TrendMiner Releases New Analytics Software Version: TrendMiner 2017 R2

March 15, 2018
The latest version of the self-service industrial analytics software introduces a recommendation engine to help subject matter experts solve complex cases, a new integration with OSIsoft PI Asset Framework and improved support for enterprise level scale-outs.

TrendMiner NV, provider of a self-service process and asset analytics offering, releases a new software version: TrendMiner 2017 R2. The release reportedly leverages global connected data sources to unlock data insights across the extended enterprise for optimizing overall plant performance and profitability.

TrendMiner software is based on a high-performance analytics engine for process data captured in time series. Subject matter experts use the software to search through big production data, analyze the results and identify trends in their processes to optimize efficiency and quality. TrendMiner indexes data from historians and connected data sources to create real-time interactive analytics with instant results. Advanced search algorithms combined with pattern recognition help to uncover previously hidden relationships and identify causes of process behavior. With direct access to analytics insights, actionable information becomes available at all levels of the plant.

The latest TrendMiner release delivers key enhancements to support companies with globally dispersed sites and teams. The self-service analytics platform delivers extended connectors, time saving features and a new offering to help users to solve previously unsolvable cases. TrendMiner 2017 R2 includes a recommendation engine to help subject matter experts solve challenging cases. The recommendation engine can proactively suggest tags that may be relevant to the analysis, including influence factors in upstream process sections with potentially significant time delays. In combination with the powerful search and discovery capabilities, users can now examine a process anomaly of interest, search for more instances of similar behaviors, and have TrendMiner generate hypotheses for diagnostics based on all selected occurrences, all within the same interface.

The automated suggestions guide users on where to find the most relevant results and help them to proactively generate a hypothesis about the root cause of behaviors. Instead of relying only on their individual knowledge, users can gain insights from all the indexed data and solve production issues faster. Any sensor measurements that have been indexed by TrendMiner can be used to inform suggestions, regardless of the originating historian or connected data source.