68588results about "Machines/engines" patented technology

The invention relates to the technical field of wind turbine generator fault monitoring. The invention provides a wind turbine generator fault monitoring method and system. The method comprises the following steps: synchronously acquiring gearbox and environment temperature and humidity data, and generating a time-frequency energy fusion matrix through adaptive wavelet packet transformation; adopting mutual information entropy weighted improved variational mode decomposition to screen out an intrinsic mode component set related to a fault mode; constructing a space-time double-flow residual network based on the intrinsic mode component set, and fusing two branch outputs of the space-time double-flow residual network through a dynamic feature gating mechanism to obtain a multi-dimensional feature vector; and inputting a multi-dimensional feature vector obtained by fusion into a lightweight fault classifier, and outputting a real-time fault probability and a component health degree evaluation index based on a sliding window mechanism. The problems of low efficiency, high false alarm rate, missing detection of early faults, reduction of prediction precision, incapability of mining multivariable coupling relations, need of massive annotation data, and high delay caused by insufficient edge side computing power existing in an existing wind turbine generator fault monitoring mode are solved.

The invention relates to the technical field of wind power prediction. The invention provides a wind power prediction method and system. The method comprises the following steps: acquiring multi-dimensional meteorological time series data, three-dimensional elevation data and unit operation data of a target wind power plant; constructing a spatial-temporal feature fusion network, extracting time sequence dynamic features, and performing weighted fusion on the spatial correlation features and the time sequence dynamic features to obtain a fusion feature vector; establishing a hybrid prediction model, and taking the fusion feature vector as input to obtain a wind power initial prediction result; introducing a terrain correction factor, constructing a turbulence intensity compensation function, and performing micro-terrain disturbance correction on the wind power initial prediction result; and outputting a final power prediction curve and a confidence interval. The problems that in an existing wind power prediction method, a physical model is insufficient in complex terrain microclimate modeling precision, high in calculation complexity and difficult to meet the real-time requirement, a statistical learning method is limited in high-dimensional nonlinear time sequence feature expression capacity, and prediction errors are remarkably increased under the abnormal working condition are solved.

An online testing and diagnosis method for vibration characteristics of blades of wind turbine is disclosed. Steps of testing and diagnosing blade vibration comprises: S1: installing vibration sensors at key positions of a blade, designing an adaptive data acquisition strategy, and automatically adjusting a sampling rate according to a vibration amplitude and environmental changes monitored in a real time; S2: extracting key features reflecting health status of the blade from massive data, and evaluating an impact of wind speed, temperature, and environmental factors on vibration characteristics; S3: designing a customized deep learning model for damages of the blade of a wind turbine, extracting a time sequence data and a vibration signal, identifying a damage among different types of damages and evaluating a damage degree; and S4: automatically adjusting a warning threshold based on a real-time data stream and a historical trend, and drafting a preventive maintenance plan.

The invention provides a wind generating set fault monitoring method and system based on voiceprint recognition, and the method comprises the steps: collecting gear box voiceprint signal data in real time, recording signal fluctuation caused by gear surface wear, and obtaining an original signal data set containing a frequency spectrum high-frequency component enhancement feature; a time-frequency analysis method is adopted for the original signal data set, non-linear interference of vibration signals is recognized, multi-scale decomposition is carried out, initial wear frequency spectrum narrow-band characteristics and medium-term harmonic components are separated out, and a frequency spectrum component set is obtained; extracting characteristic parameters related to modulation depth abnormal fluctuation from the frequency spectrum component set, identifying a gear pair meshing frequency change rule, and determining a distribution mode of a tooth surface contact noise proportion; and if the feature matching result shows that the deviation between the spectrum high-frequency component diffusion distortion feature and the reference feature library exceeds a threshold value, adding the tooth surface fatigue crack noise feature into the feature library to obtain a target feature library.

The invention relates to the technical field of state monitoring of wind generating sets, and discloses a fault early warning and service life prediction method and system for a wind generating set, and the method comprises the steps: obtaining first state data, second state data and image data of a target wind generating set, and forming multi-dimensional data; fusing the multi-dimensional data by using a multi-modal fusion model to obtain multi-modal data fusion features of the target wind generating set; and performing fault early warning and / or life prediction on the target wind generating set based on the multi-modal data fusion features. By integrating the multi-modal data, the problem that fault features are difficult to comprehensively capture by a single data source is solved, fault early warning and service life prediction are performed by utilizing the multi-modal data fusion features, the false report and missing report rate of faults is reduced, accurate quantitative prediction of the remaining service life of the wind generating set is realized in combination with the data driving model, and the prediction efficiency is improved. By improving the accuracy of fault early warning and life prediction, the wind generating set is effectively operated and maintained in advance.

The invention relates to the technical field of wind turbine generator data analysis, and discloses an offshore wind turbine generator operation data analysis and prediction system. The system comprises a marine environment data integration module for collecting data to generate a multi-source time-space synchronization data set; the multi-modal feature fusion module is used for extracting cross-modal correlation features to generate a high-dimensional fusion feature tensor; the dynamic fault prediction module is used for constructing a two-way gating circulation network model to predict the degradation probability and the residual life of key components of the equipment; and the self-adaptive optimization control module is used for constructing a multi-target dynamic programming model to optimize a fan operation strategy. In addition, the system is further provided with a feedback correction module for correcting prediction model parameters, and a virtual sensor module based on a physical information neural network is used for monitoring tower stress and diagnosing sensor faults. According to the system, comprehensive monitoring, accurate fault prediction and optimal control of the offshore wind turbine generator are realized, the operation efficiency, reliability and safety of the wind turbine generator are effectively improved, and the operation and maintenance cost is reduced.

The invention provides an intelligent detection method and system for the health state of a wind generating set in an intelligent wind field, and relates to the technical field of intelligent wind field multi-source monitoring. The method comprises the following steps: firstly, collecting multi-source operation data such as a transmission chain, structural parts and environment working conditions, and performing time reference unification; performing noise reduction, calibration, compensation and time window segmentation on the original data to form a preprocessed data set; extracting and aligning features in multiple domains to construct fusion feature representation; establishing a health baseline model based on historical normal samples and working condition variables to generate a self-adaptive alarm threshold value; inputting a health discrimination model to obtain an anomaly index, and generating an early warning event according to a trigger condition; and comprehensively fusing the features, the health base line and the judgment result to calculate a health index and output early warning information, thereby realizing accurate detection and risk early warning of the whole life cycle and the whole working condition.

The invention discloses a wind power fault diagnosis operation and maintenance method based on multi-source data and knowledge retrieval enhancement. The method comprises the following steps: S1, collecting and preprocessing multi-modal data; s2, knowledge base construction: performing knowledge extraction, and constructing a corresponding wind turbine generator operation and maintenance knowledge graph database; s3, multi-granularity problem decomposition and retrieval optimization are carried out, and a sub-problem list is generated by deeply grading and splitting problems in combination with doubt degree and dependency analysis; s4, knowledge retrieval enhancement generation: combining knowledge graph sub-graph retrieval and SCADA real-time data dynamic weight adjustment to generate a final answer; and S5, result generation and feedback optimization are carried out, and the validity of the diagnosis result is verified through a real-time verification mechanism. According to the method, the accuracy, the reliability and the real-time performance of fault diagnosis can be remarkably improved in a limited fault data environment.

The invention relates to the technical field of wind turbine generator control, and discloses a wind turbine generator control optimization method based on dynamic changes of wind speed and wind direction. According to the method, real-time wind speed time sequence data and three-dimensional wind direction vector field data of a target wind field are received, a wind field dynamic analysis model is constructed by using a space-time convolutional neural network, and a wind field energy density distribution matrix and a turbulence intensity probability graph are generated. And constructing a multi-target adaptive optimization model on the basis, generating a unit control parameter instruction set, optimizing a cooperative adjustment coefficient according to a preset unit load-power generation efficiency balance equation, and outputting an optimal control action sequence to a wind turbine generator master control system through Bayesian optimization framework iterative updating. The method can accurately sense the wind field change, effectively balance the unit load and power generation efficiency, realize multi-unit cooperative control, improve the wind energy capture efficiency, and improve the operation stability and economic benefits of the wind turbine generator.

The invention discloses a multi-modal large model interpretable diagnosis method and system for a wind turbine generator, and relates to the technical field of wind turbine generator fault diagnosis, comprising the step of combining multi-modal data (vibration, time sequence, image and text) and topological information to realize fault diagnosis through cross-modal contrast learning and topological modeling. The method comprises the steps of multi-modal feature extraction, standardization and alignment, and feature fusion through topology embedding optimization and a cross-modal attention mechanism. In the fault diagnosis process, dynamic correction and path reliability evaluation are introduced by using a regular Agent and a topology consistent Agent, weighted fusion is performed on each modal feature and a topology structure, and finally an accurate fault type and a component positioning result are output. Through combination of knowledge retrieval and a multi-Agent decision model, the adaptability and precision of fault diagnosis are improved, especially in a complex environment, the fault mode of the wind turbine generator can be effectively identified, and the system reliability is improved.

The invention relates to the technical field of hydrological monitoring, in particular to a multi-satellite collaborative hydrological monitoring system. The method comprises the following steps that a water level height measurement module obtains radar pulse recovery time delay data through a height measurement satellite and calculates the water level height, abnormal points are removed to generate river water level data, a water remote sensing module collects river water images through a remote sensing satellite to extract water boundary features, water area change data are deduced, and the water level height measurement module calculates the water level height. The meteorological wind shear analysis module obtains river channel meteorological data through a meteorological satellite, microwave scattering measurement and wind speed inversion are carried out, wind-induced shear stress parameters are generated, and the flow estimation module carries out section dynamic analysis and estimates the water flow in combination with river channel water level and water area data. And the flow correction module corrects the river water flow based on the wind-induced shear stress parameter and carries out real-time monitoring, and data are synchronously uploaded to the control terminal, so that comprehensive dynamic monitoring of the hydrological state of the river is realized. According to the invention, a more efficient multi-satellite collaborative hydrological monitoring system is realized.

The invention provides a permanent magnet motor and a variable-frequency scroll compressor, and relates to the technical field of scroll compressor design and manufacturing, and the permanent magnet motor comprises a stator iron core and a rotor iron core; the stator iron core comprises stator slots which are uniformly distributed along the circumference, copper windings are embedded in the slots, stator yokes which are uniformly distributed are arranged outside the bottom of each slot, each stator yoke is provided with an arc-shaped long slot, stator teeth are arranged between every two adjacent stator slots, and the tops of the stator teeth are sunken to form through-flow slots; the rotor core is internally provided with magnetic steel grooves which are uniformly distributed, two same first magnetic isolation grooves are arranged between two adjacent magnetic steel grooves, magnetic steel is arranged in the magnetic steel grooves, and the top of each magnetic steel is provided with two second magnetic isolation grooves which are symmetrically distributed. The outer side radius Rs1 of the stator core, the inner side radius Rs2 of the stator core and the axial length Lis of the stator core meet the following conditions: 1.282 < = Lis / (Rs1-Rs2) < = 2.778, and 34 < = Rs1-Rs2 < = 39 mm. The power density of the motor can be increased, the cost is reduced, the efficiency is improved, and the harmonic content is reduced so as to suppress torque ripple.

A method, system, and device for wind speed prediction and layout optimization in wind power generation are provided. The method includes: obtaining a basic wind resource dataset of a target region; constructing a physics-informed neural network model based on the basic wind resource dataset; obtaining wind speeds data at a specific location in a velocity field based on the physics-informed neural networks and constructing a training dataset; training the physics-informed neural network model based on the training dataset; reconstructing a wind speed distribution within the velocity field and predicting wind speeds for a next time period with a wind farm using the trained physics-informed neural network model; and optimizing a layout of a wind turbine cluster based on a reconstructed wind speed distribution within the velocity field. The present application reconstructs a two-dimensional velocity field of the wind farm by training the PINN and enables accurate ultra-short-term wind speed prediction.

The invention relates to the technical field of wind power generation. The invention provides a fan main shaft backward movement monitoring and evaluating method and system. The method comprises the following steps: acquiring an axial displacement signal, a vibration signal, a bearing temperature signal and a lubrication state parameter of a fan main shaft in real time; based on a multi-parameter fusion algorithm, performing coupling analysis on the axial displacement, the vibration amplitude, the bearing temperature and the lubrication state to generate a comprehensive risk index; performing dynamic diagnosis on the comprehensive risk index in combination with a main shaft backward movement mechanism model, and identifying at least one fault root cause of axial force imbalance, thermal stress abnormality or lubrication failure; an alarm threshold value is dynamically adjusted according to wind speed change, temperature fluctuation and load working conditions, and graded early warning is triggered based on the fault root cause; and based on historical operation data and a real-time monitoring result, evaluating a main shaft backward movement risk level, and generating a decision report containing a maintenance priority and a technical improvement suggestion. The problem that existing fan main shaft backward movement monitoring depends on manual inspection and offline detection technologies, and limitation exists is solved.

The invention discloses a water turbine fault classification diagnosis method based on multi-modal fusion and meta-learning. The method comprises the following steps: step 1, respectively extracting time domain features and Mel-language spectrogram features of monitoring noise signals of a water turbine through a feature extraction module; step 2, performing cross-modal attention mechanism fusion on the time domain features and the voiceprint features through a multi-modal fusion module, and adjusting a fusion weight based on a dynamic weight distribution mechanism; and step 3, performing small sample training optimization on the fused features through a meta-learning module, and improving the classification capability of the model for new fault types in combination with a Triplet Loss-KNN algorithm and twin network pre-training. The fault diagnosis method based on the time domain-voiceprint fusion network and meta learning has the advantages of being rapid in diagnosis, accurate in classification, high in generalization ability and the like, and the fault diagnosis precision of the water turbine based on noise signals can be effectively improved.

A heat management system includes: a first flow passage in which a reserve tank is not provided; a second flow passage in which a reserve tank is provided; a switching device configured to switch between coupling and uncoupling of the first flow passage and the second flow passage to and from each other; and a control device configured to control the switching device. The control device is configured to, when predetermined coupling conditions are met, make the heat medium flow through the first flow passage and the second flow passage that have been coupled to each other by the switching device. When the control device determines that air bubbles exceeding the allowable volume are present in the heat medium, the coupling conditions switch from being unmet to being met.

The invention belongs to the technical field of liquid level measurement and industrial process control, and particularly relates to a radar liquid level meter system fusing an adaptive algorithm and multi-modal data. Comprising a data acquisition unit, a signal acquisition and preprocessing unit, a signal denoising and optimizing unit, a data fusion and anomaly detection unit, a self-adaptive calibration and drift compensation unit and a display and alarm terminal. The system has the advantages that the radar liquid level meter, the auxiliary sensor, signal processing, data fusion, anomaly detection, self-adaptive calibration, drift compensation and other technical means are organically combined, and the system can achieve high-precision and real-time monitoring of the liquid level under the complex working condition.

The disclosure is directed to an apparatus or a system for generating energy in response to a vehicle wheel rotation. The apparatus or the system may include a roller configured to be positioned in substantial physical contact within a groove of a wheel of the vehicle. The roller may be configured to rotate in response to a rotation of the wheel. A flexible arm may be rotatably couplable to the roller such that rotation of the roller causes the flexible arm to rotate. The flexible arm may exert a downward force on the roller to increase the friction between the roller and the groove of the wheel. A first generator may be operably coupled to the flexible arm and configured to generate an electrical output based on the rotation of the flexible arm shaft and convey the electrical output to an energy storage device or vehicle motor.

The invention discloses a wind turbine generator operation and maintenance knowledge base construction method based on a large model and mechanism self-learning. The wind turbine generator operation and maintenance knowledge base construction method comprises the steps of wind turbine generator operation and maintenance domain knowledge Schema definition and large model cue word template design used for wind turbine generator operation and maintenance knowledge extraction; obtaining operation and maintenance multi-modal data of the wind turbine generator, performing preprocessing, and performing knowledge extraction through a large model based on a designed cue word template; a dynamic knowledge association and wind turbine generator operation and maintenance knowledge base fault mechanism self-learning updating mechanism is established, operation and maintenance data and a knowledge graph are associated in real time, and the knowledge base is automatically learned and updated through an exception triggering mechanism; and constructing and storing a wind turbine generator operation and maintenance knowledge graph based on a knowledge extraction result, generating a semantic association sub-graph through clustering, generating a sub-graph clustering report, and realizing efficient knowledge retrieval. Based on the above content, the wind turbine generator operation and maintenance knowledge base which is efficient, accurate and updated in real time is constructed.

A sheath is provided. The sheath includes a leading edge portion having pressure and suction sides for respective association with pressure and suction sides of an airfoil and first and second wings respectively extending from the pressure and suction sides of the leading edge portion. The first wing includes a first elongate portion and a first trailing edge disposed at an end of and thinned relative to the first elongate portion. The second wing includes a second elongate portion and a second trailing edge disposed at an end of and thinned relative to the second elongate portion.

The disclosure relates to an air guide control system with a flap arrangement including flaps which can be rotatably inserted into a joining partner. An actuator drives a damper and a coupling rod transmits the torque of this driven damper to the remaining dampers of the damper arrangement. In the event of a flap fracture of at least one flap, a spring, locks the flap arrangement against further movement, so that error information is reported to the actuator via the coupling rod.

The invention discloses a digital twin-driven deep and far sea fan platform prediction operation and maintenance method and system. The method comprises the following steps: acquiring real-time multi-source heterogeneous data of a deep and far sea target fan platform based on a sensor network; constructing a digital twinborn body of a multi-physics field integrated target fan platform, and inputting the real-time multi-source heterogeneous data into the digital twinborn body for simulation processing to obtain fault probability distribution of the target fan platform; and executing an operation and maintenance strategy corresponding to the fault probability distribution. According to the deep and far sea fan platform prediction operation and maintenance method provided by the embodiment of the invention, the deep and far sea fan platform prediction operation and maintenance efficiency is improved.

The invention discloses an air compressor frequency conversion and energy saving control method and system based on data analysis, and particularly relates to the technical field of air compressor control. Aiming at the problems of frequency jitter and motor instability of the permanent magnet variable-frequency screw air compressor in a high-frequency air consumption fluctuation scene, the method comprises the following steps of: acquiring exhaust pressure, air consumption flow, motor current, frequency converter output frequency and environment temperature data in real time, performing trend prediction by utilizing a data analysis model, and constructing a multi-dimensional working condition characteristic curve; a gas consumption fluctuation mode is accurately recognized, and predictive judgment is made before frequency fluctuation is about to exceed the limit; according to a prediction result, a frequency converter control strategy is dynamically adjusted, the frequency response rate is buffered in advance, an energy-saving mode is automatically triggered when the load change trend is abnormal, rotating speed control is optimized in real time, and according to the method, the operation stability and response precision of the air compressor are effectively improved, energy consumption is remarkably reduced, and the service life of equipment is prolonged; the method is suitable for the intelligent energy-saving operation requirement in a high-beat and high-precision manufacturing scene.

An airfoil is provided that includes a first end, a second end, a leading edge, a trailing edge, a pressure side and a suction side. The leading edge and the trailing edge are joined by the pressure side and the suction side to provide an exterior airfoil surface extending in a spanwise direction from the first end of the airfoil to the second end of the airfoil. The exterior airfoil surface are formed in conformance with a plurality of cross-section profiles of the airfoil described by a set of Cartesian coordinates set forth in Table 1. The Cartesian coordinates are provided by an axial coordinate scaled by a local axial chord, a circumferential coordinate scaled by the local axial chord, and a span location. The local axial chord corresponds to a width of the airfoil between the leading edge and the trailing edge at the span location.

The invention belongs to the technical field of wind power generation, and discloses a wind driven generator multi-drive variable pitch control method based on big data, which comprises the following steps of: constructing a multi-source data acquisition network covering an environment, a unit, a power grid and a driver, and combining preprocessing means such as denoising and standardization and feature extraction methods such as principal component analysis; high-quality input is provided for a short-term prediction model; the model can accurately pre-judge wind conditions and power requirements in the future 10-30 minutes, and is matched with a global optimization algorithm to dynamically adjust a variable pitch strategy, so that the wind driven generator can still keep stable power output under complex working conditions of gust, power grid fluctuation and the like, and particularly has outstanding performance in areas with unstable wind resources. According to mechanical coupling characteristics of the multi-drive variable pitch system, mechanical parameters such as a gear transmission gap and shafting rigidity are fully included in global optimization, and power output, mechanical fatigue and load distribution are balanced through a multi-objective optimization algorithm.

The invention relates to the technical field of wind power generation. The impeller imbalance detection method based on multi-source data fusion comprises the steps that operation state data of a draught fan unit and environment data of corresponding time are obtained, and a multi-source data set is obtained; preprocessing the multi-source data set to obtain a preprocessed data set; feature parameters in the preprocessed data set are extracted, data fusion is carried out, and a multi-source feature fusion vector is constructed; according to the multi-source feature fusion vector, constructing a time sequence prediction model based on a gating circulation unit, and predicting a theoretical vibration baseline value under the current working condition in real time; and calculating a deviation degree between an actual vibration value and a predicted baseline through residual analysis, dynamically adjusting a detection threshold, and triggering imbalance early warning when the deviation degree exceeds the threshold. The problems that the accuracy of a detection result of a traditional impeller imbalance detection method is affected by various factors, misjudgment and missed judgment are prone to occurring, and the requirement of a modern wind power generation system for high-precision fault detection cannot be met are solved.

The invention discloses a fan gearbox fault diagnosis and early warning system based on oil and vibration parameter fusion, and the system comprises an oil parameter and vibration signal collection module which is used for collecting oil parameters and vibration signals of a fan gearbox in real time through an oil sensor and a vibration sensor; the data fusion optimization module is used for optimizing the fusion weight of the oil and vibration data by adopting a firefly algorithm; the feature extraction and subset selection module is used for performing feature extraction and optimization on the fused oil parameters and vibration signals; the fault diagnosis module is used for inputting the optimized feature vector into a fault diagnosis model; the fault early warning generation module is used for generating a fault early warning signal in real time according to the output of the fault diagnosis model; and the closed-loop updating module is used for forming a closed-loop optimization-diagnosis-feedback iteration mechanism. According to the invention, fault diagnosis and early warning of the fan gearbox based on oil and vibration parameter fusion are realized for users.

The invention discloses a wind power generation system operation optimization method based on intelligent control, and the method comprises the following steps: S1, collecting the real-time data during the operation of a wind power generation system, including the wind speed, the wind direction, the rotating speed of a generator, the output power, and the environment temperature; s2, constructing a performance model of the wind power generation system according to the real-time data; s3, based on the performance model, adjusting the operation parameters of the generator in real time to optimize the power generation efficiency of the system; and S4, when detecting that the system is abnormal, automatically triggering a fault diagnosis module, generating a fault processing suggestion and executing an optimization strategy. Through real-time data acquisition, performance model construction, operation parameter dynamic adjustment and intelligent fault diagnosis, the problems that a traditional wind power generation system is low in efficiency and not timely in fault response are solved, and the system has the advantages of dynamically optimizing power generation efficiency, timely processing system abnormity and improving operation stability.

Systems and methods for delivering multiple ocular implants to reduce intraocular pressure are disclosed. The ocular implants can be implanted at multiple sites within a single human eye without requiring removal of the delivery apparatus from the eye. A system for delivering multiple ocular implants can include at least two implants preloaded within a delivery device and configured to be implanted within the eye, a metering device configured to transfer energy to the implants for delivery at selected locations within the eye, wherein the metering device is configured to meter a variable amount of energy for each implant delivery event in the eye. The system can further include an injector mechanism configured to serially engage and drive each of the implants.

The invention discloses a centralized unloading type power generation robot which comprises a mute box type diesel generator set and further comprises at least two sets of lifting supporting rod assemblies, and the lifting supporting rod assemblies are arranged on the two sides of the mute box type diesel generator set so as to lift the mute box type diesel generator set in the loading and unloading process. According to the centralized unloading type power generation robot, by arranging the lifting supporting rod assembly, the problem that a traditional generator set loading and unloading vehicle depends on hoisting equipment is solved, automatic lifting and unloading of the loading and unloading vehicle can be achieved in the environment without the hoisting equipment, the transportation efficiency is improved, the transportation cost is reduced, and it is ensured that the equipment can be rapidly transferred to the needed place to be put into use.