Data validation: a case study for a feed-drive monitoring

The monitoring of machine-tools implicated in the metal cutting process is the subject of increasing developments because of requests on control, reliability, availability of machine-tools and on work-piece quality. The use of computers contributes to a better machine and process monitoring by enabling the implementation of complex algorithms for control, monitoring, … The improvement of monitoring of the main machine-tools devices, the feed-drives and the spindles that drive the cutting process, can be realised by estimating their fault sensitive physical parameters from their continuous-time model. We have chosen to use a continuous-time ARX model. We particularly focus on slow time varying phenomena. This estimation should run while there is no machining process to avoid false detection of faults on the machine due to the cutting process. High speed motions, that occur at least for each tool exchange, are exploited. Some functional constraints require the use of an off-line estimation method, we have chosen an ordinary least squares method. Estimating the physical parameters is insufficient to obtain an efficient monitoring. A measurement analysis and validation are necessary as the validation of the estimated physical parameters. An approach of the measurement and physical parameter estimation validation for a NC machine-tool feed-drive is proposed

Area topology for road extraction and topographic data validation

CISRG discussion paper ; 1

Data Validation and reconstruction for performance enhancement and maintenance of water networks

In a real water network, a telecontrol system must periodically acquire, store and validate data gathered by sensor measurements in order to achieve accurate monitoring of the whole network in real time. For each sensor measurement, data are usually represented by one-dimensional time series. These values, known as raw data, need to be validated before further use to assure the reliability of the results obtained when using them. In real operation, problems affecting the communication system, lack of reliability of sensors, or other inherent errors often arise, generating missing or false data during certain periods of time. These wrong data must be detected and replaced by estimated data. Thus, it is important to provide the data system with procedures that can detect such problems and assist the user in monitoring and processing the incoming data. Data validation is an essential step to improve data reliability. The validated data represent measurements of the variables in the required form where unnecessary information from raw data has been removed. In this paper, a methodology for data validation and reconstruction of sensor data in a water network is used to analyze the performance of the sectors of a water network. Finally, from this analysis several indicators of the components (sensors, actuators and pipes) and of the sectors themselves can be derived in order to organize useful plans for performance enhancement and maintenance. Nice practices have been developed during a large period in the water network of the company ATLL Concessionària de la Generalitat de Catalunya, S.A.Postprint (author's final draft

Five Data Validation Cases

Data-validation routines enable computer applications to test data to ensure their accuracy, completeness, and conformance to industry or proprietary standards. This paper presents five programming cases that require students to validate five different types of data: (1) simple user data entries, (2) UPC codes, (3) passwords, (4) ISBN numbers, and (5) credit card numbers

Solar radiation data validation

This paper describes and applies a procedure for the quality control and validation of solar radiation data for two independent co-located measurement systems based at Loughborough University, United Kingdom. An assessment of the measurement error of simultaneous data from four pyranometers was undertaken over a range of averaging periods. A data filter of 0-1500W/m2 was found to reduce measurement errors by a factor of between 2 and 4 with observed hourly, daily and monthly errors of approximately 9%, 5% and 3.5% respectively for all sensors. These errors were greater than those found in the literature, indicating the possible presence of a systematic component of error. Analysis of the temporal variation of measurement error and its relationship with incident irradiance indicated the presence of an inter-system discrepancy in sensor offset. The close proximity of the two systems indicated that this was due to differences in system calibration, maintenance or response rather than environment and the results can therefore be used for future system re-calibration and to improve data accuracy. This paper demonstrates that straightforward validation procedures can yield meaningful results and greater emphasis on data validation is recommended for the solar community as a whole

Simplifying Satellite and Ground Data Validation with Level-2 Subsetting

We demonstrate that scientists can simplify their satellite data validation workflow with the use of NASA Godddard Earth Sciences Data and Information Services Center (GES DISC) subsetting services. We perform a sample validation of Aura ozone products collocated with ground-based ozone measurements using subsetting services to trim satellite data to only the relevant user-defined variables and spatio-temporal region. Because the subsetting service automatically returns only relevant data granules that adhere to a set of user-defined coincidence criteria, user workload is greatly reduced. Moreover, the resultant data files are substantially smaller than full data granules due to the subsetting service further culling the data to the relevant geospatio-temporal coincidence criteria, user-defined variables, and user-defined dimensions of variables. This decreases data download throughput and file storage requirements. The validation presented here quantifies the time and file size savings that can be achieved by utilizing subsetting services within the satellite data validation workflow