April 30, 2020 – Software AG’s TrendMiner has announced the release of TrendMiner 2020.R1. This release extends the capabilities to create soft sensors by introducing 'nested calculations.' What physical sensors cannot measure can be done by combining multiple correlated parameters within formulas.
Engineers can now extend the monitors for operational performance through creating highly advanced soft sensors by using 'nested calculations.' This release further helps optimize overall performance and product quality - in particular for process manufacturing companies in the chemicals, oil & gas, water & wastewater, utilities, pharmaceuticals, food processing, and metals & 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. Oftentimes, 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. Now in the 2020.R1 release, we have extended the capabilities with nested calculations, allowing users to create soft 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 graphically shown in the same way as any other sensor or tag, allowing application of core capabilities to better analyze, monitor and predict process outcomes. This will help process and asset experts to improve operational performance and reach new heights. 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.
An important aspect in assessing the criticality of contextual data is event duration. 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 now better prioritize which situations need the most attention, provide new insights via loss assessments and focus on new areas for improving operational performance.