511 results about "Fault model" patented technology

A system for analyzing a boiler fault and a diagnosis method, the system comprising: a sensor system, a data acquisition and analysis system, and an early warning system. The sensor system is used to monitor temperature, pressure, and water level indicators inside of a boiler and transmit data. The data acquisition and analysis system is used to acquire, count, and analyze the temperature, the pressure, and the water level inside of the boiler in real time. The data acquisition and analysis system comprises a data acquisition module and an intelligent algorithm module. The data acquisition module is used to acquire real-time temperature, pressure, and water level data inside of the boiler. The intelligent algorithm module is used to analyze and process data and to predict and diagnose an operation state of the boiler. The early warning system comprises an early warning module. The early warning module is used to receive a signal of a fault model and to send, on the basis of the signal, a first-stage early warning signal and second-stage early warning information. Thus, real-time monitoring and early warning of the operation state of the boiler is achieved, and problems are promptly discovered and measures can be taken, therefore normal operation of the boiler is guaranteed, and production safety is guaranteed.

The invention discloses an industrial equipment health monitoring and fault early warning system based on edge computing, belongs to the technical field of edge computing, and aims to solve the technical problem of how to efficiently realize health monitoring and fault early warning of equipment in real time and identify different types of faults. Each edge computing node is used for taking local data as input, predicting a fault category and a probability value corresponding to the fault category through a local model, outputting the fault category and the probability value as a prediction result, encrypting the local data and the prediction result through the cloud edge collaboration module and then sending the local data and the prediction result to the central server layer, the fusion model comprises three fault diagnosis models, namely a first fault model constructed based on LSTM, a second fault analysis model constructed based on a rule expert model and a third fault analysis model constructed based on a physical model; and the central server layer is used for fusing prediction results based on a self-adaptive weight mechanism.

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 real-model experiment distribution line fault positioning method and system. The method comprises the following steps: acquiring a topological structure diagram of a target power distribution network in a real-model experiment; based on the topological structure diagram of the target power distribution network, combining a preset diagram input feature selection logic, and obtaining target power distribution network node and edge features as input of a power distribution network fault model to be established; according to the input of the to-be-established power distribution network fault model and the output of a preset power distribution network fault model, training the power distribution network fault model based on the improved graph neural network, the output of the preset power distribution network fault model being the probability of the line fault in the power distribution network; and according to the trained power distribution network fault model based on the improved graph neural network, real-type experiment power distribution line fault positioning is completed. The system and the method have good expansibility and adaptability, can be suitable for power distribution networks of different scales and structures, and have wide application prospects.

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 relates to the technical field of industrial automation, in particular to an industrial equipment self-diagnosis system based on algorithm model prediction, and the system comprises a model building module which comprises a historical fault model; the data acquisition module divides a plurality of to-be-detected point locations and acquires and converts vibration signals; the period judgment module performs periodic judgment on the real-time vibration graph, determines that the vibration period is abnormal according to the judgment result and gives an alarm, or analyzes the abnormal condition of the point location; the data analysis module adjusts a preset frequency based on the point location abnormal conditions, or determines an overall abnormal condition based on a plurality of point location abnormal conditions so as to determine a real-time fault degree, and performs fault early warning on a comparison result of the real-time fault degree and a preset fault degree, or adjusts a preset interval; the model comparison module inputs a plurality of pieces of real-time vibration data into a historical fault model and calculates a fault degree difference value; the model calibration module calibrates the historical fault model based on the fault degree difference. According to the invention, the accuracy of the model fault prediction result is improved.

The invention provides a dry-type transformer turn-to-turn short circuit multi-physical field simulation analysis method considering insulation aging and harmonic influence, and relates to the technical field of simulation analysis, and the method comprises the following steps: constructing a physical simulation model of a to-be-tested dry-type transformer; setting physical parameters and multi-physical field operation conditions of the physical simulation model; calculating a corresponding physical field operation calculation result under each physical field operation condition; performing post-processing on the physical field operation calculation result; by establishing a detailed simulation model and accurately assigning physical property parameters of each component, physical property changes of the turn-to-turn insulating material at different aging stages and under different temperature and frequency conditions are emphatically considered; turn-to-turn short circuit fault modeling is further introduced, magnetic-thermal-flow coupling simulation calculation is carried out under multi-harmonic excitation, the multi-physical field distribution characteristics of the dry-type transformer during different turn-to-turn short circuit faults are explored, and a reliable basis is provided for design optimization and safety evaluation.

The invention belongs to the technical field of electromechanical equipment fault diagnosis, and discloses a wind turbine generator transmission chain online fault diagnosis method, which specifically comprises the following steps: S1, adopting an SCADA (Supervisory Control and Data Acquisition) system to collect environment, electrical and vibration multi-source monitoring parameters of a wind turbine generator in real time and pre-process the parameters, thereby effectively improving the accuracy and availability of data; s2, through the design of a multi-source feature association fusion module, complementarity of environment, electrical and vibration parameters is fully utilized, and the accuracy and reliability of fault diagnosis are improved; s3, a teacher-student network framework is constructed, online fault model construction and real-time diagnosis are achieved, the real-time performance and the intelligent level of fault diagnosis are improved, due to the application of a class balance sampling mechanism and a positive and negative combined learning strategy, the influence of continuous state label offset is eliminated, pseudo label noise interference in online samples is effectively reduced, and the real-time performance of fault diagnosis is improved. And the adaptive diagnosis capability of the model in a working condition environment dynamic change scene is improved.

The invention discloses an unmanned aerial vehicle automatic test method, which comprises the steps of building a high-fidelity semi-physical simulation platform which comprises an unmanned aerial vehicle motion simulator, a three-dimensional environment simulator, a flight control module and a ground control station, and loading a complete machine fault model and a dynamic environment model; designing an intelligent multi-fault injection scheme, and defining a single fault scene, a combined fault scene and a fault evolution scheme; configuring a dynamic environment simulation scene including static environment parameters, dynamic environment elements and environment, and a fault interaction scene; executing a self-adaptive test process, automatically generating a test case, intelligently scheduling a test sequence and dynamically adjusting test parameters; and performing multi-source data intelligent analysis and evaluation, collecting test data, performing fusion analysis on fault influence, and generating an evaluation report. By constructing a whole machine fault and dynamic environment model, the scene authenticity is improved by 40%, and for example, the motor fault yaw deviation under strong wind is closer to a real machine compared with a traditional method.

The invention discloses an automatic control method of a molded case circuit breaker operating mechanism, and particularly relates to the technical field of molded case circuit breakers. Electrical parameters are monitored in real time and transmitted to a control system, and comparative analysis is performed based on a preset threshold value and a fault model, so that faults are detected in time and a control instruction is generated, and the system can identify and sort multiple fault scenes, automatically execute switching-on or switching-off actions and ensure the reliability and stability of a power system; a judgment mechanism based on the circuit recovery condition is introduced, after the circuit is disconnected, whether recovery operation is carried out or not is reasonably judged according to the state of the circuit breaker and the recovery condition of the power system, the self-recovery capacity and the fault diagnosis capacity of the power system are effectively improved, the method can adapt to the influences of high loads, complex environments and power grid fluctuation, and the reliability of the power system is improved. And the reliability and the precision of an automatic control system are obviously improved.

The invention discloses a spring operating mechanism fault detection and optimization method and a related device, and the method comprises the steps: carrying out the real-time collection of key data of a spring operating mechanism through a stress sensor, a speed sensor and a vibration sensor; based on the collected key data, constructing a multi-dimensional data framework to obtain a multi-dimensional data set; fault feature extraction and data analysis are carried out on the obtained multi-dimensional data set, an adaptive fault model is constructed based on the extracted fault features, and then a real-time fault detection result is obtained through multi-modal data fusion; and optimizing a stress sensor, a speed sensor and a vibration sensor by utilizing automatic calibration and optimization of reinforcement learning according to the real-time fault detection result. Compared with a traditional fault detection method, high-precision real-time monitoring and fault prediction of the spring operating mechanism are achieved through the multi-modal data fusion and deep learning technology, and comprehensive and reliable technical support is provided for safe operation and maintenance optimization of equipment.

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 relates to the technical field of circuit board detection, and discloses a circuit board detection method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting historical design parameters and simulation results of a circuit board, processing the design parameters and the simulation results, and forming a data set # imgabs0 #; executing a prediction model training strategy according to the data set, and generating an output prediction model of the circuit board; obtaining current design parameters of the circuit board, and applying the design parameters to the output prediction model to obtain a model result; executing the simulation dynamic adjustment strategy, and dynamically adjusting the simulation process of the simulation tool; historical fault design parameters of the circuit board are collected, a fault model training strategy is executed, and a fault prediction model of the circuit board is generated; applying the current design parameters of the circuit board to the fault prediction model to obtain a fault probability; setting a fault probability threshold; if the fault probability is not less than the fault probability threshold. And performance prediction, simulation optimization and fault early warning and correction in the circuit board design and manufacturing process are improved.

Systems and methods for integrating system information for communication and / or electrical systems to provide a mapping for diagnostic trouble codes generated by both systems. A unified mapping is generated using design outputs for both systems. When a set of diagnostic trouble codes is generated by a system, the unified mapping is applied by a troubleshooting tool to direct maintenance personnel to a failure mode and the source of malfunction. The result may be applicable to a troubleshooting tool for a vehicle system.

The invention provides a new energy equipment system maintenance decision-making method and system based on a fault coupling model, and relates to the technical field of equipment predictive maintenance, and the method comprises the steps: collecting system structure information and system fault data of a complex new energy equipment system, and carrying out the maintenance decision-making of the complex new energy equipment system according to the system structure information and the system fault data; respectively constructing a function model view, a system level fault model view and each flow fault model view; constructing a fault coupling model corresponding to the complex new energy equipment system; probability estimation is carried out on the system fault data according to a kernel density estimation method, the fault probability of each fault node in the complex new energy equipment system is obtained, and key fault nodes in all the fault nodes and key node information corresponding to the key fault nodes are obtained; and mapping the key node information into a risk level corresponding to the key node information, and obtaining an optimal maintenance decision based on a decision tree analysis method and a plurality of maintenance decisions. According to the method, multi-dimensional analysis of a complex system can be realized, and fault modes of different components and subsystems can be identified more clearly, so that the fault detection and diagnosis efficiency is improved.

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 discloses an offshore wind turbine generator fault prediction method and device considering the influence of environmental factors, and the method comprises the steps: dividing an offshore wind turbine generator into a sensor system, a mechanical system and a tower structure system according to the correlation between the fault rate and the environmental factors, and building fault detection models respectively; time convolutional network prediction models are respectively established in combination with unit operation data and environment data to predict the operation state of each system, and fault occurrence is predicted in combination with a fault prediction model; coupling each system fault prediction model, constructing a coupling fault prediction model based on unit operation and an offshore wind turbine generator fault tree, carrying out label classification on predicted faults of each system fault model, carrying out verification through the fault tree, carrying out analysis and classification on fault causes, and supplementing the fault tree when judging that a new fault type appears; according to the method, the correlation between the offshore environment and the fault generation increments of different parts in the wind turbine generator is considered, so that the fault generation prediction accuracy of the offshore wind turbine generator is improved.

The invention relates to a hydroelectric generating set flat pressing pipe working condition monitoring method based on MFCC and a fault model library, and the method comprises the steps: collecting a voiceprint signal of a flat pressing pipe through a voiceprint sensor, and carrying out the monitoring of the working condition of the flat pressing pipe according to the known historical data of a voiceprint signal, a liquid level signal, a vibration signal and a pressure pulsation signal of a flat pressing pipe fault; a voiceprint model library of common faults including typical voiceprint features is established and is used for carrying out rapid comparison according to the voiceprint signals of the current flat pressing pipe and identifying and judging whether the common faults occur in the current working condition state or not; and a fault model library containing voiceprint, liquid level, vibration and pressure pulsation characteristic data is constructed and is used for identifying and judging whether a known fault occurs in the current working condition state according to comparison of voiceprint, liquid level, vibration and pressure pulsation signals of the current unit. According to the invention, real-time on-line high-precision automatic identification of the fault of the flat pressing pipe is realized, the efficiency and the precision of fault diagnosis are obviously improved, and the downtime and the maintenance cost caused by misdiagnosis or missed diagnosis are reduced.

The invention discloses a simulation method and device for power cable fault positioning and a medium, and the method comprises the steps: collecting a power cable fault instance, and building a fault model for simulating a plurality of power cable fault types according to the power cable fault instance; determining a training stage where the user is located, and selecting a target power cable fault type matched with the training stage from a plurality of power cable fault types corresponding to the fault model; loading the fault model and a preset electromagnetic interference model to generate a simulation training scene for the fault type of the target power cable, and receiving an operation instruction sent by a user through interaction equipment in the simulation training scene; and converting the operation instruction into an operation parameter corresponding to the to-be-positioned power cable, and positioning a fault existing in the power cable according to the operation parameter and a power cable parameter corresponding to the power cable to obtain corresponding fault information, thereby realizing fault simulation positioning of the power cable.

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 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 discloses a fault diagnosis device for energy storage equipment based on an artificial intelligence technology and a use method, and relates to the technical field of fault diagnosis of energy storage equipment, the fault diagnosis device comprises an energy storage unit and a fault diagnosis unit, and the energy storage unit comprises an energy storage cabinet, a battery pack, a circuit breaker and a sealing door. According to the invention, the data acquisition unit is used for acquiring the operation data during the operation of the equipment and transmitting the operation data to the microcontroller, the microcontroller transmits the operation data to the central processing unit for analysis and processing, the operation data is compared with the operation data of a normal model, and if the similarity of the two operation data is greater than or equal to a preset value, the central processing unit sends the operation data to the central processing unit. If the similarity of the two items of operation data is smaller than a preset value, the fault state is automatically judged and positioned, the central processing unit transmits the operation data to different fault models, and the fault state is judged and positioned by comparing the similarity probability of the operation data of the fault models and the normal operation data. And the fault type of the energy storage equipment can be obtained according to the similarity probability.

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.