37 results about "Density curve" patented technology

The invention discloses a method for calculating fatigue life reliability of an asphalt pavement, and relates to the technical field of road engineering. According to the method, actually-measured temperature field historical data and traffic axle load data of an existing asphalt pavement structure are obtained through an asphalt pavement long-term performance observation network, the actually-measured temperature field historical data are extracted to construct a temperature field historical data set, and a temperature cumulative probability distribution curve is generated; then, utilizing traffic axle load data to generate an axle type cumulative probability density curve and an axle load interval cumulative probability density curve, and based on Monte Carlo simulation, performing inverse function sampling in each cumulative probability density curve during each simulation to obtain the structural response of the asphalt pavement structure to determine the standard fatigue life; and determining the reliability of the fatigue life by counting the probability that the standard fatigue life obtained by Monte Carlo simulation is greater than the standard cumulative action times within the design period. According to the method, the fatigue life of the asphalt pavement structure is accurately estimated, and the design reliability of the asphalt pavement structure is effectively guaranteed.

The invention relates to a probabilistic load flow calculation method for new energy and load correlation, and the method specifically comprises the steps: building a probabilistic model of new energy and load based on the distribution types and parameters of the new energy and load, inputting network structure parameters, the probabilistic model information of the new energy and load, and correlation coefficient information, the method comprises the following steps of: setting a median Latin hypercube sampling scale and an iteration number marking variable, then sampling mutually independent random variables such as new energy and load and random variables such as new energy and load with a correlation so as to generate random samples, and finally, based on a Newton-Raphson method, calculating a maximum iteration number of the new energy and the load so as to obtain a maximum iteration number of the new energy and the load. Performing power flow calculation on the obtained random samples in sequence, finally performing statistics on power flow results such as node voltage, line transmission power and line loss rate obtained by each power flow calculation, drawing a probability density curve, and performing probability power flow calculation of new energy and load correlation by using the probability density curve; the method has the advantages of being accurate in calculation, fast and efficient.

The invention provides a data reading method of a memory, storage equipment and a storage medium, and the data reading method of the memory comprises the following steps: carrying out reading operation on a programming unit to obtain target data; according to the bit flipping information of the target data, determining a corresponding failure bit counting exception type; wherein the failure bit counting exception type comprises a first type of exception caused by read voltage offset and a second type of exception caused by trough elevation of a probability density curve of a break-over voltage of a storage unit; in response to the first type of abnormality, adjusting the read voltage; or in response to the second type of exception, performing data refreshing operation or garbage collection operation. By means of the mode, the abnormal type causing reading voltage deviation is judged so as to judge the data state, then the rereading rate of a host is reduced, and the reading effectiveness is improved.

This invention discloses a method, system, and program product for identifying the fault severity of underwater robot thrusters under conditions of relatively mild faults, belonging to the field of underwater robot fault diagnosis technology. The invention first obtains a thrust deviation curve based on the bow angle and lateral thruster control voltage. Then, based on different parts of the curve, it uses difference and multi-time-window sliding Fourier transform methods in the time and frequency domains respectively to obtain multiple thrust losses. Next, it introduces a kernel density estimation method to obtain the time-domain and frequency-domain fault severity probability density curves based on the thrust losses obtained in the time and frequency domains. Finally, it uses a method of averaging at the same location to fuse the curves, and identifies the fault severity based on the fused fault severity probability density curves. This invention achieves high identification accuracy and is particularly suitable for fault severity identification in ocean current environments and under conditions of relatively mild faults.

This invention relates to a method, apparatus, medium, and equipment for analyzing the overpressure characteristic values ​​of combustible gas explosions in marine oil and gas production facilities. The analysis method includes the following steps: geometrically modeling the marine oil and gas production facility; calculating the congestion degree of the process area of ​​the marine oil and gas production facility; calculating the volume of the combustible gas cloud under different filling ratios in the process area of ​​the marine oil and gas production facility; performing transient CFD simulations of combustible gas explosions with varying gas cloud positions and ignition positions to obtain the overpressure value of the protected target changing over time; determining the maximum overpressure value of the protected target under combustible gas explosion conditions; expanding the sample and performing statistical analysis and characteristic value calculations to obtain probability density curves and characteristic values; and determining the explosion-proof level of the protected target according to protection requirements.

The present application relates to the technical field of rock fracture modeling, and discloses a method and a processor for modeling a multi-scale fracture network of a tight reservoir. The method comprises: obtaining a strike attribute of a fracture; generating a fracture strike conforming to a preset mode according to a cumulative probability density curve of the strike; taking a fracture position interpreted from a core and an imaging logging as hard data of a fracture position in a fracture network; dividing the hard data into seed hard data and correction hard data; correcting a cumulative probability curve of fracture development intensity according to the seed hard data, and generating a fracture element set according to the corrected probability density curve; generating a discrete fracture network through similarity fusion criteria, mechanical cause fusion criteria and intersection criteria; comparing the established discrete fracture network with the correction hard data, and correcting the number of fracture elements and the cumulative probability curve. Through iterative inversion of the seed hard data and the correction hard data, a more optimal multi-scale fracture network model that is more consistent with the hard data is generated.

The application provides a curve reconstruction method and device based on deep learning, equipment and medium, the method comprises the following steps: obtaining the logging curve data of the non-expanded section, and establishing a training set and a test set; a density curve reconstruction model is established and the training set is used to train the density curve reconstruction model, the density curve reconstruction model is a deep learning model; the test set is used to test the density curve reconstruction model, and a reconstructed density curve is generated, the density curve reconstruction model is adjusted until the correlation of the reconstructed density curve and the original density curve of the non-expanded section and the relative error of the density curve reconstruction model reach a preset condition; the density curve of the target well is reconstructed by using the adjusted density curve reconstruction model. The application simplifies the operation process, especially in complex formation conditions, can accurately reflect the actual situation of the formation, and improves the calculation accuracy of the reservoir parameters.

The invention discloses a short-term power load probability prediction method based on an improved neural network, and belongs to the technical field of power system load prediction. The method comprises the following steps: acquiring and preprocessing load and related data; the method comprises the following steps: constructing a DCS-CNN-BiLSTM-Attention-QR probability prediction model fusing a convolutional neural network, a bidirectional long short-term memory network, an attention mechanism, quantile regression and kernel density estimation; automatically optimizing key hyper-parameters of the model by utilizing a difference creation search algorithm; carrying out model training by adopting a quantile loss function to obtain load prediction values under different quantiles; and finally, generating a probability density curve of the prediction interval through kernel density estimation. According to the method, deterministic point prediction is expanded into probabilistic interval prediction, the uncertainty of load prediction can be quantified, the model precision and generalization ability are improved through an intelligent optimization algorithm, and richer and more reliable decision information is provided for power system scheduling and risk management.

This invention relates to the field of data processing technology, specifically to an efficient data processing method for communication networks. The method includes: acquiring a network data sequence to be processed; obtaining a feature value sequence based on the differences between adjacent network data in the network data sequence; obtaining suspected drift anomaly data based on the similarity between the normal distribution curve and probability density curve corresponding to the feature value sequence; obtaining an optimal second data window corresponding to each suspected drift anomaly data based on the difference between the feature value corresponding to each network data in each second data window and the mean corresponding to the feature value sequence; obtaining each drift anomaly data based on the first data window and the optimal second data window; and obtaining the target network data sequence based on the optimal second data window. This invention can eliminate the impact of drift anomaly data on detection accuracy, resulting in higher detection accuracy.

The invention relates to a data analysis method and device for a wind turbine generator, electronic equipment, a storage medium and a computer program product. The data analysis method comprises the steps of obtaining historical data of target parameters of the wind turbine generator; calculating a target probability density curve of the target parameter based on the historical data of the target parameter; and evaluating the operation performance of the wind turbine generator based on the target probability density curve. Therefore, the evaluation of the operation performance of the wind turbine generator does not depend on any input information, but calculates the probability density curve corresponding to the parameters of the wind turbine generator based on the real operation data of the wind turbine generator in the historical operation process, and then evaluates the operation performance of the wind turbine generator based on the calculated probability density curve. According to the method, the actual operation rule of the wind turbine generator is fully combined, namely the design principle and the control characteristics of the wind turbine generator are fully utilized, the control mode of the wind turbine generator can be truly restored, and the accuracy of evaluating the operation performance of the wind turbine generator is improved.

The invention discloses a weight-free health degree analysis method for industrial equipment, which belongs to the technical field of electric digital data processing, and comprises the following steps: in a health state, according to a data index type for analyzing the health degree, respectively acquiring industrial time sequence data in at least one historical time window of the industrial equipment as reference data, collecting industrial time sequence data in the current time window as comparison data; according to the reference data and the comparison data, probability density curves are obtained through kernel density estimation one by one, so that probability distribution is obtained; and according to the data index type, obtaining JS divergence values for measuring data distribution similarity under the data index type according to the probability distribution of the comparison data and the probability distribution of the at least one piece of corresponding reference data, so as to obtain the health degree of the industrial equipment according to the plurality of JS divergence values. According to the invention, based on kernel density estimation and JS divergence, the distribution difference is quantified, weight setting is not needed, and the sensitivity and consistency of health degree assessment are improved.

The invention discloses a wind turbine generator bearing fault detection method and system. The method comprises the steps of collecting power, wind speed, generator bearing temperature and generator rotating speed of a wind turbine generator in different set days; performing data box separation and quartile distance anomaly detection based on power and wind speed; dividing the operation data matrix with abnormal values removed by day, and fitting the wind speed and power in each sub-matrix into a sigmoid curve as a wind speed and power curve of each day; eliminating data of days in a non-full power generation state based on a wind speed power curve of each day; dividing the operation data matrix in the normal state into a plurality of samples, fitting the generator rotating speed and the generator bearing temperature in each sample into a set nonlinear function, constructing a probability density curve of a degradation value of each sample, obtaining the bearing degradation probability, and if the bearing degradation probability exceeds a set degradation probability threshold, determining that the bearing is not damaged; if yes, determining that a fault exists. According to the invention, the overall operation and maintenance cost is saved, and the power generation efficiency is improved.

The invention relates to the technical field of electricity market analysis and prediction, in particular to a probabilistic prediction method for the clearing price of an electricity market. The technical problems are that when an electricity market clearing price probabilistic prediction method in the prior art is practically applied, multi-peak distribution caused by market state structural change is difficult to accurately capture, a predicted price probability density curve is often too smooth, and a prediction result has systematic deviation; according to the technical scheme, the probabilistic prediction method for the clearing price of the electricity market comprises a market state mode division step, a mode probability prediction step, a condition price distribution prediction step and a probability distribution synthesis step. According to the method, recognition and division of the market state mode are introduced, and a complex probability prediction problem is decomposed into a plurality of simple sub-problems, so that the multi-peak characteristic of the clearing price probability distribution is more accurately captured, and the prediction precision is improved.

The application provides a shear wave velocity prediction method based on deep learning, comprising the following steps: step 1, obtaining a longitudinal wave velocity curve Vp, a neutron porosity curve CNL, a density curve DEN, a porosity curve POR, a shale content curve SH and a corresponding measured shear wave velocity curve Vs; step 2, pre-processing the logging curves; step 3, constructing a shear wave velocity prediction model; step 4, continuously modifying the model parameters until the model stability and prediction accuracy meet the requirements; and step 5, predicting the shear wave velocity of a well to be predicted by using the shear wave velocity prediction model. The shear wave velocity prediction method based on deep learning can accurately predict the logging shear wave velocity, can replace the empirical formula method and the rock physics modeling method, can accurately and quickly predict the logging shear wave velocity by using the logging curves and logging interpretation results, has strong generalization ability and has practical popularization and application significance.

The present invention belongs to the field of power systems and automation thereof, and particularly relates to a method for determining reserve capacity and allocating its cost taking into account the random fluctuation characteristics of renewable energy. The method comprises the following steps: utilizing historical load / renewable energy forecast data and fitting the probability density curve of each load / renewable energy source based on an uncertainty estimation method of non-parametric kernel density; determining the total reserve capacity by simulating and generating a reserve demand scenario based on the Monte Carlo method; constructing a market clearing model with minimizing the unit operating cost and the rotating reserve cost as the objective function; solving the clearing model with a quadratic programming algorithm to obtain the energy price and the reserve price, and determining the total amount of reserve auxiliary funds to be allocated; calculating the replacement value of each load / renewable energy source based on a VCG mechanism, and allocating the reserve auxiliary fees according to the relative proportion of the value. The present invention can reasonably determine the total reserve capacity and effectively avoid the problem of imbalance between revenue and expenditure, and can be widely applied to the power spot market of the power system.

The application discloses a fan vibration online diagnosis method and system based on generalized extreme value distribution, and the method filters old historical vibration time series data, extracts characteristic values and calculates fitting distribution conditions, obtains a generalized extreme value distribution curve of the fan, calculates a cumulative probability density curve of the curve, that is, a fitting model, calculates key point extreme value data according to the curve, enables the fan master control to directly judge a theoretical occurrence probability of a vibration condition at the moment according to the fitting model, flexibly adjusts a control strategy based on the probability, and realizes real-time vibration online diagnosis; the application is simple to implement and low in cost, does not need to additionally add other equipment on the basis of the original, does not need internet and other facilities, only needs to modify a fan master control program code, and meanwhile, the bottom logic is simple and clear, so that the master control corresponding operation logic is convenient to debug and modify.

The application discloses a temperature-pressure decoupling control method based on an isodensity curve and related equipment, and the method comprises the following steps: after determining a target cabin for temperature-pressure control, obtaining an initial state and a target state of the target cabin; judging whether the initial state and the target state are located in the same isodensity curve range based on an isodensity temperature-pressure decoupling control graph of the target cabin; if not, calculating at least two intermediate target states by using the change relationship between the temperature and the pressure under the quantitative premise based on the target temperature and the target pressure in the target state; and performing temperature and pressure control on the liquid in the target cabin based on the at least two intermediate target states, so as to adjust the liquid from the initial state to the target state. By using the isodensity temperature-pressure decoupling control graph of the liquid, the temperature and the pressure of the liquid in the target cabin are adjusted by using the mode of separately controlling the temperature and the pressure, so as to solve the problem that it is difficult to simultaneously and accurately control the temperature and the pressure in the existing high-temperature and high-pressure environment.

The invention relates to a multi-temporal-spatial-scale air conditioner load demand response potential quantitative evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: classifying each building and an air conditioning system according to the building data of each building in a target area and a preset classification standard, and building a reference simulation model; a plurality of air conditioner load models are determined, and baseline working conditions before demand response of all the air conditioner load models are determined; carrying out analogue simulation on the air conditioner load models corresponding to various building air conditioner loads, and obtaining a demand response potential probability density curve by utilizing each simulation result and the corresponding baseline working condition; obtaining a total demand response potential probability density curve by using the demand response potential probability density curves of the various building air conditioner loads; according to the total demand response potential probability density curve, the demand response potential of the target area under different confidence degrees is determined, quantitative evaluation of the air conditioner load adjusting potential under multiple spatial and temporal scales can be achieved, and the method has the advantages of being high in precision and generalization.

A kind of harmonic modal detection and removal method based on blind source separation, rotating machinery generates harmonic excitation to structure in running state, based on the vibration response acceleration signal obtained by measurement, harmonic modal will appear in modal parameter identification, cause modal parameter identification error, low identification accuracy and other problems, harmonic modal should be detected and removed.In this method, based on the high similarity between modal expansion and blind separation principle, the vibration signal is analyzed under the condition of underdetermination sparse component, the energy peak scatter is clustered to estimate the mixing matrix by using the maximum energy method, and then the source signal is recovered based on the principle of minimum L1 norm;The probability density curve of each source signal is calculated, and the position of the harmonic modal is preliminarily determined;For the source signal containing harmonic, a spectral kurtosis curve diagram is drawn, so as to detect the frequency value corresponding to the harmonic modal;The corresponding harmonic source is removed, and the signal without harmonic interference is reconstructed.

The application discloses a variable stiffness composite wing aeroelastic analysis method containing multi-source uncertainty, comprising the following steps: dividing a probability space according to the conditions of random uncertainty factors, and determining the value of a random vector at each sampling point and the probability value of a subdomain where the random vector is located; regarding the random vector at each sampling point as a deterministic parameter, and calculating interval boundary values of aeroelastic response variables which need to be analyzed at each sample point through a loop; based on the interval boundary values, calculating the reliability of a structure at each sample point only under the influence of interval factors; discretizing initial conditions of a generalized probability density evolution equation to obtain the probability density of the reliability of the structure in each subdomain; superimposing the probability densities of each subdomain to obtain a probability density curve of the reliability of the structure considering multi-source uncertainty; and calculating the reliability of the structure based on the probability density curve. By adopting the technical scheme, the aeroelastic analysis of the variable stiffness composite wing containing multi-source uncertainty is realized.

The invention relates to the technical field of private domain reaching, and discloses a middle-aged and elderly private domain user reaching method and device based on dynamic preference prediction.The method comprises the steps that original behavior data and external environment data of a target user are obtained and preprocessed, and a structured time sequence data table of the target user is obtained; inputting the structured time sequence data table into a dynamic preference prediction model for prediction to obtain a touch response probability density curve of the target user in a future preset time period; obtaining to-be-reached content metadata, and generating a reaching task list of the target user in a future preset time period in combination with the reaching response probability density curve; and extracting the reaching content and the reaching time in the reaching task list, and pushing the reaching content to the target user according to the reaching time. According to the method, the response probability curve is generated based on the behavior and environment data, the user disgusting and shielding rate is reduced, the high response probability window is matched in combination with the content metadata, and the time and the content are synthesized, so that the pushed content is accurately reached in the user active period.

The present application relates to a method, apparatus, and computer device for identifying variant-enriched regions. The method comprises: S1, determining a bandwidth for density estimation based on the variant position and number of variant samples in an amino acid sequence, and generating a kernel density curve based on the bandwidth and variant position; S2, determining initial parameters of a Gaussian distribution based on the maximum and minimum values ​​of the kernel density curve; S3, determining the posterior probability that each variant sample belongs to each Gaussian distribution based on the initial parameters, and obtaining updated parameters based on the posterior probabilities when an iteration does not meet an iteration stopping condition; S4, replacing the initial parameters of S3 with the updated parameters, and repeating S3 until the iteration stopping condition is met; S5, determining a candidate region in the amino acid sequence based on the updated parameters, and determining the candidate region as a pathogenic variant-enriched region or a benign variant-enriched region when the ratio of the first sample to the second sample in the candidate region meets a preset condition. This method can accurately identify variant-enriched regions.

The invention discloses a method and a device for establishing a low-frequency model of a complex construction area based on multi-parameter constraint, electronic equipment and a medium. The method comprises the following steps: obtaining a longitudinal wave velocity curve, a transverse wave velocity curve and a density curve; obtaining an interval velocity body after well correction, and determining seismic attribute features representing the reservoir; establishing a low-frequency model in an ultralow-frequency band range based on the seismic horizon velocity body after well correction, and establishing a low-frequency end low-frequency model based on seismic attribute features; combining the low-frequency model in the ultralow-frequency band range with the low-frequency end low-frequency model to obtain an initial low-frequency model; and fusing the special abnormal geologic body information into the initial low-frequency model to obtain a low-frequency model of the special geologic body characteristics. According to the method, the low-frequency model is established in a complex high and steep structure area under the condition of special abnormal geologic body development, so that the reliability of inversion reservoir prediction is improved, the uncertainty of a prediction result is reduced, and a data foundation is laid for analysis of a subsequent reservoir prediction result.

The invention discloses a sand-to-ground ratio prediction method and system for constructing a virtual well optimization variation function by using seismic attributes, and the method comprises the steps: determining a lithology division critical value based on the intersection analysis of a gamma curve and a density curve of a real drilling well, carrying out the statistics of the accumulated thickness of sandstone and mudstone of each layer section, and calculating the sand-to-ground ratio data of the real drilling well; extracting seismic attributes from the seismic data of the target area to generate a seismic attribute plane graph; associating well point sand-to-ground ratio data with seismic attribute values to draw a cross plot, constructing a linear regression relational expression, calculating a sand-to-ground ratio determination coefficient of seismic attributes, and screening to obtain sensitive seismic attributes; designing a virtual well network based on the sensitive seismic attributes and assigning the sensitive seismic attributes to form a virtual well data set; on the basis of the virtual well data set, variation function graphs under different well distances are drawn, and optimized variation function parameters are obtained through variation function graph statistics; and based on the optimized variation function parameters, adopting a Gaussian random function algorithm to generate a sand-to-ground ratio plane distribution diagram. Accurate prediction of the sand-to-land ratio of the reservoir is realized.

The application relates to a hybrid life prediction method of a complex electromechanical device, which comprises the following steps: preprocessing full-life data collected by the complex electromechanical device to obtain a labeled training sample set; obtaining a set of training sample subsets in a segmented mode; setting the horizontal and vertical coordinates of the best fitting curve as the 'feature value-weight value' pairs of the training sample subsets; obtaining an improved VPM model by summarizing all the models; obtaining a set of test sample subsets, and predicting each test sample subset according to the improved VPM model to obtain a preliminary prediction result of life evaluation; and obtaining a final life prediction value of the complex electromechanical device. im The application takes the independent variable probability as the weight w of the mean and variance, and reduces the influence of abnormal samples on the classification accuracy; the improved VPM model can be used for calculating the similarity of the probability density curves of a section of data of the measured continuous time sequence, and greatly reduces the influence of abnormal samples.

This invention relates to a method and apparatus for predicting the depth of the overpressure top surface before drilling in exploration wells. The method includes: collecting formation pressure test data from drilled wells in the study area, including multiple drilled formation pressure test points; obtaining the density curves of the exploration wells; and calculating the overlying strata pressure P of the exploration wells based on the density curves. O and the hydrostatic pressure P of the exploration well 静水 According to the overlying strata pressure P of the exploration well O The hydrostatic pressure P of the exploration well 静水 Based on the aforementioned multiple drilled formation pressure test points, the pre-drilling overpressure top surface depth H of the exploration well was determined. TOP This invention relates to a method for predicting the depth of the overpressure top surface before drilling in exploration wells. It utilizes the trend of density variation with burial depth and analysis of formation pressure test data from drilled wells to avoid the uncertainty caused by relying solely on formation velocity to predict the overpressure top surface, thereby improving the accuracy of overpressure top surface prediction before drilling in exploration wells.

The invention provides a post-earthquake structure evaluation and repair method and device based on two-stage time-history analysis, and relates to the technical field of structure damage evaluation. The method comprises the following steps: performing two-stage elastic-plastic time-history analysis on a multi-layer structure through a fiber beam model to obtain structure response data; building a deep learning model based on MLP and LSTM, and predicting structure response data of a second stage; performing weighted summation dimension reduction processing on the predicted structure response data of the second stage through an evaluation index dimension reduction method to obtain a one-dimensional damage evaluation index after the earthquake of the structure, and calculating 95% quantile of a probability density curve; constructing a one-dimensional damage evaluation index limit value of the structural mechanical response state as a damage grade division basis; according to the post-earthquake structure safety assessment method and the post-earthquake structure safety assessment system, rapid assessment of post-earthquake structure safety can be achieved, and a basis is provided for post-earthquake repair decision making.

The invention discloses a Bayesian update-based industrial equipment residual life prediction method and system, and the method comprises the steps: building a prior model of an equipment degradation process through historical failure data, and obtaining an initial parameter through maximum likelihood estimation; collecting state monitoring data of the equipment, and performing feature extraction and normalization processing to form a degradation amount observation value; carrying out recursive updating on posterior distribution of degradation model parameters by utilizing a particle filtering algorithm and a Bayesian theorem; through a Markov chain Monte Carlo sampling technology, outputting a point estimation curve, a confidence interval curve and a failure probability density curve of the residual life; an equipment maintenance plan is dynamically adjusted according to the prediction result, and early warning is triggered when the failure probability exceeds a preset threshold value, so that decision support is provided for predictive maintenance; the system correspondingly comprises a data acquisition and preprocessing module, a prior model construction module and the like. According to the method, through fusion of probability classification and statistical reasoning, prediction accuracy is improved, and a reliable decision basis is provided for predictive maintenance of equipment.

The application relates to the technical field of emergency diesel generator monitoring, and discloses an emergency diesel generator fault monitoring method, device, equipment and product, which comprises the following steps: obtaining operation data, health state parameters and operation data of an emergency diesel generator at different time points in a historical period; adopting a reinforced mean clustering algorithm, combining with working condition auxiliary data, performing clustering on feature data to obtain a plurality of clustering clusters, and determining the working conditions corresponding to the clustering clusters; respectively determining initial alarm threshold values of various features according to probability density curves; correcting the alarm threshold values according to the health state parameters; and determining a fault state according to current operation data and the alarm threshold values; in the application, the data obtained through detection points are used for monitoring mechanical faults, the alarm threshold values are determined in combination with operation data obtained through the detection points, the fault state at the current time is judged based on the alarm threshold values, the problem of insufficient mechanical fault monitoring effect in a traditional monitoring method is solved, and the monitoring result is more accurate.

The application discloses a kind of industrial equipment residual life prediction method and system based on bayesian updating, the prior model of equipment degradation process is established by historical failure data, and initial parameters are obtained using maximum likelihood estimation;Collect the condition monitoring data of equipment, after feature extraction and normalization processing, form the observation value of degradation amount;Using particle filtering algorithm and bayes theorem, the posterior distribution of degradation model parameters is recursively updated;Through Markov chain Monte Carlo sampling technology, the point estimate, confidence interval and failure probability density curve of residual life are output;According to the prediction result, dynamically adjust equipment maintenance plan, and trigger early warning when failure probability exceeds preset threshold, provide decision support for predictive maintenance;The system corresponds and contains data acquisition and pretreatment, prior model construction and the like module.The application is fused by probability classification and statistical inference, improves prediction accuracy, and provides reliable decision basis for equipment predictive maintenance.