by utilizing a sensing domain feature enhancement method based on dual-working-condition background field coherent modulation, and the optical fiber acoustic signals after coherent noise reduction are formed. According to the method, a normal acoustic feature reference model library under double working conditions (static and operation) is constructed, a sensing domain feature enhancement method based on double-working-condition background field coherent modulation is introduced, and coherent noise reduction enhancement is performed on real-time acoustic signals by using a cross-correlation analysis and adaptive filtering technology.

The invention discloses a pipeline leakage position positioning method based on excitation signal synchronous collection and electronic equipment, and the method comprises the steps: loading an excitation signal into a target pipeline, and obtaining pipeline fusion noise signals collected by a plurality of collection points; segmenting the obtained pipeline fusion noise signal by taking an excitation signal in the pipeline fusion noise signal as a reference to obtain a pipeline fusion noise signal segment corresponding to each acquisition point; calculating the signal transmission time delay of each acquisition point, and carrying out phase callback calibration on the pipeline fusion noise signal segment corresponding to each acquisition point by using the transmission time delay; respectively removing the excitation signal to obtain an original pipeline noise signal segment corresponding to each acquisition point after phase callback calibration; then cross-correlation analysis is carried out, the delay inequality of the original pipeline noise signal reaching the multiple collection points is obtained, and the leakage position is positioned by combining the set positions of the multiple collection points, so that the phase deviation caused by clock asynchronism among the signals of the multiple collection points is eliminated, and the pipeline leakage positioning accuracy is improved.

The invention relates to the technical field of radars, in particular to a weather radar distance resolution calibration method and system. The method comprises the following steps: collecting oversampling original baseband I / Q data of a point target in a plurality of continuous pulse periods; determining a pulse signal-to-noise ratio, and screening out effective pulses according to a threshold value; carrying out sub-sampling stage time alignment on the effective pulses based on cross-correlation analysis, and carrying out incoherent averaging on power module values of all the aligned pulses to generate an average pulse power envelope; converting the average pulse power envelope to a logarithm domain, and selecting discrete data points in a main lobe region; according to the discrete data points, a nonlinear least square method is adopted to carry out iterative fitting on the generalized Gaussian mathematical model, and a continuous analytic function describing the pulse envelope is obtained; according to the continuous analytic function, determining a pulse time width when the power is reduced to a peak value of-6dB; and determining the real distance resolution according to the pulse time width. The measurement robustness is improved, and sub-meter calibration precision is achieved.

The invention discloses an osmotic pressure analysis method and device fusing model optimization and physical inversion and a medium, and the method comprises the steps: calculating correlation coefficients of a specified reservoir water level and osmotic pressure under different time lags based on cross-correlation function analysis, and recognizing a differential nonlinear feature corresponding to the maximum correlation coefficient; aiming at the differentiated nonlinear characteristics of different dam types, constructing a plurality of corresponding diagnosis prediction models in parallel, and establishing a model selection decision rule to select an optimal diagnosis model; according to delay parameters obtained through cross-correlation analysis, a permeability coefficient is reversely deduced through a pore medium heat transfer diffusion theoretical formula; according to the regression slope and the theoretical value attenuation factor of the optimal model, a permeability coefficient is reversely deduced in combination with a correction diffusion equation, credibility evaluation and weighted fusion are performed on the permeability coefficient, and a comprehensive permeability coefficient is calculated; and diagnosing the soil type and the permeability characteristic grade. According to the method, the propagation delay characteristic of reservoir water level change in the pore medium can be accurately quantified, and the inversion precision of the permeability coefficient and the engineering applicability are improved.

The invention discloses an intelligent disturbance and analysis method and system for hemodialysis data, and relates to the field of hemodialysis data analys.The intelligent disturbance and analysis method comprises the steps that in the hemodialysis process, disturbance signals are superposed through a blood input pump, so that blood parameters input into a dialyzer generate preset configuration changes, and therefore input disturbance signals are formed; in the disturbance applying period, blood parameters of the blood output end of the dialyzer are collected in real time, and corresponding output response signals are formed; performing cross-correlation analysis on the input disturbance signal and the output response signal, and calculating a time delay parameter between the input disturbance signal and the output response signal; periodically and repeatedly detecting, carrying out regression analysis on the time delay parameter, and when the time delay parameter is continuously increased along with time, judging that the dialyzer has a blockage trend and outputting early warning information. According to the method, early detection on the dialyzer blockage is realized through the dynamic time-lag characteristics under the condition of not increasing a sensor, and the safety of a hemodialysis system is improved.

The invention provides a tidal reach water level prediction method, device, equipment and medium, and the prediction method comprises the following steps: collecting historical data and current time period data, the historical data and the current time period data comprising actually measured runoff data, actually measured water level data and actually measured tidal range data; lagging adjustment is conducted on the actually measured runoff data and the actually measured tidal range data with the actually measured water level data as the benchmark through a cross-correlation analysis method; performing EEMD decomposition to obtain respective intrinsic mode functions and residual errors; and outputting the eigenmode function of the water level data of the analysis station in the future time period and the predicted value of the residual error through the LSTM model, and carrying out superposition to obtain the predicted value of the water level of the tidal reach. According to the method, the water level data, the runoff data and the tidal range data of the tidal reach are subjected to conjoint analysis, the problems of nonlinear signal processing and low precision of the water level of the tidal reach are effectively solved through EEMD decomposition and the LSTM model, and the prediction accuracy of the water level of the tidal reach is greatly improved.

The invention relates to a broadband distributed radar interference waveform estimation and suppression method and device, and the method comprises the steps: inputting a receiving sampling signal of a distributed radar, including an interference signal, a target echo and receiving noise; constructing an observation matrix according to the interference parameters; through Fourier transform, converting the time domain signal model of the distributed radar for confronting multiple interferences into a frequency domain model; interference parameters are initialized through design of orthogonal waveforms and cross-correlation analysis, interference signals, a complex coefficient matrix and the interference parameters are optimized through internal and external nested loops, and interference waveform estimation is achieved. And eliminating the interference signal after waveform estimation from the received signal to complete interference suppression. Therefore, the problem that a traditional airspace interference suppression algorithm fails due to interference decorrelation of a current broadband distributed radar system is solved.

The invention discloses an engineering vehicle chassis transmission system operation noise source accurate positioning system and method, and the method comprises the steps: S1, installing a vibration sensor on a key part of an engineering vehicle chassis transmission system, and collecting the vibration signal of each key part; s2, carrying an acoustic sensor on a mobile device, wherein the acoustic sensor can sequentially move to the corresponding position of each key component along with the mobile device to carry out acoustic signal acquisition; s3, performing cross-correlation analysis on the acquired acoustic signals and vibration signals, calculating cross-correlation coefficients among the signals acquired by different sensors, and preliminarily determining position information of a noise source by comparing the cross-correlation coefficients of all key components; and S4, performing cepstrum analysis on sensor signals near the preliminarily positioned noise source position, identifying periodic components in the signals, verifying the position of the noise source, and accurately positioning the noise source in combination with a cross-correlation analysis result and a cepstrum verification result.

The present application relates to the technical field of noise diagnosis, in particular to a method and device for identifying abnormal sound source applied to a vehicle transmission system, the method comprising: acquiring abnormal sound noise data, and confirming an abnormal sound generation period according to the abnormal sound noise data; acquiring transmission system vibration data, and performing time-frequency domain analysis on the transmission system vibration data and the abnormal sound noise data according to the abnormal sound generation period to obtain abnormal sound time domain data; the transmission system vibration data is obtained by vibration detection of the vehicle transmission system at a previous time; performing cross-correlation identification of the abnormal sound source according to the abnormal sound time domain data to obtain an abnormal sound source identification result; the abnormal sound source identification result is used to represent that the abnormal sound source is a gearbox and / or a drive axle. By considering the state of strong coupling connection between the gearbox and the drive axle in the vehicle transmission system, cross-correlation analysis is performed on the abnormal sound time domain data to identify the abnormal sound source, thereby improving the accuracy of identifying the abnormal sound source of the vehicle identification system.

The invention relates to the technical field of heart sound signal processing, and particularly discloses a heart sound signal respiration interference suppression method and system based on multi-source sensing fusion, and the method comprises the steps: constructing a multi-source synchronous signal set through synchronously collecting heart sound signals and respiration monitoring signals; separating a heart sound principal component, a breathing interference component and a noise component by utilizing an adaptive modal decomposition and multi-dimensional feature clustering technology; constructing a time-frequency domain interference template through multi-scale cross-correlation analysis and phase registration by taking a respiration monitoring signal as a reference; based on the template, adopting time-varying gain control and an iterative optimization strategy to realize accurate suppression of respiratory interference, and generating a pure heart sound signal; and through multi-resolution feature enhancement and multi-index quantitative evaluation, generating a purity report containing a signal quality grade and a clinical applicability scheme.

The application discloses a kind of gear engagement loop resistance measurement based on gear face lubrication state judging method, belong to gear face lubrication technical field.This method includes: installation angle sensor identifies the engagement start-stop point of each gear tooth;Constant current is applied to collect loop resistance signal, and is divided into the engagement period resistance waveform of each gear tooth;The similarity matrix between waveform and the discrete degree value of its row mean are calculated, compared with the reference value under initial good lubrication state to obtain relative deviation degree;According to relative deviation degree, elastohydrodynamic lubrication, mixed lubrication or boundary lubrication state is judged, and the gear tooth number of similarity abnormality is output simultaneously.The application can also extract oil film extrusion depth coefficient and recovery residual coefficient, and identify lubricating film dynamic instability through inter-circle cross-correlation analysis.The method realizes gear tooth level lubrication state evaluation and abnormal positioning, is robust to working condition change, and is convenient for online monitoring and predictive maintenance.

A method of monitoring a rotating machine configured for energy transfer having a plurality of subsystems, each subsystem includes one cylinder. In the method a measurement signal of a vibration of the rotating machine is obtained and the vibration measurement signal (2) is sampled into a plurality of vibration subsignals (21, 22, 23, 24, 25), each vibration subsignal (21, 22, 23, 24, 25) corresponding to one full revolution of the rotating machine. A reference vibration subsignal (20) is determined based on an average of the plurality of vibration subsignals (21, 22, 23, 24, 25). The reference vibration subsignal (20) is sampled into a plurality of signal snippets (201, 202, 203, 204, 205), each signal snippet (201, 202, 203, 204, 205) assigned to one subsystem of the rotating machine. A cross-correlation analysis of the plurality of signal snippets (201, 202, 203, 204, 205) is performed for identifying a potential fault state of the rotating machine. The disclosure further discloses a rotating machine configured for energy transfer having a plurality of subsystems, each subsystem including one cylinder, is provided, wherein the rotating machine further includes a control unit adapted to perform a method according the disclosure.

The invention particularly relates to an in-situ calibration system for a pipeline sodium flowmeter. The in-situ calibration system comprises a primary instrument and an in-situ calibration device, the primary instrument is arranged in a pipeline through which sodium liquid flows and is used for generating a cross-correlation flow signal; the in-situ calibration device is used for collecting cross-correlation flow signals generated by the primary instrument, sodium flow measured by the sodium flow meter, electromotive force output by the sodium flow meter and sodium temperature, conducting cross-correlation analysis on the cross-correlation flow signals, calculating the instrument coefficient of the sodium flow meter, updating the instrument coefficient of the sodium flow meter according to the calculated instrument coefficient, and calibrating the sodium flow meter according to the updated instrument coefficient. And completing the in-situ calibration of the sodium flowmeter to be calibrated. Measurement data and fitting correction coefficients under corresponding temperature and flow points are obtained through cross-correlation flow measurement and then serve as standard sodium flow to conduct reverse calibration on the sodium flow meter, and in-situ efficient calibration of the precision and sensitivity of the sodium flow meter is achieved.

The invention relates to the technical field of power system automation and relay protection, and discloses a low-current grounding system fault line selection and closed-loop control method and device, and the method comprises the steps: applying preset parameter disturbance to an arc suppression coil in a system after the system is detected to have a grounding fault; in the process of applying the parameter disturbance, synchronously collecting zero-sequence current of each outgoing line as a response signal; analyzing the response signal, and determining a fault line and a fault property; and executing a corresponding closed-loop control strategy according to the determined fault line and the fault property. After a fault is detected, an arc suppression coil is utilized to inject a high-frequency pseudo-random binary sequence as a disturbance signal, and a cross-correlation analysis algorithm is adopted to process the zero-sequence current response of each line, so that a fault feature component related to a disturbance source can be accurately extracted from strong background noise; therefore, high-precision selection of the fault line is realized.