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A Tool to Optimize Compression Settings in OSIsoft's PI System


"A tuning tool for the OSIsoft PI system that recommends optimal exception and compression settings based on analysis of real time signal characteristics."

In any analysis, such as optimization and modeling, it is crucial that the source data contains as much relevant information as possible, without confusing the picture with redandance. To strike this balance, the PI system uses a combination of exception reporting and compression testing to evaluate and archive the source data. To tune these settings properly can be an overwhelming task, especially if the number of tags being considered reaches into the thousands. CompressionInsight is a plug-in to the PI System and automatically makes recommendations on optimzed exception and compression settings by evaluating the data being archived with the current settings and comparing them to raw data "snapshot" values. With this information, an optimization problem is formulated and recommendations made that achieve maximum data fidelity with minimum data storage.

Using the same core technology found in Discover*e, CompressionInsight uses advanced statistical algorithms to perform the data analysis.

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Once the tags are selected for analysis, CompressionInsight performs the analysis in real time and produces a ranked, summarized list of results. A colour coded legend pinpoints tags requiring the most attention. The user can automatically accept and update the settings with the recommended values, or drill down, on a tag-by-tag basis, for more detailed information. With CompressionInsight, it is easy for system administrators to tune and maintain their PI systems so that they can achieve maximum value from their data for downstream analysis.


Features

  • Connnect to PI servers and import thousands of tags simultaneously
  • Interactively visualize individual tag data (snapshots, current archive and optimal recommendation
  • Monitor and tune exception and compression settings in an automated background process
  • Automatically update settings, individually or in bulk, or export setting recommendations for bulk updating through PI data link
  • Experiment with different fidelity targets and what-if scenarios
  • Run multiple instances of the application for scalability

Benefits

  • Improve the quality and fidelity of data that is used for downstream modeling and decision making
  • Reduce uncertainty by eliminating the need for guess work, personal bias and “rules of thumb” associated with setup of PI tags
  • Promote consistency and save time during commissioning stages or on existing PI installations
  • Establish greater confidence that the data being collected has the fidelity to support ongoing analysis and performance improvement initiatives