We collected data from OPC server ， but it always include some noise value ,like pic
How can I Filter it ?
The key question for this is how to determine if a pattern is noise or an actual signal?
Typically you should suppress noise using the ExcDev settings on the PI Point that sets a deadband for the changes in value. In this case (i can't see the scale here so i use the grid) the setting for ExcDev should be >5.
If you need to do post-filtering, then you could evaluate a similar rule in an AF Analysis (either ad-hoc or saved to a PI Point).
And then you can use more advanced noise filtering options, like simply showing a moving average, which does not remove the noise but drops the amplitude of the noise.
Thank for your reply
If value>0 and keep 10 mins will recorded value else will set to 0 ， how can I evaluate in AF?
If the value needs to be stable for 10 minutes and only then saved, the only practical way is to create an analysis that saves the data to a new PI Point. Create an Analysis like:
If ('attribute' > 0 AND TagAvg('attribute', ParseTime("*-10m"), ParseTime("*"))='attribute') Then 'attribute' Else 0.
This checks if the value > 0 and if the Average equals the current value, which is an approximation of "keep 10 mins".
NOTE: Untested, written in freehand.
Roger's solution works with some reservations:
Complex noises will require complex filter methods that AF might not help with.
In case your noises are just these spikes which amplitude are lower than actual signal you might be able to filter them, and just them, using an expression like: IF 'Attribute' > [Spike Amplitude] THEN 'Attribute' ELSE 0.
Thanks for thinking along and finding the edge cases!
Agree, noise filtering is a tricky subject, but this might be a start. It depends on how critical the data is, if it does not need to be 100% correct, then the edge cases might not matter that much.
My quote of the day: "Values that are accurate enough and on time are more valueable than values that are impossible to determine and too late"
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