All plants constantly collect measurement data on their production processes and this data contains tremendous amounts of detailed information on how efficient the processes are. However, the measurement data also contains a great deal of both stable errors and errors that change over time. The data cannot be used for effective monitoring and improvement of process efficiency unless the quality of the measurements is separately validated.
In today's world, sharing data over networks is affordable and safe. Co-operation with a third party service provider enables quicker implementation of digital applications. IndMeas has extensive experience with such co-operation in both measurement quality validation, and in monitoring and improving process efficiency.
Process measurements have plenty of overlap, i.e., a given measurement value can often be given an estimate on the basis of other measured values. When all of the measured values are correct, consistency between different estimates is high. The continuous validation of measurement quality is based on the constant analysis of data consistency using analytics and correlations. Erroneous measurement positions are identified and their measurement errors are estimated.
The absolute level of measurements is validated with traced on-site calibrations in some of the key measurement positions of the process and, if necessary, in the erroneous positions discovered through continuous analysis.
Based on experience, there is also reason to ensure that the data chains are in order all the way from the validated data of the automation system to the facility's production, environment and other reports.