3990results 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.

A system-side harmonic impedance estimation method and apparatus, and a storage medium and an electronic device. The method comprises: acquiring an h-th harmonic parameter equivalent circuit simplified on the basis of power grid characteristics, h being a positive integer greater than or equal to 2 (S1); on the basis of the h-th harmonic parameter equivalent circuit, establishing a system-side harmonic impedance estimation mathematical model (S2); acquiring amplitude and phase data of a harmonic voltage and a harmonic current at a PCC (S3); dividing the amplitude data of the harmonic voltage at the PCC into several stable intervals (S4); on the basis of the amplitude and phase data of the harmonic voltage and the harmonic current in the several stable intervals, establishing a variable coefficient regression model between a harmonic voltage phasor and a harmonic current phasor (S5); selecting an optimal window width of a kernel function of the variable coefficient regression model (S6); and calculating a system harmonic impedance on the basis of the variable coefficient regression model and the optimal window width (S7).

The invention relates to the technical field of line monitoring, in particular to an electrical engineering high-voltage line fault monitoring system. The system comprises a data acquisition module used for acquiring current signals of monitoring nodes on a mining high-voltage line at all moments; the anomaly analysis module is used for constructing a harmonic anomaly characteristic value of each current signal according to current signal change and data mutation characteristics in an adjacent time period of each current signal; obtaining an abnormal score correction value of each current signal based on the probability density of each current signal and the harmonic abnormal characteristic value; the power transmission control module is used for acquiring all abnormal current signals based on the abnormal score correction value by using an abnormal detection algorithm; and obtaining a judgment result of an abnormal fault based on all the abnormal current signals. Therefore, the mining high-voltage line is monitored, the accuracy of monitoring the abnormal fault of the power supply of the mining high-voltage line is improved, and the stability of the power supply of the mining high-voltage line is effectively ensured.

A sensing system is provided for contactless sensing of current. The system includes a conductor for generating a magnetic field as electric current flows through the conductor, the conductor having a predetermined width and comprising a hole with a predetermined hole width passing through the whole thickness of the conductor, and a magnetic sensor for measuring at least one component of the magnetic field generated by the conductor. The magnetic sensor overlaps the hole. The current sensing is done based on the measured magnetic field. The sensor is positioned at a predetermined distance over a top surface of the conductor. The width of the hole is at least 0.15 times the width of the conductor.

The invention discloses a current detection system based on an inductance element, and relates to the technical field of current detection, and the system comprises a power switch module which is used for adjusting the conduction angle of a power switch device; the driving control module is used for improving the signal driving capability and lowering the driving potential; the current sampling module is used for detecting an inductive current change rate; the output detection module is used for adjusting a first fault threshold value and a second fault threshold value according to the aging degree and carrying out first-class short circuit or second-class short circuit detection; the conduction detection module is used for detecting saturated conduction voltage drop; the aging judgment module is used for detecting the aging degree; the detection adjusting module is used for adjusting a third fault threshold value according to the aging degree and cooperating with the short-circuit judgment module to perform second-class short-circuit detection; and the intelligent control module is used for signal receiving and module control. According to the current detection system based on the inductance element, the sensitivity of fault current detection is improved, whether the power switch module is normal or not is accurately judged, and the system safety is improved.

The embodiment of the invention provides a differential Hall current sensor and a manufacturing method thereof, and belongs to the technical field of semiconductors. The differential Hall current sensor comprises a current bar; the insulating layer is bonded on the upper surface of the current bar; the at least two Hall elements are symmetrically arranged on the two sides of the upper surface of the insulating layer in the current direction; and the at least one deflection magnetism gathering block is attached to the magnetic induction surfaces of the at least two Hall elements and is used for deflecting a magnetic field component of the current magnetic field in the direction parallel to the magnetic induction surfaces of the Hall elements into a magnetic field component in the direction perpendicular to the magnetic induction surfaces of the Hall elements. According to the differential Hall current sensor, the current magnetic field is gathered through the deflection magnetism gathering block to form a conduction magnetic circuit, the directions of the magnetic field components are changed, the Hall element obtains the magnetic field intensity formed by superposing the magnetic field components in the two directions, and the measurement sensitivity is improved.

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 relates to the technical field of ground fault detection, in particular to a centralized substation terminal with a data real-time acquisition and analysis function. Performing difference on the current effective value sequence to obtain a target moment; obtaining a local number according to the historically connected differential value of the target moment; acquiring a local current segment and a corresponding spectrogram according to the target moment and the local number; obtaining a harmonic ratio coefficient according to the number of peak points in the spectrograms of the target moment and the historical adjacent target moments and the amplitudes of the peak points; obtaining a target coefficient according to a difference value between the target moment and a historical adjacent target moment and a harmonic ratio coefficient, and carrying out clustering; and obtaining a detection reference value according to the discrete characteristics of the target coefficient in the cluster. The abnormal characteristic value is obtained according to the target coefficient and the detection reference value at the current moment; and high-resistance grounding fault monitoring is carried out according to the abnormal characteristic value, so that the fault detection accuracy 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 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.

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 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 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 relates to the technical field of power battery testing, in particular to a BMS function testing system and method for a new energy automobile. The method comprises the following steps: constructing battery pack structure model data according to battery pack CAD design drawing data; collecting vehicle working condition charging and discharging sample data; carrying out adaptive current excitation sequence processing according to the vehicle working condition charging and discharging sample data, and generating multi-working condition disturbance pulse excitation sequence data; performing disturbance magnetic field simulation on the battery pack structure model data through the multi-working-condition disturbance pulse excitation sequence data to obtain working condition test magnetic disturbance response data; and performing battery pack BMS monitoring data acquisition based on the working condition test magnetic disturbance response data, performing BMS current distribution monitoring performance evaluation, and generating BMS current monitoring performance evaluation data. By simulating current excitation and magnetic field response under multiple working conditions, comprehensive evaluation of the BMS performance is realized, and the test accuracy and efficiency are improved.

Disclosed in the present invention are a method and system for determining a short-circuit current of a grid-forming power electronic device. The method comprises: establishing a short-circuit equivalent model for a grid-forming power electronic device, and initializing a dynamic compensation current value to 0; acquiring a node voltage prediction value and a fixed branch short-circuit current prediction value on the basis of the present dynamic compensation current value and the short-circuit equivalent model for the grid-forming power electronic device; determining a power electronic device short-circuit current prediction value on the basis of the present fixed branch short-circuit current prediction value and the present dynamic compensation current value; determining the maximum current limit value, an active current limit value and a reactive current limit value of the power electronic device under an actual short-circuit fault; performing three-level amplitude limiting, and determining an active current value and a reactive current value after amplitude limiting; calculating a compensation current dynamic correction amount; and if the present compensation current dynamic correction amount is less than or equal to a preset threshold, determining that the present power electronic device short-circuit current prediction value is the short-circuit current actual value of the grid-forming power electronic device.

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 current measurement, and discloses an open-close type current transformer which comprises a lower shell, one side of the top of the lower shell is rotatably connected with an upper shell, semicircular iron cores are embedded in the lower shell and the upper shell, a copper wire is wound on the iron core located in the lower shell, two ends of the copper wire are fixedly connected with butt joints, and the butt joints are fixedly connected with the upper shell. The butt joint is embedded in the outer wall of one side of the lower shell, the side, close to the butt joint, of the lower shell is rotationally connected with a mounting plate, butt joint rods are fixedly connected to the sides, close to the butt joint, of the two ends of the mounting plate, two limiting holes are formed in the mounting plate, and two limiting seats are fixedly connected to the side, close to the mounting plate, of the lower shell. Each limiting seat is aligned with the corresponding limiting hole in the same side, mounting grooves are formed in the tops and the bottoms of the limiting seats, limiting blocks are slidably connected into the mounting grooves, and the problem that due to the fact that the positive electrode and the negative electrode are reversely connected, wiring copper wires need to be detached, and then winding work needs to be conducted by replacing the positions, and operation is troublesome can be solved.

The invention relates to the technical field of temperature measurement, in particular to an electric energy meter wiring terminal temperature detection method and system, and the method comprises the steps: obtaining the temperature data and current data of each wiring terminal of each electric energy meter in different time periods within a preset time period, the environment temperature of each electric energy meter, and the structure data of each electric energy meter; determining a heat dissipation condition coefficient of the wiring terminal in the time period; selecting a first time period which simultaneously satisfies a subtraction interval in the current fitting curve of all the wiring terminals of the electric energy meter, and selecting a second time period in which the mean value of the current data of each wiring terminal is the lowest from the first time period; determining an external influence index of the wiring terminal in the time period; calculating the electromagnetic interference intensity of the wiring terminal in the time period; and after parameters of Wiener filtering are set to be electromagnetic interference intensity, temperature data of the wiring terminal in the time period are subjected to noise reduction processing. According to the invention, the noise reduction effect of the temperature detection data is improved, and the authenticity and accuracy of the temperature detection data after noise reduction are further 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 provides a lightning protection intelligent monitoring method and system in a power distribution network, and the method comprises the steps: extracting the transient energy of a polarity current from the lightning stroke current of each lightning monitoring node, determining the cross-correlation between all transient energy, carrying out the state response evaluation of the lightning stroke current of the lightning monitoring node based on a fuzzy evaluation mechanism according to the cross-correlation, and obtaining the state response of the lightning stroke current of the lightning monitoring node. Obtaining the monitoring confidence of the thunder and lightning monitoring node; according to the historical lightning stroke current, determining a time sequence dependency relationship of the lightning stroke current between the adjacent lightning monitoring nodes, and determining a response characteristic of lightning stroke current attenuation between the adjacent lightning monitoring nodes through all the time sequence dependency relationships and the topological structure of the target power distribution network; and according to the monitoring confidence and all the response characteristics, confidence correction is carried out on the lightning stroke current of the lightning monitoring node, the corrected lightning stroke current is obtained, and then the lightning stroke occurrence position is positioned. By adopting the scheme of the invention, correlation confidence correction of lightning stroke current between monitoring nodes can be realized, so that the positioning precision of a lightning stroke occurrence position is improved.

The invention relates to the technical field of grounding current detection, and particularly provides a power equipment fault diagnosis method based on multi-dimensional feature attention enhancement, which comprises the following steps: acquiring first historical grounding current time sequence data and preprocessing to obtain a first preprocessing time sequence matrix; inputting the first preprocessing time sequence matrix into a trained grounding current prediction network model to output predicted grounding current time sequence data; the grounding current prediction network model comprises a multi-scale spatio-temporal feature extraction module, a double-stage attention mechanism module and a long short-term memory network module, and the loss function comprises classification loss and prediction error loss; and comparing the preset fault interval with the predicted grounding current time sequence data, and if the predicted grounding current time sequence data is in the preset fault interval, determining that the power equipment has a fault. Accurate grounding current prediction data are obtained through the grounding current prediction network model, and potential faults of power equipment can be prevented and eliminated.

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 discloses a high-voltage cable terminal parallel connection lightning arrester total current online monitoring method and system, and relates to the technical field of power grid equipment, and the method comprises the steps: obtaining the cable coupling coefficient of each cable terminal to a current lightning arrester under the single-phase effect at a test stage, and obtaining the cable coupling coefficient of each cable terminal to the current lightning arrester; obtaining an inter-phase coupling coefficient between the current lightning arrester and the remaining lightning arresters under the single-phase action; in actual work, the leakage current detection module is adopted to obtain the leakage current induction value of the current lightning arrester on line; the power frequency calibration coefficient is adopted to calibrate the leakage current induction value, and a leakage current calibration value of the current lightning arrester is obtained; based on the leakage current calibration value, the cable coupling coefficient and the interphase coupling coefficient, obtaining a current leakage current actual value of the lightning arrester; in the scheme of the invention, coupling interference caused by spatial distribution is fully considered, correction is carried out based on the cable coupling coefficient and the interphase coupling coefficient to eliminate interference caused by spatial distribution parameters, and the current leakage amount of the lightning arrester can be accurately monitored.

The invention relates to the technical field of cable fault testing, in particular to a multi-core cable fault testing system and method, and the method comprises the steps: determining a fault generation coefficient of a target core wire testing current signal based on the difference between periods where all adjacent signal wave crests in the target core wire testing current signal are located and a periodic parameter; determining the composition richness of the target core wire test current signal based on the similarity of related data sets between the target core wire and all other core wires under different voltage conditions and the signal amplitude distribution near any maximum value point in the target core wire test current signal; and performing forward fusion on the composition richness and the fault occurrence coefficient, adjusting a preset initial decomposition interval scale, screening part of component signals for reconstruction, and performing fault detection on the reconstructed signals. According to the invention, accurate analysis of the test current signal is realized, and the accuracy of fault diagnosis is improved.