Software AG’s TrendMiner releases TrendMiner 2020.R1. Engineers can now extend the monitors for operational performance by creating highly advanced soft sensors by using “nested calculations.” What physical sensors cannot measure can be done by combining multiple correlated parameters within formulas. The latest release helps optimize overall performance and product quality - in particular, for process manufacturing companies in the chemicals, oil and gas, water and wastewater, utilities, pharmaceuticals, food processing and metals and mining sectors.
TrendMiner enables production experts in the process manufacturing industries to analyze, monitor and predict operational performance with use of sensor-generated time-series data. Often, even the most advanced sensors cannot measure what is most crucial for a production process. TrendMiner users can create soft sensors, for example, to measure product quality by using formulas with use of the data from physical sensors. Users can now:
● Improve the structure, overview and logic in formulas
● Combine formulas and their results for use within higher level formulas
● Use a large number of variables within formulas
● Share and reuse complex formulas for use by others
The result of the soft sensor can be shown graphically in the same way as any other sensor or tag, allowing application of core capabilities to better analyze, monitor and predict process outcomes.
Operational contextual data can help identify new areas for performance improvement. 2020.R1 creates this from events captured during process monitoring or from data residing in other business applications, such as the maintenance management or laboratory information management. In the latest TrendMiner release, context items can be filtered and sorted based on event duration. In combination with the new current state filters (such as “in progress” or “under inspection”), users can better prioritize which situations need the most attention, provide new insights via loss assessments and focus on new areas for improving operational performance.
