1737results about "Continuity testing" patented technology

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 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.

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 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 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 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.

The invention provides a helicopter cable fault detection system and method, and belongs to the technical field of helicopter electrical system detection. The system comprises a positioning and matching device and a multi-modal signal analysis device, the positioning and matching device determines the relative position with a to-be-detected aviation plug through a dual-spectrum camera, generates a contact coordinate matrix and a contact and probe mapping table, injects a test signal to identify the contact material so as to obtain a topology result, and detects the contact pressure; and the multi-modal signal analysis device is used for matching parameters based on the topology result and the mapping table, setting a detection signal, positioning the contact, applying a signal in combination with pressure, acquiring a response signal, establishing a mapping table, and analyzing the response signal to generate a contact and fault result mapping table. According to the invention, the whole process automation from aviation plug positioning, signal detection to fault analysis is realized, the system can complete the core links of contact coordinate generation, signal mapping, fault determination and the like without manual intervention, and the detection efficiency is greatly improved.

The invention discloses an adaptive learning method and system for underground cable damage risk assessment, and relates to the field of cable damage risk assessment. The method comprises the following steps: constructing state evolution vectors of M cable damage events, carrying out neighbor clustering on the M state evolution vectors, generating K state evolution clusters, determining first similarities between the K state evolution clusters and real-time evolution vectors, constructing a similar cluster sequence according to the K first similarities, and in the similar cluster sequence, carrying out neighbor clustering on the M state evolution vectors to generate K state evolution clusters; calculating a variance of a plurality of relative time zone codes in the Q similar clusters, determining an adaptive learning cluster according to the variance of the plurality of relative time zone codes in the Q similar clusters, determining a second similarity in the adaptive learning cluster, and calculating a damage risk index based on the second similarity; according to the method, the damage risk index is generated based on the weighted relation between the L2 norm of each historical state evolution vector in the adaptive learning cluster and the second similarity, and visual quantification of damage risk assessment is realized.

A monitoring device is provided. The monitoring device comprises one or more circuitry operable to obtain measurements of one or more pins of a battery system, the one or more circuitry comprising at least one circuit for monitoring current, which comprise: at least one other circuit for amplifying variance comprising at least one differential amplifier, one voltage reference in electrical connection with a first input of the differential amplifier; at least one transconductance means comprising at least one output in electrical connection with a second input of the differential amplifier and at least one input in electrical connection with an output of the differential amplifier; at least one current source in electrical connection with at least one output of the at least one transconductance means; and an output in electrical connection with at least one output of the at least one transconductance means.

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

The invention discloses a line diagnosis method and circuit, and the method and circuit are used for a data communication system, and the method comprises the steps: detecting a first current on a high-side switching tube and / or a second current on a low-side switching tube; judging the states of the high-side switch tube and the low-side switch tube based on the detection result; and judging whether open circuit and short circuit exist at the line positive end and the line negative end and whether short circuit exists between the line positive end and the line negative end and the power supply voltage and the ground voltage based on the states of the high-side switch tube and the low-side switch tube. According to the line diagnosis method and the diagnosis circuit, the accuracy of fault diagnosis in the bus power supply and power-on process can be improved, false report and missing report of faults are reduced, and chip and line burnout caused by long-time overcurrent is avoided; by means of fault detection of the communication state, the line fault diagnosis precision in bus power supply communication is improved, false alarms and missing alarms are reduced, and overcurrent is avoided.

An apparatus for detecting disconnection of a power cable of a motor may include a computing device including a processor and a storage medium on which one or more programs configured to be executable by the processor are recorded, wherein the one or more programs may include: obtaining a plurality of sensing current values from a plurality of current sensors respectively sensing currents flowing in a plurality of windings of the motor corresponding to a plurality of phases of the motor; determining a plurality of correction current values by removing a common mode current from each of the sensing current values; and detecting disconnection in each of the phases based on a cable disconnection index based on the plurality of correction current values.

The utility model provides a high-voltage interlocking detection system and a vehicle. The high-voltage interlocking detection system comprises a detection circuit and a microcontroller. 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 corresponding detection signals based on the connection states of the low-voltage detection connectors; the microcontroller is connected with the plurality of detection branches to receive detection signals from the plurality of detection branches, and the microcontroller 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 plurality of detection signals. According to the system, the detection signals of different detection branches are received and processed through the same microcontroller, the response speed is high, the multiple detection signals are received through the same microcontroller, the synchronism of receiving the detection signals is high, the detection real-time performance and the detection accuracy of the microcontroller are guaranteed, additional sub-control units do not need to be arranged, and the cost is low. The manufacturing cost is saved, and the system structure is simplified.

The embodiment of the invention provides an electronic lead sealing strip and a detection method based on the electronic lead sealing strip, the electronic lead sealing strip comprises a shell, a first lock cylinder, a second lock cylinder, a pressure detection module and a control module, the first lock cylinder, the second lock cylinder, the pressure detection module and the control module are arranged in the shell, and the first lock cylinder and the second lock cylinder are arranged in parallel; a pressure detection module is arranged at the vertical position of the first lock cylinder, and the first lock cylinder is further connected with a connecting line. The control module is connected with the first lock cylinder, the second lock cylinder and the pressure detection module. And under the condition that the connecting wire is inserted into the second lock cylinder and is stably installed, the control module determines the connecting state of the connecting wire by judging a loop of the first lock cylinder and the second lock cylinder and / or a pressure value collected by the pressure detection module. Whether the electronic lead sealing strip is cut off or not is judged according to the detected pressure value, and the reliability of the electronic lead sealing strip is improved.

The invention relates to an on-line monitoring method for the on-off state and abnormal behavior of a synchronous rectification MOSFET of a power system. According to the method, a drain-source voltage VDS signal and a drain current Isd signal of a synchronous rectification MOSFET are collected in the working process of a system, and on-line monitoring is conducted on the on-off state and the abnormal state of the MOSFET. The method specifically comprises the following steps: conducting state monitoring: when VDS is smaller than or equal to a conducting judgment threshold value, judging to be conducted, and otherwise, judging to be turned off; monitoring an oscillation state, and judging switch oscillation when the VDS generates multiple complete oscillations in a set time window; early turn-off monitoring is carried out, early turn-off is judged according to whether I sd is higher than a threshold value or not during turn-off in the DCM mode, and early turn-off is judged according to the current descending trend and zero intersection point deviation in the CCM mode; withstand voltage monitoring: when the VDS transient peak value exceeds the rated withstand voltage or margin of the device, determining that the device is unsafe; and mis-opening monitoring: in the DCM mode, when the VDS is close to the opening threshold value during the logic turn-off period, determining that the mis-opening risk exists. The method can improve the reliability of the power supply system.

The invention relates to an open-circuit fault detection method and device, a domain controller, a system and a vehicle, and relates to the technical field of open-circuit fault detection. According to the method, the three-phase current data of the cooling water pump can be firstly obtained, and under the condition that at least one phase of current data in the three-phase current data meets the pre-judged open circuit condition, at least one of the following motor parameters including the three-phase current unbalance degree, rotor data and water pump vibration data is monitored. If it is monitored that any motor parameter in the three-phase current unbalance degree, the rotor data and the water pump vibration data is abnormal, it is determined that the current data meeting the pre-judgment open-circuit condition occurs, and an open-circuit fault occurs to the target phase corresponding to the current data meeting the pre-judgment open-circuit condition. Therefore, under the condition that the suspected open circuit of the corresponding phase is predicted according to the single-phase current, prediction can be performed by fusing at least one motor parameter of the cooling water pump, so that the misjudgment rate of the open-circuit fault of the cooling water pump can be reduced and the open-circuit phase positioning precision can be improved through multi-data cooperative detection.

The invention provides a converter open-circuit fault diagnosis method and device based on adaptive attention. According to the converter open-circuit fault diagnosis method based on adaptive attention, the non-stationary time sequence characteristics of the industrial converter open-circuit fault can be effectively captured under the small sample condition, the fault diagnosis accuracy and generalization ability are improved, the detection delay is reduced, and the fault diagnosis efficiency is improved. And rapid adaptation and high-precision identification of different working conditions and equipment types are realized.

A thermosensitive wire includes a pair of conductive wires each covered with an insulator that softens at a preset temperature, and the pair of conductive wires are configured to come into contact with each other to be short-circuited in response to the insulator softening. A power supply system includes: a second power source configured to supply power to the pair of conductive wires; a short circuit detector configured to detect that the pair of conductive wires are short-circuited; a disconnection detector configured to detect that the pair of conductive wires are disconnected; and a controller configured to perform, for power supply to a power supply wire, abnormality control that differs between in the case where the short circuit detector detects that the pair of conductive wires are short-circuited and in the case where the disconnection detector detects that the pair of conductive wires are disconnected.

The invention relates to the technical field of fault prediction and health management, and discloses a membrane envelope wire breakage detection method, which comprises the following steps: constructing a static impedance reference model containing viscoelastic characteristics, and extracting a time constant representing the physical relaxation velocity of an insulating material; performing phase correction operation on the instruction motion attitude coordinate by using the instant motion velocity vector and the time constant to obtain an equivalent physical attitude coordinate; and querying theoretical electrical characteristic parameters in the static impedance reference model by using the equivalent physical attitude coordinates, and carrying out difference calculation on the theoretical electrical characteristic parameters and the measured values to calculate a residual error characteristic sequence so as to evaluate the health factors. And due to measurement reference deviation caused by viscoelasticity lag of the high polymer material, accurate capture and trend prediction of weak early damage signals are realized.

The invention discloses a photovoltaic array fault diagnosis method, a computer and a storage medium, and belongs to the field of photovoltaic array intelligent detection, and the method comprises the steps: extracting fault feature parameters based on an I-V curve and a P-V curve of a photovoltaic array; carrying out local feature extraction on the fault feature parameters by adopting a CNN (Convolutional Neural Network) to obtain a spatial feature vector; inputting the spatial feature vector into LSTM for time sequence modeling to obtain a space-time fusion feature; carrying out weight distribution on the space-time fusion features based on a multi-head attention mechanism to obtain an optimized feature vector; performing global optimization on the parameters of the CNN, the LSTM and the multi-head attention mechanism by adopting a frost ice optimization algorithm to determine an optimal parameter combination; and based on the optimal parameter combination, constructing an RIME-CNN-LSTM-MATT classification model, and carrying out classification diagnosis on the photovoltaic array. According to the invention, multiple fault types can be accurately distinguished, and the problem of insufficient composite fault identification capability of a traditional method is solved.

The utility model relates to the technical field of filament detection equipment, and discloses a vehicle H7 bulb filament detection mechanism, which comprises a working platform, and a tray is arranged at the top end of the working platform. According to the vehicle H-shaped bulb filament detection mechanism, due to the design of a second air cylinder and a first air claw, a plurality of bulbs can be accurately clamped and conveyed into a moving block, due to the design of a first air cylinder, the bulbs on the moving block can be sequentially aligned with the second air claw, and due to the fact that the second air claw is provided with two grippers, the bulbs can be accurately clamped and conveyed into the moving block. Due to the design of the power-on detection module and the current sensor, power-on detection can be conducted on the bulbs, and when lamp filaments of the bulbs break down, the current sensor stops the device and gives an alarm to attract attention of operators. And due to the design of the camera and the illuminating lamp, the lamp filament can be photographed and detected, so that the bulb can be efficiently and accurately detected.

The invention discloses a distribution line fault identification and positioning method based on wavelet transform and traveling wave distance measurement technology, and the method comprises the steps: building a three-dimensional digital twin distribution network model, deploying a traveling wave detection device on a distribution line, collecting a traveling wave signal in real time when a fault occurs, and carrying out the fault identification and positioning of the distribution line; the method comprises the following steps: carrying out multi-scale decomposition on an acquired traveling wave signal by using a wavelet transform technology, extracting time-frequency characteristics of fault traveling wave data, and when a line fails, realizing high-precision positioning under a complex branch network by adopting a double-end-B type traveling wave distance measurement algorithm and combining Beidou / GPS time service synchronization and topological parameters; a lightweight convolutional neural network is adopted to construct a fault classification model, and a fault type is quickly identified; and generating a fault positioning report containing three-dimensional coordinates, fault features and historical processing schemes, and marking the specific position of a dynamic fault point on a visual interface. According to the method, the fault positioning precision and the anti-interference capability of the complex power distribution network are remarkably improved, and fault identification-positioning-decision intelligent closed loop is realized.

The invention discloses an open-circuit fault diagnosis method and system based on cooperation of a current residual error and a zero-sequence circulating current, and relates to the technical field of fault diagnosis of a deep-sea wind power system. The method comprises the following steps: establishing a continuous time model based on two parallel two-level wind power converters in a wind power parallel conversion system; performing predictive current control according to the continuous time model, and calculating a current residual error; setting a residual threshold value, starting a window statistical mode based on the converters which trigger residual threshold crossing for the first time, and recording the residual threshold crossing state of each converter by using a plurality of windows; and carrying out open-circuit fault diagnosis and positioning on the wind power parallel conversion system according to the residual threshold crossing state and the zero-sequence circulating current direction information. According to the invention, rapid detection and accurate positioning of the open-circuit fault of the power device in the wind power parallel converter system are realized.

The invention provides a fault diagnosis system and method for a light-emitting device, and the system is used for carrying out the fault diagnosis of the light-emitting device in a display panel, and comprises a power supply circuit, a current detection module, a control module, and a storage module. The display panel comprises a plurality of pixel units, and each pixel unit comprises at least one sub-pixel. The current detection module detects actual current data provided by the power supply circuit to the plurality of sub-pixels under different display pictures of the display panel; and the control module calls the corresponding reference current data from the storage module according to the actual current data so as to determine the fault type of the light-emitting device based on the actual current data and the reference current data. Real-time detection and positioning of faults of the light-emitting device in the display panel are achieved by collecting actual current data under different display pictures in real time through the current detection module, judging the short circuit or open circuit fault type of the light-emitting device through the control module in combination with reference current data pre-stored in the storage module, and achieving real-time detection and positioning of the faults of the light-emitting device in the display panel. And the detection precision is high and extra hardware support is not needed.

The present invention provides a server for diagnosing a defect in a battery, the server including a server communication unit configured to receive battery data including at least one of a battery voltage, a battery current, and a battery temperature, which is a temperature of the battery, from a battery management system (BMS); a server storage unit configured to store a plurality of internal resistance values of the battery calculated based on the battery data at each diagnosis time point for diagnosing the defect in the battery; and a server control unit configured to extract a plurality of previous diagnosis time points corresponding to a predetermined number of samples based on a diagnosis time point, calculate a moving average, compare an internal resistance value with an upper band threshold, and a lower band threshold, and diagnose the defect in the battery.