D.I. Wilson (New Zealand)
Data reconciliation, paper machine, gross errors, diagnostic model processor
This paper compares the techniques of Data Reconciliation and the Diagnostic Model Processor applied to an indus trial full scale two-ply paper machine using actual operat ing data. Data reconciliation optimally adjusts the raw data to satisfy known constraints whilst simultaneously identi fying gross errors. The DMP searches for faults that create the observed discrepancies in these constraint equations. The DR strategy worked well, reconciling the raw measure ments and correctly identifying gross errors while the DMP was over enthusiastic in its attempts to identify assumption violations.
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