2046results about "Particle size analysis" patented technology

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 discloses a solar photovoltaic panel cleaning method and device based on a multi-mode cleaning technology, which can plan a traversal path according to parameters of a photovoltaic panel, primarily remove dust by using a wind blowing and ultrasonic vibration form, plan an energy-saving cleaning path for still existing cleaning points, and improve the cleaning efficiency of the photovoltaic panel. And secondary cleaning is conducted along the cleaning path, dust is adsorbed and cleaned through an electric field generated by the electrode plate, meanwhile, the output parameter set is trained and optimized through a trained deep learning model through input parameters extracted in real time, optimal cleaning parameters are obtained, and intelligent cleaning is achieved.

The invention provides a method for measuring the content of carbonate minerals, and belongs to the technical field of process mineralogy. The method for measuring the content of the carbonate minerals comprises the steps of ore grinding, reselection, automatic mineral analysis sample preparation, acid treatment, analysis comparison sample preparation, automatic mineralogy analysis testing, data correction and the like. Compared with the prior art, the analysis comparison sample is prepared in a targeted manner by introducing the analysis comparison sample for correction and combining automatic mineralogy analysis data, so that the analysis comparison sample is closer to an actual sample and is more representative, the test data is more accurate, and reliable data support is provided for efficient utilization of such resources.

Provided in the present invention are an imaging device and method for turbid oil abrasive particles based on polarized image enhancement, which belong to the technical field of image processing. The imaging device of the present invention includes a CMOS fixing device, a Stokes polarized CMOS sensor, an optical lens sleeve and an optical magnifying glass, where the Stokes polarized CMOS sensor includes a CMOS sensor and a Stokes analyzer. By using a polarization characteristic difference between scattered light of lubricating oil media and reflected light of abrasive particles, Stokes vector information is measured in real time under turbid lubricating oil scenes, the scattered light of the media is filtered out, image quality of the abrasive particles is improved, coverage duration for monitoring abrasive particle imaging is effectively prolonged.

The invention relates to the technical field of boiler pulverized coal on-line detection, and discloses an automatic pulverized coal on-line detection device which comprises a pulverized coal sampling pipe suitable for being communicated with a pulverized coal pipe at an outlet of a coal mill; the screening devices are sequentially arranged in series, the first screening device is communicated with the output end of the pulverized coal sampling pipe, and the last screening device is communicated with an air extracting pump; the number of the weighing balances is multiple, the weighing balances and the screeners are arranged in a one-to-one correspondence mode, and the weighing balances and the screeners which are correspondingly arranged communicate with each other. Through automatic weighing and data recording, personal errors can be reduced, and the automation level of detection and the data reliability are improved. Through the arrangement that the pulverized coal sampling pipe is matched with the multi-stage screening device and the multi-stage weighing balance, the fineness of pulverized coal in the pulverized coal pipe at the outlet of the coal mill can be analyzed in real time, so that an operator is guided to timely and quickly adjust the coal mill in real time by utilizing the fineness analysis, and a boiler is enabled to operate in an optimal state; meanwhile, the cost of pulverized coal is controlled.

The invention relates to the technical field of copper rod detection, in particular to a copper rod grain size detection method and system based on double-frequency eddy current analysis, and the method comprises the steps: synchronously obtaining a high-frequency eddy current response signal and a low-frequency eddy current response signal of the surface of a moving copper rod according to an orthogonally arranged double-frequency eddy current probe, and collecting the moving speed of the copper rod in real time; performing phase demodulation on the high-frequency eddy current response signal and the low-frequency eddy current response signal to respectively obtain a first phase angle and a second phase angle; calculating an instantaneous difference value between the first phase angle and the second phase angle, and generating an original phase difference characteristic quantity; performing dynamic compensation on the original phase difference characteristic quantity based on the movement speed of the copper rod to generate a speed compensation phase difference characteristic quantity; and inputting the speed compensation phase difference characteristic quantity into a pre-calibrated grain size mapping relation model to obtain a real-time grain size grade value. According to the invention, eddy current response interference caused by speed change in the continuous movement process of the copper rod is effectively eliminated, and high-precision detection of the grain size grade is realized.

The invention provides a grain impurity rate and breakage rate monitoring system and method for a combine harvester. The grain impurity rate and breakage rate monitoring system comprises a grain sampling device, an image acquisition device and a control device, the grain sampling device is installed on the side wall of the vertical grain conveying barrel, a grain inlet of the grain sampling device is connected with a grain outlet hole in the side wall of the vertical grain conveying barrel through a hose, and a grain outlet of the grain sampling device is connected with a feeding port of the image acquisition device through a connecting pipe; the image acquisition device is connected with the control device, an image acquisition module of the control device controls the image acquisition device to acquire a grain image, and online calculation of the impurity rate and the breakage rate of the grain is realized on the grain image through a YOLOv8-Seg instance segmentation model of the image processing module in combination with an impurity rate and breakage rate mathematical model; finally, a detection result is displayed on a display screen in real time, visual numerical values can be provided for an operator of the combine harvester, and the operator is helped to adjust working parameters of the harvester. The method can be used for harvesting rice, wheat, oilseed rape and soybeans, and has wide applicability.

The invention provides an environment detection method and system. The method comprises the following steps: detecting spectral information of each soil sample through an intelligent spectral sensor; extracting a reference spectral value from the spectral information of each soil sample, and determining an absorption spectrum of the pollutants according to the reference spectral value and the spectral information; performing principal component analysis on the absorption spectrum to obtain a spectral characteristic value of each soil sample in principal component, and determining a spectral fluctuation quantity based on each spectral characteristic value; acquiring granularity information of each soil sample by using an intelligent sound wave granularity sensor, and determining interaction characteristics of each soil sample according to the granularity information of each soil sample; and determining a confidence characteristic spectral line according to all the spectrum fluctuation quantities and all the interaction characteristics, and further detecting the content of the pollutants in the target environment through the confidence characteristic spectral line. By adopting the scheme provided by the invention, the influence of heterogeneity of the soil sample on the characteristic spectral line can be avoided, so that the content detection precision of pollutants is improved.

The invention discloses a method for measuring the particle size of metal powder by using a scanning electron microscope, which comprises the following steps: step 1, sample treatment: adhering a small conductive adhesive tape on a sample table, sprinkling metal powder on the adhered conductive adhesive tape, and purging to obtain the conductive adhesive tape adhered with the metal powder; step 2, parameter setting: setting acceleration voltage, working distance, diaphragm, image contrast, brightness, magnification times and scanning speed of a scanning electron microscope to observe the conductive adhesive tape adhered with the metal powder obtained in the step 1 to obtain an electronic image of metal powder particles; and processing and calculating the electronic image of the metal powder particles by using software to obtain the average particle size of the metal powder particles. According to the method disclosed by the invention, a solvent is not needed to disperse the sample, pollution of the solvent to the sample is effectively avoided, and the result is more accurate than that of directly measuring the particle size by using length software.

The invention relates to the related technical field of dust detection, and provides a mining induced charge and light scattering fusion dust detection device and method.The dust detection device comprises a workbench, a sampling assembly and a detection assembly, and the sampling assembly comprises a sampling air pump, a conical airflow homogenizer and a closed sampling cavity; the detection assembly comprises an annular sliding rail, a light scattering detection module and an electrostatic induction detection module; the dust detection method comprises the following steps: S1, device initialization and zero calibration; s2, sampling preparation of dust-containing airflow; s3, dual-module synchronous signal acquisition; s4, data processing and temperature and humidity compensation; s5, carrying out anti-interference protection assembly; and S6, data fusion and result output. S7, device cleaning and standby; through cooperative work of the double detection modules, dust detection signals in different concentration intervals are accurately captured, wide-range coverage from low concentration to high concentration is achieved, and synchronous monitoring of dust concentration and particle size characteristics is achieved.

Disclosed is a system for optimizing a particle size distribution of a lost circulation material. The system comprises a data acquisition module, a communication module, an experimental module, a particle size distribution optimization module, and a plugging and filling module. The data acquisition module is configured to obtain a target feature particle size. The experimental module is configured to obtain the target feature particle size, and set a plurality of experimental schemes for fracture plugging; conduct fracture plugging experiments to obtain experimental results; evaluate a plurality of fracture plugging effects of each of the plurality of experimental schemes. The particle size distribution optimization module is configured to obtain the plurality of fracture plugging effects, determine a particle size distribution rule, and obtain optimized particle size distribution data by optimizing the particle size distribution of the lost circulation material. The plugging and filling module is configured to perform a plugging operation.

The invention discloses a wind power generation equipment gearbox oil state detection method and system, and the method comprises the steps: obtaining an oil sample, carrying out the quantitative analysis of solid, liquid and gas pollutants through an oil pollution degree analysis model, and processing the parameters such as kinematic viscosity through an oil physicochemical property analysis model; and after the tensors of the two results are fused, feature decoupling and deep feature mining are performed, and finally, the oil state is judged by a state evaluation decision model. The system is correspondingly provided with six units such as a sample acquisition unit and a pollution analysis unit to realize the functions. According to the scheme, the defects that traditional detection is single and correlation analysis is lacked are overcome, a certain index is not independently detected any more, pollution and physicochemical property information is comprehensively integrated, the relation between the pollution and the physicochemical property information is deeply analyzed through tensor fusion, feature decoupling and other technical means, accurate evaluation of the oil state of the gearbox is achieved, and the detection accuracy is improved. And misjudgment caused by one-sided detection or no consideration of mutual relations is effectively avoided.

The invention provides a system and method for detecting free carbon particles in transformer oil based on a microelectrode array, and the system comprises a sampling module which comprises an oil inlet pipeline and a filtering device, an oil sample is conveyed to a microelectrode array sensor module from a transformer oil tank through the oil inlet pipeline, and the filtering device is located in the oil inlet pipeline; a filter membrane is arranged in the filter device to remove impurities in the oil sample; the microelectrode array sensor module comprises a microelectrode array sensor, the microelectrode array sensor is nanoelectrodes arranged on a silicon substrate, the surface of the microelectrode array sensor is coated with graphene oxide to adsorb free carbon particles in an oil sample, and when the free carbon particles are adsorbed to the surface of the microelectrode array sensor, an adsorption signal is generated; and the signal processing unit is used for extracting the characteristic frequency and intensity of the vibration of the carbon particles in the adsorption signal, and classifying the carbon particles according to the characteristic frequency and intensity of the carbon particles. The device is compact in structure, the maintenance cost is reduced, and the operation process is simplified.

Methods and systems for water quality assessment are disclosed. The method includes obtaining a first input data indicative of properties of a first liquid sample, the first input data including turbidity data and total suspended solids data for the first liquid sample, where the first liquid sample is acquired from a liquid source. The method further includes determining, using a computer processor and a machine learning model, a first predicted particle-size distribution of the first liquid sample based on the first input data, where particle-size distribution is controlled, at least in part, by a set of dosage parameters configurable by a water quality system. The method further includes determining, with an optimizer applied to the machine learning model, an optimal set of dosage parameters based on the first predicted particle-size distribution and adjusting the set of dosage parameters of the water quality system to the optimal set of dosage parameters.

The invention relates to a detection method and application of a bainite steel structure and / or grain boundary for nuclear power equipment, and relates to the technical field of new material detection. According to the detection method, the problem that the structure and the grain boundary of bainite steel for nuclear power equipment are difficult to display can be solved, a major breakthrough of a grain size detection technology is achieved, the clear and complete grain boundary can be accurately presented, and scientific evaluation of the grain size through a comparison method, a cut point method, an area method and the like according to standard requirements is further supported. When the detection method is used for evaluating the grain size, the results are highly consistent, the repeatability is good, and the problems of subjective experience dependence and measurement errors caused by blurred grain boundary display in a traditional nitric acid and alcohol corrosion method are effectively avoided.

The invention belongs to the technical field of sediment particle size monitoring, and particularly relates to a sediment particle size monitoring device for river flow monitoring, which comprises at least three conical barrels sequentially sleeved from left to right, and the outer barrel wall of the conical barrels sequentially sleeved from left to right is in non-contact design with the other outer barrel wall. Meshes formed in the cylinder wall of the conical cylinder located on the left side are located in the conical cylinder located on the right side, the diameters of the meshes formed in the cylinder walls of the three conical cylinders from left to right are sequentially decreased, and a particle size monitoring assembly is detachably installed at the cylinder opening of the conical cylinder located on the leftmost side through a supporting mechanism; the at least three conical barrels are installed in a river channel to be monitored in a settling mode through the supporting settling assemblies. The monitoring device provided by the invention not only can be used for monitoring the particle size of the sediment particles mixed in the river, but also can be used for collecting and sampling the monitored sediment particles in a grading manner, so that the application range of the monitoring device provided by the invention can be widened.

The present invention discloses an emulsified asphalt storage stability analysis method and system, the method comprising: judging whether a first diameter difference between a first layer average particle diameter and a second layer average particle diameter is greater than a preset threshold value; if greater than the preset threshold value, selecting a target first layer average particle diameter from other first layer average particle diameters according to a preset selection strategy; subtracting a target second layer average particle diameter corresponding to the target first layer average particle diameter from a second layer average particle diameter to obtain a second diameter difference, and judging whether the second diameter difference is greater than a preset threshold value; assigning weights to the first stability result and the second stability result according to a first ratio of the first diameter difference to the target difference and a second ratio of the second diameter difference to the target difference. The first stability result can be corrected, thereby improving the authenticity of the emulsified asphalt stability determination in a storage tank as much as possible.

The invention discloses a soil nitrogen leaching capacity monitoring method based on a frequency domain reflectometry, and relates to the technical field of soil water environment monitoring, in particular to a nitrogen leaching online monitoring method based on the frequency domain reflectometry (FDR), the Darcy law and a Van Genuchten (VG) model. And a monitoring method with high precision and sensitivity, multi-parameter synchronous detection and low cost is provided for the soil nitrogen leaching capacity. Comprising the following steps: 1, collecting and pretreating a soil sample; 2, placing the treated soil sample in a soil experiment cylinder; 3, inserting the multi-parameter sensors into the soil experiment cylinder in a layered manner; 4, 1000 ml of water is injected, and data acquisition is carried out; and 5, realizing soil nitrogen leaching capacity monitoring in combination with a Darcy law and a VG model.

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.

Analytical methods and systems for assessing analyte distribution uniformity in cosmetic compositions are provided. An analysis system (1) for evaluating analyte distribution uniformity of a cosmetic composition comprises: a binarization and calibration module (41) for binarizing and calibrating a chemical image of the cosmetic composition to obtain a binarized and calibrated image; the image segmentation module (42) is used for splitting the binarized and calibrated image into small grids; and an analyte distribution uniformity evaluation module (43) for calculating the number of pixels corresponding to the analyte in the small grid to obtain an analyte distribution uniformity.

The invention provides a method for measuring particle granularity and morphology, and belongs to the technical field of process mineralogy. According to the method, the particle size of the particles is analyzed on the basis of particle centroids in combination with geometric and statistical parameters, and meanwhile, morphological characteristics of the characterization particles are analyzed. A specific particle size measurement result is an equivalent radius result based on the centroid and can directly reflect the overall size of the particles, and further, a morphological characteristic result is based on centroid distance statistics and can quantify irregularities of the particles, such as roundness, length-diameter ratio and roughness; according to the test method, measurement is carried out on a two-dimensional plane, the measurement result is not influenced by a selected direction, accurate granularity and morphology characterization can be provided for particles with complex morphology (such as a dendritic or porous structure), and a reliable test method is provided for analysis of complex particle systems (such as geological sediments, industrial powder or biological particles).

A method and apparatus used to determine atmospheric extinction coefficients in conjunction with an unmanned aerial vehicle are provided. The unmanned aerial vehicle may include a central body, at least one motor operatively connected to the central body, a condensation particle detection payload, and a data acquisition board including memory operatively connected to the data acquisition board, and a processor configured to generate extinction information.

The invention discloses a clean workshop dynamic rating management system based on multi-modal sensing data. The system comprises a data acquisition module which is used for deploying a modal sensor to acquire environment data and personnel behavior original multi-modal data; the risk analysis module is used for preprocessing the original multi-modal data, extracting basic modal features, introducing a cross-modal early fusion algorithm based on the basic modal features to realize cross-modal feature association mining, and obtaining a risk event feature set; the cleanliness quantification module is used for constructing a particle reference model, predicting theoretical particle concentration by training the particle reference model based on the risk event feature set, calculating a ratio of actual to theoretical, and obtaining a dynamic interference coefficient for quantifying the influence of personnel equipment behaviors on cleanliness; and the rating management module is used for generating a pollution thermodynamic diagram in the digital twin body based on the dynamic interference coefficient, and triggering standard exceeding early warning to realize dynamic rating of the clean workshop.

The invention provides a method for measuring and calculating the article position of continuous silver ore with the particle size of 0.037 mm or above, and belongs to the technical field of mineral grade detection. The silver grade in the sample is measured and calculated through screening; leaching and measuring the content of silver monomers and continuous silver minerals, and reselecting to obtain a heavy sand product; carrying out fluorescent dyeing on the heavy sand product, and selecting monomer silver minerals; analyzing the color of the silver mineral through point scanning data of a scanning electron microscope, melting the monomer silver mineral to obtain the volume of the monomer silver mineral, and calculating the content of the monomer silver mineral; and carrying out conversion to obtain the continuous silver ore article position with the size of more than 0.037 mm. According to the method, the target mineral is rapidly and accurately obtained by adopting density medium reselection and fluorescent dye tracing; introducing a scanning electron microscope to measure and calculate the actual density of the silver mineral to replace the theoretical density, smelting the silver mineral in a mold, and carrying out volume measurement and calculation to obtain the actual metal amount closer to the actual silver mineral; measuring and calculating errors caused by impurities and irregular shapes of mineral particles are reduced; and the calculated grade of the silver mineral is more accurate.

The present application provides a method for detecting lunar soil particle size using computed tomography (CT) technology, which achieves non-contact, non-polluting and lossless detection. The present application also provides a method for detecting particle size of lunar soil samples using a combined three-dimensional X-ray microscope, an optical microscope and / or a scanning electron microscope, thereby making full use of the measurement advantages of different detection methods, effectively making up for the shortcomings of a single method, and improving the accuracy and reliability of particle size measurement.

The invention provides a reverse particle size analysis method and system for a lithium ion battery, and the method comprises the steps: carrying out the detailed disassembly, cleaning, adhesion, stripping and high-temperature treatment of a to-be-analyzed cell, can accurately analyze the particle size distribution of an electrode active material in the positive and negative electrode materials of the lithium ion battery, guarantees the purity and activity of the electrode active material, and improves the detection accuracy. Therefore, the particle size analysis result is more accurate and reliable. According to the invention, the performance of the battery material is optimized, the safety of the battery is improved, the service life of the battery is prolonged, and important data support is provided for research, development and improvement of the battery material.

The invention relates to the technical field of iron ore grinding and separation processes, in particular to an iron ore grinding granularity soft measurement model method based on a depth forest, which comprises the steps of collecting iron ore grinding production data and preprocessing the data, extracting input features of an iron ore grinding granularity soft measurement model of the depth forest, and setting initial parameters for a depth forest model. Comprising the number n of decision trees in each forest, the size di of a sliding window, the step length stepi and the upper limit of the number of sliding times, multi-granularity window sliding and model generation, and generation of an iron ore grinding granularity prediction result; the deep learning method based on the random forest has higher prediction precision and stronger generalization ability when processing complex ore grinding process data, can monitor the granularity change in the ore grinding process in real time, and helps operators to carry out more accurate control, so that the production efficiency and the resource utilization rate are improved, and the energy consumption and the cost are reduced.

The invention provides an acoustic emission ore pulp granularity online detection system and method based on a Hertz collision mode, and belongs to the technical field of ore dressing process monitoring and control. The system comprises an ore pulp bypass circulation module, a particle size calculation module and a particle size measurement control module. A built-in acoustic emission sensor is used for collecting acoustic emission signals generated by Hertz elastic collision between signal ore pulp particles and a probe; multi-band energy features and time domain features are extracted in combination with wavelet packet decomposition, and a fusion feature vector is constructed by fusing ore pulp flow velocity and density information; the fusion feature vector is input into a BP neural network model after dimension reduction processing, and the proportion of particles with the particle size smaller than a set threshold value in the ore pulp is predicted online. Full-process management of sampling, preparing, measuring and liquid discharging is achieved through automatic control, and the system has the advantages of being high in measuring precision and automation degree, low in cost, high in anti-interference capacity and the like, is suitable for continuous online monitoring of the ore pulp granularity on the ore dressing site and provides data support for flotation process optimization.

A method is provided for particle morphology classification. The method includes obtaining input imagery of a particle sample that includes powder particles that are sintered together. The method also includes generating an input dataset for clustering based on the input imagery, including (i) detecting and segmenting powder particles, (ii) extracting and standardizing powder particle images, and (iii) calculating morphology metrics of the powder particles. The method also includes identifying categories, based on geometry or morphology-based similarities between different particles, using K-means clustering on Hu invariant moments of the powder particle images. Some implementations include receiving labels for the categories from a user, and subsequently using the categories to analyze or quantify future batches of particulate based on those labels. Some implementations include pre-categorizing the powder particles, based on size or other characteristics prior to clustering, in order to eliminate larger-scale differences between the powder particles.