43 results about "Process measurement" patented technology

Measurement system for process automation in industrial or private environments, comprising a first field device for acquiring initial process measurement data and a computing arrangement for evaluating the acquired initial process measurement data, wherein the computing arrangement is configured to determine a new installation position and / or a new parameterization of the first field device based on the evaluation of the acquired initial process measurement data in order to improve the measurement result.

The invention discloses a monitoring method, a monitoring device, equipment, a medium and a product of a transformer substation monitoring meter. The method comprises the following steps: acquiring initial measurement data; determining a target processing strategy corresponding to the initial measurement data, and processing the initial measurement data of the monitoring meter based on the target processing strategy to obtain processed measurement data; if the processed measurement data meets a correction condition, correcting the processed measurement data to obtain target measurement data; according to the technical scheme, the general monitoring module for different types of transformer substation monitoring meters is provided, and the monitoring stability and reliability of the transformer substation monitoring meters can be improved.

The invention relates to the technical field of instrument and process measurement, in particular to a multi-range pressure gauge self-adaptive switching system and method based on time sequence feature perception, and the system comprises a data collection module which generates a smooth pressure time sequence signal in real time; the time sequence feature sensing module receives the smooth pressure time sequence signal and generates a pressure prediction trend signal of a future short-time window based on the pressure change trend feature; the self-adaptive decision-making module compares the pressure prediction trend signal with a preset current measuring range threshold value range; the switching execution module responds to the pre-switching trigger signal when the switching locking signal is in an unlocking state, and outputs the pre-switching trigger signal to the multi-range sensor group to complete switching of working channels; and the data fusion module performs weighted fusion processing on the two paths of output signals according to the switching process state signal provided by the switching execution module. According to the invention, the problem of switching lag of an existing pressure measurement system in the face of a pressure transient working condition can be solved.

The invention discloses an online calibration method for a mass flow meter, and belongs to the technical field of industrial process measurement and metering. Comprising the following steps: constructing a dual-channel data acquisition system; instantaneous mass flow data and accumulated mass flow data of the calibrated mass flow meter and the standard reference flow meter are collected in real time, and a standardized data sequence is obtained; extracting flow stability characteristic parameters by adopting multi-time scale analysis, and identifying a flow change event; the current operation state of the calibrated mass flow meter is subjected to grading judgment, and the corresponding working condition grade is determined; error comparison data used for representing the measurement deviation of the calibrated mass flow meter is generated; carrying out integrity, stability and consistency evaluation on the error comparison data, and judging whether the error comparison data meets a preset data quality criterion or not; and when a data quality criterion is met, obtaining a corresponding basic weight parameter according to the current working condition grade, and carrying out correction and normalization processing on the basic weight parameter to obtain a corrected and normalized weight parameter.

The invention relates to the technical field of industrial process measurement, and particularly discloses an online maintenance method and system based on a pressure transmitter, and the method comprises the steps: synchronously starting a pressure signal sampling window and a background noise monitoring window, and collecting pressure data and auxiliary diagnosis parameters; carrying out physical validity test based on signal continuity on the pressure data, removing abnormal data segments, generating a credible data set, and further calculating a robust center estimation value of the credible data set; dynamically adjusting a data quality acceptance threshold according to the auxiliary diagnosis parameters, and performing composite verification on a robust center estimation value and data distribution based on the threshold; executing zero point writing with verification and generating a complete traceability record only when the verification is passed, and otherwise, interrupting and outputting a diagnostic warning; according to the method, through multi-dimensional data fusion and intelligent decision making, instantaneous interference is effectively filtered out, absolute reliability of an online calibration reference is ensured, and traceability of a maintenance process is improved.

An online measurement gear precision evaluation method considering geometric errors of a large-specification gear making machine tool is characterized in that a measurement path is planned based on the large-specification machine tool which is provided with an online measurement system and contains X, Y, Z and C axes, an online measurement system geometric error model is established, the influence of the geometric errors on a measurement result is analyzed, and the precision of the large-specification gear making machine tool is evaluated. And obtaining a theoretical comparison position corresponding to the actual measurement point, and evaluating the processed measurement result. A measurement path is planned according to an involute helical gear measurement principle and shaft configuration characteristics of a large-specification gear making machine tool; establishing a geometric error model in combination with the measurement path; and analyzing the influence of the geometric error on the measurement result, correcting the measurement result, and evaluating the corrected measurement result. According to the invention, the online measurement evaluation accuracy of the large-specification gear making machine tool is improved.

The invention relates to a field device (100) comprising a measuring arrangement (10) which is configured to detect a process measurement variable, a screen (20) which has a first screen area (21) and a second screen area (25), wherein the first screen area (21) is a touch-sensitive area for detecting an operator input from a user, and wherein the second screen area (25) is a touch-insensitive area, and a control arrangement (30) which is configured to process the operator input for operating the field device (100).

The present application relates to the field of instruments and process measurement technology, in particular to a multi-range pressure gauge adaptive switching system and method based on time sequence feature perception, the system comprising a data acquisition module that generates a smooth pressure time sequence signal in real time; a time sequence feature perception module that receives the smooth pressure time sequence signal and generates a pressure prediction trend signal for a future short time window based on pressure change trend characteristics; an adaptive decision module that compares the pressure prediction trend signal with a preset current range threshold range; a switching execution module that responds to a pre-switching trigger signal when a switching lock signal is in an unlocked state and outputs to a multi-range sensor group to complete switching of a working channel; and a data fusion module that performs weighted fusion processing on two output signals according to a switching process state signal provided by the switching execution module. The present application can solve the problem of switching lag in existing pressure measurement systems when facing pressure transient working conditions.

The invention relates to a field device (100) comprising a measuring arrangement (10) for recording a process measurement variable and a display arrangement (20) with a display (21) for displaying a display page (1) and a display module (22), wherein the display module (22) is configured to generate the display page (1) based on at least one template (2) and to transfer the display page (1) to the display (21).

The in-process measurement system (H) of the present application includes an in-process measurement device (20) and an output device (30). The output device (30) acquires measurement data measured by the in-process measurement device (20) by relatively moving a measurement position on the surface of a workpiece (W) at least in the circumferential direction relative to the workpiece (W), and performs a plurality of analyses related to the machining quality of the workpiece (W) using any one of a low-frequency component, a high-frequency component, and the three of the low-frequency component and the high-frequency component in the frequency component of the measurement data, and outputs a plurality of analysis results.

The invention relates to the field of mine safety, and provides an in-situ dynamic monitoring method and system for the damage depth of a bottom plate. The base plate damage depth in-situ dynamic monitoring method comprises the following steps: constructing in a working face base plate to form a three-dimensional monitoring network consisting of a plurality of drill holes; a fixed-point water injection section is isolated at the bottom of each drill hole through a packing device; along with advancing of the working face, multiple water injection tests are conducted on each fixed-point water injection section, and the water injection flow and the water injection pressure are recorded; and calculating a permeability coefficient according to the water injection flow and the water injection pressure, and taking sudden increase of the permeability coefficient as a criterion for the rock stratum to enter a damaged state so as to determine the damage depth of the floor. According to the in-situ dynamic monitoring method for the damage depth of the floor, the whole dynamic evolution process of the damage depth of the floor rock stratum in the mining process can be continuously and accurately obtained in real time, and the crossing from final-state actual measurement to process actual measurement is realized.

The application discloses an engineering project cost whole-process collaborative management method and system, relates to the technical field of engineering project cost management, and comprises a cost matter main line construction module, which is used for receiving project estimation, design budget estimate, bidding control price, contract price, process measurement, change visa, claim and item data of final account, performing unified matter code mapping and cross-stage integration processing, and generating cost matter unit data. In the application, the project estimation, the design budget estimate, the bidding control price, the contract price, the process measurement, the change visa, the claim and the item data of the final account are subjected to the unified matter code mapping and the cross-stage integration processing by the cost matter main line construction module, the cost matter unit data is generated, cross-stage item integration record data is formed by the different-stage item data, and thus a stable main line structure is established at the data level, and consistent organization and centralized management of the cross-stage item data are realized.

The application provides a method and system for in-machine measurement based on a double-turntable machine tool and triggered by rotation of a turntable, which comprises the following steps: determining a generatrix height of a workpiece to be measured based on a workpiece size on a 2D drawing of the workpiece to be measured, and setting a measurement parameter according to the generatrix height; calculating positions of each theoretical measurement point according to the measurement parameter, and obtaining a distribution of each theoretical measurement point of the workpiece to be measured in a 3D space; generating an in-machine measurement path according to the distribution of each theoretical measurement point, processing the in-machine measurement path into an executable measurement program; and measuring the workpiece to be measured by using rotation of a fifth axis of the machine tool according to the measurement program, and obtaining measured data of each measurement point of the workpiece to be measured. The application does not need a 3D model of the workpiece to be measured, only needs some necessary parameter settings of a user according to a size on a 2D drawing, and can realize in-machine measurement by relying on rotation of the fifth axis of the machine tool, so that more applicable scenarios are added for in-machine measurement.

The invention discloses a transformer on-load tap-changer on-line sensing method, which comprises the following steps of: constructing an action time sequence based on action time sequence accurate identification of electromagnetic transient process measurement; based on the action time sequence, synchronously extracting electrical, mechanical and thermal multi-dimensional state features from multi-source sensor data, and constructing a multi-dimensional feature vector; based on the multi-dimensional feature vector, performing defect identification and state quantitative evaluation through a comprehensive health index and a mahalanobis distance to obtain a defect probability; and performing state evolution trend analysis and adaptive updating of a normal state model based on the state quantitative evaluation result. The method solves the problems that the traditional monitoring technology is difficult to accurately capture the action time sequence of the tap switch in a complex electromagnetic environment, the multi-source state information fusion is difficult and the dynamic self-adaptive capability is lacked, and realizes the high-precision, multi-dimensional and online comprehensive sensing and early defect sensitive identification of the operation state of the on-load tap switch of the transformer.

The application relates to the technical field of industrial process measurement, and particularly discloses a pressure transmitter online maintenance method and system, a pressure signal sampling window and a background noise monitoring window are synchronously started, pressure data and auxiliary diagnosis parameters are respectively collected; physical validity inspection is carried out on the pressure data based on signal continuity, abnormal data segments are removed, a reliable data set is generated, and then a robust center estimation value is calculated; a data quality acceptance threshold is dynamically adjusted according to the auxiliary diagnosis parameters, and the robust center estimation value and data distribution are compoundly checked based on the threshold; only when the checking is passed, zero point writing with verification is executed and complete traceability records are generated, otherwise, the process is interrupted and a diagnostic warning is output; through multi-dimensional data fusion and intelligent decision, the application effectively filters out transient interference, ensures the absolute reliability of the online calibration benchmark, and improves the traceability of the maintenance process.

The invention proposes a method for the computer-assisted prediction of values of a first process variable, comprising: automated training of a computer-implemented model, which comprises relationships between the captured process measurement values of the second process variable and the values to be predicted of the first process variable, using the previously captured process measurement values, the following steps being carried out in an automated manner in the course of the training: i) scaling the process measurement values, in particular standardizing the process measurement values, ii) carrying out a data imputation in order to supplement missing process measurement values, iii) detecting and removing outliers in the process measurement values, iv) performing a feature selection in the process measurement values, v) increasing a quantity of the process measurement values, vi) using a specific window size for the process measurement values when training the computer-implemented model, vii) selecting a specific type for the computer-implemented model and hyperparameters for training the computer-implemented model, predicting the values of the first process variable by applying the previously trained computer-implemented model.

The present disclosure provides a software development whole-process measurement method and system, relating to the technical field of computers, wherein the method comprises: obtaining development process data; wherein the development process data comprises at least one of code repository data, merge request information, project management data, task information and unstructured document data related to the project; based on a preset association rule and the merge request information, performing cross-source association processing on the code repository data, the project management data, the task information and the unstructured document data related to the project to generate fusion data; and calculating a composite measurement index based on the fusion data. Through automatic association and unified analysis, all-round measurement covering process efficiency, collaboration quality and knowledge inheritance is realized, and natural language processing technology is used to intelligently identify risks and knowledge gaps from unstructured documents, provide early warning and accurate problem tracing, and improve development transparency and decision-making efficiency.

The application discloses a kind of face gear tooth surface machining error on-machine measurement and correction method considering geometric error, comprising the following steps: step one: on-machine measurement geometric error compensation: 11) builds on-machine measurement platform;12) on-machine measurement geometric error modeling;13) on-machine measurement geometric error identification;14) with the linear axis several errors and rotation axis geometric error obtained by identification, compensate on-machine measurement geometric error;Step two: tooth surface error on-machine measurement: 21) planning measurement point on tooth surface, and obtaining the normal vector of each measurement point;22) establish measurement coordinate system;23) sequentially to each measurement point is measured, obtains the actual tooth surface point of face gear in each measurement point;24) according to the actual tooth surface point of face gear in each measurement point and theoretical tooth surface point, obtains the profile error and pitch error of face gear;25) according to the profile error and pitch error of face gear obtained, to face gear is carried out reverse adjustment correction.

The invention relates to a measurement system for process automation in an industrial or private environment. The measurement system comprises a first field device (110) and a computing device (111, 141, 120, 130). The first field device (110) is configured to acquire first process measurement data. The computing device (111, 141, 120, 130) is designed to evaluate the acquired first process measurement data. Wherein the computing device is arranged to determine a new installation position and / or a new parameter setting of the first field device based on an evaluation of the acquired first process measurement data, in order to improve the measurement results.

The application discloses a kind of intermittent process measurement data anomaly detection methods based on double support vector data description, first modal division is carried out to intermittent process using fuzzy clustering method, obtain each modal dataset, and fusion each stable mode and adjacent transition mode data construct fusion modal dataset;Then according to each modal dataset and fusion modal dataset, construct double SVDD process measurement data anomaly detection model respectively;Finally, the inner and outer control limits of double SVDD model and data anomaly discrimination strategy are used to realize the abnormal detection of intermittent process measurement data.The application fully considers the data change relation between different modes of intermittent process, detects the measurement data anomaly of intermittent process according to the inner and outer control limits of the double SVDD model and data anomaly discrimination strategy, which can effectively reduce the false detection rate of process measurement data anomaly detection and improve the accuracy of intermittent process measurement data anomaly detection.

Computer-implemented method for generating a process model (601) for use in the analysis of the operation of a process (300) which can be operated in a number of different process stages as defined by a stage variable associated with the process (300), comprising: Acquiring training data from the process (300) during the operation of the process (300), wherein the training data include a value for each of a set of process parameters, a value for the stage variable, and a value of an outcome variable associated with each of a variety of different process measurement times; Dividing the training data into time fractions of the data using a computer processing device (102, 13) to produce a set of time fractional data for each time fraction of the data, wherein each set of time fractional data includes a value for each of the set of process parameters, a value for the stage variable and a value for the result variable; Storing sets of time-proportional data in a computer memory; using a computer processing device (102, 13) determine a set of process stage means from the training data, wherein the set of process stage means includes a stage variable mean for each of the process stages and one or more process parameter means for each of the process stages; Storing the sets of process stage resources in a computer memory; using a computer processing device (102, 13) determining a set of time-part means for each of the time parts of the data using the stored process stage means, wherein each set of time-part means includes a time-part mean for each of the process parameters; using a computer processing device (102, 13) developing a set of deviations from the mean for each time fraction of the data, wherein the set of deviations from the mean for a given time fraction of the data for each process parameter within the given time fraction of the data includes the use of the process parameter value of the given time fraction of the data and the time fraction mean for the process parameter for the given time fraction of the data to develop the deviation from the mean for the process parameter for the given time fraction of the data; and using a computer processing device (102, 13) generating a process model (601) using the sets of deviations from the mean for the time components of the data and the result variable values ​​for the time components of the data.

Methods of determining, and using, a process model that is a machine learning model. The process model is trained partially based on simulation or based on a non-machine learning model. The training data may include inputs obtained from a design layout, patterning process measurements, and image measurements.

Techniques disclosed herein relate to determining a calibrated measurement value indicative of a physiological condition of a patient using sensor calibration data determined based on fabrication measurements. In some embodiments, the techniques involve obtaining one or more electrical signals from a sensing element of a sensing arrangement, where the one or more electrical signals are influenced by a physiological condition in a body of a patient; obtaining calibration data associated with the sensing element, where the calibration data is based on fabrication process measurement data for the sensing element and a calibration model for a certain physiological condition; and determining, using the one or more electrical signals and the calibration data associated with the sensing element, a calibrated output value indicative of the physiological condition.

The present disclosure relates to the technical field of semiconductor manufacturing, and particularly relates to a semiconductor machine control method, device, medium and product. The method comprises: training an initial weight prediction model according to training time series data until a weight prediction error is not greater than a preset target value, to obtain a target weight prediction model; inputting test time series data into the target weight prediction model to predict a target weight; the test time series data comprises process parameter time series data, machine state parameter time series data and process measurement result time series data; obtaining process parameter prediction data of a current batch of wafers according to the predicted target weight, actual feedback data and historical feedback data of the current batch of wafers; and controlling the semiconductor machine to perform a target process on the current batch of wafers according to the process parameter prediction data. At least the machine process parameters can be tracked and predicted in real time according to the dynamic changes of the actual working conditions of the machine.

The present application relates to the field of in-process measurement path planning, and proposes a kind of in-process measurement path adaptive optimization method of rotary feature.The present application carries out adaptive optimization to measurement path according to the geometric parameters of rotary feature of workpiece and the planning measurement point target section, measurement point number and measurement safety strategy and other parameters, and sets up adaptive circular arc between the measurement safety point corresponding to adjacent single measurement point to transition, to avoid the interference between measuring needle and rotary feature in moving process, cause the damage to measuring needle;At the same time, set adaptive circular arc to transition, can avoid the problem of interference between measuring needle and workpiece in moving process by increasing the number of measurement points or increasing the moving distance of measuring needle ball, the length of entire measurement path can be effectively shortened by optimizing measurement path through adaptive circular arc transition, improve the execution efficiency of in-process measurement, to reduce the use cost of machine tool.

Medical devices, systems, and methods are provided. A method involves obtaining process measurement data for a plurality of instances of a sensing element; obtaining reference output measurement data from the plurality of instances in response to the reference stimulus; determining a predictive model for the measurement output of the sensing element as a function of the machining process measurement variable based on a relationship between the machining process measurement data and the reference output measurement data; using the predictive model to generate a simulated output measurement profile across the machining process measurement variable range; identifying a performance threshold of the measured output based on the analog output measurement profile; obtaining output measurement data from the instance of the sensing element in response to the reference stimulus; and verifying that the output measurement data meets a performance threshold prior to calibrating a subsequent instance of the sensing element.

The magnetic field components measured around and along the remelt furnace and other measured furnace parameters are used to estimate the concentricity of the electrodes in the furnace crucible, to estimate the distribution of drip shorts across the gap between the electrodes and the molten pool, or to detect, locate, and categorize anomalous events during the remelt process. They may be used to control the operation of the furnace during the remelt process or incorporated into the resulting longitudinal or three-dimensional map of the ingot. Artificial intelligence, machine learning, or neural networks may be employed.