3768results 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 relates to the technical field of LED modules, and discloses an LED module breakpoint detection and positioning method and device, and the method comprises the steps: carrying out the voltage and current data collection of series and parallel nodes of an LED module, and obtaining an electrical feature matrix and a node topological graph; performing iterative calculation on the electrical characteristic matrix, and processing a voltage difference value and a current variation between adjacent nodes to obtain a breakpoint characteristic vector; inputting the breakpoint feature vector and the node topological graph into a multi-task neural network for multilayer convolution operation to obtain an electrical fault distribution graph; performing feature segmentation and depth feature extraction on the electrical fault distribution map to obtain fault area feature data; and establishing an electric field distribution model for the fault area feature data, calculating an electric field singular point through a least square method, and obtaining breakpoint space coordinate data, thereby improving the accuracy of LED module breakpoint detection, and realizing accurate positioning of the LED module breakpoint.

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 specifically relates to a power transmission line fault diagnosis and visual early warning system. The system comprises the following parts: line data acquisition; fault analysis; image information acquisition, image analysis, meteorological analysis, comprehensive processing, state monitoring and an early warning module. According to the invention, multi-dimensional data such as a current value of a power transmission line, a leakage current value of an insulator, a lightning stroke condition and image information acquired by an unmanned aerial vehicle are comprehensively and deeply analyzed and processed to obtain a line state value, an image state value and a meteorological influence value, and an early warning value is comprehensively obtained. According to the invention, the fault hidden danger of the power transmission line can be accurately diagnosed, the accuracy and timeliness of fault diagnosis are improved, and the risk and potential loss of fault occurrence are effectively reduced.

The invention relates to the technical field of wire harness fault diagnosis, and discloses a vehicle-mounted wire harness fault diagnosis method and device and a storage medium. The method comprises the following steps: carrying out equivalent circuit modeling on a measurement node and a wire harness section of the vehicle-mounted wire harness to obtain a node connection relation matrix; performing feature analysis on the voltage data and the current data of the measurement nodes to obtain a fault feature vector and a fault type discrimination rule base; inputting the voltage data, the current data and the node connection relation matrix into a recursive variational graph automatic encoder for data reconstruction to obtain time sequence feature reconstruction data and topological relation reconstruction data; carrying out distributed sub-model division and model fusion to obtain a standardized feature vector; and performing matching analysis on the standardized feature vector and the fault type judgment rule base to obtain fault position information and fault type information, so that automatic optimization and screening of fault features are realized, and the reliability and interpretability of a diagnosis result are improved.

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 provides an anti-noise enhanced power distribution network cable fault detection and positioning method. Multi-period test signals are injected, reflected signals are collected, m-sequence denoising characteristics and self-adaptive wavelet transform are combined, denoising and normalization processing is carried out on the multi-period reflected signals, and the signal quality is improved. The power spectrum functions are averaged after multi-cycle quadratic cross-correlation weighting, and anti-noise enhanced fault positioning is realized through IFFT processing. Meanwhile, filtering signal time domain features (such as amplitude, phase and envelope) of a first period are extracted and fused with a final power spectrum function IFFT value after secondary cross-correlation weighting into a high-dimensional feature vector, and the high-dimensional feature vector is input into a deep learning model based on a convolutional neural network, LSTM and an attention mechanism. The attention mechanism is used for weighting the IFFT value of the power spectrum function after quadratic cross-correlation weighting, extracting peak value characteristics, and improving the positioning precision. The method realizes accurate classification and positioning of cable faults, has high precision and anti-interference capability, is suitable for cable fault detection in a complex environment, and provides technical support for stable operation of a power system.

The invention discloses a motor fault intelligent diagnosis method based on digital twinning, and belongs to the field of motor fault diagnosis. According to the method, in a model training stage, characteristic quantities are selected through a random forest method to train an extreme learning machine model with an autoregressive cycle function as a digital twin motor. According to the method, in an open-circuit fault diagnosis stage, a physical entity motor characteristic quantity is collected and input into a digital twin virtual motor to obtain a digital twin current, the difference between actual winding current data and the digital twin winding current is obtained to obtain a current residual error, and the difference value of the current residual error is used as a threshold value to carry out open-circuit fault diagnosis. Fault positioning is carried out by adopting a moving average window method; in the turn-to-turn short-circuit fault diagnosis stage, a current symmetric mean absolute error is used as a judgment standard to diagnose a short-circuit fault. According to the method, accurate design parameters and fault data of the motor are not needed, the open-circuit fault and the turn-to-turn short-circuit fault of the motor can be quickly diagnosed in real time only through the data during normal operation of the motor, and the robustness is very high.

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.

An energy storage apparatus includes: an external terminal 51; an energy storage cell 62; a current interruption device 53 that has one end electrically connected to the external terminal 51 and the other end electrically connected to the energy storage cell 62; a discharge circuit 110 that discharges electricity from the energy storage cell 62 via a path that does not pass through the current interruption device 53; and a failure diagnosis device 150. The failure diagnosis device 150 discharges electricity from the energy storage cell 62 using the discharge circuit 110, and diagnoses a failure of the current interruption device 53 based on a change in voltage ΔV1 of the external terminal 51 before and after discharging electricity.

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 an open-circuit fault diagnosis method for a three-phase interleaving three-level DC-DC converter, which comprises the following steps of: establishing a mathematical model of the three-phase interleaving three-level DC-DC converter by taking the sum of three-phase inductive current iL1 + iL4, iL2 + iL5 and iL3 + iL6 of inductive current iL1-iL6 of the three-phase interleaving three-level DC-DC converter as state variables; on the basis, designing an improved sliding-mode observer of the three-phase interleaving three-level DC-DC converter, and combining an observation value output by the improved sliding-mode observer with an actual value of the sum of inductive currents of upper and lower bridge arms of the same phase to construct a fault detection variable Mx (x = A, B and C) based on a residual gamma (t); a fault detection variable Nj based on the difference value between the inductive current iL1-iL6 and the current mean value is constructed by utilizing the characteristic that the corresponding inductive current declines at the fastest speed after the open-circuit fault of the switching tube, and j is equal to 1-6; and judging whether the corresponding switch tube has an open-circuit fault or not by comparing the two fault detection variables with a set threshold value.

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.

An example multiphase power converter circuit includes a first power stage is configured to provide a first phase output signal at a first switching output based on a first control signal. The first power stage includes first open fault detection circuitry configured to disable detecting and / or reporting of an open fault condition responsive to the first control signal. A second power stage is configured to provide a second phase output signal at a second switching output based on a second control signal. The second power stage includes second open fault detection circuitry configured to enable detecting and / or reporting of the open fault condition responsive to the second control signal having a value to turn off the second power stage. The second open fault detection circuitry is further configured to detect the open fault condition based on a voltage at the second switching output.

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 relates to an FTU (feeder terminal unit) broken line fault monitoring method, which comprises the following steps of: performing electrical data acquisition on an FTU line through a preset electrical mutual inductor to obtain electrical data; wherein the electrical data comprises voltage data and current data; performing phase angle calculation on the voltage data and the current data to obtain a phase angle difference value; if the phase angle difference value is not in the standard range, calling a database to obtain the attribute of the FTU line; through a preset distributed parameter model, based on the attribute of the line, the phase angle and the electrical data, carrying out broken line positioning on the line to obtain a broken line area; performing dynamic feature identification on the electrical parameters corresponding to the broken line area to obtain fault dynamic features, and performing fault type analysis on the broken line area based on the fault dynamic features to obtain a fault type monitoring report; the technical problems that a traditional fault monitoring method depends on manual inspection and a simple fault indicator, and it is difficult to meet the positioning precision and judge the fault type are solved.

A method for online monitoring of temperature in current terminal blocks based on thermal imaging are provided, including: obtaining real-time temperature distribution information of current terminals of a terminal box; using an external communication substation to upload the obtained real-time temperature distribution information to the secondary intelligent operation-and-maintenance control platform; determining whether there is an abnormality in temperature of the current terminals, if there is an abnormality, issuing an alarm message about potential danger for an open circuit of a current secondary circuit. The method provided by the present invention has a self-verification function of measurement data and can suppress the influence of ambient temperature. Through aggregation analysis of the temperatures of different current terminals of the terminal box, historical data mining of the temperature of the same current terminal is carried out to identify the hidden dangers of the open circuit of the current secondary circuit.

The invention provides a circuit test system and method and related equipment, and the test system comprises a test mold, electronic test equipment and computing equipment, can test a plurality of circuit parameters in parallel, can complete a large number of tests in a short time for circuit boards produced in batches, can run according to a preset program, and is high in test efficiency. The test result is more accurate and stable, a high-precision test instrument can be equipped, tiny voltage and current changes can be accurately measured, the device has the characteristics of high efficiency and stability, the test cost can be reduced, defective circuit boards can be found and removed in time, defective products are prevented from flowing into the next process, the defective products are prevented from flowing out, and the rework cost is reduced. The test data can be automatically recorded and stored, subsequent data analysis and quality tracing are facilitated, and the test system has obvious advantages in the aspects of efficiency, precision, cost, data management, repeatability and the like and is suitable for large-scale and high-precision circuit board production requirements.

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.