1184 results about "Acoustic emission" patented technology

The invention relates to the technical field of construction surrounding rock stability evaluation, discloses a tunnel surrounding rock stability evaluation method and system based on multi-physics field parameter inversion, and aims to solve the problems that an existing method is insufficient in data collaboration, high in parameter inversion multiplicity and poor in surrounding rock stability evaluation accuracy and timeliness. According to the scheme, the method mainly comprises the steps that an acousto-optic electromagnetic vibration drill multi-mode sensing array is arranged, and time-space synchronization is implemented; establishing a mutual interference entropy spectral density model to realize multi-physics field collaborative excitation and acquisition; a unified feature vector is obtained through data correction, feature extraction and weighted fusion; a joint inversion objective function embedded with rock physical constraints is constructed, a three-dimensional physical property parameter field is obtained through inversion, and a dynamic permeability field is calculated in combination with acoustic emission energy; and finally, dynamically updating the model by utilizing ensemble Kalman filtering, and obtaining a final risk probability based on updated parameters and seepage-uncertainty coupling correction. According to the method, the accuracy, the real-time performance and the reliability of the stability evaluation of the surrounding rock of the deep-buried tunnel are improved.

The invention belongs to the technical field of grouting compactness detection, and particularly discloses and provides an intelligent detection system and method for the grouting compactness of a bridge prestressed pipeline, and the system comprises a signal acquisition and analysis module, a defect boundary calibration module, a defect recognition and analysis module and a compactness judgment and processing module. The method specifically comprises the steps that acoustic emission and fluctuation response signals are collected in real time through a sensor array arranged on the outer wall of a pipeline, and three-dimensional slurry density distribution is recognized; constructing an inner wall three-dimensional model in combination with a pipeline design drawing, dynamically setting a slurry density threshold value according to a grouting material formula and an environment temperature, and further calibrating a slurry defect boundary; extracting multi-dimensional features such as spatial positions, effective volumes, geometric regularity and types of the defects on the basis of boundaries, and calculating grouting compactness; and finally, according to whether the compactness reaches the standard or not, a grout supplementing early warning instruction or a detection report is automatically generated. According to the method, the reliability of defect identification and compactness calculation under complex working conditions is remarkably improved.

The invention relates to the field of geological exploration and geotechnical engineering, provides a rock mass dynamic mechanical parameter sensing method and system based on while-drilling parameters in order to dynamically obtain rock mass dynamic mechanical parameter characteristics in situ in real time in the drilling process, and provides a rock mass dynamic mechanical parameter sensing method and system based on while-drilling parameters by using multi-modal signals such as vibration, acoustic emission, torque and bit pressure through uniform characteristic engineering and multi-task learning. The instantaneous elastic modulus, the dynamic Poisson's ratio, the transient uniaxial compressive strength, the cohesive force and the internal friction angle can be synchronously and dynamically predicted in single drilling, and the limitation that only a single static parameter is estimated in a traditional method is broken through; a rock mass dynamic mechanical parameter acquisition model constructed on the basis of a multi-modal adaptive graph attention sequential network can effectively couple the physical proximity and the statistical association relationship of a sensor, and integrates expansion sequential convolution and a time perception Transform module, so that the characterization capability of emergencies (such as jamming / friction resistance) and long-term trends is considered on the premise of low time delay; therefore, the prediction accuracy is improved.

The invention relates to the technical field of concrete curing, in particular to a concrete crack risk prediction and curing system based on thermo-acoustic coupling indexes. According to the scheme, a temperature field and an acoustic emission signal are synchronously acquired through the multi-modal data acquisition module; a thermal diffusion shear index and an acoustic emission energy density are calculated by a multi-modal data fusion module, and are fused into a unified thermo-acoustic coupling factor to quantify a cross-scale risk; the structural constraint compensation module corrects the factor by using pre-stored constraint information to generate a final risk index; the self-adaptive maintenance decision-making module maps a dynamic maintenance strategy according to the indexes; and finally, the maintenance robot executes precise maintenance operation. The technical problems that macroscopic thermal stress and microcosmic damage signals cannot be evaluated in a unified mode, different-source data are difficult to fuse, and high-constraint area risk identification is inaccurate are solved, and continuous prediction and self-adaptive cooperative control of cracks from a potential stage to an initiation stage are achieved. And the accuracy and the intelligent level of early-age crack prevention and control of the concrete are remarkably improved.

The invention belongs to the field of artificial intelligence, particularly relates to a method for predicting the residual life of a key part of a coal mill, and aims to solve the problems of low prediction precision and poor extrapolation caused by working condition disturbance interference and inaccurate degradation characterization in a traditional method. The method comprises the following steps: collecting vibration, temperature, current and acoustic emission multi-source synchronous data; constructing a dynamic working condition decoupling model of fusion of the variational auto-encoder and the attention mechanism, and separating degradation sensitive components; and a high-fidelity degradation index sequence is generated through fusion of time-frequency analysis and a gating circulation unit. According to the method, multi-modal sensing and physical priori knowledge are fused, the monotonicity of a degradation index is remarkably improved to 98% or above, the extrapolation error of an extreme working condition is reduced by 40%, edge-cloud collaborative deployment and digital twinborn visualization are supported, multi-component collaborative early warning is realized, and the early warning amount of non-planned shutdown is improved from 7 days to 21 days or above; and a high-precision, strong-robustness and landing life prediction solution is provided for intelligent operation and maintenance.

The invention relates to the technical field of machine tool performance analysis, and provides a machine tool functional part performance analysis method and system based on big data. Multi-source operation information, including a vibration signal, a temperature signal, a motor current signal, an acoustic emission signal and working condition information, of functional parts of a machine tool is collected, and machine tool operation performance indexes are determined based on the information; and when any index exceeds a normal interval under the current working condition, the system can judge that the functional part of the machine tool is preliminarily abnormal, multi-dimensional abnormal information is constructed according to the working condition information, the vibration information and the temperature information, the fault risk level is determined according to the multi-dimensional abnormal information, and finally early warning information is sent and user feedback is acquired. Therefore, the problem of false alarm caused by non-fault factors such as tool wear in the prior art is effectively solved, and the difficulty that the credibility of an operator to early warning information is reduced is avoided.

The invention relates to the technical field of data processing, and discloses a building construction quality real-time monitoring method and system based on a sensor network. The method comprises the steps of constructing a sensor grid through hydration heat gradient mapping, recognizing welding defects based on acoustic emission spectrum texture and wavelet packet decomposition, obtaining a quality situation by adopting maintenance age weight time-varying fusion, performing multi-scale anomaly detection by applying a residual attention mechanism, and dynamically adjusting a threshold value to generate an intervention strategy in combination with a working condition switching trigger. And intelligent construction quality monitoring is realized. Through the multi-scale feature extraction and cross-modal data fusion technology, the accuracy and real-time performance of construction quality monitoring are remarkably improved.

The invention relates to the technical field of rock mechanical tests and safety monitoring, and discloses a rock fracture prediction method based on acoustic emission and surface strain cooperative monitoring, and the method comprises the steps: building a monitoring system comprising acoustic emission and an industrial camera array, and building a unified timestamp of each subsystem through a GPS time service module; through load triggering logic, a synchronous trigger is utilized to synchronously acquire an acoustic emission signal and a sample surface image sequence, and evolution characteristics of three-dimensional coordinates of an acoustic emission source and surface full-field strain data are solved. And on the basis, calculating a main strain field variation coefficient, and performing cross-correlation analysis on the resampled energy rate and strain rate. And finally, according to multi-parameter coupling criteria of acoustic emission time sequence parameters, positioning events and surface strain, identifying rock fracture precursor types, and predicting fracture moments and areas by combining a Voight model inverse velocity method and a seismic source projection technology.

The invention belongs to the technical field of hydroelectric generating set fault diagnosis, and particularly discloses a hydroelectric generating set cavitation fault diagnosis method based on multi-channel acoustic emission signal fusion. Through the technical means of acquiring the acoustic emission signals at multiple positions, a more comprehensive cavitation phenomenon data basis is provided; the representation of time-frequency characteristics is optimized through the Mel time-frequency diagram, and the problem that the dynamic characteristics of acoustic emission signals cannot be effectively captured through a traditional method is solved; multi-channel feature extraction is performed on the Mel time-frequency graph through a preset deep convolutional neural network model, so that automatic learning of deep cavitation working conditions is realized; and multi-channel fusion and classification identification are carried out on a feature extraction result through the model, so that effective integration and fault classification of features are realized. Compared with the prior art, a more accurate and comprehensive cavitation fault diagnosis effect is realized, and the diagnosis precision is improved.

The invention discloses an MGPCC fracture damage monitoring method based on AE and DIC, and relates to the technical field of building material fracture damage monitoring, the method comprises the following steps: preparing an MGPCC fracture test piece, arranging an acoustic emission probe, installing the test piece on a 3-P-B fracture test device, carrying out displacement control loading, collecting AE signals and shooting images in real time during loading, and determining the fracture damage of the MGPCC. Then analyzing characteristic parameters, images and the like to determine FPZ related conditions, and finally establishing a fracture damage evolution model in combination with a monitoring result. Spatial distribution and characteristic parameters of a damage source are monitored in real time through the AE technology, a strain cloud picture and a displacement field are accurately obtained in combination with the DIC technology, microcrack propagation and FPZ formation and evolution laws in the MGPCC fracture process are revealed, multi-dimensional quantitative characterization of the fracture damage process is achieved, and the method is suitable for mass production. A more comprehensive and accurate technical means is provided for building material fracture damage monitoring, and the blank of MGPCC multi-scale crack resistance and damage evolution research is filled.

The invention provides a sensing system fusing optical fiber vibration and acoustic emission, a monitoring method and a device, and relates to the technical field of optical fiber sensing. The device comprises a light source module, a sensing optical fiber, a detection module and a controller, the light source module is used for respectively emitting modulation pulse light for vibration monitoring and continuous wave laser for acoustic emission monitoring; the sensing optical fiber is arranged along a monitored structure or area, and at least one optical fiber acoustic emission sensing head is connected to the optical fiber in series. The detection module collects Rayleigh scattering light and beat frequency signals at all positions in the sensing optical fiber through a circulator and a coherent light receiver, and optical signals are converted into electric signals; wherein the electric signals comprise vibration electric signals and acoustic emission electric signals; the signal processing unit carries out phase demodulation and envelope analysis processing on the collected vibration electric signals and acoustic emission electric signals so as to obtain vibration waveforms and acoustic emission event space-time information along all points of the optical fiber. According to the invention, a monitoring task fusing vibration and acoustic emission can be accurately completed.

The invention discloses a live detection method for insulation defects of high-voltage power equipment. The method comprises the following steps: firstly, synchronously arranging ultrahigh frequency sensors and acoustic emission sensors on a plurality of monitoring points on the surface of a gas insulated switchgear shell to form an array, and synchronously acquiring signals for filtering pretreatment; then, pulse events are extracted from the two types of signals respectively, and associated pulses in a time window are matched into matched pulse pairs representing the same discharge source; then, carrying out time-frequency transformation on the ultrahigh frequency and acoustic emission pulse waveform in each matched pulse pair, carrying out joint noise reduction by calculating a coherence coefficient between time-frequency distribution matrixes, and extracting a joint feature vector containing an energy ratio, a time parameter ratio and a frequency difference from the denoised time-frequency matrix; according to the invention, multi-source signals are fused, and high-sensitivity detection, high-precision positioning and high-accuracy identification of insulation defects are realized.

The invention relates to the technical field of data processing and mode recognition, in particular to a bearing fault diagnosis method based on multi-scale feature fusion, which comprises the following steps: fusing multi-source data such as vibration, acoustic emission and rotating speed, performing angle domain resampling by using rotating speed data, generating a two-dimensional order spectrogram, and stacking to construct a three-dimensional working condition information tensor; a master-slave modulation heterogeneous neural network is adopted, high-dimensional spatial-temporal features are extracted through a main branch three-dimensional convolutional network, time sequence details are extracted from an original sequence through an auxiliary branch one-dimensional convolutional network, affine transformation parameters are generated, and dynamic modulation is achieved on the high-dimensional features; and the output state vector is mapped to a fault evolution knowledge graph, probability prediction is carried out through a graph attention network and by introducing a Monte Carlo discarding mechanism, a probability mean value is calculated as a fault classification result, and the diagnosis confidence is quantified by a probability variance. According to the invention, through multi-scale feature fusion and dynamic modulation, the problem of insufficient feature discrimination caused by scale mismatch under variable working conditions is solved.

The invention discloses a fatigue test method for a cabin structural member of an offshore wind power generator. A test piece is subjected to surface treatment, a multi-sensor array is arranged, the test piece is placed in a combined system of a multi-axis dynamic loading unit and a collaborative environment simulation cabin, an intelligent control and analysis platform synchronously applies a multi-axis alternating load, salt spray corrosion and a circulating temperature and humidity environment, and real marine service conditions are simulated; the state of a test piece is evaluated in real time based on criteria such as a strain concentration coefficient and acoustic emission energy, a strengthening test is started for potential damage risks, a load strategy is adjusted to track crack propagation when microcracks are monitored, and after the test is ended, a fatigue life prediction model is established or calibrated by combining failure test piece analysis data. And outputting a life evaluation result and a structure improvement suggestion. Through the multi-factor collaborative simulation and self-adaptive control technology, the authenticity and efficiency of the test are improved, and technical support is provided for design optimization and reliability guarantee of offshore wind power cabin structural parts.

The invention discloses a blade bolt state monitoring system and method based on an acoustic emission technology in the technical field of state monitoring of wind power generation equipment. The method comprises the following steps: acquiring acoustic emission signal data of a blade bolt connection area acquired by an acoustic emission sensor array; sequentially carrying out preprocessing and feature extraction processing on the basis of the acoustic emission signal data, and then calculating a wavelet packet energy entropy value; judging whether a triggering condition of multi-sensor data fusion weighting judgment is met or not based on the wavelet packet energy entropy value; when any triggering condition is met, it is judged that the state of the bolt is abnormal, and after comprehensive state recognition is conducted through multi-sensor data fusion weighted judgment and modal acoustic emission wave velocity correction positioning, early warning information including the damage type, the severity degree and position information is output; when the triggering condition is not met, it is judged that the bolt state is normal, and circulating monitoring continues. According to the invention, accurate evaluation and early warning of the blade bolt state are realized through signal acquisition, processing, analysis and early warning.

The invention provides a method for evaluating the crack resistance of tunnel body concrete, and belongs to the technical field of wind tunnels, and the method comprises the steps: uniformly arranging multiple types of sensors on the surface of a tunnel body concrete structure to form a high-density monitoring network, employing a concrete circular ring constraint test piece to establish an anti-crack reference database, and constructing a multi-working-condition temperature stress database; acquiring multi-dimensional data such as strain, temperature and acoustic emission in real time, inputting the monitoring data into a liquid adaptive neural network model to predict crack initiation probability and expansion risk, establishing a crack risk assessment function, formulating a corresponding processing strategy according to a risk coefficient range, and starting an intelligent spray maintenance system; a closed-loop control system from monitoring perception to risk early warning to active protection is formed, and the technical problem that crack initiation and expansion risks of a hole body concrete structure cannot be accurately predicted under complex working conditions is solved.

The invention discloses an acoustic emission signal noise reduction and feature extraction method and system suitable for a deep roadway, and relates to the technical field of safety monitoring of deep mineral resource mining, and the method comprises the specific steps: arranging an acoustic emission sensor array along the deep roadway, collecting multi-source data, converting the multi-source data into digital signals, and storing the digital signals; identifying an interference type through a wavelet packet decomposition and environment correction algorithm; self-adaptive noise reduction is carried out by using a complexity sensitive penalty algorithm; time domain and frequency domain features are extracted, coupling parameters are calculated, and time domain and frequency domain features are obtained through Hilbert-Huang transform; and finally, through principal component analysis dimensionality reduction and mutual information entropy screening, constructing a feature vector and transmitting the feature vector to a safety early warning system. According to the invention, the processing precision and reliability of the acoustic emission signal are improved through the multi-source signal acquisition module, the interference identification module and the adaptive noise reduction module; signal characteristics are comprehensively described through algorithm optimization, key characteristics are output through characteristic optimization, real-time monitoring and early warning are achieved through a dynamic damage vector algorithm, and deep roadway construction safety is guaranteed.

The invention provides a novel multi-source information and damage coupled coal mine rock burst prediction method. The method comprises the following steps that rock mass fracture signals, a three-dimensional stress field and acoustic emission parameters are collected in real time through a micro-seismic monitoring array, an optical fiber stress sensor and a rock sound probe; an LSTM-attention fusion module is adopted to carry out spatio-temporal feature extraction on the collected multi-source data, an attention weight coefficient is generated, and a weighted fusion feature vector is output; calculating the rock mass damage degree based on a statistical damage mechanics Weibull distribution theory; based on the law of conservation of energy and the damage mechanics theory, a dynamic energy threshold function is constructed, the mining time effect and the rock mass damage state are incorporated into a quantitative evaluation system, and a dynamic energy accumulation rate threshold function is established by introducing a time accumulation effect and a damage coupling term; when the real-time energy accumulation rate is larger than or equal to the energy accumulation rate threshold value and the time derivative of the energy accumulation rate is larger than the sudden change threshold value, third-level early warning is triggered. According to the invention, advanced accurate early warning of the rock burst can be realized.

The invention relates to the technical field of power equipment fault diagnosis, and particularly discloses a pole-mounted circuit breaker mechanical fault diagnosis method and system, and the method comprises the steps: firstly, synchronously collecting a dynamic force-displacement signal, a high-frequency acoustic emission signal and a broadband vibration signal in the switching-on and switching-off process of a circuit breaker operating mechanism; then, the acoustic emission and vibration signals are decoupled into an impact source component and a friction source component which are statistically independent through a blind source separation algorithm; performing envelope spectrum analysis on the impact source component to extract impact characteristics, and performing energy calculation on the friction source component to obtain friction noise energy characteristics; finally, on the basis of dynamic force-displacement curve fitting analysis, in combination with impact characteristics and friction noise energy characteristics, distinguishing and delimiting of mechanical wear and lubrication degradation faults are achieved. According to the invention, the problem that the existing diagnosis technology cannot effectively identify and distinguish the coupling fault of mechanical wear and lubrication deterioration is solved, and early warning and accurate diagnosis of the mechanical fault of the pole-mounted circuit breaker are realized.

The invention discloses a cutter state real-time monitoring and compensating method, system and equipment and a medium, and relates to numerical control precision machining. In the method, a cutting edge optical image, an acoustic emission signal time domain waveform, a vibration acceleration parameter and real-time temperature data of a cutting area of a cutter in the machining process are collected; measuring the maximum width and depth of a wear belt through an image to form a wear characteristic quantized value, and judging a wear over-limit state; judging the damage state based on the continuous ultralimit time of the instantaneous mutation rate of the acoustic emission signal; judging the abnormal vibration grade and state through vibration spectrum analysis; and inputting the vibration spectrum and the temperature data into a prediction model to obtain a position offset prediction quantity. And calculating a size deviation value according to a wear / damage state, calculating a coordinate offset according to vibration abnormity, generating a cutter geometric compensation instruction or a motion trail compensation instruction, and completing real-time compensation operation. According to the scheme, the fine abnormal change of the cutter in the machining process can be captured in time, and compensation is conducted according to the fine abnormal change.

The invention discloses a roadway roof damage time prediction method and system, and belongs to the technical field of mine rock mechanics and safety engineering. Comprising the following steps: recognizing a roof rock stratum structure and lithology of each layer through drilling peeping, and drilling a representative rock sample; determining the crack initiation strength and peak strength of the rock sample through a uniaxial compression test; selecting a plurality of stress levels between the axial strain and the acoustic emission signal to carry out a creep test, synchronously monitoring the axial strain and the acoustic emission signal, and determining an accelerated creep starting moment as the damage time under the stress according to the abrupt change characteristics of the axial strain and the acoustic emission signal; establishing a stress-failure time relation model of each lithology; finally, selecting a corresponding model to predict the damage time of rocks at different layers of the roof according to the actual rock stratum structure and ground stress state of the roadway; according to the method, accurate and reliable time prediction of the progressive damage process of the roadway roof is realized.

The invention provides a sensor fusion-based pig feed crushing particle size monitoring method, which comprises the following steps of: synchronously acquiring vibration, acoustic emission and optical signals, carrying out band-pass filtering, normalization, feature extraction and wavelet packet decomposition, and dynamically adjusting the confidence coefficient of each channel in combination with working condition perception and a sensor performance knowledge base so as to monitor the crushing particle size of the pig feed. Weighted fusion and conflict evidence modulation of multi-source signals are achieved, the improved Dempster-Shafer evidence theory is introduced to improve the abnormal granularity recognition accuracy, key parameters of crushing equipment are controlled in a linkage mode through a closed-loop feedback mechanism, high-robustness detection and self-adaptive adjustment of the granularity state are achieved, and the method has the advantages of being high in robustness and high in robustness. And the stability and the automation level of the feed crushing process are improved.

The invention discloses a method for predicting the remaining service life of a cylindrical grinding wheel based on acoustic emission signals, which comprises the following steps of: acquiring acoustic emission signal data in the working process of the cylindrical grinding wheel, labeling the acoustic emission signal data according to the number of effective processing parts, and forming a data set by the acoustic emission signal data and labels; constructing an initial model which comprises a data preprocessing module, a multi-scale feature extraction module, a feature fusion module and a prediction module; training the initial model by using the data set to obtain a prediction model for predicting the residual service life of the cylindrical grinding wheel; and inputting acoustic emission signal data during working of the cylindrical grinding wheel into the prediction model so as to output the residual service life of the corresponding cylindrical grinding wheel. The invention further provides a device for predicting the remaining service life of the cylindrical grinding wheel. The method provided by the invention can predict the residual service life of the cylindrical grinding wheel so as to guide production and maintenance, reduce the rejection rate and improve the resource utilization rate.

The invention relates to a harmonic reducer fault diagnosis method based on double-branch feature fusion. The harmonic reducer fault diagnosis method comprises the following steps: synchronously acquiring multi-sensor data of a harmonic reducer acquired by vibration, acoustic emission and current sensors; processing the multi-sensor data through a one-dimensional convolutional neural network to obtain data in a two-dimensional matrix form; the data in the two-dimensional matrix form is input into a pre-trained model, and the processing process of the model is as follows: feature extraction is performed on the data in the two-dimensional matrix form through an MCWGraphKAN branch feature extraction module and a GBiMama branch feature extraction module to obtain a first feature and a second feature, the first feature and the second feature are fused and then mapped to a sample space through a full-connection layer, and the data in the two-dimensional matrix form are obtained; and fault classification is realized. The method achieves the fault diagnosis of the harmonic reducer through a deep learning method, and is used for solving the problems that a single sensor cannot fully express the operation state of the harmonic reducer, and the fault feature extraction of a single-branch network is insufficient.

The invention relates to a rolling bearing embedded lubrication state evaluation method and a computer device. The rolling bearing embedded lubrication state evaluation method comprises the steps that a temperature signal of a rolling bearing and a frequency domain spectrum amplitude sequence are spliced to generate a multi-source spectrum fusion feature vector; a random forest model is adopted to screen mean value features extracted from the temperature signals and time domain features and frequency domain features extracted from the vibration signals, the sound signals and the sound emission signals to obtain a sensitive feature set; inputting the sensitive feature set into a support vector machine model to output a first lubrication state evaluation result; inputting the multi-source spectrum fusion feature vector into a MobileNet V2 model to output a second lubrication state evaluation result; and fusing the first lubrication state evaluation result and the second lubrication state evaluation result through a D-S evidence theory to obtain a rolling bearing lubrication state evaluation result. The multi-source fusion evaluation method considering accuracy, robustness and engineering practicability is constructed, so that the problems of high limitation, insufficient fusion layers, high model complexity and the like of an existing single signal are solved.

The invention provides an industrial guide rail drilling method and system. The method comprises the following steps of material pretreatment and positioning reference generation; guide rail hydraulic self-locking clamping; a reference hole laser positioning and drill bit path planning step; self-adaptive drill bit spacing adjustment is carried out; double-sided synchronous drilling and vibration suppression are achieved; integrating online burr detection and burr removal; a cutting force closed-loop control step; cooling, lubricating and self-adaptive spraying; a step of continuously discharging and automatically sorting; and predicting the service life of the cutter and replacing the cutter. The scheme has the following advantages that through the V-shaped clamping mechanism and the hydraulic compensation system, synchronous clamping of the four guide rails is achieved, and the clamping time is shortened to 2 minutes per batch; drilling units are symmetrically arranged on the guide rail up and down, double-face hole group machining is completed at a time, and 0.08 mm average positioning deviation caused by the overturning procedure is eliminated; the integrated acoustic emission sensor monitors the abrasion state of the drill bit in real time, when the cutting force fluctuation is larger than 15%, the feeding amount is automatically compensated, and the rejection rate is reduced to 1.5% from 12%.

The invention relates to the technical field of borehole fracture analysis, discloses a borehole fracture evaluation method and system based on a neural radiation field and acoustic emission, and aims to solve the problem of poor accuracy in the prior art. The scheme mainly comprises the following steps: constructing a high-fidelity hole wall three-dimensional model through a neural radiation field technology; meanwhile, time-space coordinates and energy parameters of an acoustic emission event are obtained through an acoustic emission sensor array; uniformly converting acoustic emission coordinates into a model coordinate system, and identifying and quantifying geometric features of the fissure surface; and finally, constructing a space-time diagram structure taking the fracture surface as a static node and the acoustic emission event as a dynamic node, learning a space-time evolution mode through a graph neural network, outputting a fracture surface activity index, and realizing activity grade evaluation and extension trend prediction. The method improves the accuracy of borehole fracture analysis, and is particularly suitable for safety monitoring of geotechnical engineering.

The invention relates to the technical field of grinding machine on-line detection, and discloses a grinding machine working process on-line monitoring system, which comprises a data acquisition module for acquiring vibration, acoustic emission and environment signals and generating an original multi-dimensional signal vector; the data preprocessing module is used for preprocessing the original multi-dimensional signal vector to generate a purified signal set; the working condition inversion module is used for performing real-time inversion on a technological parameter estimation value of current grinding machining; the anomaly detection module is used for calculating and quantifying an anomaly score of the anomaly degree of the current working condition based on the purification signal set; the adaptive adjustment module is used for generating a dynamic threshold value according to the process parameters and comparing an abnormal score to judge an abnormal state; and the decision fusion module fuses the abnormal state and the environment humidity and generates a decision instruction. According to the method, the material hardness of the machined workpiece is estimated in real time, and the abnormal threshold value is dynamically adjusted according to the hardness estimation value, so that normal signal fluctuation and equipment faults caused by switching of normal working conditions are effectively distinguished, and false alarms caused by process changes are avoided.

The invention relates to the technical field of geotechnical engineering and geological disaster monitoring, in particular to a rockburst tendency dynamic discrimination method, and aims to solve the limitation caused by the fact that a dynamic evolution process is simplified into a quasi-static attribute in traditional rockburst tendency evaluation. According to the method, rockburst tendency is defined as a hidden state variable evolved along with time, a physical constraint state space model containing a hidden state kinetic equation and a multi-modal observation equation is constructed, continuous stress-strain and discrete acoustic emission data are fused, hidden state probability distribution is estimated in real time through online Bayesian inference, and the probability distribution of the hidden state is estimated in real time. And in combination with adaptive information weight updating and physical consistency constraint, predicting a future state trajectory, calculating a risk probability exceeding a critical threshold, and outputting a dynamic rockburst tendency level. According to the scheme, continuous, dynamic and prospective judgment of the rockburst tendency is realized, and the accuracy and reliability of early warning are improved.

The invention relates to the technical field of boiler control, in particular to a multivariable coupling thermal process regulation and control system and method for carbon pollution governing, and the method comprises the steps: collecting multi-source data such as acoustic emission, temperature, humidity and spectrum, and constructing a feature sequence through time mark alignment and wavelet packet enhancement; a heat value characterization quantity is predicted by using a Shenchang differential equation model fused with dynamic gating, a partition equivalent thermal network model is driven on this basis, and accurate prediction of a future time domain temperature field is realized by dynamically correcting thermal resistance and thermal capacity; based on the prediction result, a control instruction is solved through multi-objective optimization under the condition that the active temperature constraint is met; and in combination with heat flow density feedback, a layered reinforcement learning controller is adopted for online compensation of a pre-feedback instruction, and stable and efficient regulation and control of the boiler are achieved.