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Status No status
Product AVEVA™ PI DataLink™
Categories Functions
Created by Matt Voll
Created on Aug 16, 2022

RELATED IDEAS

Interpolate Step=On Tags

There are tags that should be kept as Step=On but there is certain cases in which someone should be able to easily bypass the typical interpolating rules (interpolation that follows the step parameter) and allow for a true interpolation between values The cumbersome workaround is to utilize performance equation functions within the expression field . . . but that's pretty advanced usage
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  • Kenneth Barber
    Reply
    |     Aug 16, 2022
    What do you mean by "true interpolation"? Do you mean linear interpolation (i.e. treat the Step = on tag as if it were Step = off)?
  • Matt Voll
    Reply
    |     Aug 16, 2022
    yes, so that a user can force the tag's interpolation to behave as if it was Step=off, instead of is true behavior of step=on
  • Kenneth Barber
    Reply
    |     Aug 16, 2022
    In what cases would you want to keep Step = on but use linear interpolation?
  • Matt Voll
    Reply
    |     Aug 16, 2022
    For us lab data is common numerical data point that we use Step=ON for. A process sample analyzed by a lab represents a single moment in time, when the sample was taken, and due to the infrequent nature of the sample (ranging from once per hour to once a day), it would not be accurate to represent this data, visually, using linear interpolation. The process could be following any variety of behavior between the data points; roller coaster ups and downs, steady average with occasional dips, erratic with occasional spikes, etc, etc. So lacking this context the most reasonable assumption is to have step=on and have the data merely represent the state in which the process was last analyzed. Higher frequency process data, like from instruments, is sampled/scanned/advise at a higher frequency, that is reasonable to assume a linear interpolation trend between data points since they are likely less than a minute apart (ignoring any compression factors in that statement). This step behavior also helps user realize that lab data should be event-weighted summarizations and not time-weighted. a once per shift lab sample is meant to represent 8 hours of time, if the process is down or a shift sample is missed, using time-weighted summarization results in the last sample before the gap to be over represented compared to other data point That explains why lab data would be Step=ON as a default. . . now when we get to subsequent data analysis . . . well different users want to treat the data differently based on their own judgement, and possibly make different assumptions . . . even lacking the context mentioned above. Taking a big data set for a modeling effort results in numerous decisions and assumptions in an effort to clean, filter, align data . . . I don't see any reason why they shouldn't be able to actively make the decision to use linear interpolation on a step=on tag if they so choose and makes sense for their analysis