2648 results about "Current sensor" patented technology

The invention is suitable for the technical field of electrical equipment state monitoring, and provides an electrical equipment state monitoring method based on a non-contact leakage current sensor, and the method comprises the steps: collecting a leakage current signal of the surface of an insulating part of electrical equipment, and obtaining infrared thermal image data and an ultrasonic signal; variational mode decomposition is carried out on the leakage current signal to obtain a plurality of intrinsic mode functions; separating a leakage current effective component and an independent noise source based on a blind source separation algorithm; extracting a time-frequency characteristic of the effective component of the separated leakage current, extracting a local temperature gradient characteristic of the infrared thermal image data and a frequency spectrum energy characteristic of the ultrasonic signal, and generating a multi-modal characteristic vector; analyzing the space-time relevance of the multi-modal feature vector, dynamically distributing each modal weight coefficient for fusion, and generating a comprehensive fault feature; according to the method, the signal-to-noise ratio of the weak leakage current signal is improved, and the misjudgment rate is effectively reduced.

The invention relates to the technical field of power batteries, in particular to a battery aging state detection system, method and device. The method comprises the following steps: obtaining battery pack structure design data of the unmanned aerial vehicle; according to the battery pack structure design data, carrying out single battery health monitoring, and generating a multi-parameter feature vector; establishing a data mapping relation of the battery monomer indexes through the multi-parameter feature vectors, thereby obtaining initial health state data of the battery pack; collecting charging and discharging sample data of the unmanned aerial vehicle under different flight conditions; a dynamic current excitation sequence is constructed based on charging and discharging sample data, and current load changes in different flight stages are simulated, so that a multi-working-condition current disturbance sequence is generated. By simulating the load change of the unmanned aerial vehicle in different flight stages and combining the dynamic response capture capability of the high-frequency current sensor, the electrochemical characteristics of the battery can be extracted in real time, and the aging state of the battery under the complex working condition can be accurately identified.

The invention relates to the technical field of integrated circuit test and calibration, and discloses a chip test and calibration method and a chip tester. The chip test calibration method is applied to a chip test module and specifically comprises the following steps that S101, a starting signal sent by a user terminal is received, environment initialization operation is executed, and after initialization is completed, a system automatically enters a standby mode and waits for an external trigger signal or a user instruction; and S102, synchronously capturing input and output signal waveforms of the tested chip at a preset sampling frequency through a high-precision current sensor and a voltage sampling circuit. Through a dynamic parameter acquisition and real-time compensation technology, a voltage reference error is effectively reduced, the temperature control precision is superior to + / -0.3 DEG C, signal distortion caused by environmental interference is effectively inhibited, a multi-dimensional calibration model is combined with time domain, frequency domain and statistical characteristic analysis, nonlinear errors and system drift can be dynamically corrected, and the system performance is improved. And the attenuation error of a test signal transmission path is lower than 0.02 dB.

The invention relates to a multi-sensor fusion intelligent actuator fault self-diagnosis method and system, and belongs to the technical field of actuator fault diagnosis. The method comprises the following steps: realizing multi-sensor clock synchronization through a unified clock source, generating a synchronous time sequence for asynchronous data such as Hall, pressure and temperature by adopting an interpolation method, and intensively acquiring signals such as rotating speed, torque and vibration; based on a current sensor and temperature data, primary fault identification is carried out through a current overload protection model, and an adaptive processing mechanism is triggered; demodulating the noise-reduced vibration signal, extracting high-frequency energy and detecting the deviation degree between the fault frequency of the bearing and a base line; bearing energy consumption abnormity is detected, and a fusion weight is dynamically set by combining the deviation degree, the energy consumption coefficient and an overload result; and carrying out weighted fusion on the multi-modal features, inputting an intelligent diagnosis model to carry out fault mode identification, and finally outputting a fault type / position and triggering a processing strategy. Accurate and rapid self-diagnosis of the actuator fault is 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 relates to the technical field of electrical engineering and sensors, and provides a high-precision current sensor electrical performance detection method, which comprises the steps of collecting an original output signal of a current sensor and performing multi-dimensional signal preprocessing; extracting multi-dimensional feature parameters from the preprocessed signals based on a dynamic feature extraction algorithm; establishing a dynamic compensation model according to the multi-dimensional characteristic parameters, and generating a compensation control signal; a compensation control signal is injected into the detected current sensor to form closed-loop feedback, and a compensated output signal is collected; calculating a sensor performance degradation parameter according to the signal difference before and after compensation; an electrical performance assessment report is generated based on the performance degradation parameter. According to the invention, the measurement precision and reliability of the sensor can be improved, the service life is prolonged, the maintenance cost is reduced, and a powerful guarantee is provided for safe and stable operation of a power system and electronic equipment.

The invention discloses an insulation detection method for an electric automobile, belongs to the technical field of high-voltage safety monitoring of the electric automobile, and solves the problem that the insulation detection precision is reduced due to electromagnetic interference, impedance fluctuation and capacitive effect coupling in a dynamic load switching process. According to the scheme, a test sequence of discrete frequency points is applied to a high-voltage system; measuring a response current value through a current sensor and calculating a ground distributed capacitance value; connecting a test voltage source between positive and negative electrodes of the high-voltage system through a coupling circuit; applying a first pulse signal during no-load, dynamically setting the width according to the capacitance value, measuring a first response current value, and calculating an initial insulation resistance value; a second pulse signal with the same parameter is applied in the load state window, a second response current value is measured, and differential filtering is carried out in combination with the initial value to eliminate load noise; and comparing the filtered insulation resistance value with a threshold value, and outputting a fault signal when the insulation resistance value is lower than the threshold value. The method is used for monitoring the insulation state of the high-voltage system in real time and accurately early warning faults.

The invention relates to the technical field of the Internet of Things, in particular to a short circuit fault monitoring system and method based on the Internet of Things. According to the method, fault mode identification is carried out based on current characteristic waveforms, prediction is carried out based on historical data, current waveform data are captured in real time by using a high-speed sampling current sensor, and abnormal changes in the current waveforms are identified and marked; an RNN deep learning model is constructed, features are extracted from waveforms, the features are compared with known fault modes, potential short circuit faults are recognized, the constructed deep learning model conducts reasoning based on real-time data, abnormal waveform features are quickly recognized, and therefore quick response is achieved, the fault detection time is greatly shortened, and the fault detection efficiency is improved. According to the method, short-circuit faults in a high-speed circuit are effectively handled, power flow data of multiple nodes are processed in real time by adopting a distributed data aggregation and analysis technology, a Bayesian network model is constructed, potential short-circuit faults are identified when power flow is abnormal, and specific nodes and paths where the faults occur are positioned.

The invention relates to the field of cable fault identification, in particular to a high-voltage cable fault identification method for on-line monitoring of sheath current, which comprises the following steps of: firstly, monitoring in real time through a sheath current sensor, a fault traveling wave collector and a cable temperature sensor at key positions along a line, collecting cable line parameters, analyzing normal operation data, and determining the fault of a high-voltage cable according to the normal operation data; establishing a sheath current and temperature characteristic model, calculating the sheath current in real time and comparing the model, if the sheath current exceeds a threshold value, preliminarily judging a fault, and then calculating a fault area through traveling waves; temperature change data are obtained in a fault area, and an average temperature highest point area is found through calculation to accurately calibrate a fault position. According to the method, cable operation conditions are comprehensively mastered by multiple means, faults can be found in time, fault areas and positions can be accurately determined, fault types can be accurately judged according to multi-aspect information, the accuracy and timeliness of high-voltage cable fault recognition are effectively improved, safe and stable operation of a power system is powerfully guaranteed, and the working efficiency is improved. The operation and maintenance cost is reduced; and the cable management level is improved.

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 transformer bushing high-frequency partial discharge signal on-line monitoring method and system, and relates to the technical field of electrical equipment insulation state monitoring, and the method comprises the steps: synchronously collecting signals based on a high-frequency current sensor and an ultrahigh-frequency sensor; performing mixed noise reduction and feature extraction according to the collected signals; and carrying out discharge type identification on the processed data by constructing a lightweight convolutional neural network, and carrying out early warning and positioning in combination with a dynamic alarm threshold. The method can significantly improve the signal-to-noise ratio and enhance the weak discharge signal detection capability by combining the dual-mode sensor with a hybrid noise reduction strategy of wavelet packet decomposition and adaptive filtering, adopts the lightweight convolutional neural network, supports edge equipment to implement reasoning, realizes intelligent identification of the discharge type, and improves the detection accuracy of the weak discharge signal. The insulation state of the transformer bushing is monitored online in real time, off-line dependence is reduced, the service life of equipment is prolonged, and the intelligent level of operation and maintenance of a power system is improved.

A chip includes a power grid, power switches coupled between the power grid and a circuit, and current sensors configured to generate sense currents based on load currents passing through the power switches. The chip also includes a readout circuit having an input and an output, and signal routing coupling the current sensors to the input of the readout circuit.

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 discloses a method for diagnosing an IGBT open-circuit fault and a current sensor fault of a T-type three-level inverter, belongs to the technical field of inverter fault diagnosis, and is used for solving the problems of diagnosis and positioning when a three-level T-type inverter IGBT switch tube and a sensor have faults. Comprising the following steps: classifying and marking and numbering fault types of an open-circuit fault of a T-type three-level inverter IGBT and a fault of a current sensor; fault samples are collected and divided into a training set and a test set according to the proportion of 7: 3; a TCN-LSTM-SelfAction fault diagnosis model is constructed, and model training is carried out by using the training set; the trained model is used for evaluating the accuracy of fault type diagnosis of the training set and the test set. According to the diagnosis method, the fault types and the fault positions of the IGBT open-circuit fault and the current sensor fault can be diagnosed at the same time, the TCN and the LSTM are selected to be combined, local information and global relation of fault features can be captured at the same time, and meanwhile, the performance of a model is improved by combining a self-attention mechanism in the LSTM, and the accuracy is improved.

The invention discloses a high-precision electric power measurement method based on an intelligent electric meter, and relates to the technical field of electric power measurement. According to the invention, a plurality of high-precision voltage and current sensors with temperature compensation are uniformly arranged in a circuit, a low-noise front-end conditioning and multi-channel high-resolution ADC synchronous sampling technology is adopted, and a digital phase-locked loop is introduced to realize accurate correction of a sampling clock. And performing multi-stage weighted fusion, time domain instantaneous power calculation and FFT frequency domain decomposition compensation on the acquired data, and finally accurately calculating the real power in a power grid period. According to the method, measurement errors caused by temperature excursion, phase deviation and harmonic interference in the prior art are effectively solved, the measurement precision and stability of the system are remarkably improved, and the method is suitable for high-precision power monitoring in a complex power grid environment.

The invention relates to the technical field of dynamic correction and intelligent compensation of measured data of a current sensor, and discloses a self-adaptive data compensation method for the current sensor, which comprises the following steps of: acquiring an original output signal and environmental parameters in real time, identifying multi-dimensional error sources such as temperature drift, nonlinear error and electromagnetic coupling error, establishing an influence factor model, and calculating the dynamic correction and intelligent compensation of the measured data of the current sensor. Dynamic compensation is realized by fusing directivity judgment of each error source and a weighting mechanism, and online learning and model parameter updating are carried out by introducing a long-short-term memory network structure, so that the problems of poor environmental adaptability, limited compensation precision and lagging model updating in the prior art are effectively solved.

The invention discloses a method and system for measuring earth differential leakage current of a GIS shell, and relates to the technical field of leakage current detection, and the method comprises the steps: arranging a point position optimization scheme based on a sensor, arranging a current sensor, and synchronously obtaining a current signal collected by the current sensor; filtering the collected current signal by using a self-adaptive differential filter to obtain a real leakage current signal result; and performing real-time detection and analysis on signals of different frequency bands of a real leakage current signal result by using a multi-stage frequency domain filter. Based on a method of combining adaptive differential filtering, multi-sensor fusion, multi-frequency-domain analysis and intelligent threshold detection, the leakage current signal can still be accurately identified and extracted in a complex noise environment through the test of an analog signal, and the reliability and effectiveness of GIS equipment monitoring are guaranteed.

The invention provides a test system and method for calibrating high-power large-current parameters, and belongs to the technical field of power electronic component testing and metering, and the system comprises a high-power large-current standard device, a standard current sensor and a high-speed data acquisition unit. The high-power large-current standard device is used for providing high-power large current for the load to be tested; and the standard current sensor and the high-speed data acquisition unit are used for calibrating the large current of the high-power large-current standard device. The high-power large-current parameter traceability is realized, and the high-power large-current parameter quantity value is ensured to be accurate and reliable.

The invention discloses a simple evaluation method and device for the insulation state of an electric cabinet based on cooperative detection of three sensors, and the method comprises the steps: employing a digital temperature sensor, a capacitive humidity sensor and a leakage current sensor to synchronously collect the temperature, humidity and leakage current parameters in the electric cabinet, and calculating a condensation risk index, a surface dirt coefficient and an insulation deterioration degree through a processor; an insulation diagnosis conclusion is obtained in combination with the three-dimensional judgment matrix, the state of the sensor is judged through a self-checking mechanism of temperature-humidity consistency checking and leakage current reference checking, and the upper computer displays the result and generates a report. According to the scheme, reliable insulation detection and early warning are achieved through a low-cost sensor, and the method is suitable for the marine high-humidity variable-temperature environment.

The invention discloses a multi-mode sensing electromechanical actuator fault prediction system and a control method thereof, and relates to the field of intelligent maintenance of electromechanical integrated equipment. Comprising the following steps: S1, multi-mode sensor deployment and signal acquisition: cooperatively deploying a vibration sensor, a current sensor and a temperature sensor at a bearing, a motor winding and a driver of an electromechanical actuator; and S2, bionic pulse coding and time synchronization: converting the three-mode signal into a sparse pulse stream with time synchronization through a self-adaptive integral distribution model with dynamic threshold adjustment. Through bionic multi-mode sensing, brain-like edge calculation and dynamic fault-tolerant control, real-time health management of the high-precision electromechanical system is realized, the equipment reliability is remarkably improved, and the operation and maintenance cost is reduced. The method is suitable for industrial application with precise assembly requirements such as automobile welding lines, aerospace machining and metal 3D printing.

The invention discloses a new energy boost transformer abnormity early warning system and method, and the system comprises a data collection module which employs a sensor technology, a data collection card carries out the collection of the data of a new energy boost transformer, and carries out the early warning of the new energy boost transformer. The sensors comprise a temperature sensor, a voltage sensor, a current sensor, a vibration sensor and an oil level sensor. Feature vectors are selected by adopting a wavelet transform algorithm, a boost variation anomaly positioning model is established by adopting an improved artificial neural network to determine an anomaly position, parameters of an anomaly detection model are determined by adopting a genetic algorithm, and an anomaly type is judged. The MFE-TS model is established to optimize the objective function so as to improve the accuracy of new energy boost variation abnormity identification, the man-machine interaction module is adopted to display the abnormal position and the abnormal type on the user interface, and the new energy boost variation abnormity early warning capability is improved.

The invention discloses a communication equipment fault intelligent diagnosis method and device, equipment and a medium. The method comprises the steps of collecting equipment operation logs in real time, and extracting an abnormal point set; clustering the abnormal points, and matching a hardware fault, a software error and a network congestion type in combination with a preset mode library; dimensionality reduction is carried out on temperature, voltage and current sensor data based on principal component analysis, and a time-synchronized fusion feature vector is constructed; analyzing features and abnormal modes, and outputting potential fault points; associating the classification model to generate a fault priority by extracting the dynamic characteristics of the network flow; and a dependency network is constructed in combination with Bayesian reasoning, so that accurate fault positioning is realized. The system supports incremental learning, updates an abnormal mode library and automatically expands a knowledge base. Through multi-dimensional data fusion and spatio-temporal feature joint modeling, the problems that a traditional method depends on manual rules and is high in false alarm rate are solved, and the method is suitable for complex scenes such as a 5G base station and a data center.

The invention discloses an anti-electromagnetic interference high-precision current sensing circuit and a current sensor, and relates to the technical field of current measurement. The system comprises a signal conditioning circuit used for carrying out voltage division on power supply voltage to obtain direct current bias voltage, superposing the direct current bias voltage with a sensor signal collected by a TMR sensor, carrying out amplification processing on the superposed sensor signal, and transmitting the amplified sensor signal to a signal filtering circuit; the signal filter circuit is used for filtering interference signals and clutters of the amplified sensor signals, and the filtered sensor signals are sent to the MCU for signal processing. The anti-electromagnetic interference high-precision current sensing circuit has the beneficial effects that firstly, the anti-electromagnetic interference high-precision current sensing circuit analyzes related interference sources and propagation paths of electromagnetic interference in a power grid operation environment, optimizes and selects types of electronic components and device layout, and effectively reduces the interference of the electromagnetic interference on the current sensing circuit; secondly, a hierarchical protection system structure is followed, and the influence of electromagnetic interference on a measurement circuit is reduced;

The invention provides a transformer substation residual current intelligent monitoring method and device based on multi-parameter fusion, and relates to the technical field of power system monitoring, and the method comprises the steps: collecting the residual current value of each loop through a residual current sensor, and collecting environment parameters at the same time; performing dynamic confidence fusion on the residual current values of the plurality of residual current sensors through a confidence function and the consistency factor to obtain a fused residual current value; calculating self-adaptive weights of the environmental parameters, and calculating a dynamic threshold value through the nonlinear mapping function of the environmental parameters and the self-adaptive weights; calculating a multi-parameter fusion evaluation index, combining the fusion residual current value, the dynamic threshold value and the multi-parameter fusion evaluation index, calculating a comprehensive fault risk index through a fault membership function, and obtaining a fault diagnosis result according to the risk index value; and outputting early warning information according to the fault diagnosis result. According to the invention, intelligent monitoring and fault early warning of the residual current of the transformer substation can be realized, and the monitoring precision and the reliability of fault diagnosis are improved.

The invention discloses a low-voltage power grid residual current monitoring and early warning system and method based on an intelligent network, and belongs to the field of safety monitoring. The method comprises the steps that a residual current sensor is installed to monitor current change, and current fluctuation is intelligently analyzed; establishing a machine learning model based on historical data, and training normal fluctuation characteristics of the residual current; the sudden change of the current is judged through a time sequence anomaly detection algorithm, and a new data optimization model is regularly utilized; the residual current fluctuation is graded, when the current deviation reaches a threshold value, the system automatically sends a notification of the corresponding grade according to the early warning grade, the position of a fault node is automatically judged through the geographic position of a sensor and a network topology structure, and a fault section is judged in combination with power grid topology data; after early warning is triggered, the system performs statistical analysis on abnormal current data, and the system automatically optimizes the acquisition frequency and the early warning threshold according to the real-time current data and the fault probability. According to the invention, through monitoring and intelligent early warning, the safety and response speed of the power grid are significantly improved.

The invention discloses an unmanned aerial vehicle risk assessment method and system based on a behavior recognition mode. The method comprises the steps of obtaining a target acoustic signal and a target image sequence of an enemy unmanned aerial vehicle; analyzing and processing the target acoustic signal to obtain sound source positioning information; analyzing and processing the target image sequence to obtain optical flow data; fusing the sound source positioning information and the optical flow data to obtain target fusion data; inputting the target fusion data into a target neural network for identification to obtain a target behavior mode of the hostile unmanned aerial vehicle; calculating the target behavior mode and the current sensor data to obtain probability distribution data of candidate behavior modes corresponding to the target behavior mode; and inputting the probability distribution data into a target risk assessment model for assessment to obtain a target risk level. The method can improve the risk assessment accuracy of the hostile unmanned aerial vehicle, facilitates the formulation of targeted coping strategies and defense measures, and can be widely applied to the technical field of security defense.

The invention relates to the technical field of sensors, in particular to a non-intrusive passive wireless self-energy-taking quantum current sensor. The quantum current sensor comprises a self-energy-taking module and a quantum current sensing module. The self-energy-taking module is used for collecting environment energy of the environment where the tested device is located and converting the environment energy into electric energy so as to supply power to the quantum current sensing module through the converted electric energy; the environment energy comprises at least one of electric energy, light energy and magnetic energy; and the quantum current sensing module is used for sensing a current signal of the detected device under the condition of detecting that the detected device generates the magnetic field.

The invention discloses a DC fault arc identification method and device based on a wavelet transform algorithm, and the method comprises the steps: collecting a DC line current signal through a high-bandwidth current sensor, carrying out the digitalization of a signal conditioning circuit and a high-speed ADC, and sequentially carrying out the preprocessing of the elimination of baseline drift through differential filtering and the suppression of specific frequency interference through adaptive notch; performing 3-8-layer multi-scale decomposition on the pure signal by adopting a Daubechies wavelet basis, preferably selecting high-frequency detail coefficients from the first layer to the third layer, and creatively fusing a multi-scale energy entropy representing energy distribution randomness and a time domain kurtosis representing transient impact to form a composite characteristic quantity; a threshold value (threshold value = mean value + adaptive coefficient * standard deviation) is dynamically generated based on sliding time window historical data, the adaptive coefficient is dynamically adjusted according to the load type, the resistive load is 1.8-2.2, and the inductive / capacitive load is 2.5-3.5; and when the composite characteristic quantity exceeds a threshold value and the sample proportion is greater than or equal to 85% within the duration of 0.5-5ms, a tripping execution mechanism is triggered.

The invention discloses a multidimensional monitoring system of an on-load tap-changer for a converter transformer and a fault prediction method, the system comprises a motor, a change-over switch core and other parts, and a data acquisition module is provided with a current, vibration and oil chromatography acquisition unit and a vacuum tube life monitoring unit. The current sensor collects motor current, the vibration sensor collects vibration signals of the change-over switch, the oil chromatography collection unit analyzes an oil sample, and the vacuum tube life monitoring unit evaluates the residual life of the vacuum tube. And a data transmission and processing analysis module. The fault prediction method comprises the steps of data acquisition, preprocessing, multi-dimensional data fusion, fault prediction model construction, feature extraction and selection, fault prediction and evaluation and the like. And screening characteristic parameters and fusion data by adopting a plurality of technical means, and constructing a machine learning model to predict faults. According to the invention, multi-source data acquisition and multiple analysis methods are integrated, the equipment state can be comprehensively reflected, the monitoring reliability is improved, faults can be predicted in advance, and the equipment fault rate is reduced.

The invention provides an intelligent adjusting clamping device and method for composite material resistance welding. The intelligent adjusting clamping device mainly comprises a control system, a resistance welding machine, a current sensor, an automatic clamping device, an image recognition system and an infrared thermometer. According to the device, the type of a composite material is automatically recognized through an image recognition system, and optimized welding parameters are called according to material characteristics; the automatic clamping device is provided with a telescopic and rotatable clamping arm, a pressure sensor is integrated in the clamping arm, accurate clamping can be achieved, and the clamping pressure can be monitored in real time; the infrared thermometer and the current sensor are used for monitoring the temperature and the current in the welding process, the control system achieves closed-loop adjustment of welding parameters based on sensor data, and therefore the welding quality is ensured. The device and method can remarkably improve the welding stability, efficiency and consistency of the composite material, and are widely suitable for high-performance composite material connection in the fields of aerospace, automobile manufacturing and the like.