6184results about "Dynamo-electric machine testing" patented technology

The invention provides an integrated power generation equipment health state supervision system. Comprising the following steps: acquiring operation parameters and environment parameter data of an asynchronous motor precision point inspection diagnosis device, a TLI-X8 battery online monitoring system, a collecting ring intelligent data acquisition system, a transformer oil dissolved gas and micro water online monitoring system, a generator partial discharge online monitoring device and a wireless temperature measurement system; and training prediction is performed to obtain fault prediction data, so that accurate fault diagnosis can be realized on the basis of extracting original data features by deep learning. According to the application, an asynchronous motor precision point inspection diagnosis device, a transformer oil gas analysis system, a partial discharge detection system, a wireless temperature measurement system, a battery online monitoring system and the like are organically combined, and power generation equipment faults are predicted through the mother convolutional neural network model and the sub-machine learning model, so that early fault discrimination is realized; and the accuracy and efficiency of equipment fault identification are greatly improved.

The present invention relates to the technical field of power system equipment and control. The method of the present invention comprises: installing sensors to acquire data for online real-time monitoring, and constructing a comprehensive condition online monitoring model for comprehensive data analysis; comprehensively evaluating the health condition of a generator on the basis of a comprehensive analysis result, and identifying fault causes; and constructing a generator comprehensive data intelligent monitoring and dynamic early warning model to perform insulation degradation trend analysis and prediction on the generator. In the present invention, by comprehensively monitoring the condition of a turbine generator, various key indicators of the generator are captured in real time, and trends and patterns underlying the data are revealed, providing technical support for accurately evaluating the health condition of the generator; and continuous monitoring for the insulation condition of the generator allows for proactive identification of potential risks, thereby providing decision-making support for preventive maintenance, helping operators take measures promptly, preventing faults, improving the reliability and safety of the generator.

The invention provides a hydropower station generator set excitation system fault prediction method and device, and relates to the technical field of intelligent power grids, and the method comprises the steps: carrying out the continuous sampling of a generalized state observation vector through a sliding time window, so as to form an operation parameter observation tensor; fusing the operation parameter observation tensor and the boundary constraint type operation parameters of the generator, and constructing a fault observation tensor fused by multi-source operation parameters; introducing a structural causal atlas in a modeling layer, establishing a causal path network embedded based on causal reasoning and topological time sequence for a fault observation tensor, and dividing system interaction faults encountered by excitation system prediction into a plurality of typical modes; and performing graph attention modeling on the evolution trend and the instability boundary of each key parameter in the fault causal chains of different typical modes contained in the causal path network, and dynamically identifying the path characteristics of the excitation system entering the fault critical state. According to the method, the interaction type fault of the excitation system of the hydropower station generator set can be predicted.

The invention discloses an intelligent fault detection system and method for a wind driven generator, and relates to the technical field of deep learning, and the method comprises the steps: collecting multi-dimensional data, and carrying out the preprocessing; extracting multi-dimensional features from the preprocessed multi-dimensional data, fusing the multi-dimensional features through a graph neural network and short-time Fourier transform, and generating a time-frequency domain signal; key features are extracted from the time-frequency domain signals, and fault features of the operation state of the wind driven generator are detected through a support vector machine model; based on the fault features, a fusion neural network model is constructed, fault prediction is generated, a diagnosis report is output, and a maintenance suggestion is provided; according to the method, the neural network model is fused, the convolutional neural network and the long-short-term memory network are combined, the spatial features and the time sequence data are extracted, and multi-dimensional characterization of the fault features of the wind driven generator is achieved.

The invention relates to the technical field of motor anomaly detection, in particular to a motor data anomaly detection method based on the Internet of Things. The method comprises the following steps: acquiring motor operation data, evaluating motor operation demand overload and load response conditions, and detecting bearing kinetic energy chain coupling recession and motor structure connection coordination decline trends, so as to determine dynamic magnetic field unbalance conditions in motor operation; based on the dynamic magnetic field unbalance condition, estimating the electromagnetic interference risk and the frequency oscillation growth trend of the motor, monitoring the error accumulation condition, and detecting the abnormal state of the motor; performing optimal configuration processing on motor operation parameters in combination with an abnormal detection result; according to the invention, the motor operation is more stable and efficient through the motor data anomaly detection.

The invention relates to the technical field of industrial motor faults, in particular to an industrial robot motor fault early warning method and system. The method comprises the following steps: acquiring a motor control instruction of the industrial robot in real time, and then judging an instruction value change event to obtain a motor load change event; monitoring transient working condition characteristic data of the motor in real time according to the motor load change event; flexible gear jump response abnormity evaluation is carried out based on the motor transient state working condition characteristic data, and a flexible gear comprehensive response abnormity score is generated; electromagnetic fault feature analysis is carried out according to the transient working condition feature data of the motor to obtain electromagnetic fault feature coupling data; and according to the electromagnetic fault feature coupling data and the flexible gear comprehensive response abnormal score, performing motor flexible gear health state analysis so as to realize industrial robot motor fault early warning. According to the invention, through working condition jump point detection and motor flexible gear health state analysis, the motor fault can be accurately identified, and multi-stage fault early warning is realized.

The invention discloses a grinding machine internal part temperature anomaly detection method based on vibration signal analysis, which comprises the following steps that grinding data are collected through sensor deployment, and the sensors comprise a temperature sensor, a vibration sensor, an infrared thermal imaging sensor, a magnetic resistance current sensor and an inductance type particle sensor; carrying out preprocessing and feature extraction on the collected data; model construction and training are carried out based on data of preprocessing and feature extraction; according to the method, the abnormal condition of the temperature of the part is predicted by detecting the vibration signal of the internal part, the content of metal particles in lubricating oil is detected through the oil analysis sensor, and the abrasion degree of the bearing is judged in combination with the vibration signal. And motor current harmonic characteristics are monitored, and overload or rotor imbalance problems are identified.

The invention discloses a data model dual-drive motor life prediction system and method. The system comprises an information acquisition module, a model fusion module, a model training module and a weight adjustment module. The information acquisition module is used for acquiring state data of different motors; the model fusion module is used for effectively fusing the data driving model and the physical information model; the model training module is used for training and optimizing the fused mechanism-data dual-drive model according to the motor state data provided by the information acquisition module; the weight adjusting module plays a key adjusting role in the whole model training process. The method not only depends on a data-driven model for prediction, but also combines a physical information model to make up for the problem that a pure data-driven method lacks interpretability. Multi-physical-field factors such as environmental stress, vibration impact, thermal management and an electromagnetic field are brought into modeling, so that the performance degradation of the motor is accurately described and evaluated, and the accuracy and physical interpretability of a prediction result are enhanced.

The invention discloses a three-dimensional warehouse stacker driving motor health state diagnosis method based on a knowledge graph, and the method comprises the steps: obtaining sample data of a to-be-diagnosed three-dimensional warehouse stacker, and carrying out the preprocessing of the sample data, and obtaining target domain data; inputting the target domain data into a target domain convolutional neural network, and outputting a system layer fault type; based on the fault type of the system layer and abnormal data in the sample data of the to-be-diagnosed three-dimensional warehouse stacker, a reasoning traceability report is obtained; wherein the construction of the target domain convolutional neural network comprises the following steps: constructing a knowledge graph based on unstructured knowledge data of a three-dimensional stocker, and generating a knowledge graph attention mechanism; constructing a teacher model and a student model based on the source domain structured data of the general motor fault data set; in the knowledge distillation process, knowledge graph rule guidance is carried out on the student model through a knowledge graph attention mechanism; and irrelevant feature migration is inhibited through a knowledge graph attention mechanism, and a student model is migrated to a model of a target domain convolutional neural network.

The invention discloses a sampling implementation method and system suitable for motor protection quick response, belongs to the technical field of motor protection, and can increase the sampling frequency and improve the quick response of motor fault protection. Comprising the steps that motor phase current and bus voltage are collected, after anti-aliasing filtering is conducted, parallel sampling is conducted through a main ADC module and a redundant ADC module which are independently configured, and the following operations are synchronously executed based on a double-buffer-area framework: in a first buffer area, a compensation coefficient updating period is dynamically adjusted, and convergence is restrained; in the second buffer area, generating a voltage gradient prediction sequence, and compensating data abrupt change; when the harmonic frequency spectrum amplitude or the voltage gradient prediction sequence exceeds the limit, an oversampling mode is triggered, a redundant ADC module is allocated to load a multi-order digital filter, and an independent hardware acceleration unit is started to process harmonic compensation and gradient prediction in parallel; and generating a dynamic safety envelope, and if the gradient extreme value in the preset continuous window breaks through the safety envelope, generating an overvoltage fault signal.

The invention discloses a variable working condition rolling bearing fault diagnosis method and system, and the method comprises the steps: firstly obtaining bearing vibration signal data, temperature data and current spectrum data of a motor under different working conditions, carrying out the adaptive decomposition through employing an EEMD algorithm, and extracting a stable feature subset; then, constructing a cross-working-condition diagnosis model by utilizing transfer learning, and introducing an HMM (Hidden Markov Model) time sequence model to capture signal dynamic characteristics; during diagnosis, the working condition of the signal is firstly judged, and the corresponding model is selected for fault diagnosis. And if the diagnosis confidence is low, triggering an online updating mechanism, and performing incremental learning by using new data. Through continuous data collection and model updating, a dynamic evolution system capable of automatically adjusting the diagnosis strategy according to the working condition change is constructed, the problem that the diagnosis performance is reduced due to the working condition change in bearing fault diagnosis is effectively solved, and high-accuracy self-adaptive diagnosis is achieved.

The invention discloses a real-time monitoring and early warning method and system for a variable-frequency energy-saving motor, and the method comprises the steps: collecting the operation parameters of the motor in real time through a multi-source sensor group disposed on the variable-frequency energy-saving motor; determining a current motor working condition according to the motor operation parameters, and determining a real-time load rate of the motor based on a preset load working condition correlation model; acquiring historical operation data of the variable-frequency energy-saving motor, and generating a dynamic threshold curve according to the historical operation data; generating a dynamic threshold according to the motor operation parameters and the dynamic threshold curve, performing health degree calculation on the variable-frequency energy-saving motor based on the dynamic threshold, and determining a life prediction value of the variable-frequency energy-saving motor according to the health degree; and when the life prediction value of the variable-frequency energy-saving motor is lower than a preset value, automatically triggering a frequency reduction instruction, switching to a field-oriented vector control mode, and synchronously generating an alarm and equipment health file of the variable-frequency energy-saving motor.

An electronic starting switch assembly and control methods for a dryer having a split-phase induction motor. The assembly comprises a microcontroller unit (MCU) configured to execute a control method that dynamically manages the motor's operation by monitoring the forward magnitude current. The MCU determines a stabilized startup forward magnitude current and calculates a crossover condition, allowing for precise control of the motor's transition to single-phase operation by disconnecting the auxiliary winding and connecting the heater element based on real-time forward magnitude current analysis and processing. The system can also monitor for overload conditions indicative of rotor speed drops and can re-engage the auxiliary winding to maintain motor performance. The assembly includes a housing with a heat-sink for efficient thermal management and environmental protection of electronic components. The disclosed method provides a robust solution for efficient dryer operation by ensuring accurate control of motor start-up, running conditions, and overload protection.

The invention relates to the technical field of electrical monitoring, provides a general generator electrical monitoring system with a fault self-diagnosis function, and aims to deeply excavate potential correlation between electrical and mechanical parameters by judging a coherence coefficient and a phase difference between a current harmonic component and a bearing vibration frequency band and marking a fault coupling identifier by using a coupling mode library. Electrical and mechanical coupling faults can be accurately identified, and the identification capability of complex faults can be greatly improved; meanwhile, the diagnosis threshold is dynamically adjusted in combination with the load rate and the winding temperature, so that the system can better adapt to different operation conditions of the generator, and the diagnosis accuracy and reliability are improved; the fault causal chain is analyzed through the causal inference algorithm, and the fault source is positioned, so that compared with the existing fault tracing mode lacking systematicness, the fault generation reason and process can be analyzed more comprehensively and deeply, the fault source can be positioned quickly and accurately, operation and maintenance personnel can take targeted measures in time, and the fault tracing efficiency is improved. And the operation safety and reliability of the generator are improved.

The invention relates to the technical field of motor fault diagnosis, in particular to a motor fault diagnosis algorithm based on multi-sensor fusion, and the algorithm comprises the steps: injecting a step excitation signal into a motor, synchronously collecting the original response waveforms of vibration and current sensors, and calculating the inherent response delay. Establishing a mapping relation library of delay values and current sensor filtering parameters, calling the delay values in real time according to the filtering parameters, performing reverse time offset compensation on a current harmonic signal time sequence, performing time alignment on the two types of data, finally performing cross-domain coupling analysis on the aligned data, extracting vibration pulse peak frequency and current harmonic fluctuation quantity, and determining the vibration pulse peak frequency and the current harmonic fluctuation quantity. Early faults are judged by combining the bearing outer ring fault characteristic frequency band and the load rate dynamic threshold value, graded alarm is generated by tracking characteristics, the problem of fault false judgment and missed judgment caused by sensor data space-time dislocation is solved, and the early fault diagnosis accuracy of the motor is improved.

The invention relates to the field of motor control technology, and discloses a method and device for diagnosing faults in frequency converter motor control, wherein the method comprises: collecting stator three-phase current signals, inverter output voltage signals and speed signals of a frequency converter drive motor system, and establishing a state space model; constructing an energy function coefficient matrix, and obtaining training energy function features; performing Clarke transformation on the stator three-phase current signals, and performing difference to obtain training current residual vectors; performing sample entropy calculation to obtain entropy value feature sequences, and training a deep neural network according to the entropy value feature sequences to obtain a fault classification model; inputting real-time energy function features and real-time current residual vectors into the fault classification model, and performing fault identification in combination with the distribution characteristics of the real-time current residual vectors in an αβ plane, and obtaining fault type discrimination results and fault location information, wherein the invention realizes accurate discrimination of fault types and accurate positioning of locations.

The invention relates to the technical field of servo motor fault diagnosis, in particular to a servo motor fault diagnosis method and system based on a neural network. The method comprises four steps of data acquisition and preprocessing, feature analysis and mode construction, real-time monitoring and anomaly detection, and fault diagnosis and feedback optimization. Servo motor operation data are synchronously collected through multiple sensors, features are extracted through a neural network model, and a fault diagnosis model is constructed. The state of the servo motor is monitored in real time, abnormity is recognized, the diagnosis result is optimized based on historical data, and the fault prediction accuracy is improved. The system comprises a data acquisition module, a mode training module, a monitoring and diagnosis module and a feedback optimization module which work cooperatively to realize intelligent fault diagnosis. The fault detection precision can be improved, the downtime of equipment is reduced, and the industrial production efficiency is improved.

The invention relates to the field of industrial production, and discloses a predictive maintenance system for an ultra-efficient explosion-proof motor based on digital twinning, which comprises the following steps: when key operation parameters of the motor deviate from a normal working condition interval, judging whether the key operation parameters reach a digital twinning model activation threshold value or not; based on historical operation data, sensor real-time data and environmental factors, a digital twin model is called to construct virtual mapping of motor operation, a current state evolution trajectory is generated, and whether the operation trend has a fault or not is predicted; the state track change mode is matched with a historical fault library, and whether a potential fault path exists or not is evaluated; according to the fault risk level and the predicted occurrence time, in combination with the current production plan and the equipment health tolerance, whether an advanced intervention measure needs to be taken is judged; and generating an intervention scheme according to state regulation and control parameters fed back by the digital twinning model in combination with specific working conditions. The method has the advantage that the operation safety and the maintenance efficiency are improved.

The invention relates to a multi-sensor fault feature fusion diagnosis method for electrical equipment, which comprises the following steps: synchronously acquiring operation data of the electrical equipment through a vibration sensor, a temperature sensor, a current sensor and an ultrasonic sensor, dynamically adjusting the sampling frequency according to the physical characteristics of each sensor, and the sampling rate of the temperature signal is not lower than 1Hz. Through a multi-source sensor data synchronous acquisition and time sequence alignment technology and a signal alignment method combining a dynamic time warping (DTW) algorithm and Hilbert-Huang transformation, the problem of time asynchronization of heterogeneous sensor data such as vibration and temperature is solved, so that the time alignment precision of multi-source data is improved, the feature extraction accuracy is improved, and the accuracy of feature extraction is improved. Through hierarchical feature extraction and graph convolutional network fusion, a feature incidence matrix based on mutual information is constructed, deep correlation between vibration signal TKEO features and cross-modal features such as current harmonics is mined by using GCN, the feature dimension is reduced, and the fault feature separability index is improved.

The invention relates to an axial magnetic field motor rotor assembly fault detection method and system, and the method comprises the steps: collecting vibration waveform data, temperature gradient distribution data and magnetic field intensity distribution data of a rotor assembly in an operation state, and constructing a dynamic monitoring data set; performing dynamic feature extraction on the data in the dynamic monitoring data set to generate a dynamic operation feature set comprising a vibration coupling feature, a temperature abnormal fluctuation feature and a magnetic field offset feature, and performing multi-dimensional correlation mapping processing based on the dynamic operation feature set to generate a comprehensive abnormal index set; according to a matching result between the comprehensive abnormal index set and a preset fault judgment threshold value, determining a fault type set of the target rotor assembly and a corresponding fault confidence score; according to the fault type set of the rotor assembly and the corresponding fault confidence score, a visual detection report is generated, the visual detection report is used for displaying a fault result at a terminal, and accurate detection and visual presentation of the fault of the rotor assembly are achieved.

The invention discloses an empirical knowledge and data fusion driven motor load fault diagnosis method, which comprises the steps of constructing an empirical knowledge feature space of a current / vibration signal, and obtaining a plurality of simulation signal samples; inputting the simulation signal sample into an improved generation network DDGAN for processing to obtain an expanded simulation signal sample, and combining an actual fault sample with the expanded simulation signal sample to obtain a fault signal sample; constructing an ICNLCNDTL diagnosis model, inputting the fault sample signal into the ICNLCNDTL diagnosis model for diagnosis processing, and obtaining a current signal diagnosis result and a vibration signal diagnosis result; and fusing the current signal diagnosis result and the vibration signal diagnosis result through a multi-sensing signal collaborative fusion motor composite fault diagnosis model to complete final fusion identification and obtain a fault diagnosis result. According to the invention, the composite fault cross-domain diagnosis performance is further improved.

The invention relates to the technical field of fault diagnosis, in particular to a wind driven generator fault diagnosis method and system based on Mamba-ResNet. The method comprises the following steps: carrying out feature extraction and feature fusion by utilizing preprocessed data, namely constructing adaptive window short-time Fourier transform (AW-STFT) to carry out dynamic time-frequency resolution analysis, carrying out parallel feature extraction and constructing a multi-dimensional heterogeneous feature vector, and carrying out a cross-modal adaptive gating fusion mechanism based on a bidirectional cross gating unit; the method comprises the following steps: constructing a Mamba-ResNet hybrid deep network model architecture; performing model training on the constructed network model architecture; and performing fault diagnosis on the wind driven generator by using the trained model architecture. A tedious manual feature design process in a traditional method is avoided, and the automation level and adaptability of a diagnosis system are remarkably improved.

The invention relates to the field of motor health management and predictive maintenance, in particular to a motor stator winding insulation state evaluation method and system based on a digital model. Comprising the following steps: S1, collecting high frequency of a motor stator winding, and generating multi-physical-quantity real-time data; s2, calculating a dynamic capacitance reference value according to real-time data of multiple physical quantities; s3, performing subtraction operation on the high-frequency equivalent capacitance measurement value and the dynamic capacitance reference value, and extracting an insulation degradation residual signal; s4, constructing a self-adaptive dynamic detection threshold according to multi-physical-quantity real-time data; s5, judging whether the absolute value of the insulation degradation residual signal is greater than a self-adaptive dynamic detection threshold or not: if so, judging that an insulation degradation event occurs; if not, judging that the operation state is a normal operation state; and S6, in response to the insulation degradation event, updating the insulation degradation index, and generating insulation state evaluation based on the updated insulation degradation index. According to the invention, false alarm under severe load fluctuation is avoided, and the accuracy and reliability of evaluation are significantly improved.

The present application relates to the technical field of motor operation monitoring, and discloses a factory motor energy-saving operation monitoring system based on big data analysis, including: a data acquisition module, which is used to collect multi-dimensional data of motor operation in real time; a data processing module, which pre-processes the collected data and marks abnormal data; an analysis and diagnosis module, which performs trend analysis and fault prediction on the motor operation status based on big data algorithms; a storage module, which is used to store raw data and analysis results, and supports historical data retrieval; a communication module, which is connected to a cloud server or a local terminal by wireless or wired means to achieve data interaction. Winding faults are judged by electrical parameters, and early hidden dangers are identified by audio characteristics. The characteristics of multi-dimensional data fusion enable the system to warn of faults in advance and make targeted diagnostic suggestions, reducing downtime and economic losses caused by faults.

Power tools that include a housing, a motor supported by the housing, an inverter positioned between and electrically connected to a battery pack interface and the motor, a gate driver connected to the inverter, a rotational position detector configured to detect a position of the motor, a controller configured to control the gate driver, and a short circuit detection circuit. The short circuit detection circuit is configured to monitor the current in the inverter, compare the current in the inverter to a first current threshold, determine, in response to the current in the inverter being greater than or equal to the first current threshold, whether the position of the motor is changing based on a signal from the rotational position detector, and control, in response to the position of the motor not changing, a switch of the short circuit detection circuit to disable the gate driver.

The invention provides a motor defect identification method fusing time sequence space feature extraction and reinforcement learning. The method comprises the following steps: building a Transform-GAT model architecture T-GAT, and learning time sequence relevance and spatial topological structure features by the T-GAT to form a space-time composite representation vector; designing a dual-network architecture reinforcement learning weighted fusion mechanism of a strategy network and a value network, and after the strategy network and the value network of a parallel structure receive the composite vector, constructing a strategy function to select a defect type with the maximum probability; designing potential energy function quantization parameters, and constructing a reward function to generate a reward in combination with a difference value; taking a reward function as a target, training and optimizing model parameters of the dual-network architecture in an off-line manner, running a real-time decision in an on-line manner, and storing a tetrad to an experience pool to form a'perception-decision-feedback-update 'closed-loop mechanism; according to the method, motor defect identification is realized, downtime is reduced, and motor operation reliability and equipment operation efficiency are improved.

The invention discloses an electric actuating mechanism service life evaluation method and system based on dynamic load monitoring, and particularly relates to the technical field of service life evaluation. For a fretting wear accumulation effect caused by resonance of a load frequency spectrum and a mechanical gap and lubricating oil film fracture reconstruction lag caused by the fact that the load change rate exceeds a critical value, an output shaft torque signal is collected for time-frequency domain conversion, and a load change frequency spectrum and a load change rate are obtained; and in combination with oil film state monitoring, the lubrication state deterioration risk and the wear accumulation effect are evaluated, and the failure critical risk is determined. When the failure critical risk is lower than a safety threshold value, analyzing contact surface stress distribution through a finite element to evaluate the wear degree, predicting the residual life by combining the failure threshold value of the similar electric actuating mechanism in a historical database, and judging whether the electric actuating mechanism needs to be maintained or replaced in advance, so that the actual wear and failure trend of the electric actuating mechanism under multiple working conditions can be accurately reflected; and the scientificity of maintenance decision and the operation safety are improved.

The invention relates to the technical field of motor testing, in particular to an online monitoring method and system for the health state of a carbon brush of a motor, and the method comprises the steps: determining a target operation load environment cluster of a target carbon brush at the current moment according to the pre-obtained load condition and operation environment condition of the target carbon brush in the current operation time period; screening out a reference operation time period; determining an abnormal loss factor and a potential abnormal wear feature vector of the target carbon brush at the current moment; according to the Lyapunov index of the vibration signal of the target carbon brush in the current operation time period and the temperature change and the current change of the target carbon brush in the current operation time period, determining a wear fault display possible feature vector; therefore, the health state of the target carbon brush at the current moment is determined. According to the invention, the potential abnormal wear condition of the target carbon brush at the current moment is considered, the health state of the carbon brush is monitored, and the timeliness of abnormal health state monitoring of the carbon brush is improved.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle power control system based on fault detection. The system collects state data of each component of the unmanned aerial vehicle in real time, the data calculation module accurately calculates key parameters such as battery remaining capacity and energy, fault states such as over-charging, over-discharging and temperature abnormity of the battery are rapidly judged, and meanwhile, the rotating speed and the load condition of the motor are monitored; the power management module dynamically calculates residual flight time and an optimal power distribution strategy according to data monitored in real time, power output can be rapidly adjusted when abnormal conditions are detected, and the feedback control module controls the power distribution strategy according to the flight attitude angle of the unmanned aerial vehicle. The power output of the motor is adjusted in real time, the power management and fault detection capability of the unmanned aerial vehicle in a complex environment is remarkably improved, the problems of over-charging, over-discharging and the like of the battery are effectively avoided, and the safety and reliability of the unmanned aerial vehicle are enhanced.

The invention discloses a shut-down detection and control system for a diesel generating set, belongs to the technical field of monitoring, and solves the problem that the service life of the diesel generating set is affected by damage to internal results caused by direct shut-down of the diesel generating set due to the fact that clamping stagnation of a piston ring in the diesel generating set cannot be detected through a sensor. Comprising a global data acquisition and space-time synchronization module, a data processing module and a communication module, the data processing module receives multi-sensor data flow from the global data acquisition and time-space synchronization module after synchronous clock alignment and diagnoses the clamping stagnation phenomenon of the piston ring through a full-dimensional dynamic fusion model; and the communication module is used for outputting the clamping stagnation phenomenon of the piston ring diagnosed by the data processing module. According to the method, high-precision diagnosis of the clamping stagnation fault of the piston ring of the diesel generating set can be realized through multi-source heterogeneous data fusion, nonlinear dynamic modeling and cross-domain coupling analysis.