| Summary: | Self-validating (SEVA) sensors implemented in a control process provide various metrics regarding sensed variables to a central control unit. Specifically, SEVA sensors provide measurements of the variables and validity information about the measurements, which may include fault information about the sensors themselves. A control unit utilizes the various SEVA metrics even when large numbers of SEVA sensors are used, a situation that is otherwise problematic due to difficulties in assimilating data from multiple SEVA sensors. Accordingly, the control unit distinguishes sensor faults from actual process changes, and responds as needed, even when large numbers of SEVAsensors are implemented together. Specifically, the monitoring and control unit assimilates signals from multiple SEVA sensors using a multi-variate statistical analysis, and compares results of this analysis with a model characterizing behavior of the process (where the model may take into account actuator position information) and/or historical statistical data.
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