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Found 2 results

  1. Seeq Version: R21.0.43.03 but the solution is applicable to previous versions as well. The Profile Search Tool is great for specifying a profile in Signal A and then looking for occurrences of that profile throughout time. In the screenshot below I've used Profile Search to identify when the Compressor Power Area A resembles the shape of a chair. For basics about how to use the tool check out the Seeq KB article Profile Search. However, what if I want to look for that same profile on another signal? In the screenshot below, I've added a second signal, Compressor Power Area G. I'd like to identify when the "chair" profile I previously specified for Compressor Power Area A is present in Compressor Power Area G. I can do this by using the profileSearch() function in Seeq Formula. Here is how... 1. Start with the Chair in Area A condition I previously made and use Duplicate to Formula. The duplicated formula looks like (note that $cpA refers to Compressor Power Area A): profileSearch($cpA, toTime("2019-09-02T15:43:30.135Z"), toTime("2019-09-03T06:28:09.629Z"), 98, 0.5, 0.3, 0.3) 2. Modify the formula to add $cpG (Compressor Power Area G) to the start of the function. This is an optional argument in Profile Search which allows us to use the profile identified on signal $cpA and look for when it occurs on signal $cpG. For more information on the Profile Search function, check out the documentation available in the Formula Tool. $cpG.profileSearch($cpA, toTime("2019-09-02T15:43:30.135Z"), toTime("2019-09-03T06:28:09.629Z"), 98, 0.5, 0.3, 0.3) Here is a screenshot of what it looks like in the Formula Tool: 3. View the final result. In this example two "chairs" where identified in Area G.
  2. Hi All, Suppose I have created a capsule with a trend which looks like this. a) I would like to select a capsule in these capsules and make it my "Standard Trend". b) Also I would like to find out find out which trends are NOT at least 90% of my reference trend. Basically trying to find out outlier amongst the capsules. c) Using that outlier result in an table format as (% duration out of bound/ Number of capsules which are not atleast 90% of my standard trend) etc would be my next step. Any help or direction for this path would be appreciated.
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