3270 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 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 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 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 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 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.

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 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 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 high-speed rail platform safety monitoring, and discloses a high-speed rail platform area intrusion detection and early warning alarm system, which comprises an acoustic and vibration sensor network deployed in a platform area and used for collecting environment signals in real time, the edge calculation unit extracts signal features and compares the signal features with a dynamically maintained environmental rhythm feature baseline to generate a detuning event mark, and the detuning mode analysis device identifies an abnormal mode through space-time correlation and triggers an alarm. According to the method, early perception of subtle anomalies is realized by constructing a multi-modal environment rhythm baseline, active perturbation injection and differential response analysis technologies are combined, the recognition capability of a silent target is remarkably improved, meanwhile, the monitoring continuity under an extreme working condition is ensured by using an elastic baseline adaptive mechanism, and the detection accuracy is improved. And a closed-loop security and protection system from passive sensing to active discrimination is formed.

The invention relates to the technical field of elevator fault prediction, in particular to an elevator fault prediction method and system. The method comprises the following steps: collecting an operation vibration signal of an elevator brake through a vibration sensor, performing frequency domain conversion, performing vibration frequency disorder structure analysis, analyzing abnormal vibration frequency intensity, analyzing elevator braking force increment loss, and obtaining time sequence increment data; thirdly, incremental gradient nonlinear induction is carried out on the time sequence incremental data, and an elevator fault prediction model is constructed based on a K-nearest neighbor algorithm; the elevator fault prediction technology is optimized, so that the elevator fault prediction technology is more accurate.

The invention relates to the technical field of slope disaster monitoring, particularly provides a slope landslide geological disaster monitoring method and system based on image intelligent identification, and solves the problems that image identification and hyperspectral analysis are depended, multi-modal data fusion is lacked, and the monitoring accuracy is high. The method comprises the following steps: acquiring multi-modal data through an unmanned aerial vehicle hyperspectral camera, a ground image sensor, a vibration sensor, a displacement sensor and a meteorological sensor; carrying out denoising, calibration and formatting processing on the acquired multi-modal data; extracting landslide related features from the image data, the hyperspectral data, the vibration data, the displacement data and the meteorological data; fusing the multi-modal data through an intelligent fusion algorithm to generate a landslide risk assessment result; and early warning information is generated according to a landslide risk assessment result, and an emergency response mechanism is triggered, so that the precision and efficiency of image recognition and hyperspectral data analysis are improved, and the real-time performance and response speed of the system are improved.

The invention relates to the technical field of engineering, in particular to a geomembrane defect detection system and method under the earth and stone medium coverage condition, and the geomembrane defect detection system comprises a multi-mode adaptive sensing module, an edge calculation module, a data fusion module, a digital twinning module and a decision and application module; compared with the defects that single resistivity or geological radar detection is adopted in the prior art, interference of medium conductivity and metal impurities is likely to happen, and the deep resolution is insufficient, the scheme adopts multi-mode sensor network cooperative work, and a dynamic density layout and time-phased acquisition strategy is combined; through multi-mode complementation of an electrode array, an ultra-wideband radar, a distributed optical fiber and an MEMS vibration sensor, a dominant sensing mode is automatically switched in a conductive clay area, temperature gradient interference is avoided through night acquisition, the penetration depth is increased to 15 meters through multi-band radar fusion, and metal interference is inhibited; the detection precision and the anti-interference capability in a complex medium environment are remarkably improved, and the problems of high false alarm rate and high omission ratio of a traditional method are effectively solved.

The invention relates to the technical field of industrial manufacturing digital management, in particular to a machine vision production line performance evaluation and optimization management system, which comprises a data acquisition module for triggering a high-speed industrial camera array, a vibration sensor and an RFID reader through a central synchronous controller to synchronously acquire product images, equipment operation and material circulation data; the data processing and fusion module extracts product quality features based on CNN, and fuses multi-modal data through time sequence alignment normalization and an attention mechanism; the dynamic efficiency evaluation module calculates OEE, FPY and a production line balance rate in real time by means of a deep neural network; the optimization strategy generation module is used for reinforcing the learning agent to output optimization instructions such as equipment parameter adjustment; and the control execution module converts the instruction into an industrial protocol format, issues the instruction to the PLC, and verifies the effect to form a closed loop. According to the method, the data relevance and the evaluation real-time performance are improved, the dynamic state of the adaptive production line is optimized, and the efficiency improvement is facilitated.

The invention relates to the technical field of termite monitoring and killing, in particular to a termite monitoring and killing method based on multi-modal sensing and a dynamic threshold value, and adopts the technical scheme that multi-source signal acquisition is adopted, and biological activity signals of a target area are synchronously acquired through a vibration sensor, a sonic sensor, a chemical gas sensor and a thermal imaging sensor; the anti-interference performance is improved, so that the false alarm rate is reduced; environmental parameter compensation is carried out, temperature, humidity and soil conductivity data are collected in real time, a dynamic calibration coefficient is generated to correct sensor original data, and environmental adaptability is improved; the method comprises the following steps: acquiring data of a current area, performing dynamic threshold calculation, inputting the acquired data into an LSTM-Transformer hybrid network, and outputting a self-adaptive early warning threshold of the current area so as to reduce the possibility of leak detection; through multi-stage early warning judgment, the real-time performance is improved, and the early warning delay is reduced; and starting a corresponding disposal process according to the early warning level, performing response execution, and narrowing the spraying range, thereby reducing pollution.

The invention provides an offshore wind turbine generator fault diagnosis method and system based on multi-source sensor data fusion. The method comprises the steps that a vibration signal from at least one component of a wind turbine generator is acquired through a vibration sensor; performing time-frequency conversion on the vibration signal by applying synchronous compression wavelet transform to obtain time-frequency representation of the vibration signal; when the reconstruction error exceeds a preset threshold value, it is judged that an abnormal event exists in the vibration signal; obtaining the position of a part corresponding to the abnormal event; starting an image sensor and an acoustic sensor according to the position of the component, and acquiring an image signal and a sound signal of the component according to the image sensor and the acoustic sensor; according to the DS evidence theory, the vibration signal, the image signal and the sound signal, obtaining the confidence of the fault type; the fault type of the component is judged according to the maximum confidence allocation principle, high-resolution time-frequency analysis can be achieved through synchronous compression wavelet transform (SST), and the fault feature identification degree is improved in combination with the self-encoding neural network and the D-S evidence theory.

The invention discloses a fresh concrete vibration apparent quality identification method based on a convolutional neural network, and relates to the technical field of concrete apparent quality identification, and the method comprises the steps: obtaining vibration track data, vibration frequency parameters and a concrete surface texture image of vibration equipment in an operation process, and generating an original data set; performing model training optimization based on the initial convolutional neural network model to obtain a depth feature extraction network adaptive to the vibration process; analyzing time sequence detection data in the vibration process, extracting vibration energy density distribution characteristics, aggregate distribution uniformity indexes and slurry wrapping degree parameters, and constructing a vibration quality correlation characteristic matrix; in the depth feature extraction network, the vibration quality correlation feature matrix and vibration sensing data collected in real time are fused, and a quality evaluation system is established; according to the quality evaluation system, the vibration operation process is detected, and a visual evaluation result is obtained; the fresh concrete vibration apparent quality identification method provided by the invention is more intelligent and accurate.

The invention relates to the technical field of voiceprint fault diagnosis, in particular to a multi-mode voiceprint fault diagnosis method for a converter transformer. The system comprises a multi-modal signal acquisition module, a multi-modal signal preprocessing module, a feature weighted fusion module, a depth feature extraction module and a fault identification and classification output module. A mechanical vibration signal and a voiceprint feature signal are synchronously collected through a vibration sensor and a voiceprint sensor, and a multi-modal feature vector is constructed after preprocessing; dynamic weighted fusion of vibration and voiceprint features is realized by adopting a channel attention mechanism, and a channel weight is generated through global average pooling and nonlinear mapping; and finally, voiceprint embedding vectors with time sequence distribution characteristics are extracted through attention statistical pooling, and accurate recognition of fault types is realized by adopting a Softmax classifier. According to the method, through collaborative optimization of physical signal coupling, algorithm feature fusion and deep representation learning, the detection capability of the early weak fault of the converter transformer is effectively improved.

A method for monitoring a rotating machine in order to detect a fault in a bearing, the method including acquiring, from the rotating machine, a vibration signal measured by a vibration sensor; determining a first-order spectrogram by first-order cyclostationary analysis of the vibration signal using a delta transform and spectral standardisation; determining a second-order spectrogram by second-order cyclostationary analysis of the vibration signal using averaged cyclic coherence, a delta transform and spectral standardisation; and detecting a vibration signature of the fault in the bearing on the basis of the first-order spectrogram and the second-order spectrogram.

The invention discloses a GIS online monitoring method based on multi-state quantity integration, and relates to the field of GIS online monitoring, and the method comprises the steps: deploying an array composed of an ultrahigh frequency sensor, an ultrasonic sensor, a gas density sensor, an optical fiber temperature sensor and a vibration sensor based on a GIS equipment cavity structure zone; the method comprises the following steps: synchronously acquiring an ultrahigh-frequency electromagnetic wave signal, an ultrasonic signal, a gas density signal, a local temperature signal and a shell vibration signal in an operation process of GIS equipment so as to obtain a multi-dimensional original state quantity sequence; according to the method, multiple types of sensors are accurately distributed and controlled for the GIS cavity structure partition, and through time alignment, adaptive weight fusion and time-space convolution feature extraction, hidden dangers such as a partial discharge source can be accurately positioned, and a state quantity can be predicted by means of a correlation evolution equation. Meanwhile, mechanisms such as sensor fault automatic elimination and complementation, prediction accuracy verification and the like guarantee data reliability, equipment states can be mastered in real time, and faults can be warned in advance.

The invention relates to the technical field of combustion equipment state monitoring, in particular to a combustion state monitoring method and system based on vibration and noise fusion analysis, and the method comprises the steps: collecting a combustion signal in real time through a vibration sensor and a broadband noise sensor, extracting a characteristic frequency band through noise reduction and filtering, and obtaining a combustion state; fPGA hardware-level clock synchronization is adopted to realize time alignment of bimodal signals, a vibration-noise joint feature matrix is constructed and multidimensional coupling is carried out, a combustion state feature vector is generated through a mixed deep learning model, a combustion state is determined based on a bimodal confidence weighting decision, and a linkage control instruction is generated during interruption. The system comprises a signal acquisition synchronization unit, a feature fusion and modeling unit, a state decision and instruction generation unit and a safety linkage execution unit, executes a method and activates a standby safety system, solves the problems of asynchronous bimodal data and insufficient monitoring precision, and improves the combustion state monitoring accuracy and safety response efficiency.

The invention relates to the field of fatigue driving behavior analysis based on image recognition, in particular to a fatigue driving behavior feature extraction and analysis method based on image recognition, which comprises the following steps of: acquiring an initial state set of a driver in real time through an IMU (Inertial Measurement Unit), an RGB (Red, Green and Blue) camera and an MEMS (Micro Electro Mechanical System) vibration sensor, converting the initial state set into four images such as a head attitude angular velocity oscillogram, extracting image features and inputting the image features into corresponding preset models to obtain fused feature data, dynamically adjusting weights through scene context features, calculating cognitive load indexes and dividing processing modes; according to the method, multi-modal data fusion and dynamic weight adjustment are realized, the cognitive load of the driver can be accurately evaluated and graded intervention can be performed, and the driving safety is improved.

The invention discloses a blast furnace cooling wall damage probability prediction method, which relates to the field of industrial equipment maintenance, and comprises the following steps: arranging a temperature sensor array, a vibration sensor and an acoustic emission sensor, and collecting temperature gradient data, mechanical vibration frequency and high-frequency stress wave signals in real time; calculating the slag crust thickness of the hot surface of the cooling wall based on an unsteady state heat transfer equation, introducing an in-furnace gas flow parameter correction model, and generating a dynamic slag crust thickness distribution cloud picture; establishing temperature and vibration reference threshold values under different working conditions according to historical operation data of the cooling wall, and detecting temperature over-limit accumulated duration and a vibration energy spectrum abnormal frequency band in real time; taking the temperature gradient range, the slag skin thickness variation coefficient and the vibration dominant frequency offset as input characteristics, training a multi-dimensional coupled damage probability prediction model through a random forest algorithm, and outputting a recent dynamic damage probability value of each section of cooling wall; and dividing risk grades according to the damage probability value, dynamically adjusting a threshold interval in combination with the real-time smelting strength of the blast furnace, and generating a differentiated maintenance suggestion set.

The invention discloses a structural health monitoring device for civil engineering. According to the technical scheme, the structural health monitoring device is characterized by comprising a multi-parameter sensing module used for collecting strain, acceleration, displacement, temperature, humidity, vibration frequency and structural surface crack image parameters of a civil engineering structure; the multi-parameter sensing module comprises a strain sensor group, an acceleration sensor group, a displacement sensor group, a temperature and humidity sensor, a vibration sensor and an image acquisition unit; the strain sensor group is composed of a plurality of fiber Bragg grating strain sensors which are installed at key stress parts of the structure in a distributed arrangement mode. According to the structural health monitoring device for civil engineering provided by the invention, by virtue of the unique multi-parameter sensing module, the advanced data processing module and the flexible and reliable data transmission module, various excellent effects are shown in the field of civil engineering structural health monitoring.

The invention relates to a numerical control machining dynamic error compensation method and system based on a space-time attention mechanism, and the method comprises the steps: synchronously collecting multi-source signals through a 9-axis MEMS vibration sensor, an infrared thermal imager and an acoustic emission sensor which are disposed at key parts of a machine tool, and constructing a space-time feature tensor fusing vibration energy, temperature gradient and acoustic emission features; inputting the tensor into a space-time attention network, extracting space correlation characteristics by using a graph convolution network, capturing time sequence correlation in combination with causal convolution, and generating a coupling weight matrix of thermal deformation, vibration and tool deflection; and the compensation amount is dynamically calculated based on a multi-physics field coupling formula, three-axis linkage compensation instruction generation is completed within 5ms through an FPGA hardware accelerator, and closed-loop writing is performed in a CNC system. The system comprises a multi-source sensing module, an edge computing unit and a cloud platform. According to the method, submicron real-time error compensation is achieved, the vibration suppression rate is 65%, and the machining efficiency is effectively improved.

The invention relates to an auxiliary inspection method, system and device for a box-type substation, in particular to the field of box-type substation monitoring, and the method achieves the efficient and accurate inspection of box-type substation equipment through the combination of a millimeter wave radar, an RGB-D camera, a thermal infrared imager, a vibration sensor, load current monitoring equipment and other sensors. By generating a three-dimensional topological graph and superposing sensor data, comprehensive state monitoring can be performed on equipment, electromagnetic intensity field and temperature field distribution of the equipment can be accurately predicted based on physical field distribution and a neural network model, in addition, by dynamically correcting a prediction result and calculating a fault risk index, the risk of equipment fault can be identified in real time, and the reliability of the equipment fault is improved. And the enhanced inspection instruction is triggered when the threshold value is exceeded, so that the stability and the safety of the equipment in the operation process are ensured, the intelligent degree and the fault early warning capability of inspection are effectively improved, and the maintenance efficiency of the transformer substation is optimized.

The invention discloses a remote real-time monitoring and fault early warning method for a power supply operation state, and relates to the technical field of power electronics and energy management, and the method comprises the steps: collecting the temperature data of a key part of a power supply through an optical fiber Bragg grating sensor array, and analyzing the wavelength offset; electrical parameters are collected through a differential MEMS current / voltage sensor, and a permalloy / ferrite composite shielding layer is arranged in the sensor; collecting a mechanical vibration signal through a piezoelectric ceramic vibration sensor; for device data from different manufacturers, Modbus and CAN heterogeneous protocols are matched and analyzed through a regular expression, and the Modbus and CAN heterogeneous protocols are converted into a unified RDF triple format; inputting temperature, current and vibration data into a space-time alignment module, and constructing a semantic mapping relation by adopting an ontology; calculating a sensor confidence coefficient weight, and performing weighted fusion to generate an equipment health index; operating a lightweight LSTM model at an edge node, and detecting current harmonic and vibration resonance frequency band abnormity in real time; cOMSOL multi-physical field simulation is combined at the cloud end, and the evaporation rate of the capacitor electrolyte is predicted.