98 results about "Fault grading" patented technology

The invention discloses a network equipment integrated monitoring method and system. The method comprises the following steps: collecting multi-source heterogeneous data, constructing a protocol compatible layer, and supporting multi-protocol adaptation; data fusion and intelligent analysis: constructing a dynamic topology, analyzing an equipment configuration file, and generating a network topological graph; performing time sequence prediction according to a root cause analysis model, and predicting an abnormal trend; mining association rules, analyzing historical data, and extracting fault association rules; constructing an equipment fault knowledge base under the assistance of a knowledge graph, and accelerating root cause positioning; self-adapting an alarm threshold, analyzing historical data distribution, and dynamically adjusting the threshold; visual decision making and automatic processing are carried out, a 3D topological map is provided, and layered display is supported; and performing fault grading processing, comprehensively calculating a fault influence degree score, mapping to a fault grade and a work order type according to an influence degree score interval, and formulating a dynamic work order generation rule. A protocol compatible layer is constructed by deploying a lightweight agent program, multi-protocol adaptation is supported, and various network devices can be fully covered.

The invention relates to the technical field of computer networks, in particular to a monitoring method and system based on industrial computer network fault data, and the method comprises the steps: collecting the heterogeneous fault data of each layer of equipment in an industrial control network in real time through distributed probe nodes; performing multi-modal normalization processing on the original fault data; constructing a fault knowledge graph, and dynamically associating an equipment topological relation, a historical fault mode and a current production task context; fault root cause analysis is carried out by adopting a hybrid inference engine, and a potential fault propagation path is predicted in combination with a rule-based expert system and an LSTM-GNN joint model; generating a grading alarm strategy, triggering a self-adaptive fault-tolerant mechanism, and dynamically adjusting network bandwidth allocation or starting redundant equipment switching according to the fault grade; according to the invention, by constructing the industrial knowledge graph and the adaptive fault-tolerant mechanism, efficient, accurate and interpretable fault diagnosis and prediction are realized, and the reliability and operation and maintenance efficiency of an industrial network are improved.

The invention relates to the technical field of intelligent fault diagnosis, and discloses a fault diagnosis method for new energy power generation equipment, and the method comprises the steps: obtaining a multi-physical-quantity time dynamic data set; outlier elimination is carried out on the data set, and a deviation degree sequence of each physical quantity is extracted; based on the deviation degree sequence, normalization processing is completed, and a correlation matrix among multiple physical quantities is constructed; generating a preliminary network structure through correlation screening and symmetry completion; calculating a path weight based on the initial network structure and performing topology reconstruction to obtain a final network topology structure; performing deviation propagation analysis according to the final network topology structure, and determining potential fault position distribution; based on the fault position distribution, performing fault grade classification by adopting a support vector machine algorithm, and outputting a fault risk grade label; and in combination with the fault level label and the network topology structure, node risk scoring and area identification are executed, and finally fault positioning is completed. According to the method, accurate positioning of the fault position in a complex system can be realized.

The invention discloses a data center operation and maintenance fault prediction system and method based on deep learning. The system comprises a multi-source heterogeneous data acquisition module, a data preprocessing module, a deep learning prediction model module and the like. The method comprises the following steps: acquiring multi-dimensional operation data of a data center through full-quantity acquisition of multi-source data, and inputting a CNN-LSTM-Attention hybrid model to realize fault prediction after preprocessing and feature enhancement; fault grades are divided in combination with fault grading, early warning is pushed in multiple channels, a coping strategy is intelligently generated, the effect is verified in a closed loop mode, and finally the model is iteratively optimized. According to the scheme, the fault prediction precision and real-time performance are improved, the operation and maintenance response time is shortened, the service interruption risk caused by faults is reduced, and the method is suitable for efficient operation and maintenance of large-scale data centers.

The invention discloses an automobile wire harness parameter performance test method and system, and relates to the field of intelligent monitoring and predictive protection of electrical equipment, and the method comprises the steps: extracting the trend intensity, fluctuation characteristics and abnormal frequency of resistance change based on a high-precision micro-resistance change rate data stream, and carrying out the fusion to generate a three-dimensional characteristic electric pulse signal; according to the three-dimensional characteristic electric pulse signal, a decision result is converted into a control instruction to drive an execution mechanism to execute safety diagnosis regulation and control, and an equipment operation log is generated; based on an equipment operation log, positioning an overheat area by combining a Brillouin scattering principle, measuring dielectric loss change of an insulating material, and generating a diagnosis verification report; according to the diagnosis verification report and the decision result, the neural pathway connection weight is dynamically optimized, and a wire harness performance report is generated; intelligent extraction and compression coding of resistance change characteristics under complex working conditions are realized by adaptively adjusting threshold control parameters, and three-level response decisions are driven to complete fault grading judgment and execution in a millisecond-level time window.

The embodiment of the invention provides an energy storage system fault processing method and device, equipment, a medium and a product, and relates to the field of photovoltaic power generation, the method solves the problem of low fault processing efficiency in the prior art by constructing a cloud-side-end cooperative fault processing architecture, changes the traditional static threshold judgment logic, and improves the fault processing efficiency. A decision-making mode combining multi-source information fusion and dynamic prediction is adopted, firstly, preliminary diagnosis of real-time data is achieved through edge calculation, the instantaneity of response is ensured, and then historical trend analysis, equipment health prediction and multi-dimensional real-time state evaluation are fused through a cloud platform. According to the method, the dynamic fault evaluation capable of accurately reflecting the actual severity and development trend of the fault is generated, so that the system can be adaptive to equipment aging and environment change, and a grading response strategy accurately matched with the fault grade is triggered, thereby realizing the crossing from passive alarm to active and accurate fault management and control, and improving the fault processing efficiency.

The invention discloses an insulation fault edge self-healing method and device for a power conversion system, and belongs to the technical field of fault diagnosis and automatic maintenance of power electronic equipment, and the method comprises the steps: obtaining multi-modal sensing data, synchronously collected by a multi-modal sensor array, of the power conversion system; a preset lightweight CNN-Transform hybrid model is used to process the multi-modal sensing data, and a feature vector of each modal is obtained; fusing each modal feature vector by adopting a dynamic weight attention mechanism to obtain a fused fault feature vector; classifying the fused fault feature vectors based on a fault classifier, and determining a fault level; and according to the determined fault level, sending a corresponding control instruction to an execution mechanism, and triggering a hierarchical self-healing action. According to the invention, fault identification and self-healing decision can be rapidly completed, and the accuracy of fault detection is effectively improved.

The invention relates to the technical field of cubic hydraulic press monitoring, in particular to a cubic hydraulic press health monitoring method and system based on an intelligent sensor, and the system comprises a health monitoring terminal, a data sensing module, a data processing module, a health diagnosis module, a fault management module, a diagnosis positioning module, a cooperative influence module and a rear-end execution module. According to the method, the time domain, the frequency domain and the time domain-frequency domain combined features are extracted, meanwhile, the state change slope is quantified, early warning of the equipment degradation trend is achieved, a fault type-abnormal feature-weight mapping table is constructed, a fault core component and the specific position are accurately positioned, and the fault core component is accurately determined by matching with digital twinborn visual marks. And meanwhile, the internal relation between components is quantified by constructing a component incidence matrix, the health influence coefficient of the cooperative component is calculated in combination with a fault grade coefficient, key components affected by faults are recognized in advance, a cooperative damage list is formed, and operation and maintenance personnel are assisted to intervene in advance.

The invention discloses a bus voltage multi-dimensional real-time monitoring and analyzing method, system, equipment and medium, and belongs to the technical field of power system monitoring and fault prediction.The method comprises the steps that high-frequency synchronous sampling is conducted on bus voltage signals, discrete time sequence voltage signals are obtained and preprocessed, preprocessed voltage signals are obtained, and the discrete time sequence voltage signals are obtained; the method comprises the following steps: synchronously extracting multi-dimensional characteristic parameters of voltage, obtaining a characteristic parameter set, calculating a health degree index comprehensively representing a voltage state by utilizing a dynamic weighted fusion algorithm based on the characteristic parameter set, calculating an abnormal degree of a current value according to a historical data sequence of the health degree index, and performing fault grade judgment according to the abnormal degree. And performing trend extrapolation based on the health degree index and the historical data sequence of the key parameters in the characteristic parameter set, and generating predictive maintenance suggestions. According to the method, the crossing from passive alarm to predictive maintenance is realized, and the intelligent level and the operation and maintenance reliability of state monitoring of the power system are remarkably improved.

The invention relates to a fault grading early warning method for an integrated joint, and the method comprises the steps: building a two-way communication architecture of an independent fault diagnosis module, executing multi-dimensional diagnosis through the independent fault diagnosis module, and analyzing a control instruction of an upper-layer system and operation state data fed back by the joint in real time; when it is detected that the motor temperature, the load torque and the vibration frequency spectrum exceed first threshold values, primary early warning is triggered, and control parameters of the joints are dynamically corrected; when it is monitored that the parameter change rate breaks through the boundary of the trend model or reaches a second threshold value, advanced early warning is generated, an optimization instruction set containing a fault positioning map is sent to the upper-layer system synchronously, task priority reconfiguration is triggered, and a self-adaptive protection strategy is issued to the integrated joint. Compared with the prior art, the invention provides an omnibearing, high-precision, real-time and dynamic fault monitoring and early warning solution for the integrated joint.

The invention relates to the technical field of alarms, and discloses an infrastructure catastrophe sound-light alarm system with a fault self-checking capability, and the system comprises a data collection module which is configured to synchronously collect a sound signal, an optical signal, an electrical parameter and an environmental parameter of an alarm; the data preprocessing module is configured to perform noise suppression, normalization and abnormal value elimination on the acquired data; the feature extraction module is configured to extract a sound time-frequency domain feature, an optical signal fluctuation feature, a circuit stability feature and an environment correlation feature; the fault diagnosis module is configured to perform fault type judgment by adopting a multi-modal fusion model and a predefined rule base; and the grading processing module is configured to execute emergency response, maintain early warning or self-adaptive adjustment strategies according to the fault grades. According to the audible and visual alarm fault self-checking system, multi-modal data fusion, self-calibration, mobile detection and graded response are integrated, and the reliability of industrial safety early warning is improved through multi-dimensional data collaborative analysis and dynamic adjustment.

The application relates to the field of high-voltage cable fault diagnosis, and discloses a high-voltage cable insulation fault intelligent diagnosis method and system based on multi-information fusion, which comprises the following steps: S1: acquiring operation multi-source data of a high-voltage cable, and constructing a multi-source original data matrix; S2: preprocessing to obtain a time-space aligned standardized data matrix; S3: performing feature extraction, acquiring a multi-dimensional feature vector, and learning the internal correlation among features by using a multi-modal deep network to obtain joint multi-modal features; S4: constructing a fault type recognition model based on Bayesian inference and Monte Carlo sampling, and acquiring a fault type classification result; S5: combining deep learning and a physical model to acquire interval and positioning information of fault occurrence and a fault grade; and S6: acquiring the fault grade based on the fault type classification result and the interval and positioning information of fault occurrence, and generating a diagnosis report for early warning pushing. The application realizes high-precision identification, positioning and risk assessment of high-voltage cable insulation faults.

The invention discloses a charging pile fault prediction remote maintenance system based on an edge calculation and AR technology, and relates to the technical field of charging pile maintenance, and the system comprises a data collection and transmission module which monitors the data of a charging pile in real time, and carries out the edge calculation of the data; the data processing and fault prediction module is used for performing fault prediction by combining a mixed algorithm of an LSTM (Long Short Term Memory) network and an FFT (Fast Fourier Transform) module based on the data after the edge calculation; the fault diagnosis and grading module is used for establishing a fault prediction data analysis model based on the fault prediction data and generating a fault grade and an early warning signal; the AR auxiliary operation and maintenance module is used for scanning the charging pile by using AR equipment and displaying fault information, maintenance steps and remote expert marks; and the remote support and monitoring module provides remote support and continuously monitors the state of the charging pile. According to the method, the LSTM network and the FFT technology are combined, fault prediction is carried out on the equipment, the system can predict potential faults in advance, and slow response and increase of maintenance cost of a traditional maintenance mode are avoided.

The invention discloses a lithium ion battery multi-fault grading diagnosis method and system based on a large language model, and relates to the field of lithium ion battery safety control. The disproportionation degree of a battery pack to be detected is calculated based on a battery signal; performing down-sampling on the battery signal based on the disproportionation degree to obtain a down-sampled battery signal; constructing a fault classification task prompt word based on the down-sampled battery signal, and inputting the fault classification task prompt word into a fault classification large model to obtain a fault type and a battery signal sequence corresponding to the fault; calculating a voltage variance quantile based on the battery signal sequence corresponding to the fault, and constructing a fault grading task cue word based on the voltage variance quantile; and inputting the fault grading task cue word into the fault grading large model to obtain a fault grade. With the help of the excellent nonlinear fitting capability of the large model, tiny faults are accurately captured under complex working conditions; and meanwhile, the multi-task parallel processing capability of the large model is exerted, and fault classification and diagnosis functions are realized under the same base model.

The application provides a new energy photovoltaic dynamic inspection method and system based on artificial intelligence, and relates to the technical field of intelligent inspection of photovoltaic power stations.The application triggers inspection according to weather warnings, performance alarms or timing tasks, carries out initial path planning in combination with terrain, weather and historical data, and updates the path dynamically through an RRT* algorithm, collects multi-modal data including visible light images, infrared thermal imaging, EL detection data and positioning data during unmanned aerial vehicle inspection, integrates unmanned aerial vehicle data and ground sensor data to generate a unified fault feature matrix, uses a deep neural network to locate a defect area in real time, judge a defect type and divide a fault grade, and finally scores a photovoltaic system health degree according to the fault grade and analyzes a safe operation trend.The application realizes real-time fusion of multi-modal data and dynamic path planning, improves fault recognition accuracy and inspection efficiency, reduces artificial inspection cost and risk, and provides strong support for intelligent operation and maintenance of photovoltaic systems.

The invention relates to a high-voltage bushing fault grading alarm online monitoring method and device and a storage medium, and aims to realize high-voltage bushing fault monitoring. The method comprises the steps of obtaining time sequence detection data; a time sequence oil pressure parameter, a time sequence temperature parameter and a time sequence humidity parameter in the time sequence detection data are subjected to normalization processing and then input into a pre-trained high-voltage bushing fault detection model to calculate a deviation parameter of an actual neighborhood interval difference mean value; determining whether a neighborhood interval difference mean value has a fault according to the deviation parameter and a measurement threshold value, and determining a fault level according to the degree of deviation from a normal range; wherein the measurement threshold value is obtained through a high-voltage bushing fault detection model in the training process. According to the method, time sequence modeling is carried out on time sequence detection data in a normal operation state by adopting a self-supervision mode, and distribution of a neighborhood interval difference mean value of a prediction result is modeled by utilizing a Gaussian mixture model, so that the extreme degree of the neighborhood interval difference mean value under a normal condition is determined, and high-voltage bushing fault monitoring is carried out.

The invention discloses an electric slewing mechanism safety control method and system based on fault prediction, and the method comprises the steps: obtaining time series data including the motor winding temperature of an electric slewing mechanism, the U, V and W three-phase current of a motor, the rotating speed of the motor, the torque of the motor, and the X, Y and Z three-direction vibration acceleration of a transmission shaft, and corresponding fault type labels; constructing to obtain a training data set; wherein the fault types comprise motor over-temperature, motor overload, motor stalling, gear wear and output shaft breakage; training the LSTM network by using the training data set to obtain a trained LSTM network model; based on the trained LSTM network model, electric slewing mechanism fault prediction is carried out, a fault prediction result is obtained, and the fault prediction result comprises the probability value of each fault type; and performing fault grading processing according to the fault prediction result.

The invention relates to the technical field of power plant equipment detection, and discloses a power plant equipment fault detection system based on an AI image recognition algorithm. The system comprises six core modules, namely an equipment image acquisition module, a fault feature analysis module, a fault propagation modeling module, a fault area judgment module, a fault level classification module and a fault structure output module. Wherein the equipment image acquisition module acquires real-time monitoring image data of power plant equipment, extracts equipment feature points through region segmentation, establishes a feature point topological relation in combination with equipment types, and generates an equipment feature topological graph; a fault feature analysis module performs multi-dimensional feature extraction based on the topological graph, and calculates a feature point similarity matrix to construct a fault feature association network; the subsequent modules sequentially establish a fault propagation model, screen a fault core area set and generate a fault level label group, and finally the fault structure output module generates a fault classification atlas to assist accurate detection and efficient management of power plant equipment faults.

The invention relates to the technical field of cloud computing operation and maintenance, in particular to a cloud platform fault intelligent research and judgment system and method based on multi-dimensional evaluation and topology analysis, and the system comprises a multi-dimensional evaluation matrix construction module, an influence diffusion model design module and an emergency index calculation model. The method has the beneficial effects that the system can dynamically predict the influence range of the fault by constructing the service dependency graph and adopting the improved PageRank algorithm. In addition, the system further introduces an emergency index calculation model to quantify the emergency degree of the fault and formulate a corresponding response strategy according to the emergency degree. Through continuous improvement of a mechanism, such as establishment of a fault knowledge graph and reinforcement learning optimization of model parameters, the system can continuously improve the accuracy of fault research and judgment. According to the scheme, the fault grading accuracy is remarkably improved, the influence range prediction error rate and the average repair time are reduced, and meanwhile the requirement for excessive resource capacity expansion is reduced.

The invention provides a terminal temperature monitoring and fault automatic diagnosis method for an intelligent measurement switch, which relates to the technical field of fault diagnosis and comprises the following steps of: dynamically tracking the terminal temperature to obtain long and short term temperature time-varying characteristics; if the preset temperature anomaly judgment condition is met, starting and ending timestamps are called; calling local multi-source monitoring data to obtain multi-source time sequence monitoring data; dividing the data into P multi-source monitoring data segments; performing segmented fault diagnosis based on dynamic fluctuation characteristics, and outputting P initial fault prediction results; state transition probability correction is carried out, and P-1 updated fault prediction results are obtained; and carrying out time feature fusion decision, outputting a to-be-intervened fault, and triggering fault grading early warning processing. According to the invention, the technical problem that the fault diagnosis delay and false alarm risk are increased because the fault state of the intelligent measurement switch is judged only through static fault mode identification in the prior art and intervention measures cannot be timely responded or adjusted in the fault development process is solved.

The invention provides an electric energy metering fault grading detection method and system for an intelligent electric meter, and relates to the technical field of electric energy metering, and the method comprises the steps: collecting a general meter observation signal of a transformer area general meter, carrying out the signal separation processing, generating an independent source signal, carrying out the inconsistency analysis of the independent source signal and the load data reported by a corresponding household intelligent electric meter, and carrying out the fault detection. The method comprises the steps of judging whether metering abnormity exists in a household intelligent electric meter or not, generating a first-level wide area screening list, executing directional diagnosis based on load curve waveforms, event records, power utilization behavior modes and transformer area topological association for each target user in the first-level wide area screening list, and generating a second-level directional diagnosis result. The technical problems that in the prior art, the electric energy metering detection dimension is single, and the anomaly recognition accuracy and reliability are low are solved. The technical effects of rapidly screening potential abnormal ammeters through a first level and effectively judging specific metering fault types through a second level are achieved, and the accuracy and reliability of electric energy metering abnormity identification are improved.

The application relates to the technical field of circuits, and discloses an intelligent induction conveying belt, which comprises a conveying belt module, a feeding mechanism, a sensor module, a control module, a signal processing unit, an alarm module, a fault grade judgment module and a manual reset switch, the conveying belt module is used for conveying materials, the feeding mechanism is arranged at the inlet end of the conveying belt, and the sensor module is used for detecting the running state of the conveying belt. The intelligent induction conveying belt is provided with the conveying belt module, the feeding mechanism, the control module and the fault grade judgment module, the control module realizes the hardware level time sequence control of the conveying belt module and the feeding mechanism through an RC delay circuit and a linkage control unit, the conveying belt is started first, and the feeding mechanism is started in delay; when the machine is stopped due to faults, the control module synchronously cuts off the power supply of the feeding mechanism, so that continuous feeding after the machine is stopped is avoided. The problem of material accumulation at the inlet caused by the asynchronization of starting and stopping in the traditional system is solved from the root, the system can be restarted without manual cleaning, and the production efficiency is improved.

The application relates to an intelligent isolation bearing online monitoring system, an installation process and a monitoring method. The system integrates force value, angular acceleration and displacement monitoring functions on the basis of an isolation bearing, is composed of an upper sealing plate, a rubber body, a lower sealing plate and a monitoring system, is used for simultaneously monitoring vertical loads transmitted from an upper building to a lower foundation and angular acceleration and horizontal displacement of the isolation bearing, and is composed of a ring-shaped pressure sensor, an inertial measurement unit (IMU), a displacement sensor, a data acquisition and transmission module, a cloud server and a monitoring platform. The system adopts a full-sensor external modular structure, is matched with a six-order Kalman fusion algorithm, a multi-parameter coupling early warning criterion, an IMU dynamic drift calibration model, and a special pre-tightening and anti-loosening installation process, is suitable for various working conditions, realizes synchronous acquisition of multi-dimensional parameters of a bearing, data noise reduction fusion and fault grading early warning, and can realize real-time monitoring of stress, angular acceleration and displacement states of a building isolation bearing.

The invention provides a steer-by-wire angle signal loss adaptive gain compensation method, which comprises the following steps: synchronously acquiring real-time angle signal sets of a hand feeling simulator and a steering actuator, carrying out signal loss detection and fault grading on the real-time angle signal sets to obtain a fault grade, and generating a redundancy switching strategy based on the fault grade; acquiring a historical angle signal set of a plurality of past sampling periods according to a redundancy switching strategy, and calculating an angle compensation amount according to the historical angle signal set; acquiring real-time vehicle parameters, generating a time-varying gain coefficient based on the real-time vehicle parameters, and calculating an adaptive gain compensation angle by using the time-varying gain coefficient and the angle compensation amount; and acquiring historical motor current of a past sampling period, calculating motor output current according to the adaptive gain compensation angle, performing gradient limitation on the motor output current by using the historical motor current, and performing steering control according to the motor output current after gradient limitation.

The invention relates to the technical field of application system evaluation, and provides an application system fault influence evaluation method and system, and the method comprises the steps: obtaining the customer complaint amount of different platforms for an application system fault, and calculating a reputation influence score in combination with the weights of different platforms; obtaining losses of different types of services when the application system fails, and calculating service influence scores by combining weights of the different types of services; obtaining influence quantities of different types of clients when the application system fails, and calculating user influence scores in combination with weights of the different types of clients; obtaining values of different types of performance indexes when the application system fails, normalizing the threshold sensitive index by using a tangent function, normalizing the progressive deterioration index by using an exponential function, and calculating a performance influence score in combination with weights of the different types of performance indexes; and performing weighted summation to obtain a total score, and determining the fault level of the application system. And the accuracy of fault influence evaluation is improved.

The application discloses a kind of based on big data pumped storage power station unit condition monitoring system and method, by setting multiple physical property monitoring points and multiple vibration signal monitoring points in pumped storage power station unit monitoring area, and the physical property data and vibration signal data of pumped storage power station unit are collected in real time, while the physical property data and vibration signal data collected are preprocessed by data preprocessing method, after preprocessing, the physical property data and vibration signal data after preprocessing are analyzed by data analysis method, while based on the physical property data and vibration signal data after analysis, fault diagnosis model is constructed and fault grade is determined, finally, based on the fault diagnosis model constructed, the fault real-time prediction model is constructed by digital twin method, and based on the fault real-time prediction model constructed, the state of pumped storage power station unit is monitored and early warning in real time, improve the accuracy of water storage power station unit condition monitoring.

The invention discloses an extended-range commercial vehicle whole vehicle fault processing method and system based on multistage grading and safety degradation control, and relates to the technical field of new energy vehicle control and whole vehicle fault diagnosis. Meanwhile, a system priority identifier to which a fault source controller unit belongs is obtained, a degradation control strategy type is determined through a constructed fault processing priority matrix, corresponding limiting parameters are matched, and hierarchical control is carried out on the extended-range power system; and sending the fault grade and degradation control state information to an instrument for display, monitoring the state change of the fault source controller unit, and dynamically updating the fault evaluation model. According to the invention, safe and stable operation of the extended-range commercial vehicle in a complex fault environment is realized.

The invention belongs to the technical field of equipment fault analysis, particularly relates to a power plant intelligent monitoring and fault diagnosis method based on multi-source data fusion, and solves the problem that fault classification only depends on the deviation degree and duration of a single feature and is not combined with multi-dimensional factors such as performance loss and safety risk. And fault influence range evaluation also lacks systematic conduction analysis based on topological association. According to the method, the historical anomaly template is constructed, the matching degree of the real-time features and the template is quantified by adopting cosine similarity, and multi-period accidental verification is combined, so that the conversion from experience judgment to quantitative verification is realized, misjudgment / missed judgment caused by instantaneous fluctuation of the sensor or single-period anomaly is reduced, the reliability of fault early warning is improved, and the fault early warning efficiency is improved. The fault severity is quantified in a multi-dimensional mode, the critical fault grade is supplemented, and the grading standard is refined; meanwhile, based on topological association of power plant equipment, faults are analyzed hierarchically, fault root causes are accurately positioned, and the conduction range of the faults is pre-judged.

The invention discloses a multi-gear double-electric-drive-axle fault processing method based on intelligent fault isolation, and relates to the technical field of new energy automobiles, and the method comprises the following steps: S1, fault grading detection: based on a built-in sensor of an electric drive axle, collecting operation parameters of two motors in real time, and grading faults according to the collected parameters; s2, part decoupling control: for different fault levels and fault parts, cutting off mechanical / electric signal coupling between the fault parts and other parts; and S3, dynamic strategy adjustment. According to the multi-gear double-electric-drive-axle fault processing method based on intelligent fault isolation, fault types can be recognized more accurately through fine fault grade division, mechanical / electric signal coupling of fault components and other components is cut off according to different fault grades and fault components, decoupling processing of faults of the multi-gear electric drive axles is achieved, and the fault processing efficiency is improved. Therefore, the accuracy and the efficiency of fault processing are improved, and the reliability and the safety of the system are enhanced.

The invention discloses a discharge fault grade determination method, device and equipment of a power transmission line and a medium, and the method comprises the steps: obtaining the traveling wave data of at least one discharge traveling wave of a fault phase, where a discharge fault occurs, of the power transmission line; determining a wave head moment of each discharge traveling wave based on the traveling wave data; determining a voltage initial phase angle of each discharge traveling wave based on the wave head moment and the traveling wave data; and determining the fault level of the discharge fault of the power transmission line based on the at least one voltage initial phase angle. Through the above mode, the wave head moment of the discharge traveling wave is determined according to the traveling wave data of the discharge traveling wave. And calculating the voltage initial phase angle of the fault phase at the traveling wave head moment. The distribution of the initial phase angle of the voltage is analyzed to judge the discharge intensity of the fault phase of the power transmission line so as to determine the fault level of the discharge fault, so that operation and maintenance personnel can judge the severity of the fault according to the fault level so as to take effective measures for processing, and the rationality of deploying first-aid repair personnel and distributing materials is improved.