TrendMiner Develops Industrial Process Analytics for the Non-Data Scientist

Author photo: Peter Reynolds
ByPeter Reynolds

Although plant operations typically generate vast quantities of data – both structured and unstructured – plant engineers can only leverage a small percentage of these data to make better process decisions. Most process data from plant systems is stored in on-line and off-line process historians archives. Process historian tools, although relatively simple to use, are not ideal for analyzing the data or search queries. Finding the correct historian tag and building the process context can be a time consuming and laborious task.

To improve process performance, a level of operational intelligence and understanding of data is required. Process engineers and other stakeholders must be able to search time series data over a specific timeline and visualize all related plant events quickly and efficiently. This includes the time series data generated by the process control systems, lab systems, and other plant systems and the usual annotations and observations made by operators and engineers. The challenge typically presented by historian time series data is an inability to provide a mechanism for search and the ability to annotate effectively. By combining both structured time series process data from the historian and data captured by operators and engineers, users can predict more precisely what is occur-ring or what will occur in the future with continuous and batch industrial processes. However, this typically presents a number of challenges.

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Keywords: TrendMiner, Predictive Analytics, Process Optimization, Process Historian, Data Scientist, ARC Advisory Group.

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