231 results about "Fault modeling" patented technology

The invention discloses a PLC controller fault detection system, and relates to the technical field of industrial control equipment fault detection.The system is characterized in that PLC operation environment data is acquired through a multi-physical-quantity holographic acquisition module, and after the PLC operation environment data is cleaned and subjected to feature fusion through a data preprocessing module, a fault model is constructed through a multi-physical-quantity fusion model module; the self-adaptive threshold value judgment module dynamically calculates and judges a threshold value and evaluates a state; the fault traceability analysis module constructs a propagation path diagram based on a model and historical cases, and realizes accurate traceability of a fault source and a propagation process; according to the invention, multi-physical-quantity holographic acquisition and feature fusion algorithms are integrated, multi-dimensional parameters are monitored synchronously, a comprehensive feature model is constructed, the fault identification precision is improved, and misjudgment is avoided; dynamic optimization is achieved through self-adaptive threshold judgment, the early warning accuracy is improved, meanwhile, accurate traceability is achieved through an element fault propagation algorithm, a path is optimized in combination with historical cases, the downtime is shortened through full-process intelligent support, and the maintenance cost is reduced.

The invention relates to an intelligent server fault detection method based on a BMC, and the method comprises the steps: adjusting a to-be-detected server to a debug mode of the BMC to obtain original data, judging whether the data received by the BMC is sensor fault data or not, and when the BMC receives an alarm that a sensor is abnormal; constructing a fault model and carrying out training and optimization; acquiring specified data of a to-be-detected server in real time, preprocessing the specified data, inputting the preprocessed specified data into the optimized fault model, automatically classifying the preprocessed specified data by the fault model, judging whether the to-be-detected server has a fault or a fault tendency, if so, starting an automatic repair strategy, and if not, starting an automatic repair strategy. According to the automatic decision, which repair measure is taken is determined according to the severity and the influence range of the fault, and the repair operation is executed through the automatic script or tool of the automatic operation. Through deep mining of BMC underlying data value and fusion of an intelligent model and an automatic repair mechanism, full-process optimization of server fault detection and processing is realized.

The invention discloses a topology awareness priori driven mobile energy storage power distribution network real-time scheduling method and a topology awareness priori driven mobile energy storage power distribution network real-time scheduling system under ice disaster toughness. The method comprises the following steps: constructing a line fault space-time distribution model changing along with icing evolution, and forming a multi-period and multi-scene ice disaster dynamic fault set; on the basis of the fault set and the multi-scene power flow, combining the topology and toughness indexes of the power distribution network, and screening by using a graph convolutional network to obtain a scale-controlled mobile energy storage candidate access node set; a random scheduling model is established under the power grid-road integrated constraint, and a mobile energy storage arrangement position, a driving path and a charging and discharging plan are jointly optimized. According to the method, ice disaster dynamic fault modeling is combined with candidate set screening assisted by topology perception learning, the dimensionality of a large-scale random mixed integer optimization model containing mobile energy storage is effectively reduced while line time sequence failure characteristics are fully reflected, the calculation burden is relieved, and the calculation efficiency is improved. And the toughness of the power distribution network in a multi-ice disaster scene and the timeliness of pre-disaster scheduling are improved.

The invention discloses an intelligent fault injection and performance quantification system and method for a helicopter engineering simulator, and belongs to the technical field of flight simulation and testing, and the system comprises a hierarchical combinable fault model library module which is used for the flexible construction from a part level to a system level to a composite fault; the intelligent fault injection management module is used for realizing an intelligent fault injection strategy; the multi-dimensional performance index quantification module is used for collecting data before and after fault injection in real time and setting a performance evaluation index system; the automatic test and visual analysis module is used for comparing various quantitative indexes of different control laws or different parameters of the same control law in the same fault scene to generate a comprehensive evaluation report; according to the intelligent fault injection and performance quantification system and method for the helicopter engineering simulator, efficient, objective and deep testing of robustness, stability and efficiency of an advanced flight control law in a special condition state is realized through a hierarchical fault model, an intelligent injection strategy and automatic quantitative evaluation.

The invention discloses a wind turbine blade fault diagnosis method based on aerodynamic noise and image recognition, and relates to the technical field of wind turbine blade fault diagnosis, and the method comprises the steps: building a plurality of typical fault models of a wind turbine blade based on a numerical simulation platform, and extracting simulation acoustic image data; the position of the aerodynamic noise source is positioned through an acoustic camera, and corresponding blade image information is collected by an unmanned aerial vehicle; and carrying out feature training on the image sample and the acoustic image data by using a deep neural network and executing fault matching judgment. According to the method, a fault acoustic image database under multiple working conditions is constructed through numerical simulation, outfield data acquisition is performed in combination with an acoustic camera and an unmanned aerial vehicle, and joint feature training and fault matching of acoustic frequency spectrums and images are realized by using a deep neural network. The method can break through the limitations of the prior art in the aspects of real-time performance, accuracy and intelligence, and achieves the efficient and reliable diagnosis of the fault of the wind turbine blade.

The invention relates to the technical field of chip test vector intelligent generation scheme design, in particular to a failure analysis-oriented chip test vector intelligent generation method and system. The method comprises the following steps: acquiring a chip design netlist, a fault model and a known failure mode; analyzing potential design or manufacturing defects based on the information; and according to an analysis result, a targeted test vector capable of effectively exciting and isolating potential defects is automatically generated. The system comprises a corresponding input module, an analysis module and a generation module. According to the method, the design information, the fault model and the historical failure mode are intelligently fused, automatic and precise generation of the test vector is achieved, and the defects that a traditional method is insufficient in test coverage rate, low in failure positioning efficiency and limited in automation degree are overcome. According to the method, the test completeness and the failure analysis efficiency can be remarkably improved, the dependence on artificial experience is reduced, and an efficient technical means is provided for guaranteeing the reliability of the chip.

The invention provides a model-based reusable vehicle fault joint simulation method, which comprises the following steps of: (1) creating fault mode information according to the composition and connection relationship of a system simulation model, and summarizing to form a fault mode information set associated with the model; (2) analyzing a logic causal relationship between fault events, and establishing a system-level fault mode library based on fault mode information; (3) aiming at a joint simulation requirement, screening out a fault mode needing to be injected from the fault mode library, establishing a relationship between fault mode information and a simulation model through a model grammar rule, and generating a fault model; and (4) according to the requirements of fault combination, fault time and fault injection probability in the simulation of the reusable vehicle, completing fault simulation setting, and carrying out fault joint analog simulation. According to the method, automatic mapping and injection of fault information based on the model are realized, and the analogue simulation requirements of the reusable vehicle under large-scale, cross-system and cross-linking coupling faults are met.

The invention discloses a method for realizing a redundant function memory chip test, which relates to the technical field of memory chip test, and comprises the following steps: calculating hyperedge coupling strength based on a hypergraph fault model, and generating a test vector sequence and a memory unit test instruction by adopting a weighted flipping mode according to the hyperedge coupling strength; injecting the test vector sequence into a tested memory chip, executing memory unit test instructions one by one, and capturing a failure unit coordinate and a failure mode category at the same time; constructing a Hall triad based on the failure unit coordinates and the failure mode category, executing theorem proving on the Hall triad by using a theorem proving device to verify the test completeness, generating an increment test vector when the verification fails, executing theorem proving again, and recording the result of the test completeness; according to the method, the hypergraph fault model is established, so that the coverage rate of non-independent faults is increased, the number of redundant test instructions is reduced, the test time and resource consumption are reduced, and the defect detection efficiency is improved.

The invention discloses an AGC information physical system reliability verification method and system, and relates to the technical field of power dispatching automation. The problems that an existing AGC system only adopts a static operation mode and an ideal communication condition in a simulation test, and coupling response of continuous operation and sudden disturbance of a power grid under a long time scale is difficult to reproduce truly are solved. The state evolution under event driving is realized by constructing three types of simulation event sources of a time sequence operation sample, a master station control instruction and external disturbance and setting a priority mechanism of external disturbance gt, master station instruction gt and time sequence propulsion; an uplink channel fault model is introduced to simulate on-off, delay and errors of measurement data, and a downlink channel fault model is introduced to simulate transmission abnormity of a control instruction; closed-loop simulation of frequency response and electrical quantity updating is realized by combining promotion of a rotor motion equation and dynamic load flow calculation; and finally, by analyzing indexes such as frequency deviation, tie line deviation and control response delay, comprehensive reliability evaluation of the AGC system in a complex information physical environment is completed.

The invention relates to a mechanical arm actuator fault tolerance control method based on sliding mode control and a neural network, and belongs to the field of industrial robots. The method comprises the steps that modeling is conducted on the fault condition of a mechanical arm actuator, and a fault model is established; defining a joint angle tracking error, designing a sliding mode controller, and establishing a sliding mode surface of the sliding mode controller as linear sliding; designing a mechanical arm dynamic form according to the linear sliding surface and the fault model; estimating a total dynamic item by adopting an RBFNN neural network to obtain an estimated value of a system dynamic item, and taking the estimated value as an output quantity of the RBFNN neural network; designing a control law; calculating and generating a corresponding control input quantity according to a control law by adaptively updating a fault matrix of the weight matrix; and the mechanical arm actuator controls the mechanical arm according to the control input quantity so as to achieve fault compensation. According to the method, high-precision trajectory tracking is achieved under the condition that dynamics and actuator faults are unknown, and an accurate fault model or a fault detection system is not needed.

The invention discloses a modeling method and an analysis method of a geologic body finite element model containing a fault three-dimensional fine structure, and relates to the field of earth science. According to the method, the three-dimensional fault model is properly smoothed, so that the three-dimensional geometrical characteristics of the fault are reserved, and subsequent grid division is facilitated. And meanwhile, during grid division, even if a relatively complex geometric form is adopted, hexahedron units are still adopted, so that more accurate computational simulation is facilitated, and finally, a geologic body finite element model containing a fault three-dimensional fine structure is obtained. According to the invention, flexible adjustment can be carried out according to different geological backgrounds and scientific problems, including but not limited to addition of corresponding material media, friction coefficients, physical equations, fault contact and other parameters, and geologic bodies and faults in a research area can be simulated more accurately and finely. Meanwhile, the geologic body finite element model mesh created by the method can reach million level, and more refined research, simulation and processing work are facilitated.

The invention discloses an equipment predictive maintenance system and method based on a block chain network, and belongs to the technical field of production equipment maintenance, and the method comprises the steps: collecting equipment operation data in real time through a sensor deployed on production equipment, and transmitting the data to a block chain data storage library composed of distributed nodes after preprocessing; the storage library comprises a data acquisition node, a model management node and a maintenance execution node, and equipment operation parameters and maintenance information are isolated and stored through an independent channel; based on historical data, constructing an equipment fault model, predicting a potential fault type and probability of the equipment, and transmitting the model and a prediction result to a block chain data storage library; generating a corresponding maintenance plan according to the prediction result, and automatically distributing the maintenance plan to a maintenance execution node; and after the maintenance is completed, the log is recorded to the block chain data storage library and is bound with the original plan, so that the traceability of the whole process is ensured.

The invention discloses a water turbine governor fault modeling and parameter optimization method based on iterative learning control. The method comprises the following steps: S1, system dynamics modeling; s2, iterative learning parameter updating, wherein a water turbine governor fault diagnosis method based on iterative learning control realizes progressive identification of fault features through periodically correcting model parameters; s3, fault feature extraction: calculating a residual signal of an actual output and a model predicted value, performing time-frequency analysis on the residual signal by adopting improved Morlet wavelet transform, and extracting an energy entropy feature and a time domain statistical feature; s4, fault diagnosis and dynamic optimization: based on the extracted fault features, fault detection and classification are realized through a three-level linkage decision mechanism, a dynamic adjustment strategy is introduced to carry out online optimization on a diagnosis rule base, and fault modeling and parameter optimization closed loop are completed; according to the method, the problem of modeling misalignment of a traditional method under a nonlinear working condition is effectively solved.

The invention discloses a comprehensive assessment method for weak links of a power system involving multi-dimensional indexes in extreme weather. The method comprises the following steps: firstly, establishing a multi-dimensional index system containing safety and optimized operation, wherein the system covers the line outage probability, the load loss probability, the electric energy insufficiency expectation, the line heavy load rate and the network loss rate; secondly, constructing a line fault model and a cascading fault model under extreme storm weather, describing fault probability change in combination with a Gaussian function and Weibull distribution, and simulating line segment failure by adopting Poisson distribution; a system state after a fault is calculated through a direct-current optimal power flow model, and a large amount of simulation data is obtained in combination with a non-sequential Monte Carlo method. Furthermore, a fuzzy analytic hierarchy process and a CRITIC method are adopted to calculate subjective and objective weights respectively, subjective and objective comprehensive evaluation is realized through weight fusion, and weak links of the system are identified. The method gives consideration to evaluation scientificity and calculation efficiency, and is suitable for real-time risk evaluation and weak link identification of a large-scale power system in extreme weather.

The invention relates to the technical field of intelligent management of construction elevators, in particular to a digital intelligent management system for construction elevators, which comprises an operation monitoring module used for collecting operation state data of each construction elevator in real time through a sensor network, detecting fault conditions and performing early warning; the intelligent scheduling module is used for intelligently distributing transportation tasks based on the passenger flow data, the material transportation demand data and the operation state data of each construction elevator; the predictive maintenance module is used for acquiring key component parameters of each construction elevator, predicting the residual life according to the key component parameters in combination with a fault model, and generating a maintenance work order in advance so as to reduce the fault rate; the authority management module is used for limiting unauthorized personnel to use the construction elevator; and the digital archive management module is used for recording the operation state, the fault history and the maintenance record of the construction elevator and generating a complete equipment life cycle archive. According to the invention, fine and informatization management of the whole life cycle of the construction elevator can be realized.

The application belongs to the field of power equipment, and discloses a transformer inter-turn short-circuit fault modeling method, system, device and medium, which comprises the following steps: a two-dimensional geometric model of a transformer core and winding is established, the two-dimensional geometric model is discretized and divided into a plurality of magnetic network units to obtain a discrete magnetic network model; a three-dimensional electrostatic field entity model of the winding is constructed, and a distribution capacitance matrix between turns of the winding and to the ground is calculated; a short-circuit grid point is set in the discrete magnetic network model, the short-circuit grid point is connected in parallel to a metallic short-circuit branch model to obtain a short-circuit closed branch; a transformer circuit is obtained, a coupling relationship between the transformer circuit and the discrete magnetic network model is constructed based on the distribution capacitance matrix, and a transformer inter-turn short-circuit fault model is constructed. The application can accurately describe the internal space electromagnetic distribution characteristics and the local nonlinear saturation characteristics of the core of the transformer.

The application discloses a kind of high-temperature forging dynamic three-dimensional measurement-oriented sliding mode fault-tolerant control methods, and specific implementation steps include: establishing the discrete-time singular perturbation Markov jump state space model of high-temperature forging measurement system;Design probability dynamic event trigger mechanism, utilize internal dynamic variable intelligent judgment data transmission time to save bandwidth resources;For possible fraud attacks and actuator failure, build the corresponding attack model and fault model;Sliding surface and sliding control rate function are constructed;Deduce the sufficient condition for guaranteeing system reachability and closed-loop system finite time boundedness;Iterative solution controller gain and dynamic trigger parameters.The application considers network security, resource-constrained and actuator degradation and other multiple constraints under the high-temperature forging dynamic three-dimensional measurement scene, while guaranteeing the system finite time stability, significantly improves the robust cooperative control ability and operation safety of measurement system.

This invention provides a low-computational-complexity, pre-set performance-tolerant control method for steer-by-wire systems. The control method includes: determining the control objective based on a pre-set performance control method; subsequently designing a new pre-set performance function and error transformation function; designing virtual control laws and control laws in the controller using a robust control method; establishing a dynamic model of the steer-by-wire system based on system dynamics; establishing a dead-zone model and a fault model based on the impact of dead zones and faults in the vehicle actuators on the control performance of the steer-by-wire system; simultaneously obtaining the input nonlinear model of the steer-by-wire system; subsequently establishing a state-space model of the steer-by-wire system; and finally, verifying the controller's control effect and optimizing its internal parameters using the state-space model. This invention considers fault and dead-zone issues, improving the robustness of the controller; and considers the limited computational resources of the vehicle, with a simple controller structure that improves the overall computational efficiency of the vehicle.

The invention relates to the field of fault diagnosis, and provides a multistage-flow-based electromechanical system fault diagnosis rule automatic generation method and system, and the method comprises the following steps: S1, building a multistage-flow electromechanical system fault model; s2, generating the fault propagation table, wherein the fault propagation table is generated by adopting a fuzzy cognitive graph or a bonding graph; s3, performing sensor layout optimization according to the fault propagation table; and S4, generating a diagnosis rule. The system comprises a multistage turbomachine system fault modeling unit, a fuzzy cognitive graph fault propagation analysis unit, a bonding graph fault propagation analysis unit, a sensor layout optimization unit and a diagnosis rule generation unit. According to the method, a multilevel flow fault model considering material, energy and information flow is taken as a core, a fault propagation path is dynamically simulated by using a fuzzy cognitive graph or a bonding graph, and the operation and maintenance cost is reduced by optimizing the layout of a sensor; and explainable diagnosis rules are automatically generated to realize accurate fault diagnosis of the complex electromechanical system.

The invention relates to the technical field of distribution network OCS self-healing strategies, and discloses a distribution network OCS self-healing strategy automatic simulation system, method, device and medium, and the method comprises the following steps: the system comprises a strategy analysis module, a fault modeling module, a simulation execution module, a result verification module and a report generation module; the established system can ensure that in a simulation environment independent of a real-time system, a self-healing strategy is automatically analyzed, a fault scene set covering a whole line section of a feeder line is generated, single-point / multi-point, multi-loop concurrent and remote control success configurable fault simulation is supported, and the fault simulation efficiency is improved. And a verification report containing abnormal positioning can be automatically output by triggering the self-healing logic to execute and compare the actual action with an expected result. According to the invention, reliable simulation detection of the self-healing strategy is realized, errors and omissions caused by manual operation are avoided, and the quality of the self-healing function of the distribution network is improved.

The present application relates to the technical field of power distribution network resilience evaluation, and discloses a power distribution network resilience evaluation verification method, system, device and medium based on multi-dimensional fault injection, which comprises the following steps: constructing a multi-dimensional fault model library; based on a preset test target, selecting a target fault scene from the model library and generating a fault injection strategy; executing the strategy on a hardware-in-the-loop simulation platform, and collecting system operation data in real time; calculating a performance index curve representing the overall power supply level of the power distribution network over time, and quantitatively calculating a plurality of resilience evaluation indexes; based on the resilience evaluation indexes, performing comparative analysis, identifying key scenes that significantly affect the resilience of the power distribution network, and adjusting the fault injection strategy in subsequent tests to realize closed-loop evaluation and verification of the resilience of the power distribution network. The present application not only evaluates the degree of performance loss, but also focuses on recovery speed and recovery efficiency to identify weak links affecting recovery and provide a scientific basis for formulating resilience improvement measures.

The application discloses a cross-working condition control equipment fault detection system based on causal correction and full-space modeling. The system comprises a control equipment detection instrument, a control equipment fault database, a data processing module, a full-space fault modeling module, a causal correction module and a control equipment fault display and control module. The full-space modeling module and the causal correction module respectively solve the problems of data sparsity and sample selection bias existing in traditional methods, and are intended to realize unbiased estimation of fault probability under multiple working conditions.

This invention discloses a fault-tolerant and vibration suppression control method for rigid-flexible coupled robots based on synchronous disturbance observation and dynamic event triggering. The method includes: establishing a large-deformation dynamic model of the rigid-flexible coupled robot mechanism and constructing rigid coordinates, flexible modal coordinates, and control targets; establishing an actuator effectiveness decay fault model and transforming the original underactuated system into an equivalent fully actuated model oriented towards trajectory tracking; designing a fixed-time synchronous disturbance observer based on error dynamics to estimate external disturbances and composite uncertainties online; constructing a dynamic event triggering mechanism and a time-synchronous fault-tolerant sliding mode control law to achieve synchronous convergence of the system state to the sliding surface within a fixed time; introducing a hybrid reference trajectory and virtual input to establish an augmented state model and designing a robust linear quadratic state feedback controller. This invention achieves coordinated control of trajectory tracking and flexible vibration suppression under conditions of external disturbances, actuator failures, and limited communication resources.

The invention discloses a two-point grounding fault model analysis method in a neutral point non-effective grounding mode, and aims to solve the problem that a unified accurate mathematical model suitable for a neutral point non-grounding and arc suppression coil grounding system under an in-phase two-point grounding fault is lacked in the prior art. According to the method, a fault network topology is reconstructed, a multivariate function equation set containing a fault position, ground capacitance, arc suppression coil parameters and a power supply initial phase angle is established, explicit analytical expressions of zero-sequence voltage current and phase voltage current are deduced, and a transient process is modeled in combination with a state space method. And accurate description of a fault characteristic quantity spatio-temporal evolution rule is realized. By adopting the above technical scheme, the method can provide a modeling basis which can be directly called for high-fidelity simulation platforms such as RTDS, and supports intelligent protection algorithm development and fault diagnosis system optimization.

This invention relates to the field of industry data diagnostic analysis technology, and discloses a method, system, device, and medium for attributing sensor data quality problems. The method includes: dividing raw data from multiple data layers into data windows to obtain window data; calculating the data features of the window data to obtain window feature vectors, and calculating the basic feature distance between each window feature vector and a preset normal window feature vector; performing feature distance weighting correction on the basic feature distance to obtain a corrected distance, and identifying abnormal data windows based on the corrected distance; performing threshold attribution based on the abnormal data windows to obtain an average contribution ratio, and calculating the fault probability using a pre-constructed fault model; calculating the final fusion contribution and consistency index based on the average contribution ratio and the fault probability, and generating the attribution result. This invention can improve the accuracy of attributing sensor data quality problems.

The invention discloses a quantitative evaluation method for the transverse plugging performance of a multilayer sandstone oil reservoir fault. The quantitative evaluation method comprises the steps that a layer surface model and a fault model which can reflect a stratum space framework are established; obtaining a three-dimensional lithofacies model; acquiring the contraposition condition of two sand shale reservoirs of the fault; calculating an improved transverse plugging coefficient; and performing quantitative evaluation on the fault plugging property according to data such as an oil field seismic section and tracer test and comprehensive analysis of a numerical simulation method. According to the method, the multi-layer overlapping effect is considered, the influence of the fault displacement on mudstone smearing continuity and lithologic configuration can be considered, the threshold chart of the fault static transverse connectivity is established, verification is conducted in combination with a numerical simulation method, the fault plugging performance prediction precision can be effectively improved, and the fault plugging performance prediction accuracy is improved. And a reliable basis is provided for further development and adjustment of the multi-layer sandstone reservoir.

The application discloses a tunnel unfavorable geological body advanced geological prediction detection simulation device and method, relates to the field of tunnel engineering and advanced geological prediction technology, and comprises a tunnel model, a fault model, a karst model and a boulder model. Tunnel model side walls are respectively excavated with a working face A pilot tunnel, a working face B pilot tunnel and a working face C pilot tunnel. The fault model is arranged opposite the working face A pilot tunnel. The karst model is arranged opposite the working face B pilot tunnel. The boulder model is arranged opposite the working face C pilot tunnel. The device can simulate the unfavorable geological body of the fault, the karst and the boulder respectively, has the advantages of ingenious design, strong pertinence, convenience, easy use and strong adaptability, and has a great promoting effect on tunnel advanced geological prediction. The method uses the device to perform a test first, obtains optimized detection parameters of various unfavorable geological bodies, and then uses the detection parameters to perform field detection, so that various unfavorable geological bodies in an unexcavated area can be detected and predicted at one time, and the method is convenient, fast and high in accuracy.

The application provides a path-aware fault processing method for a cloud computing environment, and relates to the technical fields of cloud computing, computer network security and system reliability. The method comprises the following steps: obtaining a real end-to-end network path of target service traffic in a network communication system for cloud computing, and representing the real end-to-end network path as an ordered path sequence; determining a fault type of a target fault to be injected and a target path node corresponding to the target fault in the ordered path sequence based on a preset fault model and the real end-to-end network path, generating a fault specification based on the fault type, the position of the target path node and the fault parameters of the target fault, and controlling the target path node to inject a fault injection primitive corresponding to the target fault; and in the case of injecting the fault injection primitive, performing correlation analysis on running data and the fault injection primitive to generate an influence analysis result of the target fault. The technical scheme of the application improves the accuracy of fault influence analysis.

This invention provides a method, system, and storage medium for enhancing transformer fault acoustic signature diagnostic samples. The method includes: constructing the acoustic wave equation for the vibration of the core and windings propagating through the medium and the medium interface boundary conditions; performing sound field simulation on a fault model based on the acoustic wave equation and the medium interface boundary conditions to obtain a fault simulation sound field; determining a physical simulation dataset based on the fault simulation sound field; performing sparse representation on the physical simulation dataset to obtain a sparse dictionary and a sparse coefficient matrix; performing transfer learning on real fault samples to obtain a transfer dictionary; and performing sample enhancement based on the sparse dictionary, the sparse coefficient matrix, and the transfer dictionary to obtain enhanced transformer fault acoustic signature samples. In this embodiment, sample enhancement using the sparse dictionary, the sparse coefficient matrix, and the transfer dictionary effectively enhances the samples of transformer faults.

This invention provides a method and apparatus for fault diagnosis of rolling bearings. The method involves acquiring the vibration acceleration signal of the rolling bearing and then obtaining its time-frequency distribution, time-domain waveform recursion graph, and spectral recursion graph. A combined tensor is then formed based on these graphs to construct a fault diagnosis model for the rolling bearing. Finally, test sample signals are input into the trained fault diagnosis model to obtain the fault diagnosis result. This invention combines the recursion graph analysis method with time-domain and frequency-domain signals, improving the diagnostic accuracy and generalization performance of the fault diagnosis model. Furthermore, through training and testing the fault diagnosis model, the diagnostic accuracy of the fault model under complex environments can be further improved.