22524results about "Engine components" patented technology

A water-turbine generator set voiceprint monitoring and diagnosing method comprises the steps that a multi-channel voiceprint collecting system is adopted, a plurality of voiceprint sensors are arranged at key parts of a water-turbine generator set, and voiceprint signals at different positions are obtained; performing time-frequency analysis on the pre-processed voiceprint signal by using wavelet transform, and extracting voiceprint features to obtain a voiceprint feature vector; if the matching degree of the voiceprint feature vector and the features in the fault feature library exceeds a preset threshold value, determining the fault type and the fault degree; classifying the determined fault type and fault degree by adopting a support vector machine algorithm to obtain a fault diagnosis result; a fault diagnosis result is optimized through a deep learning network, the diagnosis precision is improved, and a final fault diagnosis report is obtained; performing association analysis on the fault diagnosis report and the equipment operation parameters to generate an equipment operation state evaluation report; according to the equipment operation state evaluation report, an equipment maintenance plan is made, and intelligent maintenance of the water-turbine generator set is achieved.

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 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 relates to the technical field of abnormal monitoring of water-turbine generator sets, in particular to an abnormal monitoring system and method for a water-turbine generator set, and the system comprises a sensor module, an edge computing node, a cloud analysis platform and a scheduling decision center. The edge computing node performs data preprocessing, the cloud analysis platform realizes accurate early warning by applying technologies such as multi-source data fusion, a dynamic early warning model library and adaptive threshold adjustment, the scheduling decision center optimizes scheduling decisions through a resource matching engine and an emergency plan generation module, and the industrial communication network guarantees reliable transmission of data. In addition, the system is also provided with a visual interaction interface and a state evaluation report generation module. According to the invention, comprehensive and accurate monitoring and intelligent early warning of the water-turbine generator set are realized, the fault processing efficiency is effectively improved, the probability of false alarm and missing alarm is reduced, resource allocation is optimized, the operation safety is enhanced, and a powerful guarantee is provided for stable operation of the water-turbine generator set.

The invention provides a wind turbine generator array wake flow management method and system for Doppler laser wind measurement, and relates to the technical field of laser, and the method comprises the steps: collecting the real-time wind speed data, wind direction data and turbulence intensity data of each generator in the front and back direction through deploying a Doppler laser radar in a wind turbine generator array; establishing a wake flow influence model containing a wake flow influence coefficient, an attenuation coefficient and a diffusion coefficient based on the data, and calculating to obtain a wake flow influence range, loss power and a recovery distance; according to the method, the wind turbine generator array is cooperatively controlled, and the power generation efficiency of the wind turbine generator array is optimized by adjusting the yaw angle or the pitch angle of the upstream unit and the rotating speed of each unit. The wind speed data acquisition precision of the wind field is improved, the wake flow influence evaluation accuracy is enhanced, and the overall power generation efficiency of the wind field is remarkably improved.

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.

The present invention discloses a method and device for determining the effectiveness of a diesel generator set fault preventive test. The method comprises obtaining vibration data, temperature data, vibration time series, temperature time series and test data of the diesel generator set; performing denoising and dimensionality reduction processing on the vibration data and temperature data to obtain a fused feature matrix, and inputting it into a fault classification model to obtain the fault type; performing time series alignment on the vibration time series and the temperature time series to obtain time series data, and inputting it into a prediction model to obtain vibration prediction values ​​and temperature prediction values; performing deviation correction on the test data, and inputting the corrected data into a pre-built test prediction model to obtain prediction test conditions; and performing generator fault prevention operations and prevention effectiveness evaluation according to the fault type, vibration prediction value, temperature prediction value and prediction test conditions. The method solves the problem of being unable to accurately perform fault diagnosis and preventive maintenance of diesel generators.

The shutdown of a compressor (10) installed in a refrigerant circuit (2) in air conditioning or refrigeration system is controlled so as to prevent unpowered reverse rotation of the compressor. Prior to terminating electric power to the compressor drive motor (24), the pressure within the system is relieved and substantially equalized across the compressor, thereby eliminating the possibility of unpowered reverse rotation of the compressor at shutdown. Pressure relief and equalization may be achieved by reducing the operating speed of the compressor to a low forward speed for a period of time prior to deenergizing the compressor drive motor. Pressure equalization may also be achieved by driving the compressor in reverse rotation prior to deenergizing the drive motor.

The invention discloses a digital twin-driven marine diesel engine emission prediction and optimization method, which comprises the following steps: (1) constructing a comprehensive performance mechanism model, and obtaining twin data through representation and verification of the comprehensive performance mechanism model; (2) designing a calibration module based on a deep learning algorithm, taking real data and twin data as input, and outputting calibrated data through model training; (3) constructing a combustion stage mechanism module, and optimizing a gas inlet pipeline structure and improving mixing uniformity through simulation of a combustion mechanism model on oil-gas mixing characteristics and combustion efficiency; (4) constructing a data driving model, and fitting a relationship between the turbulent energy index and emission; and (5) taking the constructed comprehensive performance mechanism model, the calibration module, the combustion stage mechanism model and the data driving model fusion technology system as a fitness function, searching an optimal oil injection control parameter based on a particle swarm optimization algorithm, and formulating an optimization control strategy adapting to different working conditions.

The invention discloses a fan operation real-time dynamic adjustment control method and system based on environmental data. The method comprises the steps that multi-dimensional dynamic parameters of the environment where a fan is located are collected in real time based on a distributed sensor network; constructing a dynamic coupling prediction model, inputting the dynamic coupling prediction model into a bidirectional verification module based on multi-dimensional dynamic parameters, and obtaining an environment state prediction matrix in a future time window; based on the environment state prediction matrix, a combined adjustment instruction set of the fan pitch angle, the yaw angle and the generator torque is obtained, and the instruction set comprises main strategy parameters and an emergency tolerance interval; based on an incremental learning mechanism, model parameters are dynamically updated according to the deviation between actual environment parameters and predicted values, and the updating period is adaptively adjusted along with the change of turbulence intensity. The method has the advantages that accurate prediction and optimal control of the fan in a complex environment are achieved, the limitation that a traditional data driving model lacks physical interpretability is broken through, and the running stability and efficiency of the fan are improved.

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 a preparation method of a low-cost TC21 titanium alloy large-specification bar, which comprises the following steps: 1) carrying out two-heating cogging forging on a TC21 titanium alloy ingot: carrying out beta single-phase region high-temperature forging at the first heating to obtain an intermediate billet ingot with a square cross section; in the second heating number, low-temperature upsetting and reversing drawing-out forging are carried out, then high-temperature forging is carried out, and an intermediate billet cast ingot with the square section and the grain size being 1-2 mm is obtained; (2) three-heating-number bar forging is carried out, specifically, in the first heating number to the second heating number, the intermediate blank cast ingot is upset and then drawn out and forged into a blank with the octagonal section in each heating number, and in the third heating number, the intermediate blank is drawn out into an intermediate blank with the twelve-square section; and (3) forming and forging for one heating number, wherein the middle blank with the twelve-square section is milled into a rod blank with the round section and the required size. By means of the method, the uniformity of the bar structure is improved, and the TC21 titanium alloy large-specification bar with the structure and performance meeting the requirements of phi 350 mm can be prepared at low cost.

A vacuum pump for generating a negative pressure includes a pump housing having a pump chamber with a rotor and a motor for driving the rotor with a drive shaft rotatable about a longitudinal axis, a first and a second inlet line. The second inlet line leads through the pump housing into the pump chamber. A first outlet line leads from the pump chamber through the pump housing. The pump has a first outlet valve and a second outlet line connected to the pump housing. A connecting part is attached to a connecting surface running circumferentially to the longitudinal axis, and the outlet valve with the second outlet line is formed in the connecting part. The connecting part further has the first inlet line, with a first connecting opening formed in the connecting part housing, via which the first inlet line is connected to the second inlet line.

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.

The invention relates to a wind power plant yaw control method based on a wind process. The method comprises the following steps: 1, establishing a wind power plant simulation model; 2, acquiring operation state data of the wind power plant as an initial state of the wind power plant simulation model; 3, acquiring a wind speed fluctuation sequence and a wind direction fluctuation sequence of the wind power plant as the state of the wind power plant yaw control model; 4, performing off-line learning to obtain optimal yaw angle set values under different wind regime sequences; enabling the wind power plant yaw control model to observe the environment and guide a wind turbine generator to act, and completing online learning; the wind power plant yaw control model is established, the optimal yaw angle of each wind turbine generator in the wind power plant is obtained through the wind speed sequence and the wind direction sequence, and the total generated power of the wind power plant is greatly improved; and the timeliness requirement of solving the wind power plant yaw control model in practical engineering is met.

The invention relates to the technical field of fault detection, and discloses a diesel generating set fault detection method and system based on deep learning, and the method comprises the steps: obtaining first vibration signal data, and carrying out the time-frequency decomposition, and obtaining a dynamic change feature; de-noising processing is carried out on the dynamic change features to obtain a time-frequency feature sequence; extracting a peak energy distribution data set, and calculating each frequency band entropy value to obtain a frequency band entropy value sequence; classifying the frequency band entropy sequence, determining a random fluctuation reference mode, and separating to obtain an abnormal frequency component; calculating a spectral line spacing and amplitude ratio, obtaining a spectral line feature data set, classifying the spectral line feature data set, and determining a fault classification result; obtaining current second vibration signal data, performing similarity calculation on the current second vibration signal data and a pre-established normal mode library, and outputting a fault feature vector; and verifying the fault feature vector to obtain a final fault detection result. According to the method, closed-loop diagnosis from signal acquisition to fault classification can be realized, and the fault detection precision of the diesel generating set is improved.

The present invention relates to a computer implemented method for monitoring the blade (210) of a wind turbine (200), the method comprising fixing a sound acquisition device (110) to an outer surface of a tower (220) of the wind turbine (200), wherein the sound acquisition device (110) comprises a microphone, a sound encoder and a communication unit, acquiring a sound sample of sound emitted by the blade (210) during operation of the wind turbine (200), encoding the sound sample into a raw audio file, sending the raw audio file to a remote computing device (120), preprocessing the raw audio file, generating a spectrogram of the pre-processed audio file, detecting an anomaly by applying an image analysis machine learning model to the spectrogram, classifying the anomaly to a damage type by applying a damage type feature analysis machine learning model to frequency domain features of the audio file at the time and frequency of the anomaly, determining a damage location by applying a damage location feature analysis machine learning model to frequency domain features of the audio file at the time and frequency of the, and tracking of a damage severity by extracting audio features of the audio file at the time and frequency of the anomaly indicative of the damage severity depending on the damage type.

The invention discloses a multi-energy complementary integrated device based on wind energy, wave energy and solar energy, and belongs to the technical field of offshore power generation, the multi-energy complementary integrated device comprises a semi-submersible platform, a fan is arranged in the center of the semi-submersible platform, a photovoltaic panel is arranged on the upper plane of the semi-submersible platform, and a wave energy device is arranged in the semi-submersible platform; the bottom surface of the semi-submersible platform is connected with a photovoltaic array, the photovoltaic array comprises photovoltaic floating bodies, photovoltaic panels are arranged on the top surfaces of the photovoltaic floating bodies, and the photovoltaic floating bodies are connected through a swing type wave energy device. By the adoption of the structure, complementarity of solar energy and wave energy in space-time distribution can be complementarily utilized, multi-energy power generation output peak-valley staggered compensation is achieved, the overall output stability of the integrated system is improved, the rectification difficulty is reduced, multi-energy complementation is achieved, and the power generation power of the multi-energy integrated system can be effectively improved. Meanwhile, the self-storage capacity of the system can be improved, and the service cycle is prolonged.

The invention discloses a matrix yaw system of a wind power plant. The matrix yaw system comprises a data acquisition module, a wind plant modeling module, a parameter extraction module and a yaw control module. The data acquisition module acquires a wind field original data set containing three-dimensional space coordinates and timestamps by using a multi-source sensor; the wind field modeling module calculates correlation between points by combining a covariance function through a Gaussian process regression algorithm, and constructs a three-dimensional dynamic wind field model; the parameter extraction module is used for extracting wind regime parameter vectors in the model by adopting a nearest neighbor interpolation algorithm on the basis of actual space coordinates of a fan; and the yaw control module utilizes a depth deterministic strategy gradient reinforcement learning model to generate a yaw angle adjustment instruction in combination with the wind regime parameter vector, the current yaw state of the fan and a preset maximum cumulative reward function. Through multi-module cooperation and intelligent algorithm optimization, accurate matching of wind field dynamic modeling and yaw control is achieved, and the energy efficiency and operation stability of the wind generating set are improved.

Embodiments include a buoyant wave energy converter. In an embodiment, the wave energy converter comprises an upper chamber having a first fluid reservoir and a first gas pocket, and a lower chamber having a second fluid reservoir and a second gas pocket. In an embodiment, an injection tube is between and fluidly coupled to the upper chamber and the lower chamber, where the injection tube is to impel a fluid from the second fluid reservoir into the first fluid reservoir when the upper chamber, the lower chamber and the injection tube oscillate about a waterline with the upper chamber adjacent to the waterline and the lower chamber submerged below the waterline and vertically beneath the upper chamber. An effluent tube is fluidly coupled to the upper chamber and the lower chamber, where the effluent tube is to return the fluid from the first fluid reservoir to the injection tube.

An aeronautical thruster of longitudinal axis has a hub, an annular row of unducted upstream rotor blades and an annular row of unducted downstream stator blades. Each downstream stator blade is of variable pitch, and at least one of the downstream stator blades is in a closed-pitch configuration relative to another of the downstream stator blades in that it has a pitch angle smaller than the pitch angle of the other downstream stator blade.

This invention introduces a kinetic energy recovery wind-wave integrated system for offshore wind power generation. The system consists of a semi-submersible platform equipped with a fan and an internal wave energy device. The device includes a shell housing a Power Take-Off (PTO) system, featuring a permanent magnet synchronous linear motor and an active controller. The motor's stator is fixed inside the shell, while its mover is connected to a counterweight block outside the stator, linked to the shell's top via a spring. Limiters are installed at both ends of the shell to restrict the counterweight block's movement. This system utilizes the wave energy device to absorb kinetic energy, which otherwise affects wind turbine stability, and converts it into usable electrical energy via the PTO system. This enhances the stability and safety of offshore wind turbine power generation.

A turbomachine module includes a fan driven in rotation by a fan shaft and having a plurality of variable pitch vanes. The module further includes a pitch change system with a control means connected to the vanes via a connection mechanism, wherein the control means comprising a stationary body integral with the fan shaft and a movable body connected to the connection mechanism. The pitch change system includes an annular part which has a segment integral with the movable body, which is bell-shaped and which extends at least partly radially outwardly from the stationary body.An annular shroud is releasably attached to the annular part and has means for attaching the connection mechanism.

The invention relates to the technical field of generator monitoring, in particular to an online state monitoring method and system for a water-turbine generator set, and the method comprises the steps: collecting data, and carrying out the preprocessing of the data through a lightweight TinyML reasoning model; dimension reduction processing is carried out on the preprocessed data through PCA, and dynamic normalization is carried out on the data after dimension reduction; performing convolution feature extraction on the normalized data through a feature extraction module, and extracting attention enhancement features from the convolution features through a sparse attention mechanism; calculating a posterior probability based on Bayesian network topology through a Bayesian network feature fusion module; a fault probability vector and an integrated feature vector are obtained through combination of a multi-modal fusion model and a posterior probability; and through the fault probability vector, predicting residual life and current working condition characteristics, and outputting an early warning level, a fault type and predicted fault time. According to the scheme, the diagnosis efficiency and reliability are improved through multi-modal fusion and Bayesian reasoning.

The present invention relates to the technical field of railway-locomotive diesel-engine fault diagnosis systems, and particularly relates to a railway-locomotive diesel-engine fault diagnosis system based on a knowledge graph. The system comprises a data collection module, a transmission module, a preprocessing module, an algorithm application module, a storage module and a front-end display module, wherein one end of the data collection module is used for performing signal collection by means of a sensor, and the other end thereof is connected to the preprocessing module by means of the transmission module; the preprocessing module performs diagnosis and positioning on a specific faulty part in combination with a fault classification algorithm of the algorithm application module; and an original signal collected by the data collection module and algorithm diagnosis information are scheduled and stored in the storage module by means of back-end management software, and display interaction materials are then provided for the front-end display module. The present invention retains the previous operation mode of an expert knowledge base in terms of fault diagnosis so as to ensure the basic accuracy of cause analysis, and a neural network intelligent algorithm is introduced to reduce the manual workload and individual difference of people in terms of professionalism.

The invention relates to the technical field of wind energy control systems, and discloses a wind power energy-saving control method and system based on big data analysis, and the system comprises a data collection and transmission layer, a data storage and management layer, a data analysis and prediction layer, an intelligent control layer, and an equipment full life cycle management layer. The data acquisition and transmission layer further comprises multi-source data acquisition system construction, edge computing data preprocessing and data transmission network construction. According to the intelligent control strategy, by means of a distributed cooperative control algorithm and an intelligent variable-pitch variable-speed control algorithm, the fan blade angle, the rotating speed and the power output are dynamically adjusted according to the real-time meteorological data, the power grid load requirement and the fan running state, wind power output is smoother through accurate regulation and control, and the wind power output efficiency is improved. The impact on the power grid caused by power fluctuation is effectively reduced, the wind power acceptance capability of the power grid is remarkably improved, and the stability and reliability of the operation of the power grid are guaranteed.

The invention relates to the technical field of fan blade deicing, and discloses an intelligent temperature control type fan blade electric heating deicing control system and method, and the system comprises a meteorological sensing module which is used for collecting the environment temperature, humidity, wind speed and rainfall type; the blade state monitoring module is used for acquiring blade surface temperature distribution and ice layer thickness data; the dynamic decision-making module is used for generating a partition heating instruction based on the data of the weather sensing module and the blade state monitoring module; the temperature control execution module is used for converting the instruction into a gradient signal; the heating array module is used for executing electric heating deicing operation in the blade surface subareas; and the energy coordination module is used for dynamically constraining the total power of the system. The meteorological sensing module and the blade state monitoring module are integrated through the system, real-time collection and fusion of multi-source data of blade surrounding environment parameters and the thickness of an ice layer are achieved through the design, comprehensiveness and accuracy of icing monitoring are improved, a reliable basis is provided for dynamic decision making, and therefore the response capacity and early warning precision of the system to icing risks are improved.

The invention relates to the field of forging processes, and discloses an efficient forging process for an output shaft shared by ship accessories and mining machinery, which comprises the following steps of: 1, simulating the stress condition of a mold by adopting finite element analysis through mold design and optimization, ensuring uniform stress of the mold, and avoiding mold wear and material segregation; secondly, the forging force and the deformation rate are precisely controlled through a numerical control forging machine tool, so that stable control over the forming precision of the forge piece is achieved; thirdly, a composite forging process is adopted, wherein the composite forging process comprises a pre-forging process and a precision forging process; 4, optimizing a heat treatment process; and 5, monitoring temperature, stress and deformation data in real time in cooperation with an intelligent temperature control and monitoring system. The forging force and the deformation rate are precisely controlled through the numerical control forging machine tool, the high consistency of the shape and the size of the forge piece in the forging process is guaranteed, error accumulation in the forging process is avoided, and the forming precision of the forge piece is improved.

A method for optimizing wind farm electric power generation using multivariable system identification, which reduces energy loss caused by the wind turbine wake effect by optimizing and adjusting the yaw angle and axial induction factor of each wind turbine, so as to maximize the total power generation of the wind farm. According to the method, accurate optimization gradient estimates are obtained by using the experimental data and the identification model, and then a gradient-ascend optimization with large initial step size and variable step size iteration is carried out, so that the problems of large model errors and of high computation cost of mechanism modeling are avoided. The method is easy to implement and has a high modeling accuracy and efficiency, and thus is suitable for solving the electric power generation optimization problem of large-scale real-life wind farms.

A reduction in wake effects in large wind farms through wake-aware control can improve farm efficiency. The wake of a wind turbine presents complications for nearby turbines, depending on the atmospheric conditions, turbine characteristics, and turbine siting. The nascent field of wind farm flow control seeks to reduce the deleterious effects of the wake momentum deficit by leveraging the turbine as a flow actuator though the intelligent scheduling of either the blade pitch, rotor speed, or nacelle yaw.