Authors:
Jari Näsi
and
Aki Sorsa
Affiliation:
Control Engineering Laboratory,University of Oulu, Finland
Keyword(s):
Reliability, validation, optimal estimation.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Signal Processing and Control
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Identification
Abstract:
In an industrial process, the accuracy and reliability of process creates basics for control system and ultimately to product uniformity. Measurement results, whether from fast on-line sensors or from sample-based laboratory analyses, is the key for selecting the method for process control and analysis. Intelligent and advanced control methods, exploiting measurements, are of no benefit if the measurements cannot be trusted. This paper presents an estimation method for combining real-time redundant signals, consisting of sensor data, and analytical measurements. The validation of on-line measurement uses less frequently updated but more accurate information to validate frequently updated but less accurate on-line measurements. An estimate of the measured variable is obtained as a weighted average of the on-line measurements and laboratory analyses. The weighting coefficients are recursively updated in real time when new analysis and measurement results are available. The calculation
of optimal estimate can be used in several industrial applications for more precise process control. In addition,
pre-processed data is used to calculate a “need for maintenance indicator” to warn the operator for sensor breakdowns, wearing or deterioration and detect calibration needs. The operator’s workload is reduced in problematic situations where measurement and validation signals are not convergent, by offering calculated best estimation.
(More)