38 results about "Fault detection and identification" patented technology

The invention relates to the technical field of fault detection, and discloses a remote electric energy meter fault detection and identification system and method, and the system comprises a data collection module, a data integration module, a data inspection module, a feature extraction module, a fault analysis module, and a fault alarm module. According to the method, multiple electric energy meter target data are collected and integrated into a corresponding data set, and a complete and ordered data basis is provided for analysis; abnormal values are recognized and replaced by calculating the average value and the standard deviation, the data quality is improved, and the accurate analysis result is guaranteed; key parameters covering multiple aspects are accurately extracted from a data set, and a powerful basis is provided for fault analysis; from metering, hardware and communication fault dimensions, according to comparison of extracted parameters and threshold values, fault types and positions are accurately judged in multiple directions; the fault alarm module remotely transmits an analysis result to a monitoring center, and generates an alarm message containing detailed information such as an electric energy meter number, a fault type, a parameter value, a threshold value and the like when a fault occurs, thereby facilitating timely maintenance by maintenance personnel.

The invention belongs to the technical field of fault detection, and relates to a high-voltage transmission line fault detection and identification method. The method comprises the following steps of: dividing candidate fault sections according to a topological structure by synchronously acquiring steady-state and transient-state traveling wave signals of key nodes of a power grid; identifying a fault by using a multi-feature fusion criterion and extracting traveling wave features; executing mode recognition-based steady-state section judgment and multi-terminal ranging and polarity verification-based transient positioning in parallel to generate two types of positioning result sets; and finally, carrying out intelligent fusion judgment on the two types of results under topological constraints. According to the method, the problems that a traditional single-point signal analysis method cannot determine a fault section, lacks space positioning capability and is insufficient in adaptability are solved, section-level accurate positioning of the fault is realized, and the positioning accuracy, robustness and fault processing efficiency are remarkably improved.

The present invention relates to the fields of computer science and information technology, and specifically to a fault isolation and rapid recovery system based on a microservice architecture, which integrates automated fault detection and identification technology, intelligent grayscale release mechanism, fault isolation technology, rapid recovery strategy and elastic microservice design to provide efficient and reliable fault management and system recovery solutions. By analyzing the operating indicators and log data of microservices, using machine learning algorithms to detect abnormal behaviors, automatic fault detection is achieved; using containerization technology and Kubernetes clusters for intelligent grayscale release, and real-time monitoring of the application status of new versions; achieving fault isolation through service grids, and using Kubernetes automatic expansion strategies to increase the number of healthy Pods and quickly isolate faults; when a fault occurs, through the rollback operation in the CI / CD process, the above technical measures are used in combination to effectively improve the stability, flexibility and maintainability of the system, and are suitable for complex and changeable production environments.

The invention provides a method and a system for improving the self-healing capability of a highway micro-grid in extreme weather, and relates to the technical field of power system automation. The method comprises the following steps: deploying a sensor to collect real-time operation data, and combining meteorological data to generate an integrated system state data set through fusion processing; the digital twin engine generates system state prediction data through state estimation and dynamic simulation; the random multi-objective optimization model solves and generates a preventive scheduling instruction according to the above, and controls a power generation unit, an energy storage unit and a load unit to adjust operation points; fault detection and identification are carried out through residual analysis, a self-healing control instruction sequence is generated by using a sequence decision model, the switch equipment is controlled to isolate faults and restore power supply, closed-loop management from prediction and scheduling to self-healing is realized, and the power supply reliability and rapid recovery capability of the highway microgrid in extreme climate are improved.

The invention relates to the technical field of power distribution. The invention discloses a power distribution cabinet wire fault detection and identification method, which is characterized in that a plurality of wires with different specifications are arranged in a power distribution cabinet, and the method comprises the following steps: obtaining real-time operation parameter data of the plurality of wires, and determining a fault wire based on a reference parameter set of normal work of a to-be-detected wire and a deviation characteristic value relation of real-time parameters; fault parameter data of the faulty electric wire are input into a multi-layer fault identification model, the model comprises a plurality of sub-identification units connected in sequence, each unit detects different types of faults, and finally a fault mode is obtained. According to the method, at least three parameters are acquired through distributed sensing, and data reliability is guaranteed through noise reduction and normalization processing. On the basis of a double-threshold criterion and dynamic weight calculation, faulty electric wires are accurately distinguished, and misjudgment and missed judgment are completely eradicated. The multi-layer model carries out progressive detection, an emergency fault is preferentially recognized, and the sub-recognition units are matched with an exclusive algorithm to improve the accuracy.

The application provides a weak fault feature extraction method for an autonomous underwater vehicle propeller, and belongs to the technical field of underwater vehicle fault diagnosis, and comprises two parts: fault feature enhancement and feature fusion. First, the application optimizes parameters by judging the Gaussianity of all modes of multi-source state signals and control signals through negative entropy, completes noise reduction, and extracts and enhances fault features based on a modified Bayesian algorithm. Then, the feature signals are divided into multiple time intervals, the faults occurring in each interval are taken as focal elements, all signals except the longitudinal velocity are subjected to first feature fusion, the first fusion result is subjected to second fusion with the feature signal of the longitudinal velocity, the fault features are further enhanced, and the monotonicity between the fault features and the fault degree is presented. The application can provide a basis for subsequent fault detection and identification, and is particularly suitable for state monitoring of autonomous underwater vehicle propellers.

The invention discloses a transformer fault detection method and device, a transformer and a storage medium. The transformer fault detection method comprises the following steps: acquiring hydrogen gas concentration, methane gas concentration, ethylene gas concentration, ethane gas concentration and acetylene gas concentration of dissolved gas in transformer oil, and determining a target transformer fault area corresponding to the dissolved gas according to the acetylene gas concentration; and determining a decision coordinate value corresponding to the dissolved gas according to the hydrogen gas concentration, the methane gas concentration, the ethylene gas concentration, the ethane gas concentration and the acetylene gas concentration, and determining a current transformer fault of the transformer according to the decision coordinate value and the target transformer fault area. According to the method, the dissolved gas in the transformer oil is effectively identified and quantified, the method is suitable for low-cost online fault detection of the transformer, and the accuracy of fault detection and identification of the transformer is improved.

The invention discloses a few-sample fault diagnosis method based on a multi-scale physical information neural network, and belongs to the technical field of fault diagnosis. The method comprises the following steps: step 1, constructing a bearing vibration signal data set containing multiple working conditions, and preprocessing an original vibration signal; 2, inputting the vibration signal samples in the training set into a CCSWGAN network, generating a high-quality synthetic sample, and carrying out normalization processing on the high-quality synthetic sample; step 3, mixing the original sample and the synthetic sample and randomly disrupting the original sample and the synthetic sample to form training data, inputting the training data into an MCB-PII module, and performing multi-scale feature extraction and fault diagnosis; and 4, deploying the trained model to production equipment, and carrying out fault detection and identification on vibration signals collected in real time. According to the method, the problems of unstable performance and insufficient adaptability of the model under complex working conditions are effectively avoided, so that higher accuracy and reliability are provided for bearing fault diagnosis.

The invention discloses a sliding bearing fault detection method and system based on IGWO-VMD-CNN. The method comprises the following steps: acquiring an acoustic emission signal of a sliding bearing; introducing improved tent mapping, a nonlinear convergence operator and an improved wolf position updating mechanism, and constructing an improved grey wolf optimization algorithm (IGWO); based on an improved grey wolf optimization algorithm, reconstructing the acoustic emission signal of the sliding bearing in combination with a variational mode decomposition (VMD) algorithm to obtain a reconstructed acoustic emission signal of the sliding bearing; and constructing a sliding bearing fault detection neural network (CNN), and carrying out fault detection identification processing on the reconstructed sliding bearing acoustic emission signal to obtain a sliding bearing fault detection result. According to the embodiment of the invention, the influence of the noise signal on fault signal detection can be reduced, and the fault detection and identification precision of the sliding bearing is improved. The sliding bearing fault detection method and system based on the IGWO-VMD-CNN can be widely applied to the technical field of sliding bearing fault diagnosis.

The invention discloses a GIS equipment abnormal sound vibration identification method based on an improved residual network, and belongs to the technical field of GIS equipment fault detection and identification. According to the method, the traditional residual error network (SENet) model is optimally designed, the compression excitation model based on the improved residual error network (SENet) is constructed, and the model is relatively high in recognition accuracy and relatively small in error. The model can accurately detect and identify the mechanical vibration caused by poor contact of the isolating switch contact of the gas insulated switchgear (GIS), and the identification effect is remarkable; and a new feasible scheme is provided for researching the detection and fault identification of the mechanical abnormal sound vibration defect of the gas insulated switchgear (GIS). According to the recognition method provided by the invention, the early-stage latent problem of the gas insulated switchgear (GIS) can be found and recognized early, early warning is realized, greater loss caused by major faults is reduced as much as possible, reliable supply of electric power is further ensured, and the recognition method has very important significance and practical value.

The invention, which belongs to the technical field of electric power operation and maintenance management, discloses an operation and maintenance fault detection and identification system comprising a preparation analysis module, an acquisition module, an edge end and a cloud end. The preparation analysis module is used for performing preparation analysis on operation and maintenance fault detection and generating a corresponding equipment information graph according to target equipment; determining a target acquisition data type of each target device according to the device information graph, classifying each target device according to the target device information and the target acquisition data type to obtain edge classes, and configuring a corresponding edge end for each edge class; the acquisition module is used for performing data acquisition on each target device to obtain operation and maintenance acquisition data; the edge end is used for processing the operation and maintenance collection data to obtain target collection data and sending the target collection data to the cloud end; the cloud comprises a fault diagnosis module, and the fault diagnosis module is used for analyzing the received target collection data to obtain a fault diagnosis result.

The invention discloses a wind power plant power transmission system fault detection and identification model and system based on an SVM, and relates to the technical field of power systems, and the construction of the model comprises the steps: S1, defining a problem and a target; s2, data acquisition; s3, data preprocessing; s4, constructing an SVM (Support Vector Machine) model; s5, feature selection and dimension reduction; s6, performing model evaluation; s7, carrying out model integration; and the model is carried in a fault detection and identification system. According to the model and the system, a fault detection and identification model is constructed by using the identification capability of a support vector machine (SVM), the model can automatically learn and identify various fault signals from a large amount of data, and high accuracy can be maintained even under the condition that the data are non-linearly separable, so that the real-time monitoring capability of the system carrying the model can ensure quick response, and the fault detection and identification efficiency is improved. The time from fault detection to fault processing is greatly shortened, so that risks and potential economic losses are reduced, and the operation efficiency of the wind power plant is improved.

The invention provides a ground fault detection method and device. The device comprises an isolation DC-DC converter, a contactor KM1, diodes D1, D2 and D3, bulbs LP1 and LP2, indicating lamps LED1 and LED2, resistors R1 and R2, a voltage detection unit V and a controller. According to the invention, the power supply of the load can be switched from the DC bus to the isolated power supply without changing the power supply circuit of the transformer substation; meanwhile, load grounding fault detection and identification are automatically completed, manual operation is reduced, and detection efficiency is improved; high-efficiency isolation maintenance without power failure is realized, continuous power supply of important equipment is ensured, and safe and stable operation of a transformer substation is guaranteed.

The invention provides a feeder line terminal equipment short circuit fault rapid identification method, which utilizes a first half wave breaking phenomenon to carry out instant fault detection and identification, and comprises the following steps: establishing a fault database in a fault identification process, a longitudinal coordinate ifind at a curve intersection point of the shortest operation time t1 necessary for terminal equipment and a fault current waveform curve i2 is taken as a current instantaneous setting value S2, an absolute value of a curve slope of an intersection point p of the ifind and the fault current waveform curve i2 is taken as a setting value K2 = di2 (t) / dt of a current change rate, and fault current characteristic information (S2, K2) is stored in a fault library as a rapid identification criterion. The short-circuit fault database is continuously updated along with the fault identification process, and the short-circuit fault database is compared with the criterion stored in the fault database when the fault occurs, so that the fault identification can be completed in a 1 / 4 period, that is, the identification of the fault current can be completed in t2 time.

The invention relates to the technical field of semiconductor lasers, in particular to a semiconductor laser driving circuit which comprises an upper computer, a controller internal module, a constant-current driving circuit and a constant-temperature driving circuit. The upper computer is connected with the controller internal module, the controller internal module is respectively connected with the constant-current driving circuit and the constant-temperature driving circuit, and the constant-current driving circuit and the constant-temperature driving circuit are both connected with the semiconductor laser; the controller internal module comprises a fault detection and identification module, and the fault detection and identification module is connected with the constant-current driving circuit and the constant-temperature driving circuit; and the fault detection and identification module is used for monitoring the constant-current driving circuit and the constant-temperature driving circuit in real time, feeding back monitoring information and displaying the monitoring information to the upper computer. The laser driving circuit has the functions of automatic disconnection, fault early warning and fault prompting, can quickly position a problem and cut off a power supply when a fault occurs, improves the maintenance efficiency, can immediately cut off the power supply of the laser when the circuit fails, and improves the safety of the laser.

The present invention provides a method and apparatus for processing equipment sensor data for generalized fault diagnosis, which relates to the field of data processing technology. It determines standardized calculation parameters for multiple source domains based on multiple equipment types or operating conditions, and performs standardized processing on the sensor data of the target domain equipment accordingly, eliminating the scale differences caused by the inherent characteristics of the equipment and the sensor configuration. Furthermore, a preset autoencoder is combined with a dynamic feature decoupling matrix to decouple the time-frequency features in the mixed signal, thereby improving the interpretability and separability of the feature space. In addition, the nonlinear transformation term is used to enhance the model's ability to express complex signals, thereby extracting more representative low-dimensional features. Based on this, it can ensure that accurate and reliable fault detection and identification can be achieved under different equipment and different operating conditions.

The invention discloses a multi-mode on-orbit component irradiation fault diagnosis method and a micro module. The method comprises the following steps: acquiring voltage and current data of a to-be-tested device through an analog end circuit module; performing data preprocessing on in-phase data input and orthogonal data input acquired by the ADC; building a neural network as a fault diagnosis module; the data source of fault diagnosis is the data preprocessing output of the preceding stage; the method comprises the following steps: establishing a reverse neural network, combining a data preprocessing module, establishing a neural network training platform, training and testing the neural network through a fault data set, adaptively adjusting the learning rate of each parameter in combination with an Adam algorithm, extracting weight and bias parameters of the trained neural network, and transmitting the weight and bias parameters to an FPGA-end feedforward neural network; and fault diagnosis: inputting the preprocessed data into an FPGA-end feedforward neural network to complete calculation and output of a fault result. According to the method, more irradiation fault types are detected, the detection period is shorter, the detection precision is higher, and after training is completed, irradiation fault detection and recognition can be independently carried out according to collected information.

The application belongs to the technical field of smart grid fault detection and identification, and relates to a power distribution network intelligent inspection and fault diagnosis method based on multi-source information fusion. The method comprises an unmanned aerial vehicle inspection platform, an airborne edge computing terminal, a cloud operation and management platform, a PMS system and a mobile terminal. The application synchronously collects visible light images, infrared thermal images and independent temperature sensing data through the unmanned aerial vehicle platform, and utilizes deep learning and multi-modal fusion technology to realize data complementation and collaborative analysis at a feature level, so that real-time and accurate identification of power distribution network lines and equipment faults is finally completed.

The present application belongs to the technical field of fault detection, and relates to a high-voltage transmission line fault detection and identification method. The present application synchronously collects steady-state and transient traveling wave signals of key nodes of a power grid, divides candidate fault sections according to a topological structure; further utilizes a multi-feature fusion criterion to identify faults and extract traveling wave features; performs steady-state section discrimination based on pattern recognition and transient positioning based on multi-terminal ranging and polarity verification in parallel, to generate two types of positioning result sets; finally, intelligently fuses and judges the two types of results under topological constraints. The method solves the problems that a traditional single-point signal analysis method cannot determine a fault section, lacks spatial positioning capability and is insufficient in adaptability, realizes accurate positioning of a fault section, and significantly improves positioning accuracy, robustness and fault handling efficiency.

The present invention discloses an adaptive real-time multipath elimination and robust positioning method based on non-Gaussian distribution. The method does not make an assumption about the number of satellites that have faults or deviations, but modifies the Gaussian distribution assumption of errors in a traditional positioning model and converts it into a Gaussian mixture distribution, i.e., a non-Gaussian distribution. Parameters are solved by a maximum likelihood method based on real-time observation data, and the probability distribution of the deviation range of each satellite fault is adaptively calculated. The method can be used for identification and robust positioning under multiple faults, and can also be used for robust positioning and multipath elimination in a multipath environment. The present invention solves the shortcomings of existing RAIM and robust positioning methods. Currently, existing fault detection and identification methods have a large amount of computation, rely on a priori assumptions about the number of faults, and have a high misjudgment rate. The method provided by the present invention has a high fault detection success rate, a small amount of computation, high speed, high accuracy, and robustness.

This invention provides a method and system for detecting faults in distribution network lines based on unmanned aerial vehicle (UAV) inspection, belonging to the field of power grid fault detection and identification technology. The method includes: determining a target line based on line anomaly information and obtaining the path information of the target line; dispatching a UAV to inspect along the target line direction, obtaining magnetic field measurements, UAV position data, and the distance between the UAV and the target line, thus obtaining a magnetic field sampling sequence; constructing a magnetic field distribution map along the target line based on the preprocessed magnetic field sampling sequence; determining suspected anomaly locations, fault types, and fault location information based on the magnetic field distribution map, and outputting the magnetic field distribution map, fault type, and fault location information to a display terminal for display. This method enables rapid inspection of abnormal distribution network lines, achieving identification of suspected anomaly locations, fault type discrimination, fault location information determination, and visual display, thereby improving the efficiency of distribution network line fault inspection and maintenance.

The invention provides a distribution line fault identification model training method, a distribution line fault identification method and a distribution line fault identification system. The training method comprises the following steps: acquiring visible light images and infrared thermal imaging image data containing various distribution network line faults; and enhancing, preprocessing and classifying the image data to form a distribution line fault data set for training, and training the model based on the data set. According to the method, the advantages of visible light and infrared thermal imaging images are fully exerted, the two kinds of data are used for model training at the same time, the accuracy of fault detection and recognition is improved, and the fault detection range is widened. Data quality and model generalization ability are improved through data enhancement and preprocessing operation, the constructed model is based on a VIT transformer architecture and is integrated with a classification model of a CBAM attention mechanism, fault features can be effectively extracted, noise interference is removed, detection precision is improved, and operation and maintenance and fault diagnosis efficiency of a distribution network line is powerfully guaranteed.

The application provides a highway micro-grid self-healing ability improvement method and system under extreme weather, and relates to the technical field of power system automation. The method collects real-time operation data through the deployment of sensors, generates integrated system state data set through the fusion processing of meteorological data; the digital twin engine generates system state prediction data through state estimation and dynamic simulation; the random multi-objective optimization model solves the preventive scheduling instruction accordingly to control the generation, energy storage and load unit to adjust the operating point; the residual error analysis is carried out for fault detection and identification, and the sequence decision model is used to generate the self-healing control instruction sequence to control the switch equipment to isolate the fault and restore power supply, realizing the closed-loop management from prediction, scheduling to self-healing, and improving the power supply reliability and rapid recovery ability of the highway micro-grid under extreme climate.

The invention discloses a new energy direct current power distribution network fault identification method and system, and belongs to the field of new energy direct current power distribution network control and protection. After a fault occurs, controlling all the power electronic direct-current transformers to start a current limiting strategy, and selecting the power electronic direct-current transformers on two feeder sides to adjust phase shift angles so as to inject a dual-frequency detection signal; listing a network equation based on a one-mode circuit fault-free model, collecting voltage and current signals of each feeder line, and solving line parameters and tail end equivalent capacitance; line fault identification is realized by calculating the relative capacitance deviation and combining a preset criterion; and if all the feeders have no fault, identifying a bus fault in combination with the feeder current according to a bus criterion. The method does not need to depend on communication, adapts to multiple feeder types, can accurately recognize line and bus faults, and effectively improves the fault detection and recognition capability of the new energy DC power distribution network.

The invention belongs to the technical field of space antenna fault detection and identification, and relates to a space antenna fault detection and identification method based on three-dimensional Gaussian sputtering, which comprises the following steps of: performing coarse positioning on the position of a space antenna hinge by using an optimized YOLOv5 model; according to the position of the space antenna hinge, tracking of the space antenna hinge and calibration of a viewpoint pose are achieved through continuous image streams; constructing a 3DGS model of an intact space antenna hinge by using a three-dimensional Gaussian sputtering 3DGS technology, performing defect detection on the tracked and calibrated space antenna hinge, and quantitatively analyzing the size of the defect of the space antenna hinge; according to the invention, monitoring blind areas and fault omission can be reduced; shooting points are accurately estimated, and antenna details are restored with high precision; time resources are saved, and the detection efficiency is improved; three-dimensional Gaussian sputtering is combined with a loss function, a fault area is accurately recognized, and misjudgment and missed judgment are reduced.

The present application relates to quantum magnetic sensing and intelligent detection technical field, specifically a kind of non-contact electronic equipment ultrafast electromagnetic fault detection identification device and identification method based on diamond NV color center.This identification device includes efficient excitation microstrip array antenna module, electromagnetic fault detection identification main control board, laser modulation and coupling interface unit, photoelectric detection control interface unit, laser heat management copper-based heat dissipation assembly, voltage follower circuit control switch unit, pixel-level quantum magnetic sensing unit group, laser control circuit mainboard and photoelectric detection control mainboard;Wherein each pixel-level quantum magnetic sensing unit group includes three narrow linewidth laser excitation source, three diamond NV color center quantum magnetic sensitive element, three narrow-band optical filter and three photoelectric detector.The identification device has the characteristics of fast detection speed, high sensitivity, strong real-time, low power consumption and no need to contact the measured target, etc., can effectively improve the efficiency and accuracy of complex electronic equipment electromagnetic fault diagnosis.

The present invention discloses a multi-source fault detection and identification method for a DC power supply cable of a submarine observation network. The multi-source fault detection and identification method for the DC power supply cable can achieve the purpose of detecting and identifying multiple types of faults and multi-point faults of the DC power supply cable, including the connection of the multi-source fault detection loop of the submarine observation network cable, the detection and data acquisition of the DC power supply cable, the establishment of a fault model for a long-distance power transmission cable, and the judgment of multi-source fault detection results, etc. The beneficial effect of the present invention is that it can accurately, safely, real-timely, and efficiently detect and identify multiple types of faults and multi-point faults of the DC power supply cable, provide scientific and reliable data for the maintenance and repair of the energy supply network of the submarine observation network, and realize its long-term reliable operation.

The present invention proposes a robust positioning method that takes correlation into account, which effectively solves the problem of erroneous positioning failures caused by strong correlation between observation values ​​and improves positioning accuracy. Based on numerical simulation and result analysis, when there are many observable satellites, the correlation between satellites is weak; as the number of observed satellites decreases, the correlation between satellites becomes stronger. The chi-square global test and the w local test can effectively detect and identify faults; the correlation between satellites is weak, and the fault identification method has a high probability of correctly identifying the fault location; as the correlation between satellites becomes stronger, the probability of erroneously locating the fault location becomes higher. The robust positioning method that takes satellite correlation into account proposed by the present invention adopts a weight function processing strategy for adaptive robustness in a satellite strong correlation environment, which effectively improves positioning accuracy.

The present invention discloses a tunnel three-dimensional fault detection and identification method and system, which relates to the technical field of tunnel defect detection, and comprises the following steps: acquiring tunnel three-dimensional data, tunnel 2D image data and current positioning information in real time; associating the tunnel three-dimensional data, tunnel 2D image data and current positioning information to form a current identification, and recording the current identification in real time; extracting suspected defects from the tunnel three-dimensional data and tunnel 2D image data respectively, and generating a corresponding tunnel three-dimensional suspected defect list and tunnel 2D image defect list; associating and reviewing the tunnel three-dimensional suspected defect list with the tunnel 2D image defect list through the current identification to confirm the authenticity of the three-dimensional suspected defect; performing ellipse fitting calculation on the tunnel three-dimensional data corresponding to the real three-dimensional suspected defect to obtain suspected defect tunnel parameters, and comparing the suspected defect tunnel parameters with the reference parameters to generate a tunnel parameter comparison report.

The invention discloses an open-circuit fault diagnosis method and device for a driver of an electric excavator, and the method comprises the following steps: firstly, obtaining a three-phase current signal of an inverter; filtering and smoothing the current signal; performing positive and negative half-wave separation on the current signal to obtain an odd symmetric component and an even symmetric component; calculating an integral mean value, constructing a phase current characteristic vector, and comparing the maximum value of the integral mean value with a detection threshold value to judge whether an open-circuit fault occurs or not; calculating a positioning threshold value in real time, comparing the phase current characteristic vector with the positioning threshold value, and separating an abnormal value; and finally, obtaining a fault positioning result according to the index relation between the abnormal value and the inverter switching tube. According to the method, information containing fault characteristic essence can be extracted only by using three-phase current data, a phase current vector is constructed through odd-even symmetric components, and a distributed adaptive threshold algorithm is constructed, so that open-circuit fault detection and identification of the inverter module under a complex working condition can be realized without complex calculation.