3124results 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 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 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 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 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 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 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.

The invention discloses an underground pipeline three-dimensional deformation quantitative inspection method, which comprises the following steps: processing a multi-source sensing sequence to generate a baseline data packet; the method comprises the following steps of: performing first-stage on-line correction, acquiring a medium state agent quantity by applying micro-excitation sampling combined by a micro-amplitude angle and double power to laser, and performing joint inversion to obtain section geometric data subjected to medium correction by utilizing a forward model with endogenous refraction and scattering correction items; second-stage scale robustness processing is carried out, in a unified optimization framework, motor current-based dynamic consistency constraint, geometric conservation constraint and wellhead external reference strong constraint are fused, and a globally consistent pose track is solved; and performing three-dimensional reconstruction and deformation quantification according to the corrected section data and the pose track. According to the invention, the accuracy of geometric measurement of the pipeline and the global consistency of the pose track in a complex environment such as a humid environment can be improved.

The invention provides a gas generator set fault detection method and device, equipment and a storage medium, and belongs to the technical field of gas power generation, and the method comprises the steps: obtaining target detection data collected by different types of sensors of a gas generator set, and calculating a correlation coefficient between the target detection data and a target parameter; adding the target detection data of which the correlation coefficient is greater than a set threshold value into a fusion candidate set, and calculating a target weight corresponding to each target detection data in the fusion candidate set; based on each piece of target detection data in the fusion candidate set and the target weight corresponding to each piece of target detection data, obtaining comprehensive detection data; performing feature extraction on the comprehensive detection data to obtain time domain features and frequency domain features corresponding to the comprehensive detection data; and determining the fault type of the fault of the gas generator set based on the time domain feature and the frequency domain feature. According to the gas generator set fault detection method and device, the equipment and the storage medium provided by the invention, the accuracy of fault detection can be improved.

Disclosed are an online fault identification method for a current sensor, a diagnosis apparatus, and a fault-tolerant control system, relating to the technical field of diagnosis of faults of current sensors. The method includes defining a fault of the current sensor, and setting a corresponding label; then simulating a motor drive system, so as to obtain operation data in a normal mode and a fault mode, and making a data set; designing a neural network, and performing optimization processing on the neural network through the data set, so as to obtain an intelligent diagnosis model; and finally deploying the intelligent diagnosis model in an edge apparatus, so as to perform real-time online diagnosis on the fault of the current sensor; where the fault of the current sensor includes a saturation fault and a noise fault.

The invention provides a non-intrusive diagnosis method for typical rotor faults of a synchronous phase modifier, and the method comprises the steps: carrying out finite element modeling and fault working condition setting, carrying out simulation to obtain magnetic field distribution data under each working condition, carrying out the extraction and feature analysis of stray magnetic field signals, extracting time domain and frequency domain signals of a stray magnetic field under healthy and various fault working conditions, and carrying out the diagnosis of the typical rotor faults of the synchronous phase modifier. Fundamental wave, odd-even harmonic components and signal change trends are compared and analyzed, stray magnetic field characteristics corresponding to faults are extracted and recognized, diagnosis criterion establishment and fault recognition are carried out, diagnosis criteria and threshold values of rotor turn-to-turn short circuit, dynamic eccentricity and shaft-diameter composite eccentricity faults are established according to the stray magnetic field characteristics, and the fault diagnosis accuracy is improved. And when the specific harmonic amplitude or change characteristic of the stray magnetic field signal exceeds the criterion threshold, determining the rotor fault of the corresponding type and degree. Equipment dismounting or structure reconstruction is not needed, the detection system is simple in structure, internal fault characteristics of the phase modifier can be reflected in real time, and high sensitivity and practicability are achieved.

The invention discloses a permanent magnet synchronous motor temperature monitoring method based on digital-analog hybrid drive, and the method specifically comprises the steps: dividing a motor into a plurality of heat source nodes, including a stator winding, a stator tooth part, a rotor core and the like, connecting the nodes through a thermal resistance and thermal capacity network, building a thermal network model of the motor, and carrying out the thermal network model; calculating the initial values of thermal resistance and thermal capacity of each component of the motor, and optimizing parameters in a thermal network model by using a differential evolution algorithm based on the lumped parameter thermal network model and in combination with a data-driven strategy. Furthermore, temperature rise prediction is dynamically adjusted according to motor state data (such as stator current, rotating speed and the like) collected in real time so as to realize real-time temperature estimation. The method is high in temperature estimation precision, does not affect the performance of the motor, is simple in calculation, is good in real-time performance, and has physical interpretability. The temperature rise of the motor can be predicted in real time under different working conditions, early warning is given out, motor faults caused by too high temperature rise are effectively avoided, and the reliability and safety of the motor are improved.

The invention discloses a fault diagnosis method and a fault diagnosis system for an electric valve actuating mechanism. The method comprises the following steps: synchronously acquiring signals through an anti-EMI (Electro-Magnetic Interference) multi-source sensor; adopting complex Morlet wavelet packet decomposition to extract a 1.2-2.4 kHz energy entropy minimum frequency band, and calculating a kurtosis index; separating the third harmonic of the current through variational mode decomposition, and calculating the total distortion rate of the third harmonic; a graph attention network with 12-dimensional features is constructed, and weighted fusion is carried out through a multi-head attention mechanism; the lightweight CNN outputs a fault type, and when the confidence coefficient is less than 0.9, a knowledge graph rule engine is triggered; and updating a threshold value based on a historical diagnosis clustering result, and aggregating edge model parameters by federal learning. The system comprises a wafer-level micro-strain sensing layer, an FPGA accelerated edge computing layer, a cloud platform supporting federated learning, and an AR maintenance guidance and block chain evidence storage module. The early fault detection rate is improved, the false alarm rate under strong EMI is reduced, and the average repair time is shortened.

The invention relates to the technical field of data acquisition, in particular to a large-scale node-oriented spectrum acquisition compression system, which is characterized in that the system acquires three types of sensor spectrum data characteristics, presets four types of alarm threshold generation configuration tables, and determines sampling periods of different load intervals according to the configuration tables and node real-time loads to generate scheduling schemes. According to the scheme, a sensor is controlled to collect data, compare a threshold, mark an alarm level and generate a data set, data which does not exceed the threshold is extracted, an amplitude difference is calculated, redundancy is judged, and different data are marked according to a redundancy result to generate a standardized compression format table. According to the method, multiple types of alarm thresholds are preset by obtaining spectrum data characteristics of multiple sensors, sampling periods of different load intervals are determined by combining real-time load data of monitoring nodes, data are collected according to a scheduling period, alarm levels are marked, continuous data which do not exceed the thresholds are extracted, amplitude difference values are calculated, redundancy is judged, and the reliability of the system is improved. Corresponding identifiers are added for different data to generate a standardized compression format, invalid sampling is reduced, redundancy is accurately judged, and the data transmission amount is reduced.

The invention discloses a generator state estimation method and system considering noise and parameter uncertainty constraints. The method comprises the following steps: acquiring model parameters and dynamic state vectors of a generator, and establishing augmented state vectors; performing unscented transformation on the augmented state vector to obtain a particle set; improving to obtain robust mixed Kalman particle filtering, and in the process of performing unscented Kalman filtering on an augmented state vector, taking correlation entropy maximization of a measurement information sequence as a target function, and solving by adopting a fixed point iteration method to obtain a filtering gain; determining a filtering gain according to the updated state of the measurement information; robust mixed Kalman particle filtering is executed, physical constraints of model parameters serve as a feasible region, after resampling, projections of the model parameters in new-generation particles exceed the feasible region, the model parameters are set to be closest boundary points, and then resampling is conducted again; a weighted average value of the particle set is an optimal joint estimation value, a dynamic state estimation value and a model parameter identification result are separated, and reliable uncertainty quantization is provided for state and parameter estimation.

The invention discloses a motor fault diagnosis method and system based on color image fusion symmetric point mode, and the method comprises the steps: converting a vibration signal and an electromagnetic signal of a motor into symmetric point mode images, and generating a color signal image fusing feature information; respectively abstracting the color signal images fused with the feature information into nodes and edges in a high-dimensional semantic space so as to construct graph structure data; and performing diagnosis classification on the graph structure data of the vibration signals and the electromagnetic signals by using respective capsule graph network models, and fusing diagnosis classification results of the vibration signals and the electromagnetic signals through a voting mechanism to obtain a final diagnosis classification result. According to the method, multi-channel time domain signals are converted into image expressions with dense information and consistent geometry, and unified feature modeling is carried out on the images based on a depth map structure network with topology perception capability, so that motor fault diagnosis with high diagnosis precision and strong robustness is realized.

The invention relates to the technical field of motors, and discloses a high-temperature-resistant direct-current coreless gear motor and a test system, and the system comprises a temperature monitoring module, a current regulation and control module, a heat dissipation optimization module, a load response module, a cloud analysis module and a virtual verification module. By arranging the temperature monitoring module, multi-dimensional temperature data during operation of the motor can be acquired in real time, and a temperature distribution diagram can be generated, so that the system can comprehensively sense the internal thermal state of the motor and accurately identify local overheating risk points, thereby improving the operation reliability and safety of the motor under a high-temperature working condition; through cooperative operation and closed-loop control of the current regulation and control module, the heat dissipation optimization module and the load response module, according to real-time temperature and load changes, driving current parameters and heat dissipation resource distribution can be dynamically adjusted, and the heat dissipation efficiency is improved. And the output performance and the torque stability of the motor under high-temperature and high-load conditions are ensured.

The invention relates to the technical field of motor state monitoring, in particular to a motor state monitoring method and system based on data driving, and the method comprises the following steps: synchronously collecting a motor voltage signal amplitude time domain sample and a motor current signal amplitude time domain sample for a power frequency period in a motor operation state; and calculating an instantaneous phase difference between each corresponding sample point to obtain an instantaneous phase difference original data stream of the motor. The method comprises the following steps: synchronously acquiring voltage and current amplitudes during operation of a motor in real time, and calculating an instantaneous phase difference between the two amplitudes to obtain an electromagnetic state original data stream; further executing high-pass filtering and performing spectral analysis to filter interference signals and low-frequency trend influences, and improving the recognition precision and robustness of instantaneous phase difference jitter features; meanwhile, voltage pulses are actively injected into the winding, echo signals are collected, and rapid capturing and positioning of the abnormal state of the winding are achieved through propagation time delay calculation and impedance characteristic construction.

The invention belongs to the crossing field of artificial intelligence and mechanical engineering, particularly relates to a micro-motor fault prediction and health management system, and aims to solve the problems that early faults of a micro-motor are difficult to recognize, degradation modeling is inaccurate and maintenance lags. The system collects multi-source data through high-density sensing, combines denoising reconstruction, composite feature extraction and time-varying weighted fusion to generate health indexes, identifies health stages by using a segmented hidden Markov model, iteratively updates residual life prediction based on a Wiener process, outputs an estimation result with a confidence interval, and links a hierarchical maintenance strategy. And continuous optimization of the model is realized through federal learning. The system improves the fault early warning accuracy and prediction reliability, and reduces the operation and maintenance cost.

The invention discloses a self-adaptive motor fault diagnosis system and a fault early warning method, and relates to the technical field of motor fault diagnosis and self-adaption. Firstly, various sensors are installed at key parts of a motor, multi-source data such as vibration, temperature, current and voltage are synchronously collected at the sampling frequency of 10 kHz, the data are cleaned and subjected to normalization preprocessing, and the data are stored in a database; time domain, frequency domain and time-frequency domain features are extracted and screened, a CNN and RNN mixed model is used for training, the learning rate is adaptively adjusted in the training process, data processed in real time are input into the model, the fault type and probability are output, and if an early warning threshold value exceeds 80%, a signal is sent, and information is transmitted to operation and maintenance personnel. According to the invention, multi-source data acquisition, accurate feature extraction, multi-model training diagnosis, self-adaption to working conditions and new faults, accurate diagnosis of motor faults, timely early warning and severity evaluation can be realized; and the cloud platform is convenient to manage, guarantees data safety, improves performance after optimization, reduces cost, and guarantees stable operation of the motor.

The invention discloses a doubly-fed induction generator fault detection method combining a sliding-mode observer and a bilateral cumulative sum algorithm, and the method comprises the following steps: designing a doubly-fed induction generator rotor current sliding-mode observer based on the combination of a novel reaching law and a doubly-fed induction generator state equation; carrying out stability and convergence analysis on the designed novel reaching law by adopting a Lyapunov stability theory; and the fault detection method of the doubly-fed induction generator is designed in combination with the optimized bilateral accumulation sum algorithm. A rotor current sensor fault, an input interference voltage fault, a voltage drop fault and a stator turn-to-turn short circuit fault are introduced. The rotor current tracking performance of the designed sliding-mode observer under different doubly-fed induction generator faults is verified by using a residual value obtained by comparing a doubly-fed induction generator rotor current value estimated by the sliding-mode observer with an actual rotor current output value; and meanwhile, the reliability of the designed doubly-fed induction generator fault detection method based on the optimized bilateral cumulative sum algorithm is verified. The doubly-fed induction generator fault detection method combining the sliding-mode observer and the bilateral cumulative sum algorithm has the advantages of being high in tracking precision and accurate in fault state characterization, meanwhile, weak fault features can be effectively converted into digital signals to be output, and faults of the doubly-fed induction generator are detected.

The invention discloses a rotor acoustic anomaly detection method, system and device based on an auto-encoder and wavelet packet energy entropy and a medium, and belongs to the technical field of hydroelectric generating sets, and the method comprises the steps: collecting an original acoustic signal, carrying out the noise reduction of the original acoustic signal, and carrying out the multi-channel data fusion to obtain an integrated acoustic signal; inputting the integrated acoustic signal into a wavelet packet for transformation processing to obtain wavelet packet coefficients under different scales; calculating energy values of different frequency band signals based on wavelet packet coefficients to form an energy entropy feature vector; constructing an implicit feature model of the hydroelectric generating set rotor in a normal state according to the energy entropy feature vector; and carrying out feature reconstruction on the energy entropy feature vector, calculating a reconstruction error, carrying out dynamic statistical analysis, and carrying out real-time monitoring and abnormity judgment on the operation state of the hydroelectric generating set rotor. According to the method, the detection precision and the response speed are improved in actual hydroelectric generating set rotor acoustic anomaly detection, and automatic identification and dynamic threshold adaptive adjustment of irregular perturbation are realized.

The invention discloses a state detection method, a related device, equipment, a medium and a program product. The state detection method comprises the following steps: decomposing a first electric signal of an electric drive system into a plurality of first modal components; selecting at least one noise mode component from the plurality of first mode components; performing filtering processing on the at least one noise mode component to obtain at least one second mode component; performing signal reconstruction by using the at least one second modal component and each unfiltered modal component in the plurality of first modal components to obtain a second electric signal; and determining a state detection result of the electric drive system at least by using the second electric signal. In this way, the accuracy of state detection of the electric drive system can be improved.