6026 results about "Vibration sensor" patented technology

The invention relates to the technical field of equipment fault diagnosis, and discloses an equipment fault diagnosis and prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-modal data in real time through a plurality of sensors installed on equipment; s2, preprocessing the collected data; s3, constructing a hybrid deep learning model; s4, dynamic weighted fusion is performed on the features of different modal data by using an attention mechanism, and comprehensive feature representation is generated; s5, using the marked fault data and normal data to supervise and train the model; s6, inputting equipment operation data acquired in real time into the trained model, and judging the state of the equipment; and S7, generating a potential fault early warning signal based on a prediction result of the model. A piezoelectric vibration sensor and a thermal infrared imager are arranged on a motor bearing through vibration, temperature and sound sensors, vibration waveforms, thermal imaging slices and time-frequency diagrams are synchronously captured, and composite state characteristics such as mechanical wear and temperature anomaly of equipment are comprehensively reflected.

The invention provides a coal mill fault diagnosis and prediction method and system based on big data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: collecting a vibration signal of a coal mill bearing through a vibration sensor, carrying out the multi-scale analysis through wavelet decomposition, extracting features, constructing a deep convolutional neural network model, and carrying out the fault diagnosis and prediction of a coal mill bearing. A maximum mean value difference adaptive layer is introduced to carry out feature alignment on data of a source domain and a target domain, identification of fault types and degrees is realized, a health state evaluation index including a fault severity coefficient and a development trend coefficient is established, and the remaining service life is calculated and early warning is carried out. According to the invention, the accuracy and generalization ability of fault diagnosis are improved, accurate prediction and timely early warning of the bearing fault of the coal mill are realized, and the service life of equipment is prolonged.

The invention relates to the technical field of cable operation state detection, and particularly discloses a multi-source data driven cable operation state comprehensive evaluation method, which comprises the following steps of S1, adopting a layered distributed sensing network architecture, and deploying three types of core sensors at key nodes of a cable, through space-time calibration of the multi-source heterogeneous sensor, data consistency is improved, fusion deviation is eliminated, the problem of data islands of a traditional system is solved, and a precise evaluation foundation is laid; noise suppression and dynamic correlation modeling are adopted, environmental interference is stripped, a vibration and displacement coupling relation is quantified, limitation of a single parameter is broken through, heterogeneous fault features are captured, and evaluation comprehensiveness and sensitivity are improved; a self-adaptive threshold mechanism is constructed based on environment weight and historical data, the bottleneck of a fixed threshold is broken through, an evaluation standard is corrected along with equipment aging and environment change, misjudgment is avoided, and diagnosis robustness in different scenes is enhanced.

The invention relates to an intelligent industrial equipment state monitoring device and method, and the method comprises the steps: installing a vibration sensor, an acoustic sensor and an image sensor at key parts of industrial equipment, and comprehensively collecting the operation data of the equipment. In the data preprocessing stage, vibration and acoustic signals are filtered and denoised, and image data are denoised and enhanced. Extracting multi-dimensional characteristic parameters from three aspects of vibration, acoustics and images; a random forest algorithm is adopted for model training, and the method comprises the steps of data preparation, segmentation, decision tree construction, voting mechanism, majority voting, model verification and optimization, testing and the like. In the model identification stage, the real-time features are input into the trained model, and the equipment state is determined through decision tree prediction and voting summarization. And when an abnormal state is detected, setting a multi-level alarm response according to the probability, and generating a maintenance suggestion. According to the method, the whole-process intelligent monitoring of the industrial equipment is realized, and the fault identification accuracy and the maintenance efficiency are improved.

The invention relates to the technical field of bridge structure safety monitoring and maintenance, and discloses a road bridge real-time monitoring and maintenance system based on intelligent perception, which comprises a data acquisition module used for acquiring multi-dimensional data of a bridge operation state through a deployed multi-source sensor, the multi-source sensor comprises a vibration sensor, a strain sensor, a temperature and humidity sensor and image acquisition equipment; the data preprocessing module is used for carrying out cleaning, synchronization and feature extraction on the collected multi-dimensional data to obtain dynamic features, environment features and visual features; and a data fusion module. According to the method, the comprehensiveness and accuracy of bridge monitoring are improved through multi-source data fusion, the reliability of an evaluation result is ensured in combination with physical constraint optimization, health trend prediction and early warning functions are realized based on a time sequence model, scientific decision support is provided for bridge maintenance, and the monitoring and management efficiency is improved.

The invention provides a stratum disturbance analysis method and system under a shield construction coupling effect. Cutting vibration spectrum data are collected in real time through a cutter vibration sensor, and a feature fingerprint database containing a vibration energy distribution mode and a critical grouting interval is constructed in combination with soil parameters. And aligning the vibration spectrum with the soil bin pressure in a space-time manner, and generating a disturbance field distribution diagram for displaying an energy gradient distribution curve and a stress diffusion path topology. And matching the energy distribution curve and correcting formation interface propagation parameters through the feature matching network optimized by transfer learning, and outputting a formation type identification result and disturbance dynamic parameters. And based on the mapping relation between the parameters and the critical grouting interval, the ground surface displacement data are linked to dynamically regulate and control the grouting pressure, graded early warning and parameter regulation instructions are generated, and a stratum disturbance monitoring-regulation and control closed loop is formed. According to the technical scheme, dynamic optimization of the grouting pressure is achieved, and the stratum deformation risk caused by shield construction is remarkably reduced.

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 intelligent manufacturing and numerical control machining control, and discloses a machine tool dynamic characteristic sensing and intelligent machining control method and system based on a knowledge graph and a large model, and the method comprises the following steps: extracting frequency domain parameters through a vibration sensor, obtaining vibration displacement in combination with laser displacement, and comparing the vibration displacement with modal data to construct a graph; processing parameters and displacement are synchronously sampled, time sequence characteristics are extracted to generate tensors, dynamic characteristics are predicted and corrected, and frequency response adjustment rotating speed is matched to generate optimized track control parameters which are converted into G code instructions. According to the method, a dynamic characteristic map is constructed by fusing vibration signals and displacement, a sliding window synchronizes processing parameters and vibration data, LSTM extracts joint characteristics, GRU predicts rigidity and damping ratio, an attention mechanism dynamically corrects weight, characteristic coupling analysis and prediction precision is improved, nonlinear modeling captures dominant frequency offset and harmonic distribution, response speed is enhanced, and the method has the advantages of being high in precision and high in precision. Cutting vibration is inhibited, and the process stability is guaranteed.

The invention discloses an expressway lane control method based on real-time traffic flow. The expressway lane control method comprises the following steps of performing fusion sensing through a distributed optical fiber vibration sensor array and a millimeter wave radar; constructing a space-time diagram convolutional network model, and dynamically mapping the road topological structure into a lane connection diagram with variable weight; a layered reinforcement learning architecture is adopted, and an emergency lane dynamic management mode is triggered; and deploying a digital twin sand table to perform multi-resolution simulation verification, and when the actual traffic flow deviates from a predicted value by more than 15%, starting a causal inference engine to backtrack a decision link and update an agent strategy library. According to the method, global dynamic lane optimization is realized through a multi-source perception fusion and hierarchical reinforcement learning architecture, simulation data shows that the morning peak traffic volume is improved by 29.3%, and the accident scene traffic efficiency is improved by 28.6%; a digital twinning and causal inference engine is adopted, the strategy updating response time is shortened to 30 seconds, and the congestion propagation distance is reduced by 35%.

The invention relates to the technical field of machine tool monitoring, in particular to a numerical control boring and milling tool cutting force data intelligent monitoring method which comprises the following steps: positioning a high-speed camera in a cutting area of a numerical control boring and milling machine, continuously capturing instantaneous images of particle splashing and sparks, and collecting and analyzing particle and spark distribution of each frame in real time; a cutting site image data stream is obtained. According to the method, images generated by particle splashing and sparks in the cutting area of the boring and milling machine are captured in real time by using the high-speed camera, the prediction accuracy of tool wear and potential faults is improved by analyzing image data streams, data processing is optimized by adopting a deep learning network, and the accuracy of tool wear and potential fault prediction is improved. Particle and spark features extracted from the image provide quantitative indexes for the tool wear state, cutting force and vibration sensor data are integrated, the accuracy of abnormal point analysis is enhanced, machine tool maintenance is more predictable, and cutting parameters are adjusted in real time, so that production continuity and high efficiency are ensured, and the fault shutdown cost is reduced.

The invention relates to the technical field of concrete pouring, in particular to a concrete pouring homogeneity detection system and method based on sensing data, and aims at achieving efficient and accurate concrete pouring homogeneity evaluation and defect detection. The system comprises a multi-sensor sensing module, a multi-sensor data conversion and transmission module and a concrete homogeneity evaluation module, the multi-sensor sensing module comprises a temperature sensor, an ultrasonic sensor and a vibration sensor; the multi-sensing data conversion and transmission module is used for acquiring multi-sensing data and performing data type conversion and transmission on the multi-sensing data; and the concrete homogeneity evaluation module is used for constructing a concrete pouring homogeneity evaluation model to detect and evaluate the homogeneity and defects in the concrete pouring process. According to the invention, detection and evaluation of the concrete pouring homogeneity are realized through combination of multi-sensing data, and the accuracy of detection of the concrete pouring homogeneity is improved.

The invention provides a water pump residual life prediction method based on a large model, and the method comprises the following steps: S1, collecting the multi-source heterogeneous data of the operation of a water pump in real time through a vibration sensor, a temperature sensor, a pressure sensor and a monitoring unit, the temperature sensor monitors temperature gradient changes of the bearing and the sealing cavity, the pressure sensor records inlet and outlet pressure fluctuation characteristics, and the monitoring unit extracts three-phase current harmonic components of the motor; s2, carrying out lightweight preprocessing on the multi-source heterogeneous original sensing data at an edge computing node, wherein the lightweight preprocessing comprises vibration signal noise reduction processing based on wavelet transform, temperature and pressure data calibration normalization of load segments according to working conditions, and transient abnormal data flow filtering through a sliding time window; and S3, inputting the preprocessed data stream into a cloud large model platform, and analyzing the long-period dependency relationship of the vibration signals through a Transform encoder in a time sequence feature extraction module.

The invention discloses a primary helium fan health state evaluation method based on multi-sensor data fusion. According to the method, a vibration sensor, a temperature sensor, a current sensor and the like are used for comprehensively monitoring the primary helium fan, a deep learning model is used for carrying out feature extraction and fusion on multi-source heterogeneous data, a fault diagnosis knowledge base based on a knowledge graph is constructed, and real-time and accurate evaluation of the health state of the primary helium fan and intelligent diagnosis of a potential fault mode are achieved. Meanwhile, a reinforcement learning algorithm is introduced in the method, the maintenance strategy of the main helium fan is automatically optimized, the equipment reliability is improved to the maximum extent, and the whole life cycle cost is reduced. The method provided by the invention has good adaptability and expandability, can be popularized and applied to nuclear power and other industrial fields, and provides important technical support for improving the intelligent level and operation safety of key equipment.

The invention discloses a grinding machine internal part temperature anomaly detection method based on vibration signal analysis, which comprises the following steps that grinding data are collected through sensor deployment, and the sensors comprise a temperature sensor, a vibration sensor, an infrared thermal imaging sensor, a magnetic resistance current sensor and an inductance type particle sensor; carrying out preprocessing and feature extraction on the collected data; model construction and training are carried out based on data of preprocessing and feature extraction; according to the method, the abnormal condition of the temperature of the part is predicted by detecting the vibration signal of the internal part, the content of metal particles in lubricating oil is detected through the oil analysis sensor, and the abrasion degree of the bearing is judged in combination with the vibration signal. And motor current harmonic characteristics are monitored, and overload or rotor imbalance problems are identified.

The invention provides an aero-engine state monitoring method and device based on sound and vibration fusion and a computer readable storage medium, and relates to the technical field of aero-engine state monitoring. The method comprises the following steps: acquiring signals respectively acquired by a vibration sensor and a sound sensor; respectively intercepting a vibration signal low-frequency component and a sound signal high-frequency component based on the frequency response characteristic difference of the sensor; performing normalization processing on the intercepted signal to eliminate amplitude difference; constructing a transition region through an interpolation method, and splicing the continuous mixed frequency spectrum to obtain a sound-vibration fusion spectrum; and establishing a health benchmark based on the normal state fusion spectrum, and realizing abnormity monitoring and alarm through characteristic difference analysis. Advantages and characteristics of a low-frequency band of the vibration sensor and a high-frequency band of the sound sensor are fully utilized, frequency response limitation of a traditional single sensor is broken through, deep fusion of sound and vibration signals is achieved, the effective detection frequency range is remarkably widened, and accuracy and robustness of recognition of early faults such as aero-engine blade breakage are improved.

The invention discloses a multi-mode perception and reinforcement learning engineering machinery intelligent regulation and control method and system, and belongs to the technical field of intelligent control and industrial automation. According to the system, stratum data within the range of 20-50 meters in front of a shield tunneling machine are collected in real time through multi-mode sensing equipment such as a distributed optical fiber sensor, a cutterhead vibration sensor and an electromagnetic wave radar, and original data are processed by adopting a wavelet-Fourier combined noise reduction algorithm; and inputting the processed multi-modal data into a reinforcement learning intelligent decision-making model based on a CNN-LSTM hybrid network architecture. The model is trained through a dynamic reward function, and weight coefficient combinations can be automatically switched according to different construction scenes. According to the system, parameters such as the rotating speed, the thrust and the grouting amount of a shield cutter head are regulated and controlled in real time through the self-adaptive control module, and the slurry utilization rate is increased through the gradient pulse grouting technology. The precision, efficiency and safety of shield construction are remarkably improved, and meanwhile energy consumption and construction cost are reduced.

The invention provides a lamination bottling control method and system integrating an image and a photoelectric sensor, and the method comprises the steps: obtaining signals collected by a photoelectric sensor array, a pressure sensor and a vibration sensor, carrying out the filtering and time alignment processing, and obtaining a multi-source synchronous data matrix; performing preprocessing and feature extraction on the image acquired by the visual camera to obtain visual reference data; based on the multi-source synchronous data matrix and the visual reference data, the vibration, track and speed states of the equipment are evaluated, and a mechanism state vector is obtained; analyzing the separation, posture and stacking state of the tablets to obtain a tablet state vector; generating and optimizing speed control, time sequence control and synchronous control instructions according to the mechanism state vector and the tablet state vector; the control effect is evaluated in real time, and parameter optimization is carried out. And through multi-sensor data fusion and multi-level control strategy optimization, the control precision and stability of the bottling process are improved.

The invention provides a pump equipment state monitoring and fault diagnosis method based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the steps: obtaining a vibration signal of a target type of pump equipment based on a preset vibration sensor, and marking the vibration signal; extracting features of the vibration signal based on a preset dual-channel feature extraction model; iteratively training a preset basic fault diagnosis model based on the characteristics of the vibration signal until a preset training completion condition is reached; binding a preset number of fault diagnosis models to construct a pump equipment state reasoning model; acquiring an operation vibration signal of the pump equipment of the target category, inputting the operation vibration signal into the pump equipment state reasoning model, and outputting a fault category; through time-frequency dual-channel fusion and multi-scale perception, the fault identification precision is improved; the rationality and interpretability of the result are enhanced by using physical prior constraints; and through model integration optimization, the classification stability and reliability in a complex scene are improved.

An industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network. The system may include an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection and classification algorithms thereto. The system may perform a method of predicting a service event from vibration data captured data from at least one vibration sensor disposed to capture vibration of a portion of an industrial machine. A signal in a predictive maintenance circuit for executing a maintenance action on the portion of the industrial machine can be generated based on a severity unit calculated for the captured vibration.

The invention relates to the field of grinding and polishing, in particular to an automobile spoiler mechanical arm grinding system and method.The automobile spoiler mechanical arm grinding system comprises a path planning module, a state monitoring module, a parameter compensation module and a waste collecting module which are in communication connection, and the path planning module is used for scanning a spoiler in real time through a visual sensor and dynamically adjusting a grinding path; the visual sensor is a 3D structured light sensor or a stereoscopic visual camera; the state monitoring module is used for monitoring the vibration state of the grinding head in real time through the vibration sensor and obtaining vibration abnormal data. The parameter compensation module is used for automatically adjusting grinding parameters according to the abnormal vibration data and adjusting a grinding head for grinding; the waste collecting module is used for collecting and treating dust and waste generated in the polishing process in a centralized mode. The polishing path can be precisely planned, the polishing quality is improved, the production efficiency is improved, the cost is reduced, and dust and waste materials are effectively treated.

According to the drilling, sealing and injecting integrated anchor rod drilling intelligent monitoring system and method, a rod body is sequentially provided with a head-section wave-shaped threaded section, a middle-section large threaded section and a tail-section wave-shaped threaded section from the head end to the tail end, and a grouting channel is formed in the axis of the rod body; the drill bit and the round nut are respectively mounted at the head and tail ends of the rod body; the anti-shearing tray, the constant-torque pressing sheet and the hexagon nut are arranged on the outer side of the tail-section wave-shaped thread section in a sleeving manner; the built-in mixer is inserted into the grouting channel; the drilling machine is connected with the drilling, sealing and injecting anchor rod through a monitoring sensor arranged outside the hexagon nut in a sleeving mode. A strain type force sensor, a rotating speed sensor, a high-frequency vibration sensor, a well deviation instrument and a load sensor are arranged in the monitoring sensor. The method comprises the steps that a drilling machine rotates forwards to drive a drill bit to conduct drilling operation, deslagging operation is conducted synchronously, and in the drilling process, various sensors are used for collecting various parameters in real time; pre-tightening is conducted through reverse rotation of the drilling machine; a built-in mixer is used for grouting operation; and blocking treatment. According to the system and the method, the operation process can be intelligently monitored.

The invention relates to a multi-sensor fault feature fusion diagnosis method for electrical equipment, which comprises the following steps: synchronously acquiring operation data of the electrical equipment through a vibration sensor, a temperature sensor, a current sensor and an ultrasonic sensor, dynamically adjusting the sampling frequency according to the physical characteristics of each sensor, and the sampling rate of the temperature signal is not lower than 1Hz. Through a multi-source sensor data synchronous acquisition and time sequence alignment technology and a signal alignment method combining a dynamic time warping (DTW) algorithm and Hilbert-Huang transformation, the problem of time asynchronization of heterogeneous sensor data such as vibration and temperature is solved, so that the time alignment precision of multi-source data is improved, the feature extraction accuracy is improved, and the accuracy of feature extraction is improved. Through hierarchical feature extraction and graph convolutional network fusion, a feature incidence matrix based on mutual information is constructed, deep correlation between vibration signal TKEO features and cross-modal features such as current harmonics is mined by using GCN, the feature dimension is reduced, and the fault feature separability index is improved.

The invention provides a reinforced diesel engine friction pair on-line wear detection device and a wear detection method. The reinforced diesel engine friction pair on-line wear detection device comprises a wear calculation terminal, an NI-PXIe8115 acquisition instrument, a sampling detection circulating oil path, a cylinder pressure sensor, a rotating speed sensor, a vibration sensor, an acoustic emission sensor, a six-in-one lubricating oil physical and chemical sensor, an abrasive particle sensor, an abrasive particle visual sensor and a CAN / USB / RS485 communication interface. The method has the beneficial effects that the load of a friction pair of the strengthened diesel engine is large, the process from symptoms to failure is extremely short, and serious fault events are difficult to control, the device can quickly warn an abnormal state after being implemented, gives a corresponding action instruction to a user, and provides a reliable identification means for strengthening research, development and operation safety of the diesel engine.

The invention relates to the technical field of road surface detection, in particular to a road surface skid resistance detection system based on multi-feature fusion, which comprises the following steps: applying broadband sweep frequency excitation by using a frequency modulation vibration sensor, matching with the inherent frequency of road surface texture to generate local resonance, and collecting the temperature, humidity and rainfall of a road surface in real time; performing fast Fourier transform on the collected vibration signals to obtain a resonance response spectrum, extracting key parameters through Gaussian fitting, and fusing frequency domain, material and environment data to form a comprehensive feature set; the frequency domain features are converted into three-dimensional energy distribution of pavement microtextures, the actual contact area ratio is calculated according to the three-dimensional energy distribution, an environment temperature and humidity compensation factor is introduced, and a dynamic friction attenuation coefficient is calculated; and comparing the calculated dynamic friction attenuation coefficient with a third-level safety threshold, and outputting a corresponding anti-skid performance level. Multi-source data fusion enables a detection result to be more fit with an actual driving scene, and misjudgment caused by single data is avoided.

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

The invention discloses a petroleum pipeline abnormity early warning and monitoring method and system based on a multi-source data fusion attention mechanism and a petroleum pipeline abnormity early warning technology, and aims to realize early-stage tiny leakage abnormity detection of a petroleum pipeline through a multi-source data fusion technology and a deep learning technology. According to the technical key points, historical data of pipeline operation states are collected from sound wave, temperature, negative pressure wave and vibration sensors, and a data set is constructed; obtaining a time sequence segment code for algorithm training; constructing a fusion attention module for algorithm training; on the basis of the obtained multi-source data, an attention Transform model is fused, and an adversarial learning method is adopted for training so as to predict the future operation state of the petroleum pipeline; training a petroleum pipeline early warning model by using Gaussian distribution with learnable scale parameters; the trained multi-source data is fused with an attention Transform model to serve as a petroleum pipeline abnormity early warning model, future operation state abnormal behaviors are recognized, and the failure probability is obtained. According to the method, the multi-source data analysis technology and the deep learning technology are fused, and the innovative Gaussian kernel scaling parameters and the failure probability calculation method are combined, so that the accuracy and reliability of early warning of the abnormity of the petroleum pipeline are effectively improved, and the method particularly has important application value in the aspect of micro leakage detection.

A gait analysis is provided by passively sensing footstep-induced floor vibrations during walking using vibration sensors mounted on, within or under a floor. The approach is non-intrusive, scalable, and perceived as more privacy-friendly, making it suitable for continuous gait health monitoring in daily life. The floor vibration-based gait analysis framework estimates various gait parameters, including temporal parameters (step, stride, stance, swing time) and spatial parameters (step length, width, angle), and extracts gait health indicators (cadence / walking speed, left-right symmetry, gait balance, initial contact type).

The invention discloses a multi-scene adaptive visual acquisition system and method, and belongs to the technical field of image recognition, and the system comprises an environment sensing module which is used for collecting carrier vibration spectrum, multi-band illumination intensity and target depth information in real time; the dynamic zoom imaging module drives the focal length adjustment rate of the lens to be dynamically associated with the frequency of the high-frequency component output by the vibration sensor; and the cooperative control module executes vibration compensation, spectrum fusion and target positioning, dynamically calculates based on an illumination intensity ratio and color temperature, and dynamically configures a target positioning threshold according to a target pixel area. According to the invention, the dynamic correlation vibration frequency and the focal length adjustment rate are utilized, and mechanical anti-shake pre-focusing and electronic anti-shake optical flow methods are matched to realize accurate compensation of low-frequency continuous vibration and high-frequency impact vibration, so that the imaging stability is remarkably improved in a dynamic environment, and the phenomena of image blurring and edge blurring are effectively reduced.

The invention relates to the technical field of laser calibration, in particular to a laser optical axis calibration device and method, and the device comprises an optical axis offset detection module, a calibration parameter calculation module, a dynamic adjustment implementation module and a calibration effect evaluation module. According to the invention, the response speed and precision are enhanced by monitoring the output of the laser emitter in real time, evaluating the instant deviation of the optical axis and dynamically calibrating the optical axis, the deviation trend of the optical axis is predicted by using a data analysis technology, the future deviation path is effectively controlled, and non-critical calibration is reduced, so that the long-term performance and stability of equipment are improved; dynamic adjustment measures are adopted, the positions of an angle adjuster and an optical element are finely adjusted, so that high calibration precision is ensured, energy loss and equipment abrasion are reduced, calibration effect evaluation of a vibration sensor provides immediate feedback for operation, transparency of the calibration process is improved, an operator can make a more accurate decision based on detailed data, and the operation efficiency is improved. Therefore, the reliability and the operation efficiency of the calibration task are enhanced.

The invention discloses a multi-parameter monitoring and intelligent early warning system and method for a mine car dry disc braking device. The system comprises a data processing module, and a high-temperature fiber grating sensor, a laser displacement sensor, a piezoelectric vibration sensor, an industrial camera and an early warning execution module which are in signal connection with the data processing module. The data processing module corrects and processes the acquired temperature signal, thickness signal, vibration frequency signal and surface image signal, judges the wear state of the current brake device according to the signal processing result, and judges whether to send an early warning signal to the early warning execution module or not according to the wear state; and the early warning execution module implements corresponding early warning action according to the early warning signal. According to the method, the problem of misjudgment caused by single parameter monitoring is effectively solved, and the fault recognition accuracy is remarkably improved through multi-dimensional data cross validation. Seamless connection from state monitoring to safety protection is achieved through a closed-loop control system. And the multi-sensor cooperative working mode can effectively ensure the detection precision.