2502results about "Current measurements only" patented technology

The invention discloses an intelligent monitoring and early warning system for the safety state of an electrical cabinet, and belongs to the technical field of electrical variable measurement, and the system comprises a data collection module which is used for obtaining multi-source sensing data of the electrical cabinet, and the multi-source sensing data comprises a current waveform parameter, an infrared temperature distribution parameter and a mechanical vibration spectrum parameter; the health feature evaluation module is used for generating a health state feature vector of the electrical cabinet by using the multi-source sensing data; the instruction generation module is used for outputting an early warning decision instruction according to a matching result of the health state feature vector and a preset fault propagation rule base; and the safety protection module is used for executing a safety protection action when the early warning decision instruction meets a preset execution condition. A multi-source sensing data fusion analysis technology is adopted to construct an electrothermal mechanical composite feature vector, a fault propagation rule base is coupled to realize cross-domain conduction path modeling, composite hidden dangers such as contact deterioration and the like can be accurately identified in a fault incubation period, and graded active safety protection measures are triggered.

The invention discloses an intelligent cable fault accurate positioning and early warning method and system, and the method comprises the steps: collecting the temperature gradient, strain distribution and partial discharge signals of the whole length of a cable in real time through a distributed optical fiber sensing network, and generating a multi-dimensional feature matrix of the operation state of the cable; based on the multi-dimensional feature matrix, outputting a preliminary fault positioning coordinate; generating corrected fault coordinates according to the topological structure data of the cable laying environment and the electromagnetic interference distribution diagram; historical fault data, real-time operation parameters and the corrected fault coordinates are fused, and a fault risk thermodynamic diagram in a future preset duration is output; and based on the fault risk thermodynamic diagram and real-time monitoring data, generating fault first-aid repair information by using a dynamic priority algorithm, synchronously triggering an early warning signal, and visually displaying a fault positioning result and a risk area in a three-dimensional geographic information system. According to the embodiment of the invention, rapid positioning, accurate early warning and intelligent disposal of the cable fault can be realized.

The invention discloses a grinding machine internal part temperature anomaly detection method based on vibration signal analysis, which comprises the following steps that grinding data are collected through sensor deployment, and the sensors comprise a temperature sensor, a vibration sensor, an infrared thermal imaging sensor, a magnetic resistance current sensor and an inductance type particle sensor; carrying out preprocessing and feature extraction on the collected data; model construction and training are carried out based on data of preprocessing and feature extraction; according to the method, the abnormal condition of the temperature of the part is predicted by detecting the vibration signal of the internal part, the content of metal particles in lubricating oil is detected through the oil analysis sensor, and the abrasion degree of the bearing is judged in combination with the vibration signal. And motor current harmonic characteristics are monitored, and overload or rotor imbalance problems are identified.

The invention discloses a wire drawing machine operation state evaluation method and system based on deep learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: deploying a multi-source sensor at a preset part of a wire drawing machine, and forming a multi-dimensional operation data flow; the deep learning evaluation model is trained based on the standardized time series data set, model parameters are optimized through a cross entropy loss function, the model learns the characteristic difference between a normal working condition and an abnormal working condition, and the deep learning evaluation model is trained based on the standardized time series data set. And inputting a multi-dimensional operation data flow collected in real time into the trained deep learning evaluation model to generate a dynamic evaluation report. According to the wire drawing machine operation state evaluation method and system based on deep learning provided by the invention, a visual operation state score can be output, detailed fault type probability distribution is given, and intelligent support is provided for equipment maintenance decision.

The invention discloses a current sensor data calibration method for an industrial environment, and the method comprises the steps: collecting the original current sensor data of target equipment in the industrial environment in real time, and carrying out the dynamic preprocessing of the original current sensor data, and generating a first calibration data set; based on the first calibration data set, time domain features and frequency domain features are extracted, and a second feature vector set is constructed; iteratively calculating an optimal calibration parameter set through a self-adaptive optimization algorithm in combination with historical calibration data and the second feature vector set; and dynamically correcting the current data of the current sensor according to the optimal calibration parameter set, and outputting and updating the calibrated current value. According to the invention, through dynamic preprocessing, comprehensive time domain and frequency domain feature extraction and combination of historical calibration data, parameter adaptive optimization updating is carried out, and the problem that current sensor data calibration in an industrial environment is difficult to accurately adapt to dynamic change environment conditions in real time is effectively solved.

The invention discloses a switch loop data extraction method, device and equipment based on phase locking, and the method comprises the steps: carrying out the interval change analysis of operation data in a ring main unit, and recognizing a communication abnormal node through a phase locking technology; associating a mechanical vibration source based on the communication abnormal nodes, detecting a vibration conduction path, extracting current fluctuation characteristics, and determining position deviation; the operation current intensity is adjusted in combination with fluctuation characteristic parameters, and accurate off-position control is achieved through reverse braking force; constructing a dynamic threshold reference based on frequency domain analysis, and generating a complete operation record according to the dynamic threshold reference and the accurate parking data; performing typed grouping and transmission optimization on the data, and controlling data switching by adopting a gradually migrated beat sequence; and finally, through signal reconstruction and data fusion technologies, a switch loop data set with continuous time sequence and complete space is generated. The method can effectively cope with communication interference, mechanical vibration and other complex environmental factors, and improves the integrity and accuracy of data acquisition.

The invention discloses an equipment fault diagnosis method and system based on a large electric power model, and the method comprises the steps: collecting and fusing voltage and current waveform data and alarm log information in a short time window before and after a fault moment, achieving the context integration of a multi-dimensional feature vector through multi-level feature coding and semantic embedding, and achieving the fault diagnosis of a large electric power model. And relevant knowledge fragment retrieval is carried out in combination with a power field knowledge base. On the basis, relevant knowledge fragments, voltage-current waveform fusion feature coding vectors and fault alarm key information semantic embedding coding feature vectors are jointly embedded into a preset Prompt template and then are input into a diagnosis engine based on large model driving, so that intelligent analysis and report generation of fault types, reasons and key features are realized. Through the mode, the limitation of dependence on single data or rules traditionally is broken through, the accuracy, automation and interpretability of fault diagnosis under complex working conditions are remarkably improved, and a solid support is provided for intelligent and high-reliability equipment operation and maintenance.

The invention provides a power network state recognition method and system based on data feature analysis, which are used for continuously improving the power network state recognition precision through a data-driven optimization mechanism, providing full-process intelligent decision support for power grid operation and maintenance, and effectively promoting the power network to be converted from a passive operation and maintenance mode to an active prevention and control mode. The method comprises the following steps: acquiring a multi-dimensional power data set of a target power network, performing dynamic feature extraction processing on the multi-dimensional power data set to generate a power network operation feature set, and inputting the power network operation feature set into a debugged power state recognition model, and carrying out conjoint analysis on the voltage fluctuation characteristics, the current phase deviation characteristics and the equipment health degree characteristics through a power state identification model to generate a power network state classification result, and generating a network operation and maintenance strategy according to the power network state classification result. And the network operation and maintenance strategy is sent to the power dispatching terminal so as to activate corresponding equipment maintenance operation or load adjustment operation.

The invention discloses a non-contact magnetoelectric composite current sensor with magnetism gathering and magnetic shielding. A sensor core is composed of a magnetoelectric sensing element, a magnetofluid layer, a magnetism gathering structure and a magnetic shielding layer. The magnetoelectric sensing element is made of manganese-doped lead magnesium niobate and amorphous alloy film composite materials, the outer side of the sensing element is coated with a magnetofluid layer made of soft magnetic composite materials, the magnetism gathering structure is made of permalloy materials with high magnetic conductivity and is divided into an upper part and a lower part, the upper part accommodates the sensing element and the magnetofluid layer, and the lower part accommodates the magnetic gathering structure. And the lower part is provided with a standardized groove for placing a conductor to be tested. The outermost layer is a magnetic shielding layer composed of multiple layers of nanocrystalline alloy strips, the vertical split type design is also adopted, and it is ensured that the magnetic shielding layer is tightly matched with the magnetic gathering structure through precision machining. The upper magnetic shielding layer and the lower magnetic shielding layer are detachably connected, and field installation is facilitated. The overall structure is compact, a circuit does not need to be disconnected during installation, and the device is especially suitable for on-line current monitoring requirements in the fields of smart grids, industrial automation and the like.

The invention provides a power distribution network operation risk intelligent identification and early warning method, which comprises the steps of identifying an equipment contact failure probability through an actual wear state, and when the equipment contact failure probability exceeds a safe operation requirement, determining an equipment fault early warning signal through historical fault statistical data, identifying potential equipment failure risk points and extracting risk distribution characteristics; identifying a high-risk equipment node through the equipment fault early warning signal, evaluating whether a cascading fault of adjacent equipment overload is caused after power flow redistribution of a power grid according to the identified high-risk node, extracting a fault propagation path, and determining a system risk level distribution diagram; and carrying out risk area division on the system risk level distribution diagram, identifying key equipment nodes in a high-risk area, extracting a load transfer scheme of the high-risk area, and determining a load distribution path and a power transmission direction.

The invention belongs to the technical field of robots, and discloses a biped robot state estimation method based on multi-sensor information, and the method comprises the steps: obtaining the data of an inertial measurement unit, a joint encoder and a joint sensor on a biped robot; a self-adaptive threshold contact state detection algorithm based on current sensing information is designed, and the contact state of the sole and the environment is judged in real time; determining a supporting phase of the robot according to the contact state, and applying a dynamic reference system conversion mechanism of supporting phase perception; constructing a state estimator based on invariant extended Kalman filter IEKF, defining a state vector including the posture, position and speed of the robot, performing state prediction by using IMU data, and performing state updating in combination with contact state information, kinematics information and the like; and outputting accurately estimated robot state information. According to the method, the adaptive contact detection and the IEKF are introduced, so that the state estimation precision and robustness of the biped robot in the dynamic walking process are effectively improved.

The invention relates to the technical field of power monitoring, in particular to an intelligent filtering and trend compensation method and system for power monitoring and a storage medium, and the method comprises the steps: monitoring a current in real time to obtain an original current signal, and carrying out the abnormal data filtering of the original current signal, so as to obtain a normal current signal; smoothing the normal current signal based on a two-state space model to obtain a current decomposition signal; and performing phase compensation on the current decomposition signal based on a signal compensation formula to obtain an actual current signal. Abnormal data filtering is performed on the original current signal in real time, the trend component is extracted in combination with the improved double-state space model, and the differential-integral compensator is designed to counteract the system delay to obtain the accurate actual current signal, so that jitter elimination processing is performed on the current data in real time, and the relatively accurate current change trend is judged at the same time. And misjudgment is eliminated.

The invention relates to the field of vehicle current detection, in particular to an automobile online current detection method and terminal equipment. The method comprises the following steps: acquiring real-time current of a vehicle to obtain a multi-time-sequence sampling synchronous current waveform, performing microsecond offset identification and multi-dimensional sampling feature relevance mining, and constructing a synchronous current response matrix; a vehicle-mounted environment monitoring unit is called to collect real-time environment temperature information of the vehicle, temperature trend change mining and temperature drift factor mining are carried out, and temperature drift influence factors are extracted; current detection reference calibration analysis is carried out based on the temperature drift influence factor and the synchronous current response matrix, temperature deviation compensation intelligent optimization is carried out, and a temperature deviation equivalent regulation and control engine is constructed; the method comprises the following steps: monitoring full-band operation electromagnetic parameters of a plurality of subsystems of a vehicle, carrying out frequency-band electromagnetic fluctuation identification one by one, carrying out multi-scale decomposition, and extracting an electromagnetic interference disturbance spectral line set; according to the invention, real-time and accurate vehicle on-line current detection is realized.

A described example includes: a leadframe having a high voltage section and a low voltage section spaced from and electrically isolated from the high voltage section, and a heat slug mounted to the high voltage section. The high voltage section includes an input portion connected to the heat slug at an interior end by a mount portion, and further includes an interior lead portion that is connected to the mount portion by a flexible connect portion. An insulating material mounted to the heat slug provides a die mount area for a semiconductor die having a Hall element that is positioned so that the Hall element is proximate to the heat slug. Mold compound covers the semiconductor die, portions of the leadframe, and portions of the heat slug while a board side surface of the heat slug is exposed forming a thermal pad for a semiconductor device package.

The invention relates to the technical field of electrical measurement, and discloses a high-voltage switch cabinet contact wear on-line diagnosis method, which comprises the following steps: extracting multi-dimensional time-frequency domain characteristics of a high-voltage switch cabinet contact based on vibration and current signals of time sequence alignment to obtain contact motion characteristics; wear state matching is carried out on the features to obtain the wear degree of the contact, and then a preliminary wear diagnosis result is generated through wear label association; performing multi-factor coupling analysis in combination with historical operation times and environment temperature and humidity, evaluating a wear accumulation effect and an oxidation influence degree, and further generating a target wear diagnosis result; and finally, performing communication protocol packaging on the result to form a wear diagnosis data message, and outputting a maintenance suggestion instruction through instruction mapping. The reliability of contact diagnosis of the high-voltage switch cabinet can be improved.

Disclosed are an online fault identification method for a current sensor, a diagnosis apparatus, and a fault-tolerant control system, relating to the technical field of diagnosis of faults of current sensors. The method includes defining a fault of the current sensor, and setting a corresponding label; then simulating a motor drive system, so as to obtain operation data in a normal mode and a fault mode, and making a data set; designing a neural network, and performing optimization processing on the neural network through the data set, so as to obtain an intelligent diagnosis model; and finally deploying the intelligent diagnosis model in an edge apparatus, so as to perform real-time online diagnosis on the fault of the current sensor; where the fault of the current sensor includes a saturation fault and a noise fault.

The invention discloses an online intelligent monitoring and analyzing system for wear of a circuit breaker contact, which relates to the field of contact wear monitoring and comprises a visual module, an acoustic module, a current module, an acquisition module, an analysis module, an alarm module, a processing module and a display module. The method comprises the following steps of: analyzing and establishing a quantum driving feature selection model; constructing a contact motion mechanical control model; setting parameter prior distribution; fusing a likelihood function and prior by utilizing a Bayesian theorem; a vibration chaos non-linear loss value is calculated based on recurrence plot line segment probability distribution analysis, a jump diffusion model is constructed, the number of jumps is counted to calculate Poisson intensity, corresponding display and alarm are carried out, the accuracy of contact abrasion state evaluation is guaranteed, and early abrasion abnormity is found in time.

The invention belongs to the technical field of welding, discloses an electric arc welding process defect identification method based on multi-sensor information, and provides an electric arc welding process defect monitoring system based on welding process multi-sensor information. According to the system, current signals, temperature signals and visual image signals in the welding process are collected, the information theory and machine learning are combined, and real-time recognition of welding defects is achieved. The system aims at solving the problems of massive high-dimensional data volume generated by multi-sensor information fusion, rapid solving of appropriate model parameters and the like in a traditional welding defect detection method, and provides a welding defect identification method combining a mutual information method and a particle swarm optimization algorithm-least square support vector machine. Feature selection is carried out on multi-source sensing high-dimensional data through a mutual information method, optimization training is carried out on a particle swarm optimization algorithm-least square support vector machine model by utilizing a simplified data set, and a classification model established by the method has the characteristics of low complexity, short training and prediction time, relatively strong generalization ability and the like.

The invention discloses a method for carrying out zero sequence loop impedance calculation by using a frequency conversion signal, the calculation method is applied to a power distribution network arc suppression coil grounding system, and the power distribution network arc suppression coil grounding system comprises a control device, a grounding transformer, an injection transformer and a variable signal source; according to the method, the signal source is adopted to inject signals with different frequencies to calculate the capacitive current, the impedance parameter of the arc suppression coil and the capacitive current of the system during parallel operation are calculated by establishing a loop equation according to different system zero sequence loop impedances under different frequencies, and then the capacitive current needing to be compensated by the system is accurately calculated. The method solves the problems of the calculation of the capacitance current of the system and the automatic tracking of the arc suppression coil during the parallel operation of the two machines, is not affected by the unbalance of the system, and provides a reliable basis for the precise control of the parallel operation of the two machines of the arc suppression coil.

The invention relates to the technical field of electromagnetic compatibility testing, in particular to an electromagnetic compatibility testing device and method for the shaft current of a hub motor, and the device comprises a shielding chamber, a system master controller, a dynamometer, a hub motor tool and a testing system. A dynamometer, a hub motor tool and a test system are arranged in the shielding chamber; the hub motor tool is used for installing and fixing a hub motor to be tested. An output shaft is designed at the rotor end of the hub motor, and the hub motor is connected with the dynamometer through the output shaft. The test system is used for measuring the shaft current of the hub motor; the system master controller is used for controlling the wheel hub motor to operate under various working conditions and collecting shaft current under each working condition; and obtaining an electromagnetic compatibility test result according to the shaft current. The electromagnetic compatibility and reliability of the hub motor and the control system thereof can be improved.

The invention discloses a mass spectrometer electrometer based on a capacitance integrator, and belongs to the technical field of electrometers. Comprising a capacitance integrator, the capacitance integrator is connected with a voltage holding circuit, the voltage holding circuit is connected with an acquisition control circuit and an amplification circuit, and the amplification circuit is connected with the acquisition control circuit; and the acquisition control circuit controls electric signals input into the capacitance integrator, the voltage holding circuit and the amplifying circuit through an I / V conversion control method, so that I / V conversion is completed. The capacitance integration is used for completing I / V conversion, the problem of high-resistance forbidding can be solved, meanwhile, compared with high-resistance conversion, the capacitance integration is adopted, the temperature stability of the electrometer is improved, and noise is reduced.

The invention discloses a current sensor error compensation method and system, and the method comprises the steps: firstly obtaining a current sensor operation environment, dividing time windows, extracting the environment coding characteristics of each window, obtaining contribution degree coefficients through dynamic evaluation, and fusing the contribution degree coefficients into environment characteristics; and meanwhile, electric signal time sequence fragments are obtained and fused into joint features, and circuit state features are obtained through feature fusion network reconstruction. Environment and circuit state characteristics are aggregated, a compensation strategy module is matched according to the environment and circuit state characteristics, compensation parameter fusion operation is executed in the two modules to obtain a first compensation characteristic and a second compensation characteristic, finally, a current error compensation result is determined in combination with an error type identifier, and high-precision current sensor error compensation is achieved.

The invention discloses a Rogowski coil current transformer and a current measurement system, and relates to the technical field of current measurement. The Rogowski coil current transformer comprises a Rogowski coil and a signal processing circuit; the differential signal is converted into a differential constant-current signal to be output based on a signal processing circuit; the signal processing circuit comprises an integral amplification module for performing integral amplification on the differential signal based on the integral amplification module; the first direct current component removing module is used for removing a direct current component in the voltage signal after integral amplification based on the first direct current component removing module; the signal adjusting module is used for realizing amplitude in-phase amplification and phase compensation; the single-ended to differential output module is used for generating symmetrical differential voltage signals based on the single-ended to differential output module; and the conversion module is used for converting the symmetrical differential voltage signal into a differential constant current signal and outputting the differential constant current signal based on the conversion module. And through the integral amplification module and the phase compensation module, the differential signal is accurately converted into the current signal, so that the current measurement result is more accurate.

A magnetic field sensor is provided for sensing a current flowing in a first direction divided in at least two conductor portions separated in a second direction perpendicular to the first direction. It includes at least a first sensing element and second sensing element and at least one integrated magnetic concentrator for sensing the magnetic field at respectively first and second positions in a region between the two conductor portions. The sensing elements are adapted in combination with the concentrator to sense the field with a highest sensitivity in a sensing direction perpendicular to the first direction, wherein the two positions are separated by a predetermined distance in a spacing direction perpendicular to the sensing direction.

The invention discloses a variable frequency motor load testing device and method, and relates to the technical field of variable frequency motor testing, and the device comprises a motor testing installation platform, a fastening T-shaped bolt, two variable frequency speed regulation asynchronous motors of the same model, a drum-shaped coupler, two inverters, a rectifier, a three-phase voltage regulator, a frequency converter and an intermediate DC link. According to the invention, two frequency conversion speed regulation asynchronous motors of the same model are coaxially connected, and the power generation electric energy of the accompanying test motor is fed back to an intermediate direct current link through an inverter, so that the cyclic utilization of the electric energy and the dual-motor structural design are realized, complex load equipment does not need to be configured, and the fixed asset investment is reduced; for motors of different models, only corresponding accompanying test motors need to be replaced, the universality is improved, through voltage and current monitoring in an intermediate direct current link and motor rotating speed and torque parameter collection, and by combining a multi-dimensional performance evaluation model, precise evaluation of the motor load performance is achieved, the test efficiency is improved, and the strict test requirements are met.

The invention discloses a power distribution network cluster local control method and device, equipment and a medium. The method adopts a cloud edge end three-level collaborative architecture, and comprises the following steps: acquiring electrical parameters through a terminal equipment layer; constructing a graph model based on an electrical distance at the edge calculation layer; performing initial cluster division and issuing on the cloud computing layer; dynamically optimizing cluster division in an edge calculation layer through a lightweight deep reinforcement learning model; local control strategies such as voltage reactive power cooperative control, frequency active power balance control and fault rapid isolation and self-healing are executed based on the final division. The system comprises a terminal device layer, an edge computing layer and a cloud computing layer. According to the method, decoupling of global optimization and local real-time control is realized through cloud-side cooperation, the problems of high data processing pressure, high communication delay, expanded local fault influence range and the like of centralized control are effectively solved, and the real-time performance, reliability and disaster resistance of the power distribution network are remarkably improved.

The present invention obtains a hydraulic pressure control unit capable of accurately evaluating viscosity of a hydraulic fluid.The hydraulic pressure control unit according to the present invention includes a controller including an evaluation section that evaluates the viscosity of the hydraulic fluid. The evaluation section evaluates the viscosity on the basis of a reduced amount of a current value of a current flowing through a wire (314) at a time when the current value is temporarily reduced in a process in which the current value is increased toward a target current value at beginning of application of the current to the wire (314) of an electromagnetic valve (31).

An apparatus for detecting disconnection of a power cable of a motor may include a computing device including a processor and a storage medium on which one or more programs configured to be executable by the processor are recorded, wherein the one or more programs may include: obtaining a plurality of sensing current values from a plurality of current sensors respectively sensing currents flowing in a plurality of windings of the motor corresponding to a plurality of phases of the motor; determining a plurality of correction current values by removing a common mode current from each of the sensing current values; and detecting disconnection in each of the phases based on a cable disconnection index based on the plurality of correction current values.

The invention discloses a wear state and precision degradation prediction method for a ball screw drive system, and the method comprises the steps: employing a dual-label obtaining strategy, and constructing a'strong label + weak label 'mixed label sequence covering a whole life cycle; mapping a macroscopic working condition into a contact interface micromechanical state quantity, and converting a time domain signal into a travel domain / space domain stable sequence by adopting equal travel synchronous resampling, so as to weaken the influence of speed fluctuation on feature extraction; a double-flow physical guidance neural network is further constructed, and a physical attention mechanism is introduced to realize data feature and physical prior fusion; training is completed based on supervision loss and physical consistency constraint, and online recursive correction is carried out by using sparse laser interferometer calibration data. By adopting the method, the prediction deviation caused by equipment aging and working condition change can be reduced, and stable prediction of the wear state and the repeated positioning error degradation trend is realized.

The invention provides a direct arc current monitoring method for an optical storage power station. The method comprises the steps of 1, high-frequency sampling; 2, multi-feature extraction: respectively calculating a time domain feature kurtosis, a pulse factor and a current change rate (di / dt); 3-100 kHz frequency band energy distribution is extracted, and wavelet packet decomposition is adopted to obtain frequency domain characteristics of each sub-band energy ratio; 3, a dynamic threshold value model is established, wherein a threshold value correction formula is established based on the working voltage (vmp) of the photovoltaic array and the environment temperature (T); 4, improved D-S evidence theory fusion: introducing weighted credibility distribution, and dynamically updating the feature weight through Kalman filtering; relates to the technical field of safety monitoring of photovoltaic power stations and energy storage systems, solves the problems of high false alarm rate, long response time, incapability of distinguishing normal transient and real arcs, difficulty in adapting to complex working conditions and the like in the prior art, and realizes accurate and rapid detection of arc faults on the direct current side of an optical storage power station. And safe operation of the optical storage power station and the energy storage system is ensured.