2716results about "Continuity testing" patented technology

The invention provides a mining circuit fault self-diagnosis method and system. According to the method, current and voltage waveforms and three-dimensional vibration signals of a cable are collected, and anti-interference data are generated through self-adaptive noise reduction and time sequence synchronization; a waveform interception window is dynamically adjusted based on the correlation between the mechanical vibration intensity and the current transient rate, the wave crest slope variable quantity is extracted from the current transient segment, high-frequency harmonic energy is separated from the voltage transient segment, time-frequency analysis is carried out on the vibration signal to extract an energy sudden increase frequency point, and a mechanical damage spectrum feature set is constructed; inputting the features into a space-time correlation model, and verifying space-time consistency of current distortion and voltage abnormity to generate composite features; dynamically correcting a fault threshold based on the environmental interference factor; and finally, a short-circuit peak or open-circuit oscillation diagnosis result is output according to the association strength of the electrical and mechanical characteristics. According to the invention, accurate fault self-diagnosis of the mining cable under a complex working condition is realized.

The invention relates to the technical field of electrical equipment monitoring, in particular to a power distribution equipment state intelligent diagnosis system and method, and the system comprises a current fluctuation detection module, a voltage stability evaluation module, a heat effect diffusion analysis module, a fault point positioning module and an equipment state evaluation module. According to the invention, through extracting current change characteristics of key nodes and judging continuity of fluctuation differences, rapid identification of current anomalies can be realized, a voltage distortion region and a distribution range thereof can be positioned in combination with phase deviation and symmetry change of voltage waveforms, and the current anomalies can be identified based on a temperature gradient path and jump point screening. A hot spot diffusion trend and a thermal disturbance boundary can be described, morphological analysis and coordinate labeling are performed on the basis of multiple fault areas, multi-point concurrent positioning is realized, association between thermal disturbance and connection defects is identified in combination with temperature difference and current path change characteristics, fault diffusion trends and state disturbance blocks can be accurately presented, and the accuracy of fault diagnosis is improved. And the power distribution state identification and intervention efficiency is improved.

The present invention relates to the technical field of secondary device monitoring. Disclosed are an online monitoring method for an operating state of a secondary current loop neutral line, and a system. The method comprises: by using a high-precision sensor for collection and a composite filtering algorithm for processing, obtaining a secondary current fundamental component of each neutral line of a transformer substation which is not influenced by harmonic waves and transient states, and uploading the secondary current fundamental component to a protection information master station; retrieving originally configured access protection information master station information to obtain three-phase secondary current loop information of each bay of the transformer substation, summarizing secondary current information of all monitoring ranges, and performing grouping and classification marking on said information by transformer substation, bay and winding; and using the collection information for operation analysis, automatically identifying and confirming an operating state of the line, and on the basis of the operating state, calculating the outlier deviation linearity and the continuous dynamic trend deviation degree of each winding of each bay. The present invention solves the problem that intermittent faults in secondary current loop neutral lines are concealed and are not easily found, realizes comprehensive monitoring for the whole system, and can quickly and accurately position a specific winding and issue an abnormal early warning.

The invention provides an online monitoring method and system based on a power transmission line, and relates to the technical field of power transmission line monitoring. According to the method, the heterogeneous sensing terminal, edge calculation, the graph neural network and Bayesian reasoning are combined, multi-source data acquisition, state identification and risk prediction are realized, the fault diagnosis accuracy and the risk early warning capability are improved, and the intelligent level and the safety guarantee capability of power transmission line operation are enhanced; the monitoring data is analyzed in real time through an edge calculation unit to generate a state label, a potential fault mode is recognized by combining graph neural network modeling space-time relevance, a risk factor library is further constructed, and a real-time fault probability graph is generated based on a Bayesian network. And dynamic identification and early warning of risk types such as wire strand breakage, icing overrun and mechanical fatigue can be realized.

The invention relates to the technical field of cable asset management and fault prediction, and discloses a cable fault positioning method based on deep learning clustering analysis test waveform characteristics, and the method comprises the steps: collecting waveform and environment data in a cable operation period, and constructing a historical feature library comprising waveform, environment and position features; a self-adaptive detection model is adopted, and parameters are dynamically adjusted to adapt to different working conditions; multi-dimensional feature fusion and matching analysis are combined; a fault point distance is calculated through a signal propagation model and a time difference positioning algorithm, precise positioning is realized by fusing environment compensation and multi-point cross validation, and a three-dimensional geographic coordinate is generated by combining a laying path; and after multiple verifications, a structured report containing a fault type, a risk level, a prediction position, confidence and operation and maintenance suggestions is generated. According to the system, intelligent monitoring, fault risk prediction, asset optimization management and operation and maintenance decision support of a cable operation state are realized, and scientificity and economy of cable management in a complex environment are improved.

The invention provides a control circuit of an excitation fuse, the excitation fuse and electronic equipment. The excitation fuse comprises N excitation sources, at least one melt and multiple sections of conductors, a melt is connected between every two sections of conductors in the plurality of sections of conductors; m excitation sources in the N excitation sources are connected to the multiple sections of conductors so as to collect currents of the multiple sections of conductors; the control circuit of the excitation fuse comprises a main control module and an inspection module. The inspection module is connected with the main control module; the main control module outputs an inspection signal to the inspection module so as to control the inspection module to detect the N excitation sources; if the holding current is detected from the N excitation sources, determining that the N excitation sources are in a normal standby state; and otherwise, determining that the N excitation sources have faults, and outputting prompt information to prompt a user to carry out fault removal. Therefore, the state of the excitation source in the excitation fuse can be detected, the fault of the excitation source can be found in time, and the reliability of the excitation fuse is improved.

The invention relates to the technical field of semiconductor packaging detection, and provides a self-adaptive semiconductor packaging detection method which comprises the following steps: synchronously acquiring packaging surface heat distribution data, three-dimensional deformation data and pin electric signal data through a multi-source sensor; performing dynamic weighted fusion on the multi-modal data based on the packaging process parameters to generate a defect feature matrix; performing dynamic threshold calibration on the defect feature matrix by adopting an adaptive threshold generation algorithm according to clustering center distribution in the historical defect pattern library; performing matching judgment on the dynamic threshold value and the real-time defect feature matrix through a multi-level logic decision model, and outputting a defect type, a position coordinate and a confidence score; and based on the defect type and the confidence score, triggering adaptive compensation control of the packaging process parameters, generating a feedback control instruction, and adjusting a packaging equipment execution mechanism in real time. The packaging quality and reliability can be improved, and the production cost and the defective rate can be reduced.

The invention discloses an intelligent detection method for fault detection of an outdoor power line, and the method comprises the following steps: 1, carrying out the collection and preprocessing of multi-modal data, and carrying out the collection and preprocessing of the multi-modal data through an unmanned plane cluster, a distributed optical fiber sensor, a laser radar and meteorological monitoring equipment; visible light image data, infrared image data, laser point cloud data, vibration waveforms, temperature distribution and environmental parameters of the power line are synchronously obtained, and multi-source image data are processed, namely the visible light image data, the infrared image data and the laser point cloud data are processed; and 2, intelligent fault diagnosis: inputting the data acquired in the step 1 into a multi-task neural network model, and outputting a fault positioning and type identification result. According to the novel detection method based on multi-modal data fusion, an intelligent algorithm and closed-loop optimization, the fault identification precision, the dynamic decision-making capability and the comprehensive protection efficiency are improved, and the intelligent operation and maintenance requirements of a modern power grid are met.

The invention discloses an active power distribution network fault positioning and identification method and system based on a space-time diagram network, and relates to the technical field of active power distribution network fault positioning and identification, and the method comprises the steps: obtaining data of each node of an active power distribution network after a fault occurs, building structured diagram data, marking fault nodes and types, and constructing a sample data set; pre-training the fault diagnosis model based on the space-time diagram network by taking the sample data set as source domain data; in each iteration process, a multi-scale adversarial disturbance addition method based on gradient optimization is adopted to generate adversarial sample data, through a data category dynamic balance mechanism and a confidence evaluation mechanism, the data are screened and added to a data set, and the updated data set is utilized to train a model; introducing target domain data, and finely tuning the pre-training model by adopting a transfer learning strategy based on dynamic coring maximum mean difference; and inputting actually acquired node data into the model to realize accurate positioning of fault nodes and accurate identification of fault types.

The invention relates to a system and a method for testing the conductivity of an engine wire harness, and relates to the field of intelligent detection, a programmable switch matrix is formed by acquiring a wire harness layout diagram and arranging matrix relays, and after logic self-inspection is executed, an optimal conduction path is searched based on a hierarchical partition model and a DFS algorithm; according to the method, the path image sets of the partitions are overlapped and mapped to identify abnormal wire harness sections, and finally, abnormity is determined through analog signal injection and waveform analysis and physical repair guidance is performed, so that the technical problems of low test efficiency, poor flexibility and difficulty in adapting to complex variable wire harness layout conductivity test are solved, and the purposes of improving the test flexibility and efficiency and reducing the test cost are achieved. And a fault point can be accurately positioned.

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 fault diagnosis method and device, computer equipment and a computer readable storage medium, relates to the technical field of power systems and automation thereof, and solves the problem of fault misjudgment caused by important fault characterization of a method which is easy to lose when single modal information is used for fault diagnosis at present. The method comprises the following steps: converting a bus voltage signal into a two-dimensional time-frequency diagram based on a continuous wavelet transform model, and converting the bus voltage signal into a one-dimensional frequency spectrum sequence based on a fast Fourier transform model; performing feature extraction on the two-dimensional time-frequency graph based on a time-frequency image feature extraction model to obtain a first feature vector, and performing feature extraction on the one-dimensional frequency spectrum sequence based on a frequency spectrum sequence feature extraction model to obtain a second feature vector; performing feature fusion on the first feature vector and the second feature vector based on a feature fusion model, and obtaining a category probability vector by using a fault diagnosis model based on the fused feature vector; and determining the highest probability value in the category probability vector, and taking the corresponding fault type as a target fault type.

The invention relates to a cable detection device which comprises a first clamping part, a load applying mechanism and a real-time on-off monitoring module, the first clamping part is used for fixing one end of a cable, and the load applying mechanism is arranged on the far side of the first clamping part and used for driving the other end of the cable to generate axial stretching displacement and axial torsion angular displacement in the testing process. The real-time on-off monitoring module is electrically connected with the wire harnesses at the two ends of the cable, and is used for continuously outputting a conduction state signal in the loading process. Through the above structure combination, the cable detection device can simulate the complex composite load borne by the cable in actual use, realizes line-by-line on-off real-time monitoring of each conductor in the cable, and effectively improves the working condition reduction degree and fault identification precision of detection.

The invention provides a flexible printed circuit wiring harness defect detection method, which comprises the following steps: acquiring wiring harness resistance change data in real time through a high-precision sensor array, acquiring temperature distribution information in combination with an infrared thermal imaging technology, and performing data fusion analysis by using Fourier transform and a deep learning model to realize accurate positioning and classification of tiny defects. The Kalman filtering algorithm is also adopted to dynamically predict the defect position, and the configuration of the sensor is adaptively adjusted according to the detection result, so that the detection precision is improved. The multi-source data fusion real-time monitoring method can effectively identify hidden defects such as microcracks and poor contact in the wire harness production process, provides accurate data support for production quality control, facilitates timely discovery and solution of potential problems, and improves the production efficiency and the product reliability of the flexible printed circuit wire harness.

The invention discloses a terminal crimping quality detection method and system, and relates to the technical field of terminal crimping image detection. Comprising the steps that space coordinates of a standard datum point in a crimping image are determined, and the standard datum point is used for representing the space coordinate position, meeting the standard terminal crimping inspection standard, between a corresponding wire and a terminal notch. According to the method, the crimping samples which do not meet the standard terminal crimping inspection standard are rapidly removed in a spatial positioning mode, and then the crimping sample thermal imaging images which do not meet the standard terminal crimping inspection standard are obtained on the crimping surfaces of the crimping samples by means of trigger logic. According to the method, the crimping samples are detected, the invisible faults of the crimping samples are judged based on the set standard terminal crimping inspection standard, and finally different commands are input to the crimping equipment according to the types and severity levels of the invisible faults, so that the rapid detection of the crimping samples and the dynamic adjustment of the crimping equipment are realized, and the stability of the monitoring dimension of the crimping quality is ensured.

The invention discloses a wind driven generator blade lightning conductor broken line point positioning method based on a radio frequency transformer, and relates to the field of wind driven generator blade lightning conductor broken line point positioning. A to-be-detected line is connected in series with the radio frequency transformer before being injected into traveling waves, and the change of traveling wave current is converted into the change of transformer output voltage; therefore, the output voltage of the radio frequency transformer is detected to judge the arrival time difference of the incident wave and the reflected wave, and then the breakpoint position is judged by multiplying the wave velocity by the time difference and dividing by 2. According to the wind driven generator blade lightning conductor breaking point positioning method based on the radio frequency transformer, the position of the lightning conductor breaking point can be efficiently, accurately, conveniently and flexibly detected at low cost, positioning measurement of disconnection of a multi-core wire part can be achieved, the specific positions of the breaking point and an abnormal point of the lightning conductor can be effectively determined, and the working efficiency is improved. After the positions of the broken line point and the abnormal point are determined, specific positions are repaired in a targeted manner, so that the cost for replacing the lightning conductor is saved.

According to the power cable multi-defect nondestructive testing method and system based on the broadband impedance spectrum, impedance characteristics of a cable under different frequencies are measured, frequency domain data are converted into time domain data through inverse Fourier transform, and accurate positioning and evaluation of internal defects of the cable are achieved; the invention discloses a power cable multi-defect nondestructive testing method based on a broadband impedance spectrum. The method comprises the following steps: step 1, signal output and acquisition; the method comprises the steps that a broadband impedance spectrum tester is used for outputting a broadband sine wave voltage signal, frequency domain measurement data are collected, and the frequency domain measurement data are impedance data of a cable under different frequencies; 2, carrying out time-frequency conversion; converting the frequency domain measurement data into time domain data through inverse Fourier transform, generating a time-amplitude curve, and analyzing the electrical characteristics of the cable; 3, establishing a defect identification model; analyzing the electrical characteristics of the cable at different frequencies by using the transmission line model, and evaluating the state of the cable; step 4, defect positioning and evaluation; and 5, displaying and analyzing a result.

The invention discloses a precise positioning method of an automatic transmission conductor breaking device, and relates to the technical field of line detection, and the method comprises the following steps: obtaining eddy current detection signal data of the surface of a transmission conductor; performing sliding window segmentation processing on the eddy current detection signal data, and extracting a transient impedance sequence; the transient impedance sequence is processed through a phase sensitive demodulation circuit, and impedance real part information and impedance imaginary part information are separated; impedance real part information features are extracted, impedance imaginary part information frequency domain energy is deconstructed, and an impedance track feature vector is generated; and carrying out space segmentation on the impedance track feature vector, and calculating defect position information and severity level based on the offset of the track center point. The problem that an existing wire detection technology is insufficient in automation level and difficult to meet the requirement for accurate defect positioning of an automatic wire breaking device is solved.

The invention relates to the technical field of wire harness detection, and particularly discloses a new energy automobile wire harness conduction test system, which is used for solving the problems of low detection precision and poor abnormity identification and data tracing capabilities in a complex structure wire harness and an extreme abnormal state in the prior art. The system comprises a data acquisition module for multi-group constant current excitation and high-resolution acquisition based on a four-line method, a dynamic anomaly analysis module with sliding window processing and adaptive threshold discrimination capabilities, and a level-to-level management module for automatic multi-layer grouping according to parameterized feature tags, through high-frequency multi-round synchronous detection, trend change modeling and grouped closed-loop parameter adjustment, high-precision detection, whole-process intelligent recheck and whole-process dynamic traceability of the multi-loop conduction state and multi-type abnormity of the new energy automobile wire harness are realized; and the safety and the detection management level of the wire harness system are effectively improved.

The invention provides an electric wire and cable nondestructive on-line detection method which comprises the following steps: acquiring an electromagnetic signal, temperature distribution data, chemical component change data and mechanical vibration data of an electric wire and cable to be detected through a multi-mode sensor network; the electromagnetic signals, the temperature distribution data, the chemical component change data and the mechanical vibration data are subjected to data preprocessing, and a multi-dimensional data set is constructed; performing data fusion on the multi-dimensional data set through a multi-physics field coupling model to generate multi-dimensional feature data; performing defect identification based on the multi-dimensional feature data to generate a defect data set; and performing dynamic early warning according to the defect data set to generate an early warning data set. Electromagnetic, temperature, chemical and vibration data are comprehensively covered through the multi-mode sensing network, accurate recognition and early warning of wire and cable defects are achieved through preprocessing and multi-physics field coupling model fusion, the method has the advantages of being lossless online, comprehensive, intelligent and dynamic, and detection reliability and operation and maintenance efficiency are remarkably improved.

A battery pack includes a plurality of battery subpacks and voltage detection circuits, a current detection circuit, and a BMS. Each battery subpack and one connection component connected to the battery subpack form one to-be-detected assembly. The current detection circuit is configured to detect a current of the charge / discharge loop. The voltage detection circuit is configured to detect voltages at two ends of the to-be-detected assembly. The BMS is configured to: obtain the current of the charge / discharge loop and voltages at two ends of each to-be-detected assembly based on a preset period, generate a current sequence and a first voltage sequence of each to-be-detected assembly, calculate an impedance of each to-be-detected assembly based on the current sequence and the first voltage sequence, and when the impedance is greater than or equal to an impedance threshold, determine that the connection component in the to-be-detected assembly is abnormally connected.

The invention discloses an open-circuit fault diagnosis method for a switching tube of a dual-active bridge converter. The open-circuit fault diagnosis method comprises the following specific steps: modeling the dual-active bridge converter; analyzing voltage and inductance waveform characteristics of the dual-active bridge converter in a fault state of each switching tube on the primary side and the secondary side; comparing and analyzing fault features of each switching tube, and selecting a difference value between an inductive current average value and a bridge arm midpoint voltage average value in a fault state and a normal state as a fault feature; and designing a fault diagnosis algorithm according to the selected fault features. Compared with a traditional fault diagnosis method, only the voltage of one bridge arm of the primary side and the voltage of one bridge arm of the secondary side are selected as fault features, fewer sensors are needed, and real-time online fault diagnosis is more accurate and rapid.

The invention discloses a motor fault diagnosis method and system based on color image fusion symmetric point mode, and the method comprises the steps: converting a vibration signal and an electromagnetic signal of a motor into symmetric point mode images, and generating a color signal image fusing feature information; respectively abstracting the color signal images fused with the feature information into nodes and edges in a high-dimensional semantic space so as to construct graph structure data; and performing diagnosis classification on the graph structure data of the vibration signals and the electromagnetic signals by using respective capsule graph network models, and fusing diagnosis classification results of the vibration signals and the electromagnetic signals through a voting mechanism to obtain a final diagnosis classification result. According to the method, multi-channel time domain signals are converted into image expressions with dense information and consistent geometry, and unified feature modeling is carried out on the images based on a depth map structure network with topology perception capability, so that motor fault diagnosis with high diagnosis precision and strong robustness is realized.

The invention relates to the technical field of coal mine mechanical equipment, and discloses a frequency converter control method based on state detection and load adaptive adjustment, which comprises the following steps: when a starting command is waited after a frequency converter is shut down, calling an event at the last moment and shutdown data to judge whether a control strategy is adjusted in next starting; after the frequency converter receives the starting command, the output rotating speed, the torque and the current value are monitored in real time to trigger abnormal starting identification; if the stator resistance changes, the identification parameters are stored, and then the normal starting mode is executed; if the stator resistance is abnormal, a frequency converter fault is output; if the stator resistance does not change, self-adaptive chain clamping identification is carried out according to the output rotating speed, the torque and the current value, self-adaptive adjustment is carried out on the frequency converter in the chain clamping state, and a normal starting mode is executed after chain clamping is removed. According to the invention, dynamic adjustment can be carried out according to load changes and specific abnormal conditions, so that the stability and reliability of equipment are improved.

The invention provides a single-phase four-quadrant rectifier IGBT open-circuit fault diagnosis method under multiple working conditions, and relates to the technical field of IGBT fault diagnosis. Historical data of alternating current side voltage, alternating current side current and direct current side voltage of the single-phase four-quadrant rectifier are obtained, and a total harmonic distortion rate threshold value, a direct current side voltage threshold value and an alternating current side current first threshold value are obtained. And constructing and training an IGBT open-circuit fault positioning identification model. Detecting the total harmonic distortion rate of the alternating-current side current of the single-phase four-quadrant rectifier in real time, if the total harmonic distortion rate exceeds a threshold value, entering fault positioning, collecting and calculating the average value of the direct-current side voltage and the alternating-current side current in the period, and judging the fault of the single-phase four-quadrant rectifier under the specific working condition according to the comparison of the average value and the threshold value. Or using an IGBT open-circuit fault positioning identification model to diagnose the fault; if the threshold value is not exceeded, the next period is continuously monitored. According to the invention, the IGBT fault diagnosis efficiency and accuracy of the single-phase four-quadrant rectifier are improved.

The invention relates to the technical field of modular multilevel converters, and provides an MMC fault diagnosis method, system and device based on a sorting voltage-sharing algorithm. Comprising the steps that according to measurement data of the modular multilevel converter, a theoretical value and an actual value of bridge arm voltage of the modular multilevel converter are obtained through calculation, the theoretical value and the actual value are compared with a first alarm threshold value, and a corresponding bridge arm fault and a fault type are determined; calculating the number of fault sub-modules according to the bridge arm voltage actual value and the bridge arm voltage theoretical value; judging the switching number of the fault bridge arm sub-modules, and controlling and adjusting the bridge arm sub-modules to be switched on according to the fault type and the number of the fault sub-modules; and calculating a capacitor voltage minimum difference value, comparing the capacitor voltage minimum difference value with a second alarm threshold value, and determining a fault sub-module. According to the multi-tube fault diagnosis method, the problem that the fault positioning performance is poor under the light load working condition is solved, and the sub-module positioning performance in the full power range is improved.

The utility model provides a high-voltage interlocking detection system and a vehicle. The high-voltage interlocking detection system comprises a detection circuit, a plurality of signal processing modules and a control module. The detection circuit comprises a plurality of detection branches connected in series, the plurality of detection branches are correspondingly connected with the plurality of low-voltage detection connectors, and the detection branches output detection signals based on the connection states of the low-voltage detection connectors; the plurality of signal processing modules receive detection signals from the plurality of detection branches; and the control module determines the on-off state of the high-voltage interlocking loop and the connection state of the plurality of low-voltage detection connectors based on the detection signals processed by the plurality of signal processing modules. In the system, a plurality of detection branches are connected with a control module through different signal processing modules, wire harnesses are separated from one another, and a plurality of signal processing modules process a plurality of detection signals and then uniformly send the signals to the control module through a common node, so that the arrangement of the wire harnesses is reduced while reliable transmission of the signals is ensured, and the cost is reduced. And the whole circuit of the system is simplified.

Disclosed are a fracture detection method for a connection component of an energy storage system, and an energy storage system. The method is applied to an energy storage edge and includes: obtaining operating state parameters of the at least one secondary battery sampled and uploaded by the sampling part; determining a parameter discrete characteristic value of the operating state parameters; performing a fracture detection for the connection component according to a magnitude relationship between the parameter discrete characteristic value and a preset discrete characteristic value threshold to obtain an edge-side fracture detection result; and sending the operating state parameters and the edge-side fracture detection result to an energy storage cloud for the energy storage cloud to generate a fracture detection result of the connection component according to a cloud-side fracture detection result and the edge-side fracture detection result.

A fault diagnosis device and a fault diagnosis method for an electric vehicle charging system. The fault diagnosis device may include: a first terminal node receiving a control pilot (CP) signal from a connector of a charger through an inlet and connected to an anode of a diode; a first node connected to a cathode of the diode; a second node selectively connected to the first terminal node by a switch; a third node connected to the second node through a first resistor; a second terminal node connected to the third node through a second resistor; and a processor configured to measure a first voltage of the first node, a second voltage of the second node, and a third voltage of the third node to diagnose a fault on the CP line.

The invention discloses a TVS tube state monitoring circuit and a protection circuit of an EFUSE circuit, and relates to the technical field of electronic fuses, and the protection circuit comprises a fuse, a first switching circuit, a second switching circuit, a short circuit detection circuit, an open circuit detection circuit and a control module. And the control module judges whether the TVS tube is open-circuited or short-circuited according to feedback signals of the short-circuit detection circuit and the switch detection circuit, so that the state of the TVS tube is monitored in real time.