scholarly journals АВТОМАТИЗАЦІЯ КОНТРОЛЮ ПАРАМЕТРІВ ЕЛЕКТРОДВИГУНА

2020 ◽  
pp. 198-206
Author(s):  
Василь Іванович Петренко ◽  
Юлія Миколаївна Толкунова ◽  
Людмила Іванівна Смирнова
Keyword(s):  
Quality Control ◽  
Motor Control ◽  
Angular Velocity ◽  
Power Amplifier ◽  
Electric Motor ◽  
Reference Model ◽  
Output Parameter ◽  
Control Procedure ◽  
Technical Equipment ◽  
Velocity Sensor

The technique of the motor control technical characteristics automation is developed in the article. The control of the technical characteristics is to determine and evaluate information on deviation of the actual values from the specified ones. Quality control occupies a special place at all stages of technical products production. Efficiency of production as a whole depends on perfection of quality control, its technical equipment and organization. Automation of the control procedure allows to prevent the production of products that do not meet the requirements of standards, specifications, approved samples, design and technological documentation, while eliminating errors caused by human factors, reduces time costs, provides high reliability of control and reduces the cost of manufacturing and maintenance. The article offers a diagram of the workplace for the study of the motor technical characteristics and a mathematical model for the motor control process automation. The process of control is to compare the actual parameters of the electric motor with the parameters of its reference model. The reference model is formed on the basis of the input signals and technical parameters of the power amplifier units, the electric motor and the angular velocity sensor corresponding to the design documentation. On the difference signals of the output parameter of the reference model and the controlled sample of the motor, a rejection procedure is performed. The electric motor is represented by the first-order aperiodic link transfer function. The motion of the connection "power amplifier – motor – angular velocity sensor" is modeled by a differential equation in continuous and discrete forms. For the implementation of the reference model, the equation of state is used in a discrete form, as all the control and diagnostic procedures in the test equipment are implemented by digital computing devices. The diagnostic feature of the connection functional state is the deviation of the output parameter of the controlled motor from the output parameter of the reference model. Deviation from the reference value in excess of the tolerance is an indirect diagnostic sign of the presence of destabilizing parameters of motor. The dependence of the deviation of the output parameter on the change of the coefficient of transformation кЕM and the time constant TEM is investigated.

scholarly journals Expanding HadISD: quality-controlled, sub-daily station data from 1931

2016 ◽  
Author(s):  
Robert J. H. Dunn ◽  
Kate M. Willett ◽  
David E. Parker ◽  
Lorna Mitchell
Keyword(s):  
Quality Control ◽  
Wind Speed ◽  
Time Range ◽  
Underlying Structure ◽  
Control Procedure ◽  
Future Projections ◽  
Quality Control Procedure ◽  
Temporal Coverage ◽  
Extremes Of Temperature ◽  
Selection Of

Abstract. HadISD is a sub-daily, station-based, quality-controlled dataset designed to study past extremes of temperature, pressure and humidity and allow comparisons to future projections. Herein we describe the first major update to the HadISD dataset. The temporal coverage of the dataset has been extended to 1931 to present, doubling the time range over which data are provided. Improvements made to the station selection and merging procedures result in 7677 stations being provided in version 2.0.0.2015p of this dataset. The selection of stations to merge together making composites has also been improved and made more robust. The underlying structure of the quality control procedure is the same as for HadISD.1.0.x, but a number of improvements have been implemented in individual tests. Also, more detailed quality control tests for wind speed and direction have been added. The data will be made available as netCDF files at www.metoffice.gov.uk/hadobs/hadisd and updated annually.

scholarly journals Migration from NetWISP to Nutritics – a quality control procedure

2018 ◽  
Vol 77 (OCE3) ◽  
Author(s):  
S. Cassidy ◽  
B. Phillips ◽  
J. Caldeira Fernandes da Silva ◽  
A. Parle
Keyword(s):  
Quality Control ◽  
Control Procedure ◽  
Quality Control Procedure

scholarly journals Allan variance in dynamically tuned inertial-unit gyro error analysis

2019 ◽  
pp. 69-77
Author(s):  
M. A. Sharova ◽  
S. S. Diadin
Keyword(s):  
Angular Velocity ◽  
Error Analysis ◽  
Output Signal ◽  
Allan Variance ◽  
Measurement Information ◽  
Noise Component ◽  
Velocity Sensor ◽  
Signal Noise ◽  
Variance Method ◽  
Inertial Unit

The purpose of the study was to consider an algorithm for obtaining the measurement information from a dynamically tuned gyroscope in the mode of an angular velocity sensor and output signal noise component estimate, the algorithm being based on the Allan variance method. The results obtained were evaluated

scholarly journals Automated Quality Control of In Situ Soil Moisture from the North American Soil Moisture Database Using NLDAS-2 Products

2015 ◽  
Vol 54 (6) ◽  
pp. 1267-1282 ◽  
Author(s):  
Youlong Xia ◽  
Trent W. Ford ◽  
Yihua Wu ◽  
Steven M. Quiring ◽  
Michael B. Ek
Keyword(s):  
Quality Control ◽  
Soil Moisture ◽  
North American ◽  
Soil Layer ◽  
Control Procedure ◽  
Multiple Model ◽  
Quality Control Procedure ◽  
The North ◽  
Automated Quality Control

AbstractThe North American Soil Moisture Database (NASMD) was initiated in 2011 to provide support for developing climate forecasting tools, calibrating land surface models, and validating satellite-derived soil moisture algorithms. The NASMD has collected data from over 30 soil moisture observation networks providing millions of in situ soil moisture observations in all 50 states, as well as Canada and Mexico. It is recognized that the quality of measured soil moisture in NASMD is highly variable because of the diversity of climatological conditions, land cover, soil texture, and topographies of the stations, and differences in measurement devices (e.g., sensors) and installation. It is also recognized that error, inaccuracy, and imprecision in the data can have significant impacts on practical operations and scientific studies. Therefore, developing an appropriate quality control procedure is essential to ensure that the data are of the best quality. In this study, an automated quality control approach is developed using the North American Land Data Assimilation System, phase 2 (NLDAS-2), Noah soil porosity, soil temperature, and fraction of liquid and total soil moisture to flag erroneous and/or spurious measurements. Overall results show that this approach is able to flag unreasonable values when the soil is partially frozen. A validation example using NLDAS-2 multiple model soil moisture products at the 20-cm soil layer showed that the quality control procedure had a significant positive impact in Alabama, North Carolina, and west Texas. It had a greater impact in colder regions, particularly during spring and autumn. Over 433 NASMD stations have been quality controlled using the methodology proposed in this study, and the algorithm will be implemented to control data quality from the other ~1200 NASMD stations in the near future.

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