1861 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 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 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 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.

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 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 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 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 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 discloses an insulation resistance and capacitance measuring circuit and method based on monopulse double-edge analysis, and relates to the related field of the electrical insulation online monitoring technology, and the circuit comprises a detected object and an insulation detection circuit. The insulation detection circuit comprises a controlled pulse voltage source, a coupling resistance network, a current sensor, a switch control module, a signal modulation circuit and a processor. The measurement method implemented based on the circuit comprises the following steps: injecting a common-mode voltage pulse into a transmission line of the power transmission system by adopting a pulse transmitter; pulse current response is collected in real time, a current oscillogram is drawn, and a three-point sampling method is introduced to record a current value for insulation impedance parameter calculation; according to a load condition, a double-edge verification method is applied based on a pulse current attenuation characteristic, and insulation impedance parameters including a resistive component and a capacitive component are independently calculated by analyzing a pulse current rising edge and a pulse current falling edge. According to the invention, non-intrusive synchronous monitoring of resistive and capacitive components of line-to-ground impedance is realized, and the detection precision is improved.

The invention provides an elevator hall door lock fault detection method, device and system, and belongs to the technical field of elevator control. Comprising the following steps: S1, constructing an elevator hall door lock fault detection data set; s2, preprocessing the data and then extracting features; s3, constructing a fault detection model based on the features extracted in the step S2, and performing model training by using a cross entropy loss function to ensure that the model can accurately classify different fault states; s4, the model is trained based on dynamic learning rate adjustment and self-adaptive reweighting; and S5, deploying and using the model. Electrical signals and mechanical vibration signals of the elevator hall door lock are collected through the high-precision voltage and current sensor and the vibration sensor, fault detection and early warning operation are completed through the constructed fault detection model based on the deep neural network, adaptability to complex fault scenes is better, and detection accuracy and reliability are 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 relates to a cable joint resonance real-time diagnosis method and device and a medium, and belongs to the technical field of power equipment state monitoring, and the method comprises the following steps: obtaining a high-frequency current signal collected by a high-frequency current sensor and an ultrasonic vibration signal collected by an ultrasonic sensor array; performing signal synchronization on the high-frequency current signal and the ultrasonic vibration signal, and extracting a high-frequency current feature and an ultrasonic array feature; calculating a dominant frequency synchronization index, a normalized phase stability index and a nonlinear coupling index based on the high-frequency current characteristics and the ultrasonic array characteristics, and performing weighted summation to determine a fusion confidence coefficient function; and judging whether resonance exists or not based on the fusion confidence coefficient function, and if so, carrying out resonance source positioning. Compared with the prior art, the method has the advantages of high precision, low false alarm and high adaptability, and is suitable for key power system scenes such as new energy grid connection, urban smart power grids and wind power.

The invention provides a current sensor. The current sensor adopts a magnetic-core-free design, and adopts a diversion row group with a special structure to guide a target current so as to enhance the electrifying capability of the current sensor and reduce the adverse effect of a high-frequency current skin effect on measurement. Based on the structural characteristics of the diversion row group, the magnetic sensitive component for measuring the target current and the corresponding sensing signal processing method are arranged in a matched manner, so that the measurement precision of high-frequency large current is improved, the suppression capability of an interference magnetic field is improved, and the signal-to-noise ratio of an effective measurement signal is increased. The current sensor provided by the invention is compact in structure, simple in manufacturing process, strong in electrifying capability and high in signal-to-noise ratio of a sensing signal, not only widens the measuring range of the current sensor, but also effectively improves the measurement precision of high-frequency large current, and expands the application range of the magnetic-core-free current sensor.

The invention discloses an oil-immersed transformer active defense method and system based on pulse current characteristics. The acquisition device is used for acquiring output signals of high-frequency pulse current sensors mounted at a high-voltage bushing end screen grounding position, a high-voltage bushing position and an iron core grounding position to obtain digital pulse current signals; whether active defense is started or not is judged according to whether the instantaneous value of the pulse current signal at the iron core grounding position reaches a protection starting threshold or not; judging whether the current pulse cluster is a discharge pulse cluster with abnormal internal discharge or not based on the three groups of pulse current signal characteristics; and judging whether protection acts or not according to the number of abnormal discharge pulse clusters in a set period. According to the method, serious partial discharge in the large oil-immersed transformer can be identified, protection measures are actively taken, and the situation that serious discharge defects are further developed into arc breakdown faults is effectively avoided.

The invention provides a vehicle driving state estimation method and system in a centralized driving mode, and relates to the technical field of electric automobiles, and the method comprises the following steps: determining wheel dynamic parameters based on sensor detection information and a seven-degree-of-freedom dynamic model related to wheels, and combining a preset Dugoff tire model to obtain initial tire force parameters; a vehicle-mounted infrared camera is used for obtaining road surface image information, the road surface image information is analyzed through a deep convolutional neural network based on semantic segmentation and a vehicle dynamics inversion method, an adaptive fusion mechanism is introduced for adjustment, and a target road surface adhesion coefficient is determined; correcting the initial tire force parameter by using the target road adhesion coefficient to obtain a target tire force parameter; inputting the current sensor detection information and the target tire force parameter into a self-adaptive unscented Kalman filter to obtain vehicle driving state information; the vehicle driving state information comprises the longitudinal speed, the transverse speed, the yaw velocity and the side slip angle of each tire.

An electrical grid fault localization system including: a plurality of current sensors (measurement devices) electrically connected to the electrical grid; a memory for storing a representation of the electrical grid as a plurality of geographical markers, and for storing a plurality of sets of expected time data, each one of the plurality of sets of expected time data associated with each one of the plurality of geographical markers, each member of the set of expected time data based on an expected signal propagation time from, at least one of the plurality of geographical markers to at least one current sensor; a processor communicative with the plurality of current sensors and the memory, the processor configured to receive measured time data of a fault event from, the plurality of sensors to generate a set of fault event time data and to match the set of fault event time data to at least one of the plurality of sets of expected time data and identify in g / outputting at least one matched geographical marker. Methods and computer-readable media directed to electrical grid fault localization are also described.

The invention is applicable to the technical field of magnetic field measurement, and provides a current sensor magnetic field measurement method based on a tunneling magnetoresistance effect, which comprises the following steps of: analyzing a real-time output signal through a machine learning algorithm based on a mapping relation model of a parasitic capacitance parameter and a signal characteristic of a tunneling magnetoresistance sensor circuit; determining an actual value of a parasitic capacitance parameter; controlling a negative capacitance generation circuit and a numerical control resistance network to adjust an equivalent negative capacitance value and a resistance value according to the actual value of the parasitic capacitance parameter, and generating a matched compensation parameter set; feature extraction is carried out on the compensated signal, and an effective magnetic field component is obtained through multi-resolution time domain decomposition and phase consistency detection; calculating the magnetic field intensity and the change gradient based on the effective magnetic field component, and determining the effective working frequency range of the sensor according to the signal attenuation characteristic; and dynamically adjusting a compensation strategy according to a comparison result of the effective working frequency range and a preset target frequency band, and reducing measurement errors.

The invention relates to a charging pile multi-mode vehicle security monitoring method and system, and the method comprises the steps: collecting vehicle contour point cloud, environment parameters and voltage fluctuation data through a stereoscopic vision sensor, a temperature and humidity sensor and a current sensor, and generating a multi-mode fusion data set through time-space synchronization; comprehensive scores of vehicle deformation, environment sudden change and voltage abnormity are calculated based on a dynamic weight fusion model, low-risk, medium-risk and high-risk three-level safety judgment results are generated in combination with a quantile threshold interval, and the environment adaptability defect of a traditional fixed rule is eliminated through a dynamic weight and an adaptive threshold; triggering a hierarchical control instruction according to a judgment result, and synchronously constructing a credible evidence chain containing a timestamp, an operation log and an encryption check code to ensure that the security event is traceable and the data is tamper-proof; and finally, aggregating multi-node user feedback through a federated learning framework, updating a model weight and a threshold interval in combination with a differential privacy technology, and realizing dynamic optimization of a false alarm rate while protecting user privacy.

The invention discloses a doubly-fed induction generator fault detection method combining a sliding-mode observer and a bilateral cumulative sum algorithm, and the method comprises the following steps: designing a doubly-fed induction generator rotor current sliding-mode observer based on the combination of a novel reaching law and a doubly-fed induction generator state equation; carrying out stability and convergence analysis on the designed novel reaching law by adopting a Lyapunov stability theory; and the fault detection method of the doubly-fed induction generator is designed in combination with the optimized bilateral accumulation sum algorithm. A rotor current sensor fault, an input interference voltage fault, a voltage drop fault and a stator turn-to-turn short circuit fault are introduced. The rotor current tracking performance of the designed sliding-mode observer under different doubly-fed induction generator faults is verified by using a residual value obtained by comparing a doubly-fed induction generator rotor current value estimated by the sliding-mode observer with an actual rotor current output value; and meanwhile, the reliability of the designed doubly-fed induction generator fault detection method based on the optimized bilateral cumulative sum algorithm is verified. The doubly-fed induction generator fault detection method combining the sliding-mode observer and the bilateral cumulative sum algorithm has the advantages of being high in tracking precision and accurate in fault state characterization, meanwhile, weak fault features can be effectively converted into digital signals to be output, and faults of the doubly-fed induction generator are detected.

The invention discloses a short-circuit current limiting and short-circuit recovery transient voltage control device and method for an aviation three-stage power generation system, and the device comprises a three-stage power generator, a power generator controller, a current sensor, a voltage and current collection module, and an asymmetric half-bridge excitation control circuit. Collecting the output current iL and voltage UL of the generator through a digital processor of the generator controller; when short circuit occurs, the given voltage USet of the voltage regulating loop is linearly reduced in a piecewise mode according to the relation between the iL and a preset threshold value, so that the generator works in a constant power interval; and when the short circuit is recovered, closing the upper / lower power tubes of the asymmetric half-bridge excitation control circuit according to the relationship between UL and a preset threshold value in a grading manner, and triggering a negative-pressure follow-current mode of a main excitation winding of the generator controller to quickly extinguish the field. The method can adapt to different generators without adjusting hardware circuit parameters, has no motor ablation risk during short circuit, has transient voltage less than 350V after short circuit recovery, has no power supply interruption in the whole process, has response delay less than 1ms, and greatly improves the safety and robustness of an aviation power supply and distribution system.

The invention relates to the technical field of defect detection, and discloses a method for detecting defects of live cable equipment by using a high-frequency current detection method, which comprises the following steps of: exciting the live cable equipment to generate a high-frequency current signal, and judging the optimal frequency of the high-frequency current signal by adopting the minimum signal propagation loss; the method comprises the following steps: acquiring a current signal on cable equipment through a high-frequency current sensor, and performing multi-scale time-frequency analysis on the signal through wavelet packet transformation; carrying out noise reduction processing on the converted signal to remove an interference signal and extract a partial discharge signal; positioning the partial discharge signal based on the Bayesian theorem, and judging the position of a partial discharge source; and classifying the partial discharge signals by adopting a support vector machine. The excitation frequency of the high-frequency current signal enables the signal transmission loss to be minimized and the extraction efficiency of the partial discharge signal to be improved, and the signal transmission efficiency is improved in a complex cable environment, so that the defect detection precision and sensitivity are enhanced.

The invention provides a self-calibration method of a TMR (Tunnel Magnetoresistive) sensor and a TMR sensor array for current measurement, and relates to the technical field of quantum current sensors. According to the method, a double Wheatstone bridge structure is adopted, a to-be-measured bridge is used for measuring a to-be-measured magnetic field, and a reference bridge is used for measuring a known reference magnetic field; the self-calibration of the TMR sensor is realized by calculating the ratio of the output voltages of the two bridges, so that measurement errors caused by environmental factors such as temperature, humidity and mechanical vibration are eliminated; moreover, the invention provides a TMR sensor array for current measurement, the application of the TMR sensor array further enhances the adaptive capacity of the TMR sensor array in a complex environment, and the array is formed by a plurality of TMR sensor units, so that not only can the spatial resolution of measurement be improved, but also the fault-tolerant capability of the system can be improved through redundancy configuration, and the system reliability is improved. The array structure enables the TMR sensor to perform accurate measurement at a plurality of measurement points at the same time to obtain accurate measurement current.

The invention relates to a harmonic reducer fault diagnosis method based on double-branch feature fusion. The harmonic reducer fault diagnosis method comprises the following steps: synchronously acquiring multi-sensor data of a harmonic reducer acquired by vibration, acoustic emission and current sensors; processing the multi-sensor data through a one-dimensional convolutional neural network to obtain data in a two-dimensional matrix form; the data in the two-dimensional matrix form is input into a pre-trained model, and the processing process of the model is as follows: feature extraction is performed on the data in the two-dimensional matrix form through an MCWGraphKAN branch feature extraction module and a GBiMama branch feature extraction module to obtain a first feature and a second feature, the first feature and the second feature are fused and then mapped to a sample space through a full-connection layer, and the data in the two-dimensional matrix form are obtained; and fault classification is realized. The method achieves the fault diagnosis of the harmonic reducer through a deep learning method, and is used for solving the problems that a single sensor cannot fully express the operation state of the harmonic reducer, and the fault feature extraction of a single-branch network is insufficient.

The invention discloses a rectifier bridge arm fault identification method and system circuit based on multi-characteristic current waveform analysis, and relates to the technical field of rectifier bridge arm fault identification, and the method comprises the steps: collecting a current signal of an AC input side of a rectifier bridge through a current sensor, and obtaining an original current time domain waveform after filtering conditioning; performing period division on the original current time domain waveform based on a voltage synchronization signal, and outputting a positive half-cycle conduction interval and a negative half-cycle conduction interval in each power frequency period; in each power frequency period, performing square integral operation on the current waveforms of the positive half-cycle conduction interval and the negative half-cycle conduction interval to obtain a positive half-cycle energy value and a negative half-cycle energy value, and calculating an energy unbalance degree; and windowing fast Fourier transform is carried out on the original current time domain waveform, amplitudes and phases of third, fifth and seventh characteristic harmonics are extracted to form a harmonic characteristic vector, the vector is matched with a pre-stored bridge arm fault harmonic fingerprint template, and the matching degree of each fault type is obtained.

The invention discloses a sensor signal prediction method, system and equipment for a humanoid robot. The method comprises the following steps: acquiring a multi-source sensor training sample of the humanoid robot; training the heterogeneous neural network fusion model according to the multi-source sensor training sample through a signal amplitude loss function, a frequency loss function and a transient characteristic loss function to obtain a signal prediction model; obtaining current sensor signals of the humanoid robot, wherein the current sensor signals comprise at least two of a joint angle signal, a motor current signal or an acceleration signal; and inputting the current sensor signal into the signal prediction model, and outputting a predicted sensor signal which is a force sense signal or a torque signal. Through multi-source sensor signal fusion and multi-domain feature learning, accurate prediction of key sensor signals is realized, the number of deployed sensors is reduced, the cost is reduced, the system reliability is improved, and the method can be widely applied to the technical field of robots.

A control method for a flame retardant system of an energy storage battery pack, comprising: for each battery module, when a fire occurs in the battery module, on the basis of sensor data provided by a sensor module corresponding to the battery module, determining the fire development stage of the battery module and, on the basis of the fire development stage, controlling the initial degree of opening of a gas release module corresponding to the battery module, so as to activate flame retardation; during the flame retardation, on the basis of the sensor data provided by the sensor module, updating the fire development stage of the battery module and, on the basis of the sensor data provided by the sensor module and the degree of opening of the gas release module, calculating a pressure relief rate of the battery module; and, if the current pressure relief rate falls outside of a target rate range corresponding to the current fire development stage, on the basis of the current sensor data, adjusting the degree of opening of the gas release module.