989 results about "System failure" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a processing environment switching and recovery method, device, equipment and medium. The method comprises the steps that multi-source heterogeneous data in a main processing environment and a standby processing environment are acquired, and the system fault probability is obtained through multi-model collaborative prediction; a dynamic threshold value is generated in combination with a historical service period mode and a real-time service load, when the fault probability exceeds the threshold value, a switching strategy is generated based on the fault scene knowledge base and the service priority, and flow scheduling between the main processing environment and the standby processing environment is executed; and monitoring the business index of the standby processing environment during the scheduling period, and triggering the fusing rollback when the business index is lower than the health standard. According to the method, the fault identification precision is improved through multi-source data fusion and multi-model prediction, adaptive scheduling is realized in combination with a dynamic threshold and a switching strategy, and fusing rollback is triggered to guarantee high availability and data consistency, so that the continuity and stability of key services are enhanced.

The invention discloses an H-bridge key equipment service life and system reliability evaluation method and system for a cascade network construction type energy storage system, and belongs to the technical field of power system automation. The method comprises the following steps: firstly, extracting task profile parameters under multiple time scales, and constructing a time sequence feature model; secondly, estimating a hot spot temperature sequence of the IGBT device and the capacitor based on a multilayer feedforward neural network; then, in combination with a continuous extreme point paired temperature cycle extraction method and a Miner linear cumulative damage criterion, the damage factor and the residual life of the device are evaluated; then, task profile samples are expanded based on a generative adversarial network with gradient penalty, and life distribution and reliability indexes of key devices under different profiles are calculated; and finally, based on H-bridge series structure mapping device level information, constructing a system level reliability model, obtaining system failure rate, average fault-free operation time and a reliability function, and realizing health state perception and reliability quantitative evaluation of the energy storage system.

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 relates to the technical field of computer system structures and storage, in particular to a non-volatile memory quick recovery method based on metadata priority and on-demand loading. The method comprises the following steps: in response to a system fault signal detected by a voltage monitoring unit, freezing a processor context and traversing a page table structure to extract system configuration information; writing the system configuration information and business data codes in the volatile memory into a nonvolatile medium to generate a persistent state mirror image; analyzing the persistent state mirror image, extracting address conversion metadata, and reconstructing a mapping relation from a virtual address to a physical page frame in a volatile memory; and generating an address mapping table, wherein the physical page frame pointed by the address mapping table is set to be in an existing state but is not associated with the effective service data. According to the method, decoupling of the control flow and the data flow is realized by constructing a virtual ready state, and quick starting of the system and immediate response of key services are realized on the premise of not depending on the total capacity of a memory.

The invention discloses a PLC scheduling algorithm based on a multi-core soft real-time operating system, and relates to the technical field of voltage control, and the algorithm comprises the following four steps: 1, starting a target industrial control system, completing hardware self-inspection, memory allocation and communication module starting, and sending an initialization ready signal after confirming that an assembly is normal; 2, according to PLC task functions and real-time performance, four types of standardized tasks are divided, priority levels are defined, sub-priorities are configured, a dependency relationship is identified, and a task chain is established; 3, counting the number of tasks and task chains, distributing resources by using multiple binding cores, and constructing a global ready queue to adapt to dynamic scheduling; and 4, monitoring an interrupt event through an interrupt manager, realizing high-priority task preemption by semaphores, and allocating tasks to corresponding CPU cores, so that the problems of soft real-time multi-core task preemption and data conflict are solved, the scheduling real-time performance and the data security are improved, the system failure rate is reduced, and the industrial control efficiency and stability are improved.

The invention belongs to the technical field of cloud computing and software testing, and particularly discloses an automatic fault injection and recovery testing method based on cloud native, which comprises the following steps: acquiring test basic data of a to-be-tested cloud native system; constructing a fault test execution model according to the test basic data; according to the fault test execution model, executing an automatic fault injection operation; generating an abnormal alarm log according to the system running state after the fault injection operation, and synchronously recording a fault injection operation log; according to the abnormal alarm log, executing automatic fault recovery operation; and generating a fault test report according to the fault injection operation log, the system monitoring data and the recovery operation process log. The invention aims to solve the problems of low automation degree, narrow scene coverage, weak monitoring evaluation and passive recovery in cloud native system fault test in the prior art, and provides a comprehensive, automatic, monitorable and evaluable fault injection and recovery test scheme.

The invention relates to the technical field of industrial intelligent control, in particular to a combustion system fault correlation analysis and diagnosis method and system based on a knowledge graph. The method comprises the following steps: constructing a mechanism topological graph of a combustion system, obtaining real-time load data of the combustion system, calculating dynamic conduction lag time of a parent node influencing a child node in the mechanism topological graph based on a fluid mechanics load correction mechanism in combination with a flow resistance correction factor and reference transmission time, performing time sequence alignment based on dynamic conduction delay time, calculating causal association strength of a father node pointing to a child node and performing confidence attenuation processing, performing reverse search on a mechanism topological graph when an abnormal trigger point is monitored, calculating accumulated abnormal energy of each father node based on the causal association strength, and determining the abnormal trigger point according to the accumulated abnormal energy. And determining the father node with the highest accumulated abnormal energy as a fault root cause. According to the scheme of the invention, accurate time sequence alignment under working condition fluctuation can be realized, diagnosis failure can be prevented, and fault root causes can be accurately positioned.

The invention discloses a recirculating ball type electric power steering system fault self-checking method and system and a readable medium, and relates to the technical field of vehicles, and the method comprises the steps: storing a signal transmission path and each function node, determining the type of an influence factor which influences the instruction execution accuracy of the function node, and determining the instruction execution accuracy of the function node; analyzing and acquiring the relationship between the fluctuation amplitude of the data signal at each function node and the numerical value of the influence factor, storing the relationship as a correction model, and storing a first relationship model for reflecting the data association relationship among the function nodes on the same signal transmission path; acquiring real-time data and source data of various impact factors, and generating theoretical response data according to the first relation model, the real-time impact factor data and the correction model; the actual response data is collected, the deviation amplitude between the actual response data and the theoretical response data is calculated, whether the function node breaks down or has a potential fault is judged according to the deviation amplitude, early warning is output, and the fault detection accuracy and the potential fault pre-judgment capability are greatly improved through the technical scheme.

The invention provides a system abnormity intelligent diagnosis and recovery method and system fusing time sequence logs, and relates to the field of distributed system operation and maintaining.The method comprises the steps that operation logs are collected from a plurality of service nodes of a distributed system, time sequence alignment is conducted, and calling relation and performance measurement data are extracted; time sequence change features are calculated in the sliding time window, dynamic feature vectors are generated through fusion coding, and a cross-service time sequence association graph is constructed; identifying an abnormal service node and matching the abnormal service node with historical fault data to obtain a historical propagation path and a feature vector; calculating a propagation convergence coefficient and a characteristic deviation degree based on a historical propagation path to obtain a causal intensity score; and identifying a fault influence degree according to the score, and adaptively adjusting a resource isolation and flow scheduling strategy. According to the invention, accurate diagnosis and efficient recovery of distributed system faults are realized.

The invention relates to the technical field of intelligent control of HPLC (High Performance Liquid Chromatography) analytical instruments, in particular to an HPLC system fault early warning self-calibration method. Comprising the following steps: S1, acquiring a high-frequency pressure signal behind a pump and a chromatographic signal of a detector in real time; calculating a relative physical disturbance quantity based on the post-pumping high-frequency pressure signal; calculating a chemical state deviation degree based on the detector chromatographic signal; s2, fusing the relative physical disturbance quantity and the chemical state deviation degree, and constructing an instantaneous health index; and S3, comparing the instantaneous health index with a preset first early warning threshold value and a preset second early warning threshold value, and executing a closed-loop control strategy corresponding to a comparison result. According to the method, accurate fault early warning is realized by constructing a physical-chemical two-dimensional monitoring system, the defects of offline and lagging applicability test of a traditional system are overcome through real-time monitoring and fusion evaluation of the two dimensions, and the accuracy and timeliness of early warning are remarkably improved.

The invention belongs to the crossing field of artificial intelligence and mechanical engineering, particularly relates to a micro-motor fault prediction and health management system, and aims to solve the problems that early faults of a micro-motor are difficult to recognize, degradation modeling is inaccurate and maintenance lags. The system collects multi-source data through high-density sensing, combines denoising reconstruction, composite feature extraction and time-varying weighted fusion to generate health indexes, identifies health stages by using a segmented hidden Markov model, iteratively updates residual life prediction based on a Wiener process, outputs an estimation result with a confidence interval, and links a hierarchical maintenance strategy. And continuous optimization of the model is realized through federal learning. The system improves the fault early warning accuracy and prediction reliability, and reduces the operation and maintenance cost.

The invention discloses a deicing system real-time state evaluation and fault prediction method and system, and the method comprises the steps: simulating the fault state of a deicing system through a ground state monitoring test of the deicing system, and obtaining sensor data in different fault states; constructing a data set by using sensor data in different fault states to train a state evaluation model and a fault prediction model; the method comprises the following steps: installing a sensor on an aircraft deployed with a deicing system to collect working data of the deicing system; and inputting data acquired by the sensor into the trained state evaluation model and the fault prediction model, evaluating the real-time state of the deicing system, and predicting possible faults. According to the invention, the current working state of the anti-icing and deicing system can be evaluated according to the current state monitoring data, and possible faults of the system can be predicted.

The invention relates to the technical field of network detection, and provides a dynamic event trigger fault detection method under DoS network attack, which comprises the following steps: establishing a linear state space model of a network control system; defining a non-attack interval and an attack interval of system operation, and constraining attack frequency and duration; the observer gain is switched according to the current non-attack interval or attack interval of the system; a dynamic event triggering mechanism is constructed, and the triggering condition depends on the output state and the internal dynamic variable of the full-order switching observer and is used for dynamically adjusting the data transmission frequency; establishing a closed-loop switching system model; and analyzing system index stability according to the closed-loop switching system model, and cooperatively designing observer gain, controller gain and event triggering parameters. According to the invention, through quantification of the DoS attack model, the dynamic event triggering mechanism and collaborative optimization design, the effects of effectively detecting the system fault and improving the utilization rate of the network channel are achieved.

According to the back-end system fault self-recovery method and device, the storage medium and the computer equipment provided by the invention, in the system operation process, multi-dimensional state analysis is performed on the operation data collected by the system micro-service in real time to obtain the operation state; when the operation state is abnormal, the fault analysis model is used for performing fault analysis on the operation data, so that the fault positioning efficiency is improved; after fault data output by the model are obtained, a self-healing strategy corresponding to the fault data is obtained through matching in the dynamic knowledge base, then self-healing operation is executed on the micro-service, and quick response of fault processing is achieved. If the self-healing result is failure, self-healing data in the self-healing operation process is obtained, the self-healing strategy of the micro-service is adjusted to be more adaptive to the actual fault condition, then the self-healing operation is executed on the micro-service again based on the new self-healing strategy until the self-healing result meets the self-healing ending condition, and therefore the flexibility of system fault self-healing is improved; and the influence of the fault on the system stability is reduced to the greatest extent.

The present application relates to a method and device for intelligently processing faults in a distributed system. The method includes: starting an application service and generating a heartbeat packet; sending a heartbeat packet to a heartbeat queue at a set time interval; a monitoring service receiving and saving the data in the heartbeat queue; continuously collecting the data in the heartbeat queue from each node and preprocessing it, extracting features related to fault detection, and using an isolation forest model for model training; using an adaptive threshold adjustment method based on an isolation forest algorithm to analyze the data in the heartbeat packet; comparing the heartbeat according to the threshold, and when an abnormal heartbeat is found in a node, determining the fault location according to the feature code; generating a compensation message, and the application service executing the compensation task according to the compensation message. The method detects faults by combining a heartbeat mechanism with an AI algorithm, and recovers unfinished tasks by a compensation mechanism. The method is adaptable to complex distributed environments, has high real-time performance and sensitivity, and significantly improves the stability and reliability of the system.

The invention discloses an intelligent operation and maintenance and fault handling method based on an AI large model, and relates to the technical field of artificial intelligence and information, and the intelligent operation and maintenance method comprises the following steps: multi-source heterogeneous data fusion, cross-level fault analysis, handling strategy generation and verification, and resource optimization scheduling. The method has the advantages that multi-source heterogeneous data of the equipment layer, the software layer and the service layer are synchronously collected through the sensing layer, the space-time aligned feature matrix is constructed, the limitation that traditional operation and maintenance only depend on single-level data is solved, the time dependence and level incidence relation of the data can be mined at the same time in combination with a multi-mode AI large model of the analysis layer, and the data processing efficiency is improved. The fault root cause probability distribution and the influence hierarchy are accurately output, misjudgment or missed judgment caused by single hierarchy data deviation is avoided, and the accuracy of complex system fault positioning is remarkably improved.

The invention discloses an automatic fault repairing method for an operating system based on artificial intelligence, and relates to the technical field of automatic fault repairing, and the method comprises the steps: carrying out the feature analysis of system operation data through a pre-trained fault feature extraction model, generating a fault feature vector, inputting the fault feature vector into a fault classifier, and obtaining a fault feature vector; a current fault type is identified through a multi-classification algorithm, fault cause primary tracing is performed according to the fault type to obtain a fault generation factor, secondary tracing is performed on the fault generation factor to obtain a fault influence factor, positioning is performed based on the fault generation factor, and a corresponding repair strategy is matched from a knowledge base. The method comprises the following steps: acquiring historical system operation data with relevance on the basis of a fault influence factor, acquiring updated real-time system operation data after executing a repair operation to calculate a system optimization coefficient, judging a forward trend of a repair strategy according to a preset optimization threshold value, and updating the forward trend into a knowledge base to realize rapid and efficient automatic repair.

Providing issue resolution in a cluster system by monitoring system operation to detect occurrence of a system error, and automatically generating, upon detection of the error condition, a core file for a user node. The core file captures a current memory state of a respective node, where the current memory state comprises system statistics, system information, and logs. An intelligent core debugger extracts information from a core file to generate a core file report that is sent to a vendor for a quick determination of whether sufficient information is in the report to allow the vendor to recommend a fix, or whether further information from is required, including the core file itself, if necessary. This prevents the need to send an entire core file to a vendor in every instance of a system fault.

The invention relates to the technical field of safe operation of a power system, in particular to a power distribution network information physical system fault collaborative recovery method, which comprises the following steps of: acquiring basic operation data of a communication layer and a physical layer, and respectively constructing fault recovery models of the communication layer and the physical layer; the communication layer takes optimal link performance as a target and applies constraints such as link capacity and connectivity, the physical layer takes multiple parameters such as load recovery as an optimization target and applies constraints such as network topology and power flow, and a double-layer optimization model, an upper layer optimization communication recovery strategy and a lower layer optimization physical recovery strategy are constructed for concurrent faults; and solving by using a double-layer optimization objective function to realize power distribution network fault collaborative recovery. According to the method, the interaction between the communication layer and the physical layer is comprehensively considered, so that the fault point can be more accurately positioned, the fault area can be quickly identified, the optimal recovery strategy is formulated, the power supply reliability is improved, and the healthy development of the smart power grid is promoted.

The invention discloses an energy storage system fault database indexing method, and particularly relates to the technical field of fault prediction. The method comprises the following steps: firstly, constructing an energy disturbance vector sequence matrix in a unified time window, extracting features based on a continuous variation rate and energy residual distribution, and generating an energy disturbance feature spectrogram; establishing an energy propagation path atlas in combination with a system module topological relation, and introducing a time sequence consistency identifier; a fault evolution fingerprint is generated through graph embedding coding, similarity index matching is carried out in combination with a standard fault trajectory, and a fault type and a positioning weight are output; real-time energy indexes are further fused, a micro-fault position prediction map is generated, and dynamic early warning is achieved; according to the method, accurate identification and visual positioning of the micro-fault of the energy storage system in a complex scene can be realized, and the operation safety and the intelligent operation and maintenance capability of the system are improved.

The invention provides an MCU function security implementation system based on a domain controller, and relates to the technical field of domain controllers, the system realizes dual backup storage and on-site execution of program codes by combining a security MCU without a built-in program memory with at least two redundant plug-in nonvolatile storage units, and the security of the MCU is improved. Consistency verification is carried out on key codes or data in the running process, and system failure caused by single-point storage faults or data tampering is effectively prevented; meanwhile, read-write operation without losing data during power failure is isolated through the independent data storage unit, and resource conflict and interference between program execution and data storage are avoided; the ASIL D-level power management unit supporting two-way fault monitoring is matched, resetting can be triggered in time or the MCU enters a safe state when a power supply is abnormal or an MCU internal fault occurs, and therefore a high-reliability and high-safety domain controller MCU system is constructed on the hardware architecture level, and the capacity of the MCU for meeting the ASIL D function safety level in a plug-in storage scene is improved.

The invention relates to the technical field of hydraulic system control, and discloses a fault diagnosis method and device for a hydraulic system of fully-mechanized mining withdrawing triangular area supporting equipment. The method comprises the following steps: acquiring real-time operation data of each oil cylinder through a hydraulic system sensor, extracting differences of motion states of the oil cylinders by adopting a time sequence analysis and principal component analysis method, and calculating quantitative indexes of inertia force distribution and non-uniform force distribution of the oil cylinders. And when the quantitative index exceeds a preset threshold value, identifying the abnormal oil cylinder by adopting a clustering analysis method, analyzing the correlation between the positioning deviation and the abrasion of the sealing element through a linear regression model, and calculating the occurrence probability of an early fault signal. The fault prediction grading result is generated based on the probability distribution, the oil cylinder control parameters are adjusted through the decision tree algorithm, the oil cylinder operation parameters are updated, dynamic adjustment of the hydraulic system and optimization of the supporting structure are achieved, the fault diagnosis precision of the hydraulic system is improved, and the stability and reliability of the supporting structure are ensured through real-time compensation control.

The present application belongs to the grid-connected converter technical field, disclose a kind of droop control inverter current-limiting control method and system, the virtual impedance size and active reference value restriction condition when the fault current is equal to current-limiting value are accurately obtained by phasor analysis, virtual impedance-current difference proportional coefficient and active reference value-current difference proportional coefficient are designed considering the most serious situation of current-limiting, proportional increase virtual impedance and reduce active reference value with the change of adaptive current at fault, can meet the current-limiting requirement under different depth grid voltage drop.The current-limiting control method proposed in the present application is adaptive to fault current, without fault detection device, the transient stability of inverter is enhanced due to the reduction of active reference value at fault, and the fault current is indirectly limited by adjusting virtual impedance and active reference value, the voltage source characteristics of droop control inverter are retained, and the system fault ride-through capability is significantly enhanced.

The invention relates to the technical field of server heat dissipation monitoring, and discloses an intelligent monitoring system for energy consumption of a server cluster radiator. An energy consumption acquisition unit of the system periodically acquires radiator energy consumption parameters and sends the radiator energy consumption parameters to a threshold value judgment unit; the threshold value judgment unit generates temperature, flow or pressure abnormal signals through threshold value comparison; the heat dissipation efficiency analysis unit calculates a deviation value between the energy consumption parameter and a preset threshold value based on the abnormal signal, generates a heat dissipation regulation and control lag proportion, and counts regulation and control time consumption to obtain heat dissipation regulation efficiency; the regulation and control correction unit regulates the collection period according to the lag proportion and regulates the regulation and control intensity according to the regulation efficiency; the system stability monitoring unit counts the total amount of abnormal signals, calculates out-of-control frequency in combination with operation duration, and generates system failure signals when the out-of-control frequency exceeds a threshold value. According to the system, dynamic adjustment and stability early warning of energy consumption monitoring are realized, and the reliability and adaptability of operation of the server cluster radiator are improved.

The invention provides a power monitoring system safety operation and maintenance service and inspection management optimization method and system, and relates to the field of power system safety operation and maintenance, and the method comprises the steps: collecting equipment operation information to construct a feature vector, generating an operation track, analyzing an abnormal interval, and calculating the equipment state correlation degree to form a fault propagation link diagram; recognizing a fault source node, determining a sweep device, and establishing a routing inspection sequence and path; constructing a security isolation domain and a communication matrix deployment protection strategy; polling is executed, and feature matching is carried out to position potential safety hazards. According to the invention, accurate positioning and prevention of faults of the power monitoring system are realized, and the operation and maintenance efficiency and the system safety are improved.

The invention relates to the technical field of cooling system fault prediction, in particular to a multi-target collaborative liquid cooling system fault prediction and self-healing control method and an energy storage thermal management system.The method comprises the steps that 1, a system health state database is constructed, and data in the database is dynamically updated; step 2, quantizing a parameter change rule based on calculus, constructing a mathematical model of core parameters of the liquid cooling system through a heat balance equation, and quantizing correlation between parameter change and a system state; and step 3, based on the mathematical model in the step 2 and a machine learning algorithm, analyzing the fused data set to realize fault prediction and graded early warning. And 4, according to the fault type in the step 3, by solving a dynamic control equation, actuator adjustment parameters are automatically calculated, and self-healing operation is achieved. According to the method, multi-source data fusion and fault prediction are deeply combined, a self-healing strategy of stage processing and redundant fault tolerance is designed, and high-precision early warning and continuous operation of the system in a fault state are achieved.

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 is suitable for the technical field of system fault monitoring, and provides a system fault monitoring method based on log real-time synchronization and active scanning, and the method comprises the steps: capturing a log increment in real time through a file system event monitoring trigger, analyzing a newly added log content, and synchronizing the log increment and the newly added log content to message middleware; a distributed active scanning unit is configured, the scanning period is dynamically adjusted, and node resources are expanded and shrunk based on the containerization technology; integrating the abnormal data of the user side, the interface state data of the server side and the log stream data, and constructing a multi-modal data set; a normalized error report increment is calculated based on a 30-minute sliding window, and a pre-processing instruction is generated in combination with a random forest algorithm and Bayesian optimization; executing corresponding operation according to the pre-processing instruction; according to the method, a traditional passive mode is broken through, the fault monitoring confidence coefficient is remarkably higher than that of an industry standard, sub-health perception and active blocking of a high-concurrency scene are supported, and a real-time and accurate intelligent operation and maintenance solution is provided for a complex system.

The invention relates to the technical field of tractor power system fault diagnosis, and discloses a method and system for remotely diagnosing a power system fault of a tractor, and the method comprises the steps: monitoring a vibration signal, a sound signal and a temperature signal of a transmission gear of the tractor; extracting a frequency domain feature, a Mel frequency cepstrum coefficient feature and a temperature change trend feature of the vibration signal; carrying out weighted fusion on the signal features by adopting a dynamic weighting mechanism; constructing a fault prediction model based on an XGBoost algorithm, and outputting the gear wear degree and the fault occurrence time; and generating a fault early warning and transmitting the fault early warning to a mobile terminal APP and a cloud platform through an LTE or 5G network. Compared with a traditional fault diagnosis method in the prior art, particularly under the condition of dynamic working conditions, the technical problem that efficient and accurate fault diagnosis of the tractor power system is difficult to realize is solved, and the accuracy and response efficiency of fault diagnosis are improved by combining the convolutional neural network and the XGBoost algorithm.