388 results about "Fault modeling" patented technology

The invention discloses a power plant intelligent maintenance method and system based on multi-modal dynamic graph learning. The method comprises the following steps: acquiring structured sensor data, unstructured data and equipment physical topology data of equipment operation in real time through a multi-source sensor cluster and an industrial terminal; the method comprises the following steps: preprocessing multi-modal data, and fusing multi-modal features by using a double-flow Transform architecture and a gated attention mechanism to generate a joint embedded representation; constructing a dynamic causal graph based on equipment physical topology data and sensor time sequence characteristics, updating an edge weight through a GraphSAGE algorithm, fusing domain rule constraints, and outputting equipment state information; generating a maintenance strategy through an improved near-end strategy optimization algorithm according to the state and the equipment health index; and finally, the maintenance strategy triggers third-level early warning of the DCS through an OPC UA protocol, and a maintenance instruction is accurately issued. According to the method, the defects of a traditional method in the aspects of data fusion, fault modeling and decision making are overcome, and the safety, the economical efficiency and the operation and maintenance intelligent level of power plant equipment are remarkably improved.

An optical cable multi-dimensional state monitoring and fault prediction method and system are provided, and the method comprises the steps: obtaining optical cable environment characteristic data, historical fault distribution probability and operation risk indexes, selecting key nodes, deploying state sensors, and collecting multi-dimensional state data to generate an initial state data set; establishing a fault judgment model based on the initial data set, and calculating multi-dimensional state feature data and determining an initial distribution range in combination with historical fault probability and environment features; arranging the physical state value and the communication state value according to a time sequence, and generating a dynamic threshold range of the key node by combining dynamic correction of environment characteristic data; and when the feature data exceed the threshold range, marking abnormity, calculating a judgment parameter through a fault model, and comparing the judgment parameter with a preset value to confirm a fault.

The invention belongs to the technical field of thermal circuit model construction, and particularly relates to a high-voltage power cable intermediate connection thermal circuit fault modeling analysis method and device, and the method comprises the steps: building a high-voltage power cable intermediate connection thermal circuit fault circuit model through analyzing a cable intermediate connection temperature inversion principle; the method comprises the following steps: establishing a geometric model for multi-physics field analysis of the intermediate connection of the high-voltage power cable by utilizing Comsol finite element simulation software, setting material parameters of each component, and setting field domain parameters; an electromagnetic field module is added, boundary conditions of electromagnetic field analysis are set, and a cable intermediate connection electromagnetic field simulation model is obtained; adding a solid heat transfer module to obtain a solid temperature field simulation model; obtaining an electromagnetic-thermal coupling model, and solving to obtain the temperature, current and loss of the cable middle connection wire core and the outer surface in different insulation states. According to the method, the cable intermediate connection fault problem can be found and predicted, and the reliability and the safety of the power cable are improved.

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 discloses a fault model construction method for adaptive testing of abnormal values of a system-on-chip. The method relates to the technical field of fault construction of chip testing, and comprises the following steps: testing chips on a large scale under the conditions of different temperatures, voltages, frequencies, loads and the like, acquiring performance parameters such as signal delay and power consumption, screening abnormal values by applying a Z-score or box plot method, and processing by using a sliding window; extracting features through CNN or RNN, and normalizing the features; fault features are classified through K-Means or hierarchical clustering, and the accuracy rate is larger than or equal to 80%; a model is built through an SVM or a decision tree, faults can be predicted, self-updating can be achieved, self-adaptive adjustment can be achieved according to chip parameter changes, independent data verification optimization, one-out-of-leave evaluation and gradient descent optimization are achieved. According to the method, multi-condition testing can be carried out on the system-level chip, abnormal values are accurately screened, features are extracted, faults are classified, a fault model capable of being self-updated is constructed, self-adaptive adjustment and verification optimization are achieved, the accuracy is improved, and the chip testing and fault diagnosis efficiency and accuracy are improved.

The invention discloses a discrete element-based three-dimensional fault / fracture network modeling method and device under a backlash pushing and covering system, and relates to the technical field of three-dimensional fault modeling, and the method comprises the steps: building an initialized fault plane model based on the seismic logging information parameters of a target region; extracting stratum parameters of the target area, and determining a deformation process in combination with an equilibrium profile recovery method; the method comprises the following steps: calibrating mesoscopic parameters of discrete elements according to stratum parameters, depositing a first discrete element model, optimizing and generating a second discrete element model, setting boundary conditions to carry out back-flushing pushing-covering simulation, comparing an output result of the second discrete element model with an actual geological section, adjusting model parameters until the output result and the actual geological section are matched, and establishing a three-dimensional fault and fracture network. And analyzing and establishing spatial distribution characteristics of the target area, and carrying out dynamic evaluation and trend prediction on the activity of the three-dimensional fault. According to the method, the geomechanical model is fused, the subjectivity of manual interpretation is overcome, and the problems of fault tip extension and the like in grid reconstruction are effectively avoided.

The invention provides a rotating machine fault diagnosis method and a related system. The rotating machine fault diagnosis method comprises the following steps: S1, constructing a rotor vibration equation according to a vibration behavior of a rotor system under an operation condition; s2, acquiring real-time information of a to-be-diagnosed rotating machine, performing modal analysis based on a rotor vibration equation according to the real-time information of the to-be-diagnosed rotating machine to determine a sensitive frequency band and key features, and forming multi-dimensional feature data; s3, carrying out denoising processing on the multi-dimensional feature data and carrying out signal reconstruction to obtain a reconstructed signal; s4, inputting the reconstruction signal into a pre-trained rotating machine fault model to obtain a fault diagnosis result; and the rotating machine fault model is fused by adopting a neural network ordinary differential equation and a convolutional neural network-gating cycle unit. The method can accurately simulate and predict the continuous dynamic change of the system, improves the diagnosis precision, and enhances the real-time performance and robustness of the model.

The invention discloses a cable accessory fault accurate positioning method and system based on multi-physics field cooperation, and belongs to the technical field of cable fault detection, and the method comprises the steps: installing and deploying a plurality of signal monitoring sensors in a key region of a cable accessory, and forming a multi-physics field sensing matrix; the method comprises the following steps: monitoring a cable accessory in real time by adopting heterogeneous sampling frequency, collecting a multi-physics signal, extracting a multi-physics signal feature in the multi-physics signal based on a time-frequency domain joint feature algorithm, performing signal analysis on the multi-physics signal feature, and obtaining a multi-physics signal analysis result; a multi-physical field fault model is constructed based on a multi-field coupling positioning algorithm, and a fault point in the cable accessory is positioned according to a multi-physical field signal analysis result, so that the fault of the cable accessory can be quickly and accurately identified and positioned, and the reliability and stability of a power system are improved.

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, which relates to the processing and manufacturing field, discloses an intelligent processing and manufacturing management system comprising a data acquisition module, a data transmission and processing module, a production planning and scheduling module, an equipment management module, a quality management module, an inventory management module and a personnel management module. And the equipment management module is used for carrying out full-life-cycle management on the processed and manufactured equipment, monitoring the running state of the equipment in real time, and predicting the fault occurrence probability of the equipment through analysis of the running data of the equipment. According to the management system for intelligent processing and manufacturing, refined and intelligent management of the whole life cycle of equipment from access to scrapping can be realized, the reliability, the available duration and the operation efficiency of the equipment can be effectively improved, common fault models can be identified through the constructed diagnosis model, and the reliability of the equipment can be improved. And faults caused by multiple factors can be accurately positioned and analyzed.

Systems and methods for detecting and diagnosing machine faults are discussed. An exemplary system includes at least one sensor node to sense a signal indicative of an operation status of a machine part, and a machine health analyzer circuit to generate a computational machine fault model comprising an autoencoder (AE) network and an associative module. The AE network encodes the sensed signal into signal features in a latent feature space, and decodes the signal features to produce a reconstructed signal. The associative module transforms the encoded signal features into an associative output using a dynamically updatable codebook. The machine health analyzer circuit detects a presence or absence of fault in the machine part based on reconstruction losses determined respectively from the reconstructed signal and the associative output. The detected fault can be presented to a user or to a process such as fault diagnosis or fault correction.

The invention discloses a power distribution network grounding fault research and judgment analysis method, relates to the technical field of power distribution network fault analysis, and solves the technical problem that fault analysis is not accurate and deep enough due to the fact that fault features in a neutral point grounding complex fault scene cannot be comprehensively considered. According to the method, single-phase grounding fault models of a neutral point ungrounded system, an arc suppression coil grounded system and a small resistor grounded system are respectively constructed, grounding current, phase voltage and neutral point voltage expressions under the models are detailedly deduced, and characteristics of voltage and current before and after a fault point in the steady-state and transient-state processes are deeply analyzed; the method comprehensively considers various complex fault scenes, can more accurately grasp essential characteristics of faults in different grounding modes, and provides a more comprehensive and accurate basis for fault diagnosis and processing.

The invention relates to the technical field of motor faults, and discloses a motor fault detection method and system. Motor operation data are collected in real time through a sensor, and data preprocessing is carried out to eliminate noise and missing values. A deep learning algorithm based on an attention mechanism is adopted, historical motor operation data and fault data are combined, and a motor fault model is established. And inputting the preprocessed real-time data into the model, and outputting the probability of various faults of the motor. And when the probability of a certain fault exceeds a preset threshold value, triggering an early warning strategy. Meanwhile, the invention further provides a motor fault detection system which comprises a data acquisition module, a data preprocessing module, a model construction module, a fault detection module and an early warning strategy module, and intelligent and automatic detection and early warning of motor faults are achieved. According to the method and the system, the accuracy and the robustness of fault identification are improved, the safe and stable operation of the motor and the system is ensured, and the method and the system have wide application prospects.

The invention relates to the technical field of robot control, and discloses a mechanical arm specified performance fault-tolerant control method based on a fuzzy logic system, and the method comprises the steps: building a kinematics and dynamics model and a fault model of an agricultural robot; establishing a relative error dynamic equation between the actual position and the expected position; designing a specified performance function, and constraining the tracking error within an expected range by using the specified performance function; on the basis, designing a virtual control law by using a backstepping method under the condition of meeting error constraints; designing an adaptive control law by adopting a fuzzy logic system based on an uncertain item, unknown environment interference and a fault model in the kinetic model; a specified performance controller is designed based on a virtual control law, fuzzy logic adaptive control and full-state feedback agricultural robot, and specified performance fault-tolerant control of the agricultural robot is realized. The stability proves that the tracking error of the agricultural robot converges to any small neighborhood near zero within fixed time.

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 a fault modeling method, device and equipment based on seismic data and a storage medium, relates to the field of geological modeling, and aims to process initial seismic data by adopting a dip angle-oriented median filtering method and effectively remove noise while retaining geological structure information such as faults and the like. And then the trained fault identification model is introduced to process the target seismic data, fault information can be rapidly extracted from massive seismic data, and the identification efficiency is greatly improved. And then similarity is calculated from two dimensions of spatial distance and direction attribute based on a fault identification result, clustering is carried out by using a spectral clustering algorithm, correlation of fault lines in spatial distribution and fault extension direction characteristics are considered, and more reasonable and accurate fault line groups are obtained. And finally, constructing an initial B-spline curved surface by utilizing a grouping result, and enabling the finally constructed three-dimensional fault model not only to fit actual seismic data, but also to accord with the smoothness and direction rule of a geological structure through variation energy minimization optimization, so as to accurately reflect the real form of an underground fault.

The invention relates to a preset performance specified time tracking control method for a mechanical arm, belongs to the technical field of intelligent robot control, and solves the technical problem of low control precision of the mechanical arm. Comprising the steps of establishing a dynamical model and a fault model, designing a preset performance specified time function, constructing a preset performance specified time tracking control law, and verifying the bounded stability of the preset performance specified time tracking control law. According to the control method, the specified time preset performance function is introduced into the control law design in an error transformation mode, synchronous adjustment of the convergence precision and the convergence speed is guaranteed, and the control method is used for the mechanical arm device and can also be used for locally spraying an end effector on the surface of an aircraft.

The invention provides a power-traffic coupling charging network toughness evaluation method and system, and belongs to the technical field of power system analysis. According to the method, a power-traffic coupling charging network is constructed, a typhoon path and a Holland wind field model are simulated in combination with a Bezier curve to calculate a wind speed, a dynamic traffic flow distribution, alternating current optimal power flow and cascade fault model is established, a fault data set is generated by utilizing Monte Carlo simulation, a fault prediction model is trained through a dynamic Bayesian network, and a fault prediction result is obtained. Inputting the actually measured wind speed, outputting a dynamic probability graph, and calculating a dynamic toughness index to evaluate the toughness of the power-traffic coupling charging network. According to the method, the problem of insufficient fault propagation and dynamic evaluation between systems is considered, and the toughness evaluation precision of the power-traffic coupling charging network under typhoon disasters is 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 present invention relates to a motor turn-to-turn short-circuit fault detection method based on variational modal decomposition fused with deep learning, which solves the defects of low accuracy and slow efficiency of turn-to-turn short-circuit fault diagnosis compared with the prior art. The present invention includes the following steps: establishing a permanent magnet synchronous motor fault model; extracting fault features by variational modal decomposition algorithm; optimizing variational modal decomposition parameters by sparrow search algorithm; establishing a deep pyramid pooling residual convolutional neural network model; training of SPP-ResCNN network; real-time diagnosis of permanent magnet synchronous motor turn-to-turn short-circuit fault. The present invention uses sparrow search algorithm to optimize variational modal decomposition algorithm, eliminates other harmonic components in fault features, extracts fault features of specific frequencies, and significantly improves the accuracy of fault features. At the same time, pyramid pooling network and residual network are used to optimize convolutional neural network, which improves the accuracy and efficiency of fault diagnosis.

The invention discloses a switch cabinet partial discharge fault identification method based on multi-fault information fusion, and the method comprises the steps: building different partial discharge fault models in a switch cabinet, and carrying out the data collection of partial discharge signals generated by different models through a plurality of sensors; dividing the collected multi-fault multi-source signal data into a training set and a test set; carrying out noise reduction processing on the signal data by using an LMS-wavelet transform method; constructing time domain feature parameters for the denoised data, and extracting features by using a sliding window method; performing dimension reduction processing on the features by using a kernel principal component analysis (KPCA) method; and training a PNN model based on the signal features to realize fault identification. According to the method, monitoring information of various sensors is fused, the LMS-wavelet transform method and the KPCA kernel principal component analysis method are introduced at the same time to carry out noise reduction and feature dimension reduction on complex multi-source data, the operation time of fault recognition is greatly shortened, and the efficiency of switch cabinet partial discharge fault detection is improved.

The invention discloses a transformer fault diagnosis research method and system, and relates to the technical field of transformer fault diagnosis, and the method comprises the steps: introducing an SHAP value to carry out the feature optimization of transformer fault data based on a decision tree and an integration method thereof; dividing the optimized data into a training set and a test set, and performing data cleaning and oversampling processing on the training set; and optimizing the deep mixed kernel extreme learning machine based on the improved dung beetle algorithm, constructing a transformer fault model, and performing fault diagnosis by using the transformer fault model. According to the feature system provided by the invention, the input features of the transformer fault diagnosis model can be effectively optimized, transformer fault data can be effectively utilized, and the influence of redundant features on the diagnosis model is reduced. Compared with other heuristic artificial intelligence algorithms, the improved dung beetle algorithm provided by the invention has more excellent optimization ability, and can find more excellent parameters of the deep mixed kernel extreme learning machine in the same number of iterations and range.

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 community power grid monitoring and autonomous decision scheduling system. The community power grid monitoring and autonomous decision scheduling system comprises a man-machine interaction module; the monitoring module is configured to acquire power grid operation data in real time, and the power grid operation data at least comprise voltage, current, power and environmental parameters; the fault diagnosis module is integrated with a fault prediction model based on machine learning, generates a fault feature library through historical data and real-time data training, and is used for recognizing potential faults in advance and triggering protection actions; the autonomous decision-making unit generates a scheduling strategy based on the fault diagnosis result and is linked with the power distribution equipment to execute a protection action; wherein the monitoring module and the fault diagnosis module work cooperatively through a distributed computing architecture, original data are preprocessed by utilizing an edge computing node, and dynamic optimization of a fault model is realized through a cloud computing platform. According to the method, the fault handling capacity, the operation efficiency and the user service quality of the community power grid can be remarkably improved, and key technical support is provided for intelligent power grid construction.

The invention provides a comprehensive anomaly recognition and fault-tolerant control method based on current sensor fault modeling, offset coordinate system transformation, a full-order sliding-mode observer and current space vector error projection, which comprises an innovative algorithm for accurately modeling and classifying various typical faults and carrying out fault detection and positioning by using the models. Based on a current sensor fault detection strategy of an offset coordinate system, through specific Clark transformation and coordinate offset, in combination with current residual amplitude reconstruction and hysteresis comparison, a reconstruction error is obtained through reconstruction of a current space vector error and projection, a sliding mode surface is formed by the reconstruction error and a measurement error, and accurate reconstruction of fault phase current is achieved. According to the method, various fault types and positions thereof can be accurately identified, the faults can be more accurately positioned, and the accuracy of fault detection is improved. When the current sensor breaks down, fault phase current can be effectively reconstructed, stable operation of the system is guaranteed, and better dynamic performance and control precision are achieved.

The invention discloses an electric power system safety assessment method and system considering multiple meteorological sensitive factors, and relates to the technical field of electric power system safety assessment, and the method comprises the steps: building a fault model under the influence of multiple meteorological factors based on the coupling effect of different meteorological factors on electric power system elements; and calculating the fault probability of each element of the power system under the extreme weather condition by using a multi-modal data fusion and neural network technology. And evaluating the safety of the power system through a Monte Carlo method in combination with the fault probability of the system element, and outputting a reliability index. According to the method, the dependence of traditional safety assessment on single-factor hypothesis, a static model and low-efficiency calculation is broken through, and a full-link innovation path of multi-factor dynamic perception-deep learning fusion modeling-global state probability assessment is realized; finally, the technical effects of improving the evaluation accuracy, adapting to extreme weather evolution and enhancing the emergency response capability of the power system are achieved, and the method has remarkable social value and engineering application prospects.

The invention discloses a combined fault monitoring method and device based on a large model, a medium and equipment. The method comprises the following steps: collecting and preprocessing multi-dimensional data; extracting multi-source feature data based on a convolutional neural network and a long-short term memory network; performing redundancy elimination and fusion on the multi-source feature data; and cluster collaborative fault modeling is carried out, and global health assessment is carried out. According to the method, a deep learning large model is established through a convolutional neural network and a long-short term memory network, and effective fault features are automatically extracted from high-dimensional heterogeneous sensor data; through feature fusion of multi-source feature data, dimensionality reduction and fusion are carried out on features extracted from the sensor data, redundant information is removed, and a unified feature vector is generated; through the cluster collaborative fault modeling method, the identification problem of an equipment fault propagation cluster-level modeling path is solved, the globality and comprehensiveness of a fault monitoring system are improved, and fault monitoring of a cluster system is achieved.

The invention discloses a method and system for simulating a latent fault of a medium-voltage distribution cable. The method comprises the following steps: establishing a target cable latent fault model based on an arc model; according to the target cable latent fault model, establishing a first simulation model, and obtaining a first fault simulation waveform; according to the first fault simulation waveform, obtaining a relationship between the target parameter and a fault feature in a target cable latent fault model; and obtaining an optimal target parameter to determine a final target cable latent fault model. By simulating a cable latent fault, the potential problem of the cable can be predicted and identified more accurately, so that a maintenance measure is taken in advance, and occurrence of a sudden fault is avoided. By optimizing the target parameters, it can be ensured that the simulation result is highly consistent with the waveform feature of the actual cable latent fault, and therefore the effectiveness of fault prevention and protection measures is improved.

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.