1189 results about "Vibration acceleration" patented technology

Disclosed in the present invention are an adaptive deep transfer fault diagnosis method and system, an apparatus and a medium. The method comprises the following steps: S1: collecting vibration acceleration signals of industrial equipment under different working conditions, and dividing same into a source domain data set and a target domain data set; S2: building a self-tuning universal domain adaptive fault diagnosis model, which comprises a shared feature extractor, a known classifier and a plurality of unknown classifiers; S3: separately calculating a classification loss of known faults of the source domain, a discriminative loss of the plurality of unknown classifiers, a target domain soft consistency regularization loss and an information entropy maximization loss; S4: introducing a dynamic weighting strategy based on model uncertainty assessment to optimize the model parameters; and S5: using the model for diagnosis. The present invention can fully mine valid information in data, can establish reliable class decision boundaries, and in addition, uses the self-tuning dynamic update strategy to adjust weightings corresponding to different loss functions, thus allowing for quick generalization of the model to different industrial diagnosis scenarios.

The invention relates to the field of road compactness monitoring, in particular to a road compactness real-time regulation and control method and system based on digital twinning, and the method comprises the steps: collecting vibration acceleration, temperature and position data, and generating a time-space aligned multi-source fusion feature data set after processing; inputting a pre-trained LSTM model to construct a dynamically updated digital twinborn body, and outputting a three-dimensional compaction energy spectrum; dispersing the atlas and calculating parameters, and generating a compaction degree deviation matrix; a regulation and control instruction is generated and issued based on matrix optimization; and according to the measured data and the predicted value residual error, triggering re-optimization and calibrating the sensor. According to the method, the problems of non-uniform compaction quality and over-high energy consumption caused by poor adaptability, decision lag and error coupling of a traditional static model are solved.

The invention relates to the technical field of fan blade fatigue damage prediction. The invention provides a fan blade fatigue damage prediction method and system. The method comprises the following steps: constructing a coupling finite element model based on blade anisotropy parameters; blade surface three-dimensional strain field data, blade vibration acceleration signals, environment temperature and humidity and wind speed and direction data are obtained in real time, and a multi-dimensional monitoring data set is constructed; based on the multi-dimensional monitoring data set, nonlinear coupling features of all the load components are extracted, a multi-dimensional feature tensor is obtained, and a reference stress field matched with the current working condition is generated; inputting the multi-dimensional feature tensor and the reference stress field into a bidirectional long-short-term memory network, and establishing a data-physics combined driven damage evolution model; and positioning a damage area based on a damage probability distribution diagram output by the damage evolution model. The problems that in the prior art, prediction errors are obvious, sensitivity to early damage is insufficient, the false alarm rate is high, and accurate positioning of the damage position and quantitative prediction of the residual life are difficult to achieve are solved.

The invention discloses a guardrail collision warning method and system, and the method comprises the steps: carrying out the nonlinear phase alignment of a multi-source heterogeneous signal through an adaptive variational mode decomposition algorithm according to the vibration acceleration, strain tensor and acoustic emission signals collected in real time through a multi-mode sensor array disposed at a guardrail key node, generating a three-dimensional dynamic strain field distribution vector; inputting the three-dimensional dynamic strain field distribution vector into a nonlinear dynamics reconstruction module, and extracting a chaotic feature fingerprint spectrum of the collision event; performing collision intensity grading processing on the chaos feature fingerprint spectrum, and outputting a quantitative evaluation matrix including a collision grade, a damage radius and a residual intensity prediction value; and triggering a multi-mode alarm protocol according to the quantitative evaluation matrix, and synchronously transmitting the multi-mode alarm protocol to a traffic management center and an adjacent vehicle OBU terminal. According to the embodiment of the invention, the state of the guardrail can be monitored in real time, and a collision event can be accurately evaluated and warned.

According to the technical scheme, the automatic feeding system comprises a feeding bin, a pneumatic conveying pipeline, an air source device and a discharging bin, and further comprises a distributed sensor set used for monitoring the flux, pressure, temperature and vibration frequency spectrum of a preset point of the pneumatic conveying pipeline; comprising a flow sensor, a pressure sensor, a temperature sensor and a vibration acceleration sensor which are arranged at a plurality of preset points of a conveying pipeline, and a digital twin model building module which is used for building three-dimensional modeling of the pneumatic conveying pipeline and mapping state parameters of all positions of a pipeline distribution sensor group in real time; the blockage analysis module is used for marking a blockage position to three-dimensional modeling in real time based on data acquired by the distributed sensor group, extracting a multi-parameter change curve in a time period T before blockage, constructing a blockage feature library, and generating a blockage risk level through a weight change rate difference value of a feeding bin and a discharging bin; and the graded dredging execution module is configured with a differentiated dredging strategy, and the differentiated dredging strategy is started according to the blockage risk grade.

The invention discloses a mechanical transmission system fault trend prediction system based on dynamic feature recognition, and relates to the technical field of mechanical state monitoring. Comprising the following steps: synchronously acquiring a load torque signal and a lubrication state parameter signal of a transmission system and vibration acceleration signals of a plurality of measuring points through a signal acquisition module; the working condition decoupling characteristic generation module carries out time-frequency analysis on the vibration signal, calls a pre-stored load disturbance spectrum template according to a load torque signal to carry out adaptive differential processing so as to eliminate load fluctuation interference, and calls a correction rule set according to a lubrication state parameter signal to carry out form recombination on the signal so as to compensate the lubrication state influence; and finally outputting a working condition decoupling feature representing the health state of the mechanical part. And the trend prediction module calculates and obtains fault development trend and residual life estimation data through a pre-trained fault prediction model. According to the method, the dynamic characteristics representing the essential degradation of the part are effectively extracted, and the accuracy and reliability of fault trend prediction of the mechanical transmission system are improved.

The invention relates to an AI server cooling water pump vibration quantity self-adaptive calibration test method and system, an intelligent test platform integrates a six-dimensional force sensor and a temperature compensation vibration exciter, the platform rigidity is automatically calibrated, a water pump-pipeline system transfer function is obtained, and a rotating speed-lift-modal frequency three-dimensional mathematical model is established; a three-axis MEMS accelerometer array is arranged at sensitive parts such as a water pump bearing seat and a motor shell, and vibration, current and pressure signals are synchronously collected; carrying out time-frequency domain signal processing by adopting variational mode decomposition in combination with self-adaptive S transformation, and extracting a 128-dimensional full-frequency domain feature vector containing a modulation side frequency band; effective values of vibration acceleration, speed and displacement are calculated through a frequency domain integration algorithm, and current harmonic interference is corrected through an electromagnetic vibration compensation model; and finally, a vibration health degree evaluation model containing 18 characteristic parameters is established based on a support vector machine, the test system comprises an intelligent test platform unit, a multi-source sensing unit, an edge calculation unit and a data management unit, and microsecond-level synchronous acquisition and GB-level data throughput are realized through a time sensitive network. And online incremental learning and automatic generation of a test report are supported. The method has the effect of improving the water pump vibration quantity test precision.

The invention relates to the technical field of cold-chain transportation, in particular to a cold-chain transportation safety remote supervision system, which not only can evaluate the vibration acceleration of goods in the transportation process in real time, but also can identify abnormities in advance under the condition of sudden vibration by using a sequential probability ratio method, thereby improving the safety of the goods. Therefore, goods damage caused by sudden turning, collision and the like of the vehicle is avoided. In addition, by combining historical data, the system can accurately set normal characteristic parameters of vibration and compare real-time data, and the sensitivity to abnormal vibration is further enhanced. According to the monitoring mode based on the sequential probability ratio test method, the defects of a traditional fixed threshold value are avoided, and whether vibration is abnormal or not can be judged more accurately. In the aspect of environment monitoring, compared with traditional static threshold judgment, a prediction method based on a Hort linear trend method can reflect environment changes more dynamically, environment adjustment measures are started in advance, and goods damage caused by too large fluctuation of temperature and humidity is avoided.

The invention discloses a drill string vibration identification and regulation method based on multi-modal data fusion, and belongs to the crossing field of petroleum drilling and artificial intelligence. According to the method, multi-modal input fusing space and time-frequency information is constructed by using a three-axis vibration acceleration signal of an underground drill string and a three-channel time-frequency diagram corresponding to the three-axis vibration acceleration signal. Through a deep neural network in which BiGRU and ResNet18-SENet are combined, an attention mechanism is fused, and accurate recognition of complex vibration modes such as general vibration, stick-slip and vortex motion is realized. Further combining the recognition result, driving the self-adaptive regulation and control of drilling parameters, and realizing the closed-loop control of the vibration state of the drill string. The method has the advantages of being high in recognition precision, high in robustness, intelligent in regulation and control and the like, and is suitable for underground vibration monitoring and drilling parameter optimization in a complex stratum environment.

The invention relates to the technical field of crane line fault diagnosis, in particular to a crane line fault diagnosis system and method based on multi-source data fusion, which comprises a data acquisition and processing unit, a mechanical and electrical coupling characteristic unit and a characteristic fusion and fault quantification unit, the three-axis vibration acceleration and the three-phase current waveform of the track are obtained through the data collecting and processing unit, the mechanical and electrical coupling characteristic unit conducts three-dimensional vector synthesis and wavelet packet decomposition on vibration data, and a time-space incidence matrix of harmonic distortion and vibration is constructed. And the feature fusion and fault quantification unit outputs coupling factors by using a bidirectional long-short-term memory network and an attention mechanism, and outputs a fault probability value through a dynamic time warping matching algorithm after time-frequency domain analysis and sample entropy judgment, so that time-space correlation modeling and dynamic fault matching of multi-source data are realized. And the fault positioning precision and the diagnosis accuracy are improved.

The invention discloses a modularized intelligent tubular column conveying system based on digital twinning and self-adaptive control, and belongs to the technical field of digital twinning. The modularized intelligent tubular column conveying system is characterized in that a digital twinning model integrating a mechanical structure, electrical control and hydraulic power attributes is constructed; acquiring multi-source sensing data including an image sequence, a three-dimensional point cloud and vibration acceleration in real time, driving the digital twin model to perform synchronous simulation, and calculating a deviation value between the digital twin model and internal simulation data; on the basis of the deviation value, a control logic parameter adjusting instruction is generated through a predefined mapping rule, then updated control logic parameters are executed, and a complete pipe column conveying process is simulated to generate a predictive system state sequence; and finally, the safety-related state subsets are issued to a field controller to guide and execute preventive actions. According to the invention, deep fusion and closed-loop control of digital twinning and physical entities are realized, and the debugging efficiency, the environmental adaptability and the operation safety of a tubular column conveying system are effectively improved.

The invention relates to the technical field of automobile automatic transmission, and discloses an AMT bearing operation state detection system and method under an active and passive switching working condition, and the method comprises the steps: collecting real-time data through a vibration acceleration sensor, a current sensor, a temperature sensor and a rotating speed encoder, carrying out the preprocessing of envelope demodulation, Kalman filtering and the like, and carrying out the detection of the operation state of an AMT bearing; and generating a time-frequency characteristic matrix by using variational mode decomposition and Hilbert transform. And inputting the matrix into a probabilistic neural network model adopting a sliding time window mechanism, and outputting a bearing health state probability value. A multi-parameter state evaluation model optimized by a quantum genetic algorithm is constructed, an optimal feature combination is obtained, and early warning levels and maintenance suggestions are output through a belief rule base inference device in combination with a hierarchical diagnosis control model (including an acquisition layer, an analysis layer and an execution layer). The system is further provided with a signal verification module to ensure data reliability. According to the invention, multi-source data fusion and dynamic adaptive diagnosis are realized, and the state detection precision and real-time performance of the AMT bearing under complex working conditions are improved.

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

The invention provides a vertical axis wind turbine power prediction method based on multi-dimensional environment data, and relates to the technical field of vertical axis wind turbine power based on the multi-dimensional environment data. Multi-dimensional data are collected in real time based on a sensor network, and the multi-dimensional data comprise environment parameters such as wind speed, turbulence intensity and temperature and humidity, and operation states such as rotor rotating speed and pitch angle; secondly, performing empirical mode decomposition on a vibration acceleration signal, constructing a physical information neural network, taking environmental data and an aerodynamic load frequency domain energy spectrum as input, outputting a real-time wind energy utilization coefficient and an aerodynamic coefficient, and finally, determining the aerodynamic load based on an aerodynamic load time domain signal. A load cyclic spectrum is extracted by adopting online rain flow counting of dynamic window length, an accumulated damage value is calculated in combination with a fatigue damage model of humidity correction, and when the residual life is lower than a threshold value, load peak suppression and power generation efficiency balance are realized through cooperative control of magnetic resistance torque linear attenuation and flap fuzzy PID (Proportion Integration Differentiation).

The invention discloses a power transmission system internal source excitation load and vibration transmission analysis method and system. The method comprises the steps that firstly, gear dynamic meshing rigidity, spline equivalent bearing rigidity and a flexible shaft model are obtained based on parameters of a gear, a spline and a rotating shaft, and then a power transmission system vibration transmission analysis model is established by combining bearing parameters; the method comprises the following steps: respectively obtaining a vibration acceleration signal of each bearing seat position through experiments and simulation, extracting a signal corresponding to each meshing excitation from each bearing seat measuring point signal through a defined comb filter, and calculating the vibration energy contribution of each meshing excitation; finally, through meshing excitation vibration contribution of multiple measuring points and a load transmission rule among parts obtained through simulation, a transmission rule of endogenous excitation load and vibration in the power transmission system is disclosed, vibration characterization of each measuring point in the power transmission system can be accurately obtained based on the method, a theoretical basis is provided for selection of sensitive measuring points, and the method has a wide application prospect. Therefore, support is provided for digital twin modeling and health management of the power transmission system.

The invention relates to the technical field of fault detection, in particular to a mutual inductor fault detection method and system, and the method comprises the steps: synchronously collecting a mutual inductor iron core vibration acceleration signal, a leakage magnetic flux intensity signal and a surface temperature field distribution signal, and generating a multi-physical field original data set; carrying out mechanical resonance characteristic analysis on the multi-physical field original data set, implementing adaptive noise filtering, and outputting a de-noised characteristic set; inputting into a multi-scale feature fusion module, and generating a multi-dimensional feature tensor comprising a time-frequency feature, a spatial distribution feature and an energy evolution feature; dynamically extracting a fault sensitive factor set from the multi-dimensional feature tensor, wherein the fault sensitive factor set comprises a mechanical deformation sensitive factor, an insulation degradation sensitive factor and a poor contact sensitive factor; and constructing a fault mode recognition model, and outputting corresponding fault types, fault levels and positioning information. According to the invention, through multi-physical field synchronous acquisition and fusion analysis, the accuracy and reliability of mutual inductor fault detection are significantly improved.

The invention discloses a transformer fault ultrasonic detection system, which comprises an ultrasonic sensor module, a signal conditioning module, a data acquisition and processing module, a data fusion module, a learning module, a diagnosis decision module and a human-computer interaction interface, according to the ultrasonic sensor module, 6-8 ultrasonic sensors are arranged in a transformer shell in an array mode, piezoelectric ceramic sensors with the resonant frequency of 40 kHZ are adopted as the ultrasonic sensors, the sensitivity of the ultrasonic sensors is-65 dBV / uBar, and effective capture is weak. According to the ultrasonic detection system for the transformer fault, single ultrasonic detection is easily influenced by accidental interference, the system is synchronously connected into equipment such as an infrared thermal imager and a vibration accelerometer, and a multi-physical-quantity correlation analysis model is established. When high-frequency ultrasonic pulses are detected, the system automatically calls temperature data of the corresponding position, and if the temperature gradient exceeds 2 DEG C / cm, it is confirmed that discharging heating is conducted; and if the temperature rise does not exist and the low-frequency vibration of 10-200Hz is accompanied, the mechanical looseness is judged.

The invention discloses a vibration displacement measurement method and system based on multi-source data fusion, and belongs to the technical field of vibration measurement. The vibration displacement measurement method based on multi-source data fusion comprises the following steps: acquiring motion sequence images of an elastic tube and a rigid tube through a high-speed camera, and acquiring a vibration acceleration signal of the elastic tube through an acceleration sensor; displacement tracks, obtained by the high-speed camera, of the elastic tube and the rigid tube are extracted, and a flow-induced vibration displacement time travel curve of the elastic tube is separated; performing frequency domain analysis on the displacement track data, and determining frequency bands of the rigid tube and the elastic tube; carrying out band-pass filtering on signals of the acceleration sensor based on a frequency band of flow-induced vibration, and compensating an error of an integral trend term of the acceleration sensor by utilizing time domain displacement data of the rigid tube; and carrying out secondary integration on the filtered acceleration sensor signal, and outputting corrected displacement information. By adopting the vibration displacement measurement method and measurement system based on multi-source data fusion, the problem that effective unification of measurement precision and engineering applicability cannot be realized by an existing vibration measurement method can be solved.

The invention discloses a locomotive wheel polygon damage detection method based on a lightweight neural network, and the method comprises the steps: obtaining axle box vertical vibration acceleration response signals of a heavy-load locomotive under the combination of different operation speeds and different wheel polygon abrasion degrees, and carrying out the preprocessing of the signals, the method comprises the following steps: acquiring frequency domain characteristics of a signal through fast Fourier transform, constructing a sample data set based on the frequency domain characteristics, constructing a wheel polygon damage detection network model, training the model by using the sample data set, and identifying a wheel polygon abrasion amplitude by using the trained polygon damage detection network model. And completing damage detection of the polygon of the wheel. The method can realize accurate and quantitative detection of the polygon abrasion degree of the heavy-load locomotive wheel, has the characteristics of accuracy, high efficiency and strong robustness, and also has relatively good interpretability.

The invention relates to the field of welding pressure control, in particular to a friction stir welding pressure control method, system and equipment and a storage medium. A three-axis acceleration sensor is used for monitoring the vibration acceleration of a main shaft to start a pressure compensation algorithm, a thermal infrared imager is used for collecting temperature field distribution of a welding area to dynamically correct welding pressure, and after welding is completed, control parameters are reversely optimized through ultrasonic flaw detection to form a self-learning control loop. The invention further comprises a corresponding control system, electronic equipment and a computer readable storage medium. The technical effects that the friction stir welding pressure is accurately controlled, the pressure can be adjusted in real time according to factors such as material characteristics, vibration and temperature, the welding process is optimized, the welding seam quality is improved, and the control parameters are continuously improved through self-learning are achieved.

The invention discloses a robot track online compensation and precision control method and system based on multiple sensors, and belongs to the technical field of robot track control. The method comprises the following steps: acquiring target trajectory data and original data of each sensor, and performing time alignment on the original data of each sensor to generate a fusion data set comprising vibration acceleration data and pose data; performing time-frequency feature extraction based on the vibration acceleration data to obtain a dominant vibration frequency spectrum; constructing a mechanical transfer function model of the joint driving torque and the tail end vibration displacement; based on the mechanical transfer function model, constructing an adaptive notch filter to generate a feed-forward compensation sequence; according to the target trajectory data, the pose data and weight parameters, updated in real time, of the sensors, a trajectory fusion deviation value is obtained through calculation; and after the feedforward compensation sequence and the trajectory fusion deviation value are integrated, a joint driving current instruction is generated based on a fuzzy PID control algorithm. The robot trajectory control precision can be improved.

The utility model relates to the technical field of acceleration sensors, in particular to a piezoelectric acceleration sensor, which comprises a shell, a mounting cavity is arranged in the shell, a shielding shell is fixedly connected in the mounting cavity, a crystal base is fixedly connected at the bottom end in the shielding shell, a PCB (printed circuit board) is arranged at the upper part in the shielding shell, and the crystal base is fixedly connected with the crystal base. An external force is applied along the polarization direction of the ceramic wafer to deform the ceramic wafer, the inside of the ceramic wafer is polarized, charges with opposite polarities appear on the two stressed end faces, and the ceramic wafer is recovered to an initial state if the external force is removed, so that mechanical energy is converted into electric energy by utilizing the direct piezoelectric effect of the ceramic wafer, and then the electric energy is subjected to signal processing; the sensor outputs 100mv / g alternating current voltage, so that vibration acceleration signals are measured, triangular shearing is adopted by a measuring element, a two-core shielding cable is adopted by a cable, and the sensor is small in size, light in weight and high in applicability.

The invention discloses a rotating machine vibration protection method and system of an adaptive resonance neural network, and the method comprises the steps: collecting a millisecond vibration acceleration signal of a rotating machine through a distributed piezoelectric sensor array, obtaining the real-time working condition parameter of equipment, and generating a time-work synchronous vibration signal flow; inputting the time-work synchronous vibration signal flow into a resonance neural network, and dynamically generating a frequency domain-time varying adaptive threshold group in combination with the working condition parameters; extracting a weak impact resonance characteristic tensor by using a multi-scale resonance kernel of the resonance neural network; projecting a weak impact resonance characteristic tensor to an orthogonal fault subspace through a tensor decomposition algorithm, and separating out a decoupling fault characteristic matrix; and generating a real-time protection decision instruction set based on a comparison result of the decoupling fault feature matrix and the equipment operation historical database. According to the embodiment of the invention, the method can improve the detection sensitivity and positioning precision of a weak fault under an unsteady working condition, and achieves the real-time adaptive optimization of the vibration protection of the rotating machine.

The invention provides an axial flow pump cavitation stage identification method based on high-speed photography and vibration signal double-flow convolution fusion. The method comprises the following steps: synchronously acquiring vibration acceleration signals on an inlet and outlet pipeline in three stage states of non-cavitation, cavitation inception and serious cavitation and a cavitation image in a high-speed use shooting pump stage; performing feature extraction of fine composite multi-scale dispersion entropy improved by a granulation strategy of variable mode decomposition and local mean filtering on the vibration acceleration signal, and performing feature importance scoring by using a Laplacian fraction method to obtain a one-dimensional feature cavitation feature data set; performing image processing on the cavitation image to obtain a cavitation dynamic feature image data set; and establishing an axial flow pump cavitation diagnosis model, and outputting an identification type by the diagnosis model. According to the method, the cavitation state of the axial flow pump can be accurately identified, and the anti-interference capability is high.

A train-track-bridge coupling response prediction method based on a sparrow optimization algorithm and a long short-term memory network comprises the steps that data such as train speed, axle load, track vibration acceleration, bridge strain and environment temperature are collected in real time through a multi-source sensor, and a multivariable time series data set is constructed after wavelet denoising and standardized preprocessing; and designing an LSTM network architecture on this basis, introducing an attention mechanism to dynamically allocate feature weights of each time step so as to enhance the ability to capture key signals in the track irregularity mutation and bridge resonance interval, and adopting a sparrow optimization algorithm to globally search an optimal combination of a hidden layer neuron number, a learning rate and a time step length in order to solve the problem of LSTM hyper-parameter optimization. Through the dynamic adaptive step length strategy balance algorithm, the early-stage global exploration and later-stage local development capabilities are balanced, the local convergence defect of a traditional grid search or genetic algorithm is avoided, the calculation efficiency can be remarkably improved, errors can be reduced, and the prediction precision can be improved.

The present invention relates to the technical field of intelligent out-of-distribution fault detection for construction machinery, and discloses an out-of-distribution fault detection method and system based on energy propagation and graph learning, and the method includes: acquiring vibration acceleration signals in typical fault states, carrying out similarity calculation to obtain an adjacency matrix composed of the maximum mutual information coefficients, and taking the adjacency matrix as input in a graph neural network; carrying out feature extraction on the adjacency matrix through adopting a GraphSage graph convolution method, and generating each node representation; calculating an energy score of each node, and distinguishing between in-distribution data and out-of-distribution data; and enhancing out-of-distribution data confidence estimation for each node, and carrying out out-of-distribution data identification and out-of-distribution data detection under different working conditions of a rolling bearing.

The invention relates to a multi-machine cooperative dynamic balance driving method, system and device for a spiral lifting machine, and the method comprises the steps: carrying out the compensation of the time lag of an actuator through employing a non-linear state observation and time lag compensation combined method, and obtaining a feedforward control quantity; performing Lyapunov stability feedback processing on the vibration acceleration signal to obtain a vibration suppression control quantity; designing a multi-section S-shaped speed curve, and calculating a closed-loop feedback control quantity by adopting PID (Proportion Integration Differentiation) combined with a robust control algorithm; and according to the feedforward control quantity, the vibration suppression control quantity and the closed-loop feedback control quantity, multi-machine cooperative dynamic balance driving of the spiral lifting machine is carried out. According to the invention, a non-linear state observation and time delay compensation technology is adopted, so that the synchronization error is reduced to a millisecond level; the low-frequency vibration amplitude is reduced through dynamic feedback control designed based on the Lyapunov theory; the hydraulic impact is obviously reduced by adopting a multi-section S-shaped speed curve; external disturbance is observed in real time through Kalman filtering, feed-forward compensation is carried out, and the robustness of the system is greatly enhanced.

The invention discloses a bridge service intelligent evaluation and early warning method and system based on dynamic and static load-multi-modal data fusion, and the method comprises the steps: S1, installing a vibration acceleration sensor and a strain gauge at a bridge control part, and collecting data; locally preprocessing data by adopting an edge computing framework, extracting feature values, encrypting and transmitting the feature values to a cloud; identifying and collecting diseases, and combining a deep learning network model to realize pixel-level crack segmentation; s2, establishing a space-time-measuring point-disease three-dimensional correlation model; s3, a three-level early warning threshold system is set, different early warning levels have different requirements for vibration amplitude and crack length indexes, and when a monitoring index exceeds a threshold value, early warning information is pushed to a terminal in real time through 5G; S4, a decision engine is constructed based on a Q-Learning algorithm or a deep reinforcement learning model, early warning levels, residual life and maintenance cost parameters are input, and the early warning level, the residual life and the maintenance cost parameters are calculated. And outputting the optimal maintenance scheme.

The invention discloses a sensor bearing data-driven mechanical motion state intelligent monitoring system, which belongs to the technical field of monitoring equipment, and comprises an acquisition module, a processing module, a wireless communication module and an analysis module, the acquisition module and the processing module are arranged inside the bearing body, the wireless communication module and the analysis module are arranged outside the bearing body, and the acquisition module comprises a vibration acceleration sensor, a temperature sensor and a rotating speed sensor. The vibration acceleration sensor, the temperature sensor and the rotating speed sensor are used for collecting vibration acceleration data, temperature data and rotating speed data in the bearing operation process respectively, and the processing module comprises a microprocessor and a protocol converter. According to the method, the weighted root-mean-square acceleration is calculated, potential faults or abnormal conditions can be found in time, abnormal data are removed through the real abnormal degree, and the abnormal state of the bearing can be effectively monitored.

Disclosed in the present invention is a rock porosity prediction method based on the inversion of few-shot while-drilling data. While-drilling parameters of a drilling rig during a laboratory or on-site drilling process are collected, wherein the while-drilling parameters comprise parameters such as torque M, thrust force F, rotation speed N, penetration rate V, drill pipe amplitude A and vibration acceleration a; different types of while-drilling parameter data which have been subjected to multiple instances of denoising processing are inputted into a BP-GA model for outlier removal, data augmentation and iterative calculation, so as to form a new while-drilling data set; and while-drilling parameter time-frequency domain feature maps of rocks having different porosities are inputted into a VG-CNN prediction model, an inversion model between while-drilling parameter time-frequency domain features and the porosities is obtained by means of training and learning, and finally inversion prediction is performed on the rock porosities on the basis of on-site real-time while-drilling parameters. The present method enables fast, accurate and quantitative inversion and prediction of rock porosities on the basis of a small amount of borehole while-drilling data.