3906results about "Dynamo-electric machine testing" patented technology

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 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 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 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 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 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 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 provides an anomaly detection method and system based on motor operation vibration sound, and relates to the technical field of motors, and the method comprises the steps: obtaining an original motor signal, determining the amplitude of the original motor signal, and marking an abnormal signal therein; determining spectrum distribution characteristics of vibration signals in the abnormal signals, performing multi-scale decomposition on the vibration signals to obtain a plurality of sub-band signals, and integrating the sub-band signals to obtain a multi-band combined signal; separating the multi-band combined signal into a plurality of intrinsic mode components by adopting a variational mode decomposition algorithm, and integrating the intrinsic mode components to obtain a reconstructed signal; performing time domain analysis and frequency domain analysis on the reconstructed signal to obtain frequency domain distribution characteristics; and inputting the frequency domain distribution characteristics into a trained motor anomaly detection model to obtain an anomaly detection result. According to the invention, the multi-dimensional abnormal features in the motor operation process can be effectively extracted, and the accuracy and reliability of abnormal detection are improved.

The invention relates to the technical field of data processing, in particular to a motor performance monitoring data processing system which comprises a signal registration module, a rhythm judgment module, a trend lag module, an effect recognition module and a label scheduling module. According to the method, the waveband gravity center is established by identifying extreme points, trend comparison is performed, a gravity center alignment structure between different channels is constructed, the time period rhythm stability is judged based on the sampling interval change trend, abnormal paragraphs are marked, and the response lag time period is positioned in combination with the rotation speed slope change and the current fluctuation behavior. And detecting the sequence and direction relationship of a plurality of parameter fluctuation frames, identifying the dense segments which alternately act to construct a label labeling index, and adjusting the state output sequence according to the occurrence frequency of different labels in a continuous time period, thereby realizing the identification and dynamic sorting scheduling of the response structure relationship among different time sequence behaviors. And the time sequence consistency identification capability and the parameter linkage structure expression capability of the monitoring data in the continuous abnormal state are improved.

The invention provides an electrical equipment remote maintenance guidance method and system. The method comprises the following steps: capturing an aperiodic torque waveform and a magnetic field gradient abnormal signal by deploying a torque fluctuation sensor and a magnetoresistive sensor; performing multi-scale frequency band division on the non-periodic torque waveform, screening out a transient high-frequency component in a start-stop stage of equipment, and separating out a magnetic field polarity reversal characteristic from a magnetic field gradient abnormal signal; performing cross-domain matching on the occurrence time of the transient high-frequency component and the spatial orientation of the magnetic field polarity reversal feature to generate a mixed feature mark; calculating a frequency band overlapping degree based on the time-frequency energy distribution marked by the mixed features and generating a coherence map; and matching a fault historical case library according to the coherence map, and outputting a maintenance strategy set aiming at the rotating shaft dynamic unbalance and electromagnetic interference superposition fault. According to the invention, through fusion of torque fluctuation and magnetic field distortion collaborative analysis, the precision of mechanical and electromagnetic composite fault diagnosis of the rotating shaft is improved.

The invention discloses a motor working condition monitoring method with a high-speed data transmission capability, and relates to the field of motor operation working condition detection and information transmission. The method comprises the following steps of: synchronously acquiring multi-dimensional time sequence data of a motor under multiple working conditions, and performing data segmentation, standardization, fixed-step downsampling, frequency domain conversion and feature fusion to form a multi-dimensional feature vector; then building a BiLSTM-Attention deep learning model, extracting bidirectional time sequence dependence of the sequence, focusing key time step features, and training to obtain a working condition monitoring model; the monitoring reliability is guaranteed through a dual detection mechanism, and high-speed synchronous transmission of multi-sensor data is realized based on an EtherCAT communication protocol stack. According to the method, through collaborative modeling of multi-dimensional time-frequency feature fusion and a deep learning model, a high-speed synchronous transmission protocol stack and a dual detection mechanism are combined, motor working condition feature expression can be remarkably enhanced, and high-precision data transmission and reliable monitoring can be achieved.

The invention relates to the technical field of artificial intelligence, and discloses a motor manufacturing quality intelligent detection system which comprises a sensor data acquisition module, a data processing module, an artificial intelligence analysis module, a decision feedback module and a report generation module. Aiming at electromagnetic interference and mechanical noise generated by multiple processes, a self-adaptive signal filtering mechanism is established, transient interference components of data collected by a sensor can be reduced in real time, the extraction integrity of key characteristic parameters such as vibration and temperature is ensured, the misjudgment risk caused by signal distortion is reduced, the problem that the omission ratio is too high in traditional detection is solved, and the detection accuracy is improved. And a dynamic association rule base is constructed, so that the system can capture early fault symptoms which cannot be recognized by a traditional single-dimensional model, prediction blind areas caused by multi-field data splitting are reduced, and the product life prediction precision is improved.

The invention discloses a detection system for operation abnormity of an electric drive axle motor of a new energy commercial vehicle, and belongs to the technical field of motor abnormity detection. The system comprises a multi-source sensing module, a dynamic coupling analysis module, a harmonic distortion traceability module, a dynamic stability analysis module, an anomaly fusion decision module, a self-adaptive threshold generation module and a fault tree reasoning module. Multi-dimensional signals such as current, vibration, temperature and a rotor position angle are synchronously acquired through the multi-source sensing module, and multi-physics field collaborative analysis is performed through the dynamic coupling analysis module and the harmonic distortion traceability module, so that the fault diagnosis accuracy of operation abnormity of the electric drive axle motor of the new energy commercial vehicle is improved; the problem that in the prior art, detection only depends on a single physical quantity, and misjudgment or missing detection is easily caused by interference is solved.

The invention relates to a low-voltage motor insulation resistance online monitoring and early warning method, system and device and a medium. The method comprises the following steps: firstly, acquiring multi-dimensional operation data through a data acquisition link to form an original monitoring data set; preprocessing the original monitoring data set, and focusing core information through dimension reduction processing to obtain a key parameter set; and then inputting the key parameter set into a dynamic correlation model constructed based on a long short-term memory network, and outputting a prediction result of the variation trend of the insulation resistance value along with time and the deterioration degree quantification. And finally, carrying out early warning analysis based on a prediction result, and generating early warning fault features in combination with a real degradation judgment process. According to the method, the insulation resistance trend and the degradation degree are accurately predicted, the defect of insufficient real-time performance of off-line detection is overcome, the accuracy and the pertinence of fault early warning are improved, and equipment shutdown and safety risks caused by insulation faults are effectively reduced.

The invention discloses an asynchronous motor energy-saving monitoring and dynamic early warning method based on situation awareness, and relates to the technical field of asynchronous motor energy-saving monitoring. The method comprises the steps that S1, multi-source heterogeneous data are synchronously collected, power supply parameters and operation parameters are obtained in real time, and a frequency conversion working condition self-adaptive deviation value and a current stability coefficient are obtained; s2, generating four-dimensional situation factors including an overtemperature danger value, a current stability coefficient, a voltage stability coefficient and a frequency deviation value; s3, constructing a fourth-order transmission network, and generating a primary early warning signal based on the path mark combination; s4, calculating the motor efficiency in real time, triggering an efficiency abnormity mark, fusing the efficiency abnormity mark with the primary early warning signal, and reconstructing a final early warning instruction; and S5, executing hierarchical control according to the final early warning instruction. Through multi-parameter coupling and dynamic early warning transmission, the early-stage capture capability of the composite fault is improved, the early warning response delay is shortened, energy-saving optimization and fault protection of the motor are realized, and the method has a remarkable practical value.

The invention relates to the technical field of motor state monitoring and intelligent fault diagnosis, in particular to a motor test and diagnosis system and method based on multi-data acquisition, and the system comprises a multi-mode sensing array module, a heterogeneous data fusion module, a coupling feature mining module and a migration diagnosis decision module. Wherein the multi-mode sensing array module is used for collecting mechanical vibration time domain signals, temperature field space distribution data and near-field electromagnetic radiation spectrums of a motor; the heterogeneous data fusion module is used for generating a time-frequency matrix, a thermal field characteristic matrix and fundamental frequency harmonic energy distribution characteristics; the coupling feature mining module is used for generating a comprehensive diagnosis factor; and the migration diagnosis decision module is used for constructing a feature mapping network based on meta-learning and outputting a fault type and a confidence score. According to the invention, through multi-modal feature fusion and a metalearning-based migration diagnosis mechanism, high-precision identification and cross-model adaptive diagnosis of multi-source fault information under a complex working condition of the motor are realized.

The invention relates to the technical field of electrical variable measurement, in particular to a rolling mill speed reducer monitoring and diagnosing system based on multi-source information fusion, which comprises an electrical parameter signal acquisition module for acquiring continuous readings of three-phase voltage and three-phase current of a rolling mill driving motor and synchronizing the readings. According to the method, the three-phase voltage and current data of the rolling mill driving motor are synchronously acquired, and the amplitude and phase are calibrated, so that a more accurate calibration electrical measurement set is obtained, and error propagation in a signal acquisition stage is effectively avoided; using the calibrated electrical data to directly calculate instantaneous input electric power, deducting motor loss to obtain an accurate motor shaft power sequence, and then using a Fourier transform method to extract key harmonic characteristics in a shaft power spectrum; and meanwhile, band-pass filtering and Hilbert transform analysis are carried out on the motor stator current, so that a clearer and more recognizable spectrum kurtosis value is obtained, and the judgment capability of current impact and periodic characteristics is enhanced.

The invention relates to the technical field of sound wave detection, in particular to an intelligent detection system for a motor, which comprises a data acquisition module, an abnormality judgment module, a type judgment module, a risk determination module, an adjustment module and an alarm module. According to the invention, through combination of sound wave analysis and various real-time monitoring parameters, accurate detection of the operation state of the motor is realized, the system can acquire the sound pressure level, the vibration acceleration, the winding current, the bearing temperature, the rotating speed and the torque of the motor in real time, and rapidly determine abnormity based on a preset threshold value, and after abnormity is detected, the system is started. The system further combines current, temperature and torque information to analyze the abrasion type, and evaluates the serious risk level according to the rotating speed and the vibration acceleration, so that refined risk management is provided, and the problems of low fault judgment precision and slow response speed caused by data transmission delay and environmental interference are effectively solved.

The invention provides a rotating equipment fault diagnosis method fusing a CNN (Convolutional Neural Network) and a graph attention network. A plurality of sensors respectively capture key operation signals of each part of a motor, a bearing, a gear box, a coupler and a rotating load, the key operation signals are input to a multi-channel attention module through a GRU network and then feature sequences are output, the feature sequences are spliced into a two-dimensional feature matrix, the two-dimensional feature matrix is input to a CNN and mixed attention mechanism module, and fault features of each part are obtained; the fault features are windowed according to a time sequence and recombined after being dynamically weighted according to importance; constructing a global fault diagnosis graph by taking the recombination features of different parts as nodes and the significant interaction relationship between the nodes as edges; learning interaction information among the nodes through a graph attention network and aggregating the interaction information to obtain global fault features; and constructing a graph network joint optimization loss function, and analyzing and optimizing model parameters. According to the model, fault information is comprehensively mined through a multi-stage fusion mode, and the accuracy of fault diagnosis is greatly improved.

The invention relates to the technical field of fault diagnosis, in particular to an electromechanical equipment fault diagnosis method and system based on data analysis. The method comprises the following steps: extracting a historical operation log of a motor, analyzing a current overload state and constructing a current overload spectrogram; then, carrying out insulation layer performance loss gradient analysis based on the spectrogram, and calculating the failure probability of the motor; finally, a risk level is given to the overload current according to the failure probability, an equipment fault diagnosis framework is constructed, and the framework is sent to a control terminal to execute fault diagnosis. According to the invention, the electromechanical equipment fault diagnosis technology is optimized, so that the electromechanical equipment fault diagnosis technology is more accurate.

The invention provides a fault diagnosis system of an intelligent bamboo cutting machine, which relates to the technical field of mechanical engineering and comprises a feature extraction module used for collecting current and voltage of a motor, a spindle vibration frequency spectrum and dynamic parameters of a quadrilateral monitoring area formed by a spindle bearing, a transmission gear box, a cutter clamping end and a base supporting part in real time, and a current waveform characteristic value, a voltage harmonic component, a vibration frequency domain energy distribution parameter and an abnormal parameter combination in the quadrilateral region are extracted. Through cooperation of multiple modules, parameter acquisition, material-load analysis, dynamic threshold setting, graded early warning and accurate fault diagnosis are realized, and the operation and production continuity of the bamboo cutting machine can be efficiently guaranteed.

The invention discloses a wiper motor health state early warning system based on big data, and particularly relates to the technical field of electrical equipment state monitoring and fault diagnosis, which comprises a data acquisition module, a state interface module, a data preprocessing module, a data analysis module, an early warning decision module and an early warning information output module, electrical parameters such as current, voltage, temperature and rotating speed are collected through a sensor, and a feature set is generated through preprocessing in combination with vehicle bus working condition data; comparing the real-time electrical characteristics with a pre-stored baseline model and a fault mode library, calculating a health score based on a mahalanobis distance algorithm, identifying a fault and predicting the life; and generating graded early warning according to a multi-level threshold strategy, and outputting the graded early warning through a vehicle-mounted interface and a remote monitoring center. According to the invention, early, accurate and predictive early warning of the health state of the motor is realized, and the accuracy of state evaluation and the reliability of the system are improved.

The invention relates to the technical field of fault diagnosis, in particular to an SMT production line equipment fault diagnosis method and system based on the Internet of Things, and the method comprises the following steps: obtaining multiple types of signals, aligning a time window, judging that the trend is consistent and fluctuation is synchronous to generate a sudden change event, recognizing an intersection point, extracting a key response position, and outputting an inflection point node number. And constructing a response time sequence and a propagation path, identifying abnormal nodes, and generating a fault traceability result. According to the invention, through time alignment and trend linkage identification of current, temperature and acceleration signals, fusion of trend direction judgment and fluctuation synchronization relation, a cross-equipment linkage sudden change identification mode is established, key response points are extracted through cross positioning of a jump terminal point and a vibration peak value, and a dynamic judgment mechanism of inflection point nodes is formed. In combination with a response sequential sequence and a chain path structure, local signal fluctuation analysis is converted into perception, and the judgment accuracy and traceability of abrupt change abnormity are effectively improved.

Disclosed is an explainable method for monitoring a state of a generator of a wind turbine generator system on the basis of a spatio-temporal graph. The method includes: S1: acquiring data collected by a supervisory control and data acquisition (SCADA) system; S2: carrying out data understanding on the SCADA data, selecting features associated with the generator, and carrying out data preparation on the selected feature data, and obtaining valid data; S3: embedding the SCADA data, and forming a directed spatio-temporal graph data sequence; and S4: carrying out modeling of a normal behavior model of the generator on the constructed directed spatio-temporal graph data sequence, computing a full-graph-level residual and a node-level residual, computing a residual through an exponentially weighted moving average (EWMA) control chart method, carrying out full-graph-level state monitoring on the generator, forming a fault information transmission chain relation, and enhancing explainability and robustness of a monitoring result.

The invention discloses a water pump motor fault detection method and system, and belongs to the technical field of detection, and the method comprises the steps: building an electrical operation state model based on current, voltage and three-phase balance degree, and outputting an electrical state coefficient; predicting a resistance degradation trend by using the historical winding / insulation resistance data set, and generating a resistance prediction coefficient; combining the electrical state, the amplitude under the load and the flow of the water pump to construct a vibration-flow matching coefficient; fusing the resistance prediction coefficient, the noise index and the temperature index, and outputting a fault evaluation coefficient through a fault evaluation model; and when the fault evaluation coefficient exceeds a threshold value, shutdown is triggered. The system correspondingly comprises an electrical analysis module, a resistance prediction module, a state evaluation module and a fault decision module. According to the invention, early fault accurate detection is realized by fusing multiple parameters, and the reliability of the water pump motor is improved.

The invention discloses a diesel generator turn-to-turn short circuit fault protection method and system based on multi-parameter characteristics, and relates to the technical field of intelligent fault diagnosis of power equipment, and the method comprises the steps: carrying out the feature extraction of operation state monitoring data, carrying out the integration according to a time sequence, and generating a real-time feature sequence set; performing spatio-temporal feature coding and fusion on the real-time feature sequence set to generate a multi-dimensional spatio-temporal feature map; mapping the comprehensive matching score set into fault confidence according to a linear relation, obtaining a fault confidence sequence, judging a fault level in combination with a characteristic amplitude overrun condition, and generating a fault diagnosis result set; and executing hierarchical response based on the fault diagnosis result set, generating a hierarchical protection action execution record, and generating a short-circuit fault protection report in combination with the fault diagnosis result set. According to the method, quantitative similarity analysis of real-time features and standard modes is realized, the fault probability is accurately evaluated through a multi-parameter weighted fusion mechanism, and the reliability and interpretability of a diagnosis result are improved.