531 results about "Device failure" patented technology

The invention discloses an operation and maintenance debugging monitoring system suitable for an electric field, and relates to the technical field of operation and maintenance debugging monitoring, and the system comprises a collection unit which collects the operation data of an electric field device at a multi-dimensional monitoring point, the operation data comprises an electrical parameter, a mechanical vibration parameter, an environment parameter and chemical gas component data, and a multi-modal monitoring data set is constructed; the feature analysis module is used for carrying out spatial-temporal feature extraction on the multi-modal monitoring data set, generating a multi-dimensional feature tensor containing a time domain feature, a frequency domain feature and a spatial distribution feature, obtaining a spatial-temporal coupling relationship among the features through a tensor decomposition technology, and sending the spatial-temporal coupling relationship to the multi-modal monitoring data set; according to the method, a whole-process intelligent solution from monitoring, early warning to decision making is provided for electric field operation and maintenance, the equipment fault risk is greatly reduced, the power failure time is shortened, and the safety and economical efficiency of power grid operation are improved.

The invention discloses a method and system for predicting the health state of equipment based on large model AGENT capability. According to the method, equipment vibration, temperature, pressure, current and other data are acquired in real time through a multi-mode sensor, and data fusion and self-supervised learning are realized in combination with virtual data generated by digital twin simulation, so that equipment operation characteristics are extracted, and an equipment health index is calculated. According to the method, the health index and the change rate thereof are comprehensively considered, the equipment fault risk is dynamically evaluated, meanwhile, a dynamic weight self-adaptive updating mechanism is introduced, model parameters are optimized on line, and it is ensured that the model keeps high-precision prediction in long-term operation. The system can be widely applied to various heterogeneous devices, realizes full-process closed-loop management from data acquisition, intelligent analysis to risk early warning, and provides a scientific basis for device maintenance and preventive overhaul, so that the device failure rate is reduced, and the service life of the device is prolonged.

The invention discloses a converter valve internal temperature monitoring and early warning method and system, and particularly relates to the technical field of converter valve temperature monitoring. Temperature sensors are arranged on a plurality of key heating units, local temperature data are collected in real time, a time sequence is built in combination with cooling water inlet and outlet temperature, a temperature trend prediction model is built based on temperature rise rate and temperature gradient change, local thermal runaway risks are accurately recognized, and a prediction result is compared with a grading threshold value, so that the local thermal runaway risk is determined. Multi-stage early warning signal output is realized, and a corresponding processing strategy is linked; under the extreme conditions that the local temperature is continuously and abnormally increased and the response of the cooling system is lagged, the system can trigger emergency shutdown protection in an override mode, device failure is avoided, the sensing capacity and response speed of local thermal faults are effectively improved, and the operation safety and the intelligent level of the converter valve system are remarkably improved.

The invention relates to the technical field of partial discharge detection, in particular to a leak detection and partial discharge detection method and system based on artificial intelligence, and the method comprises the following steps: obtaining a partial discharge pulse signal, extracting a phase mutation coordinate, analyzing an incremental feature, reconstructing a propagation waveform, constructing an amplitude gradient matrix, and screening a direction vector; and a backtracking path calculates a change rate to generate a time track, and track mapping and space positioning are completed in combination with image data. According to the method, the accuracy and the response speed of partial discharge detection are improved through multi-stage signal processing, extreme points and phase abrupt change are analyzed, more accurate discharge feature capture and equipment defect positioning are achieved, phase and amplitude data are combined, the discharge mode is recognized, a signal propagation path is reconstructed, the fault source positioning accuracy is improved, and the fault source positioning accuracy is improved. Through dynamic rendering, the intuition of a detection result is enhanced, real-time decision is supported, the method is more intelligent and efficient, the equipment fault risk is greatly reduced, and the safe operation level of power equipment is improved.

The invention relates to the technical field of equipment fault intelligent diagnosis, in particular to an equipment fault intelligent diagnosis method and system based on a Modbus protocol. The method comprises the following steps: acquiring real-time operation data from target industrial equipment through a Modbus protocol, dynamically adjusting an initial sampling frequency based on an equipment operation state, and performing multiple verification and compensation correction on the acquired data to obtain a stable data stream; performing multi-scale decomposition and feature enhancement processing on the stable data stream, extracting a time-frequency domain mixed feature set, and constructing a feature evolution trajectory; inputting the feature evolution trajectory into a double-branch diagnosis model integrating equipment state prediction and fault classification, and outputting an equipment health degree score and fault type probability distribution; and constructing a dynamic fault threshold curved surface, carrying out multi-dimensional fusion decision by combining the equipment health degree score and the fault type probability distribution, and generating a graded fault early warning and maintenance strategy. According to the invention, the accuracy, timeliness and adaptability of industrial equipment fault diagnosis can be greatly improved.

The embodiment of the invention provides an equipment fault prediction management method and device based on a large model, and the method and device achieve the accurate evaluation of a state through the innovative design of a multi-modal feature fusion model, data integration and feature learning. A fault diagnosis system is constructed, and a reliable root cause analysis mechanism is established in combination with semantic analysis and case retrieval. Maintenance guidance is introduced, and the feasibility of a maintenance scheme is ensured through experience precipitation and priority evaluation. According to the method, the defects of the traditional technology in the aspects of feature extraction, fault diagnosis, maintenance guidance and the like are effectively overcome, and technical guarantee is provided for equipment management.

The invention discloses an equipment preventive maintenance and diagnosis method based on multi-dimensional data verification, relates to the technical field of equipment preventive diagnosis, and solves the technical problems that fault reasons are difficult to comprehensively and deeply analyze, and maintenance measures are lack of pertinence due to the fact that the fault reasons are often misjudged or cannot be positioned. According to the method, the fault diagnosis model is constructed, the adaptation algorithm is adopted for different types of data, the diagnosis accuracy of the model to equipment faults and the adaptability of the model to complex working conditions are improved, the equipment fault type and the abnormal state can be rapidly and accurately recognized, and the fault diagnosis accuracy is improved through the methods of fault phenomenon matching, parameter correlation analysis, historical data deep backtracking and the like. According to the method, fault reasons can be accurately determined, the performance degradation trend and potential risks of the equipment can be effectively identified in combination with a hybrid similarity measurement method, correlation among data is further mined through a secondary analysis mechanism, the potential risks are accurately pre-judged in combination with historical data, and real preventive maintenance is achieved.

The invention relates to the technical field of fault diagnosis, in particular to an electromechanical equipment fault diagnosis method and system based on data analysis. The method comprises the following steps: extracting a historical operation log of a motor, analyzing a current overload state and constructing a current overload spectrogram; then, carrying out insulation layer performance loss gradient analysis based on the spectrogram, and calculating the failure probability of the motor; finally, a risk level is given to the overload current according to the failure probability, an equipment fault diagnosis framework is constructed, and the framework is sent to a control terminal to execute fault diagnosis. According to the invention, the electromechanical equipment fault diagnosis technology is optimized, so that the electromechanical equipment fault diagnosis technology is more accurate.

The invention discloses a water meter equipment fault self-checking maintenance management system, particularly relates to the field of water affair metering management, is used for solving the problem of fault missing report risk hidden danger pain points caused by self-checking failure, and is characterized in that self-diagnosis drift monitoring and heartbeat frame extremely simple coding are triggered through pseudo-flow rate pulses, and the energy consumption increment of an uplink link is reduced to a milliwatt level; the health score drives the rhythm to be rechecked, so that the deep sampling only occurs at a necessary moment; a differential patch is combined with a digital twinborn model to accurately position a failure branch, pushing and loading are only loaded with micro bytes, and downlink consumption and refreshing time are greatly shortened; shape-frequency complementary indexes are fused with unsupervised boundary learning, so that deviation judgment is sensitive and stable; the dual-redundancy monitoring timer verifies that the patch takes effect and then uploads an abstract, and the maintenance center can carry out remote self-repair under the condition of not interrupting metering and communication; and therefore, instant capture and directional repair of silent faults are realized, the autonomous reliability of the water meter is remarkably improved, and the operation and maintenance cost and the field intervention frequency are greatly reduced.

The invention belongs to the technical field of intelligent engineering management, and discloses an automatic monitoring and control method of an engineering management system based on the Internet of Things. According to the method, a four-layer Internet of Things architecture comprising a sensing layer, a network layer, a platform layer and an application layer is constructed, and the four-layer Internet of Things architecture is uploaded to a cloud platform through a 5G / narrowband Internet of Things dual-mode transmission channel. A digital twinborn technology is innovatively adopted to construct a three-dimensional visual engineering model, intelligent identification and prediction of abnormal working conditions are realized through a machine learning algorithm, and a multi-stage linkage control mechanism is established. And when construction deviation or equipment failure is detected, the system automatically generates an optimization control strategy and issues the optimization control strategy to the execution terminal, so that accurate regulation and control of the construction machinery and intelligent pushing of early warning information are realized. According to the method, a manual inspection mode of traditional engineering management is broken through, full-process automatic supervision is realized through data fusion analysis and closed-loop control, the construction quality supervision precision is effectively improved by more than 30%, the safety accident rate is reduced by 50%, and the engineering management efficiency is remarkably improved.

The invention is suitable for the field of equipment fault prediction, and provides an underground equipment multi-source fault prediction method and system based on a large model, and the method comprises the following steps: synchronously collecting an acoustic signal, a visual image, a vibration signal, environment data and working condition parameters of underground equipment in real time, and integrating the acoustic signal, the visual image, the vibration signal, the environment data and the working condition parameters into a multi-modal time sequence data stream; performing time alignment and cleaning on the multi-modal time sequence data stream, extracting deep features of each modal, and generating a multi-source fusion feature vector; inputting the multi-source fusion feature vector into a preset prediction model to obtain prediction information of the underground equipment, and generating fault traceability information; continuously optimizing parameters of the prediction model based on fault data collected in real time; and dynamically displaying prediction and traceability results, and automatically generating a maintenance strategy and an equipment scheduling scheme based on the results. According to the invention, panoramic perception of the operation state of the equipment and accurate capture of early weak fault features are realized, and the comprehensiveness and accuracy of fault detection are greatly improved.

The invention relates to the technical field of wind power operation and maintenance management, and particularly provides an abnormal state detection method and system for a wind driven generator system. Joint feature extraction is carried out on the multi-source monitoring data flow through a feature coding network, a state evolution feature sequence and a cross-source association feature set are generated, a pre-trained anomaly diagnosis model is called to carry out fusion analysis on the features, and an anomaly diagnosis result containing anomaly confidence distribution information is generated; and after the abnormal type, the time dimension development track and the space dimension influence component identifier are analyzed, an equipment early warning instruction containing time and space positioning information is generated and sent to the target management terminal, so that the abnormal state of the wind driven generator system can be comprehensively and accurately detected, detailed abnormal information is provided in time, and the safety of the wind driven generator system is improved. And the equipment fault risk and the operation and maintenance cost are effectively reduced.

The invention discloses a dynamic correction method and system for equipment fault mode occurrence degree, and belongs to the technical field of power plant electrical overhaul and maintenance, and the method comprises the steps: collecting operation parameters, maintenance records and historical fault data of target electrical equipment, and recognizing a potential fault mode set of the target electrical equipment; failure mode and influence analysis is carried out on each failure mode, and the initial occurrence degree, severity and detectability of each failure mode are determined; calculating an initial risk priority number of each fault mode, and determining an initial risk according to a preset risk grade division rule; dynamically adjusting the initial occurrence degree, recalculating the updated risk priority number, and generating a corrected risk level; and constructing a dynamic maintenance decision model, and outputting a maintenance priority and a maintenance time window for each fault mode. According to the method, the defects of real-time performance, comprehensiveness and flexibility of electrical operation and maintenance of the power plant are overcome, and efficient and accurate data support is provided for operation and maintenance of electrical equipment.

The invention provides an equipment fault maintenance method and system based on a large model, and a storage medium. The method comprises the following steps: determining fault equipment according to equipment fault description; obtaining current operation data and a current operation image of the fault equipment, and inputting the equipment fault description, the current operation data and the current operation image into a pre-trained large model for fault identification to obtain a target fault reason; determining a priority troubleshooting step according to the target fault reason, and sending the target fault reason and the priority troubleshooting step to the user; a troubleshooting feedback result for the priority troubleshooting step is received, and a real fault reason is determined according to the troubleshooting feedback result; and generating an equipment fault maintenance result according to the equipment fault description, the current operation data, the current operation image and the real fault reason. According to the embodiment of the invention, the target fault reason and the priority troubleshooting step are sent to the user, thereby effectively assisting the equipment fault maintenance, and improving the equipment fault maintenance efficiency.

The invention provides a method and a system for evaluating the health degree of audio and video conference equipment, which comprehensively utilize a QFD thought and AHP and AI methods to construct an equipment fault model and a fault risk transfer model, solve the field problem of fault influence dependency analysis among an equipment principle, a real-time operation state and a historical operation state in equipment fault detection, and improve the reliability of the equipment fault detection. The evaluation method comprises the following steps: scanning a basic state in real time and detecting an environment in real time; verifying the integrity of the signal link; carrying out equipment performance standard test; the influence of the hardware loss on functionality is evaluated; performing actual use state testing, including limiting condition testing and fault injection testing; and performing health degree modeling and grading, and establishing a multi-dimensional comprehensive evaluation model. According to the method, a traditional regular maintenance mode is exceeded through multi-dimensional dynamic modeling and a predictive intervention mechanism, real-time environment sensing and hardware loss modeling are fused, a fault window can be pre-judged, the equipment operation fault rate is reduced, and preventive maintenance is achieved.

The invention belongs to the field of new energy battery pack flying welding equipment, and discloses power battery pack flying post-welding detection, dust removal and milling three-in-one equipment which comprises a three-axis rack, a dust removal mechanism and a detection and milling module, and the dust removal mechanism and the detection and milling module are fixed to the moving end of the three-axis rack. The equipment failure rate is low, and the equipment maintenance and operation cost is reduced.

The invention provides a rail transit remote monitoring method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the fault causal reasoning and fault propagation simulation of an equipment fault event based on artificial intelligence when an equipment abnormal event occurs in a rail transit scene through remote monitoring; and performing multi-modal early warning based on a fault causal reasoning result and a fault propagation simulation result. According to the rail transit remote monitoring method and system based on artificial intelligence, fault causal reasoning and fault propagation simulation are carried out on the equipment fault event based on artificial intelligence, multi-modal early warning is carried out based on the fault causal reasoning result and the fault propagation simulation result, and the reliability of the system is improved. The system has sufficient monitoring efficiency to a great extent when facing sudden faults, complex fault modes and large-scale data analysis in a rail transit scene, and monitoring accuracy and response timeliness are improved.

The invention discloses an intelligent fire-fighting equipment fault prediction method, and belongs to the technical field of intelligent maintenance of fire-fighting equipment. According to the method, pressure, electrical waveform and environmental data are dynamically acquired through a multi-source sensor, spatial-temporal features are extracted in parallel by adopting a bidirectional LSTM and a multi-scale convolutional network, and feature fusion is realized by utilizing a cross-modal attention mechanism; outputting a fault probability based on a gradient boosting decision tree model, and predicting the remaining service life in combination with Bayesian regression; the firmware upgrading authority is controlled through a health degree dynamic scoring system, and the upgrading safety is guaranteed through AES-256-CBC encryption and SM3 signature verification; and generating an optimal maintenance path based on an improved genetic algorithm, and scheduling an engine to respond to a sudden fault in real time. Tests show that the fault early warning accuracy rate reaches 93.1%, the maintenance cost is reduced by 45%, the upgrade failure blocking rate is 100%, and the intelligent level and reliability of operation and maintenance of the fire-fighting equipment are remarkably improved.

The invention relates to the technical field of fault early warning of power plant equipment, in particular to a method for generating a fault early warning model of power plant equipment. The method comprises the following steps: acquiring power plant equipment operation data, extracting operation coordination relation data, and constructing a power plant equipment operation simulation model; identifying the coupling interference increase condition of the power plant equipment based on the operation data, and detecting the dynamic suppression load increase condition of the equipment by using the operation simulation model; on the basis, the power plant multi-equipment linkage amplification effect is recognized, and the abnormal fault risk is detected; carrying out extension chain analysis, point location tracing and cause identification on the abnormal fault to form a fault correlation structure chain; constructing a fault early warning model based on fault causes, point locations and extension chain data; according to the invention, the fault early warning model of the power plant equipment is constructed, so that the power plant equipment fault identification is more accurate.

The invention provides a computer cross-scale integrated analysis method based on a multi-physics field coupling mechanism, and the method comprises the steps: constructing a multi-level finite element model from a system level to a micro-area level, and integrating thermal-mechanical-electric coupling modeling; therefore, the technical problems that in a traditional method, multi-physics field coupling lacks, local area modeling is not fine, and electrical performance evolution is not introduced are solved, high-precision simulation of a typical device failure mechanism is achieved, and the method is suitable for device packaging reliability evaluation in special electronic systems such as military and high-reliability civil use and the like. The method can be widely popularized in microelectronic structures such as a power supply module, a processor and a memory, and can improve the evaluation precision of the high-frequency signal transmission performance of complex electronic equipment, thereby improving the precision of predicting the service life.

The embodiment of the invention provides an electromechanical equipment fault prediction method and system based on a BAS system, and the method comprises the steps: firstly constructing an equipment state reference library based on a historical operation log of the BAS system, and then obtaining an equipment multi-source monitoring data set through a real-time monitoring network of the BAS system, and covering part sensor data, system control instruction data and environment feedback data. And then performing state alignment verification processing on the multi-source monitoring data set and the equipment state reference library to generate a state alignment verification result indicating a matching degree. And performing fault feature level derivation processing based on the result, and generating an equipment fault precursor feature chain containing an abnormal transmission path. And finally, an equipment fault dynamic risk assessment report is generated according to the evolution trend of the fault precursor feature chain and is synchronized to an operation and maintenance decision module of the BAS system, so that the probability of equipment fault occurrence is effectively reduced, the operation reliability and stability of electromechanical equipment are improved, and economic loss and safety risks caused by equipment faults are reduced.

The invention discloses an electric power system fault protection system and method based on multi-modal data fusion, and relates to the technical field of electric power systems.The method comprises the steps that an electric power equipment fault analysis model is constructed, and historical electric power fault data is collected to train the electric power equipment fault analysis model; inputting the electrical measurement abnormal condition, the equipment state abnormal condition and the environment meteorological abnormal condition into the power equipment fault analysis model to judge abnormal equipment, positioning a fault point of the abnormal equipment, analyzing the abnormal condition of a module corresponding to the fault point based on the electrical measurement data of the fault point of the abnormal equipment, and determining the abnormal condition of the module. Meanwhile, the influence degree of equipment outage caused by the fault of the fault point is evaluated, the equipment fault severity degree is analyzed based on the abnormal condition of the module corresponding to the fault point and the influence degree of equipment outage caused by the fault of the fault point, and an equipment first-aid repair sequence is generated based on the equipment fault severity degree; the accuracy of power failure degree judgment and the emergency response capability of a power system are improved.

The invention discloses a comprehensive monitoring platform for an auxiliary system of an intelligent substation, and the platform comprises a sensing layer which is provided with diversified sensors and is used for collecting the operation state, environmental parameters, security and protection information and fire-fighting information of equipment in the substation in real time, and the sensors have the self-diagnosis and self-calibration functions; the edge computing layer is composed of a plurality of edge computing gateways; the cloud platform is used for carrying out deep fusion, storage, analysis and intelligent decision making on the data uploaded by the edge computing node; and the application layer is used for providing an interactive interface and a functional module for a user, and the user can log in the technology through various terminal devices. Through a multi-level architecture and an intelligent analysis technology, comprehensive perception, intelligent analysis and automatic control of equipment states are realized, faults are predicted in advance, downtime is reduced, and reliability and stability of a power system are improved. The response time is shortened by an intelligent linkage strategy and an edge computing local decision-making mechanism, sudden safety events and equipment faults can be quickly handled, and accident loss is reduced.

The invention provides a multi-parameter cooperative control method and system for a chemical product production workshop, and relates to the technical field of intelligent control, and the method comprises the steps: constructing a multi-parameter sparse coupling topology according to the dynamic mutual information amount between parameters; performing topology path sensitivity evaluation, identifying a key coupling chain which has the greatest influence on the reaction stability, and tracking the collaborative change trend of parameters on the chain in real time; establishing a parameter prediction model, and executing the adjustment priority automatic rearrangement of the intra-chain parameters based on a constraint migration mechanism; constructing a self-evolution cooperative control strategy generator, and generating a cooperative regulation and control instruction of the reaction process through online evolution; and the materials are delivered to corresponding heating equipment, cooling equipment, pressure discharging devices and feeding equipment. The technical problems that in the prior art, adjustment is generally conducted on a single parameter, deep analysis on interaction among multiple parameters is lacked, the system cannot be kept stable when the multiple parameters fluctuate, and then equipment failures or safety accidents are caused are solved.

The utility model discloses an overturning positioning device for machining detection of mechanical parts, which comprises a table body, a clamping positioning assembly is arranged right above the table body, two sliding blocks are driven by a two-way lead screw to drive clamping arms to move face to face or back to back, and preliminary clamping positioning of the mechanical parts is realized. Meanwhile, the adjusting rods are matched with the adjusting openings to flexibly adjust the distance between the suckers according to the sizes of the accessories, so that the suckers can be accurately attached to the surfaces of mechanical accessories of different specifications. A vacuum generator is connected with a connecting port through a connecting pipe, the negative pressure principle is used for enhancing the adsorption force of a suction cup to the parts, a multi-layer and multi-direction stable clamping structure is formed, the situation that the mechanical parts are separated from the clamping device in the overturning process is effectively avoided, the risks of part damage and equipment failure are greatly reduced, the safety of operators is guaranteed, and the working efficiency is improved. And the stability and the production efficiency of the processing detection process are obviously improved.

The invention provides a power device failure positioning method and system based on multi-physics field coupling, and the method comprises the steps: applying dynamic electric stress to a tested power device, and synchronously collecting the thermal field data, electric field data and mechanical field data of the tested power device; wherein the thermal field data is used for representing the surface temperature distribution of the to-be-measured power device, the electric field data is used for representing the electrical parameters of the to-be-measured power device, and the mechanical field data is used for representing the internal morphology of the to-be-measured power device; synchronizing the thermal field data, the electric field data and the mechanical field data through timestamps, and aligning in a preset three-dimensional coordinate system to obtain multi-dimensional alignment data; and generating a failure probability distribution diagram according to the multi-dimensional alignment data so as to determine corresponding failure point coordinates. Through multi-physics field data coupling analysis, the problem of failure point fuzzy positioning in a complex packaging structure is solved, and the detection sensitivity under a dynamic working condition is improved.

The invention discloses a pressure regulating and speed regulating system of a drilling machine derrick lifting hydraulic cylinder. The pressure regulating and speed regulating system comprises a hydraulic system, a derrick area, a regulating control box and a safety area display monitoring unit. The adjusting control box is connected with the hydraulic system, the derrick area and a diesel generating set of an original lifting system through lines, and the hydraulic system is connected with the diesel generating set and the derrick area through lines. The diesel generating set supplies power to the whole system, the derrick area measures the inclination angle of a derrick and the displacement of an oil cylinder and transmits the measurement result to the adjusting control box, the adjusting control box sends an instruction to the hydraulic system according to the measurement result to conduct system pressure reduction, and the safety area display monitoring unit is used for displaying various parameters of the system. And an operator can check conveniently. The invention further discloses a method for regulating the pressure and the speed of derrick lifting by using the system. The device solves the problems that an existing system does not have closed-loop control, misoperation is prone to occurring, out-of-tolerance cannot be adjusted by itself, and equipment failures are caused due to the fact that pressure reduction cannot be achieved.

The invention relates to the technical field of fault prediction, and provides an equipment fault prediction method and device and electronic equipment. According to the method, the equipment operation data is acquired, the fault prediction model is constructed based on the equipment operation data, and the constructed fault prediction model is used to predict the real-time operation data of the equipment at the second moment to obtain the fault prediction result. And when it is determined that the target index of the fault prediction model is smaller than a preset index threshold value based on the fault prediction result and the rechecking mark information of the fault prediction result, the equipment operation data is updated based on the real-time operation data at the second moment, and then the fault prediction model is updated based on the updated equipment operation data. And finally, the updated fault prediction model is used for predicting the equipment fault, the real-time data of equipment operation can be analyzed and processed, the fault prediction model is constructed in combination with an incremental learning method, model parameters are updated in real time to improve prediction accuracy and real-time performance, the maintenance cost is reduced, and the equipment safety is improved.

The invention provides a gate-source short-circuit SiC MOS device failure analysis method and related equipment, and the method comprises the steps: responding to the determination that the functional characteristics of a source and a drain have no abnormal conditions, carrying out the analysis of the cause of a short-circuit fault of a gate and the source based on the characteristics of a silicon carbide material and an MOS device failure mechanism, and carrying out the analysis of the short-circuit fault of the gate and the source. Comprising the following steps: judging whether gate source chemical lap joint caused by internal water vapor exists or not through a dry chip; unsealing the chip to expose the interior of the chip, testing the chip, and judging whether a metallization layer migration short circuit caused by high temperature occurs or not; analyzing whether breakdown caused by high electric field stress exists in the gate oxide layer; according to the failure analysis method provided by the invention, the reason of the failure of the SiC MOS device can be accurately analyzed, and the problem that the microdefect of the SiC MOS device cannot be positioned by a traditional means is solved.

The invention relates to the technical field of hydropower station operation and maintenance, and discloses a hydroelectric generating set guide bearing performance evaluation method and device, equipment and a medium. The operation time sequence of the unit is segmented, time sequences of different operation time periods are determined, and the operation time periods comprise the stable operation time period and the transient operation time period; extracting state features of the time sequences in different operation periods to determine the operation state of the unit; and determining a performance evaluation result of the guide bearing based on the unit operation state. According to the method, the unit operation states including steady-state operation and transient-state operation can be identified in the real-time monitoring data, so that the performance of the guide bearing is evaluated based on the monitoring data in the steady-state operation state, manual intervention is not needed in the process, the monitoring precision of the states of key equipment such as the unit guide bearing is improved, and the working efficiency is improved. The downtime caused by equipment faults is reduced, the operation reliability of the hydropower station is enhanced, and the operation monitoring efficiency is improved.