1649 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 an equipment state monitoring method and system based on multi-modal data fusion. According to the method, numerical data of equipment temperature, strain and vibration acceleration and operation record text data are acquired, an index table is established based on a maintenance event to realize heterogeneous data time sequence alignment, equipment dynamic features are extracted by combining Fourier transform and wavelet transform, and text semantic features are extracted by using an LSTM network. And designing a multi-head cross attention mechanism to realize deep feature fusion, constructing a state evaluation model by adopting CatBoost, and introducing KL divergence detection to dynamically update the model. The technical problems that in equipment state monitoring, multi-source heterogeneous data is difficult to align, numerical features and text features are difficult to effectively fuse, and a model is difficult to adapt to data distribution changes are solved, and the accuracy and reliability of state evaluation are improved.

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.

The invention relates to the technical field of equipment fault prediction, in particular to a coal mine equipment fault prediction system and method based on big data, and the method comprises the following steps: collecting the thickness of a biological membrane on the surface of equipment, synchronously collecting the vibration acceleration of the equipment and the environmental hydrogen sulfide concentration, and constructing a corrosion multi-mode data set; performing electrochemical noise separation on the corrosion multi-modal data set, and constructing a corrosion activity fingerprint spectrum; decoupling a coupling effect of mechanical vibration and microbial corrosion, and generating a corrosion-fatigue damage correlation map; and constructing a dual-channel life prediction model based on the corrosion-fatigue damage correlation map, and setting an early warning mechanism. Microbial corrosion characteristic current is accurately extracted through wavelet packet decomposition and wavelet entropy analysis, a corrosion-vibration heterogeneous graph is established by adopting a graph attention network, corrosion-induced structural vibration enhancement effect analysis is realized, and compared with a traditional prediction method based on a single signal, the method has the advantages that the method is simple and convenient to operate. And the problems of limited local corrosion influence range, unstable vibration response and the like are effectively solved.

The invention relates to the technical field of bridge structure monitoring, in particular to a bridge structure health monitoring system based on an intelligent sensor network and deep learning, which comprises a data acquisition module used for setting a plurality of acquisition points on a bridge, obtaining bridge structure monitoring data related to the bridge, and sending the data to a server; wherein the bridge structure monitoring data comprises temperature and humidity, strain, deflection, cable frequency data and vibration acceleration on a bridge; the data monitoring module is used for matching and associating the bridge structure monitoring data, setting an abnormal index corresponding to the bridge structure monitoring data, and obtaining abnormal related data corresponding to the abnormal index; the modal anomaly analysis module is used for identifying the abnormal related data, determining an abnormal label of the abnormal related data, and clustering according to the abnormal label to obtain an abnormal classification result; the accuracy and reliability of bridge structure health state evaluation are realized.

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 provides a surface quality monitoring method and system based on image-signal multi-modal data, and relates to the technical field of machining process detection, and the method comprises the steps: obtaining a workpiece surface image and a main shaft vibration acceleration signal in a milling process, inputting the workpiece surface image and the spindle vibration acceleration signal into a surface roughness classification model to obtain a machining state recognition result; wherein in the surface roughness classification model, shallow texture features of a workpiece surface image are extracted through an image processing module, and frequency domain features of a spindle vibration acceleration signal are extracted through wavelet transform and a frequency attention mechanism; and splicing projection features of the shallow texture features and the frequency domain features into a combined feature vector, carrying out dynamic weight distribution and fusion on the combined feature vector based on a self-adaptive fusion strategy of a gated attention mechanism to obtain weighted fusion features, inputting the weighted fusion features into a grade classifier, and outputting the grade of the surface roughness.

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 sliding bearing-rotor system stability test system and method under an impact load, and relates to the field of rotating machinery test in mechanical engineering. According to the technical scheme, different types of impact loads are applied through a vibration acceleration platform, and starting, stopping or external impact force in actual working conditions is simulated. In the testing process, a high-precision sensor and a multi-channel acquisition board card are used for monitoring the vibration characteristic and the rotating speed of the system in real time, it is guaranteed that signals of different positions and types can be synchronously acquired, and comprehensive system state information is provided. Then, in combination with time domain and frequency domain analysis technologies, key features are extracted from mass data by applying an advanced Fourier transform algorithm, the resonant frequency, modal characteristics and potential fault modes of the system are identified, and accurate evaluation of the stability of the system is realized. Experimental results show that the real-time monitoring and analysis method can effectively improve the stability test precision of the sliding bearing-rotor system under the impact load, and provides powerful technical support for engineering application. The method is used for evaluating the stability of the sliding bearing-rotor system under the impact load.

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 rolling bearing fault diagnosis method and system based on multi-channel adaptive fusion, and the method comprises the steps: obtaining a one-dimensional vibration acceleration signal, carrying out the normalization, and converting the one-dimensional vibration acceleration signal into a two-dimensional gray-scale map; inputting the two-dimensional grey-scale map into a convolutional neural network model for processing, specifically comprising the following steps: a multi-scale feature extraction module extracts feature information of the two-dimensional grey-scale map, adaptively weights the importance weight of multi-channel information, and outputs a first multi-scale feature map; a double-attention cavity convolution module extracts the spatial position and channel information of the first multi-scale feature map, and outputs a second feature map; and the residual dense module performs deep feature extraction on the second feature map through residual connection and dense connection, and outputs a rolling bearing fault diagnosis classification result. According to the method, the problems of low model fault diagnosis effect and poor generalization ability caused by strong noise interference and large load change of the rolling bearing can be solved.

The invention relates to the technical field of fault diagnosis, and discloses a bearing fault diagnosis method and system based on multi-source domain deep transfer learning, and the method comprises the steps: collecting vibration acceleration data signals of a bearing part in a typical fault state under different working conditions; establishing a backbone network of the diagnosis framework, calculating a feature distribution distance between each source domain and a target domain in training, constructing a multi-source domain loss function, and setting a multi-source domain loss dynamic adjustment mechanism according to a Wasserstein distance; and obtaining to-be-diagnosed target domain label-free signal data, preprocessing the data, inputting the preprocessed data into the trained diagnosis model, outputting a diagnosis result, storing the diagnosis result, and visually displaying the features. The influence of negative migration is effectively reduced by reducing the attention of the model on the classification effect of the multiple source domains, support is provided for further fault processing, and it is ensured that fault handling of the system is more accurate and safer.

This invention relates to the field of battery testing technology and discloses an ice-water impact testing system, comprising: a temperature control unit for regulating the temperature of the testing environment, the temperature control unit including a liquid nitrogen cooling device and an electric heating device, and using a PID algorithm to regulate temperature fluctuations within ±0.5℃; an impact unit for simulating water flow impact and mechanical vibration of battery samples during testing, the impact unit including a high-pressure water pump and a mechanical vibration device, capable of adjusting water flow pressure and vibration acceleration; and a data acquisition and analysis module for real-time acquisition of temperature, humidity, pressure, and battery voltage data during the testing process, and evaluating battery performance through intelligent analysis algorithms. By combining the liquid nitrogen cooling device and the electric heating device, and employing a PID control algorithm, precise adjustment of the temperature control unit is achieved, enabling rapid temperature switching within the range of -40℃ to 80℃, while maintaining temperature fluctuations within ±0.5℃.

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 provides a method and a system for testing noise of a hybrid electric drive assembly of a hybrid vehicle with a small-capacity battery. The method comprises the following steps: arranging a microphone at a noise testing point; arranging a vibration acceleration sensor at the vibration measuring point; under the pure electric working condition, the vehicle speed starts from 0 km / h, the motor torque is controlled through an accelerator pedal, slow acceleration is conducted at the motor torque of 30 Nm, 50 Nm and 70 Nm respectively till the electric quantity of a battery is consumed to the lower limit, and the hybrid working condition is started; synchronously recording vibration noise of each measuring point and motor rotating speed and torque data; working condition testing is carried out, and acceleration signals of all the vibration measuring points and sound pressure signals of all the noise measuring points under different working conditions are obtained; and analyzing and processing the test data, and confirming the noise source distribution and the noise source resonance frequency. According to the method, the electric drive howling can be effectively tested, analyzed and evaluated through specially set vibration noise test working conditions and data processing parameters of the electric drive assembly in the whole vehicle state of the hybrid power vehicle with the small-capacity battery.

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 monitoring and early warning method for wind-induced abnormal vibration of a cable-stayed bridge cable, and the method comprises the steps: calculating an energy concentration coefficient R according to a vibration intensity peak value P1st corresponding to a first large peak value and a vibration intensity peak value P2nd corresponding to a second large peak value; adding the energy concentration coefficient R into the array {R} to obtain an array {R} of bmin, and calculating the acceleration root mean square of the vibration acceleration data of the inhaul cable bmin according to the vibration acceleration data of the inhaul cable bmin; if k continuous energy concentration coefficients R in the array {R} of bmin exceed the early warning lower limit value and the acceleration root mean square exceeds the acceleration RMS upper limit threshold value, an early warning signal is sent out. The method has universality and effectiveness in identifying the second large vibration intensity peak value of different stay cable spectrograms, a bridge management and maintenance unit can conveniently know the abnormal vibration condition of the stay cable in time and maintain the stay cable, the service life of the stay cable is prolonged, and a guarantee is provided for safe operation of a bridge.

The invention belongs to the field of accelerometers, and particularly discloses an accelerometer and a vibration rectification error compensation method thereof, and the design of the accelerometer needs to meet the condition that the quality factor of the accelerometer is smaller than or equal to the ratio of the accelerometer range to the maximum vibration acceleration in an application environment; the square of the resonance angular frequency of the accelerometer is greater than or equal to the ratio of the accelerometer range to the check mass displacement upper limit; the ratio of the resonance angular frequency to the quality factor is smaller than or equal to a preset value related to a preset mechanical thermal noise upper limit; the quality factor is the gain at the resonant angular frequency of the accelerometer, and the gain is the ratio of the accelerometer response corresponding to the acceleration input at different frequency points to the accelerometer response corresponding to the constant acceleration input with the same amplitude. According to the invention, the sensitivity of the mechanical structure of the accelerometer can be improved, output signal saturation is avoided, the sensitivity of the accelerometer is ensured, accurate vibration rectification error compensation can be carried out on the accelerometer, and the overall performance of the accelerometer is improved.

The invention provides a method for predicting the sand-bearing state of a ballast track bed based on deep learning. The method comprises the steps that a discrete element simulation model of a sandstorm track bed is established, and rigidity parameters of the sandstorm track bed in different positions and depth areas are obtained through analysis; based on the rigidity parameters, a vehicle-desertification ballast track coupling finite element simulation model is established, and the vibration acceleration of the sleeper in different sand-containing states is obtained through analysis; and inputting the vibration acceleration as training data into the constructed sand-bearing state prediction model to train the sand-bearing state prediction model to obtain a trained sand-bearing state prediction model, and performing sand-bearing state prediction by using the trained sand-bearing state prediction model to obtain a sand-bearing state prediction result. The sand-bearing state prediction model is a deep learning model based on a convolutional neural network and a long short-term memory network. According to the invention, the sand-bearing state of the ballast bed can be predicted with high precision. The method has important reference value for guiding line maintenance work and guaranteeing stable operation of the train.

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.