(HASSELT, BELGIUM — April 27, 2023 — TrendMiner Release via EINPresswire.com) —TrendMiner, a Software AG company, is introducing the “Digital Twin Manager” in its latest 2023.R1 release. With the Digital Twin Manager, industrial companies can create the foundation for making a digital process twin. TrendMiner allows engineers to analyze historical process and asset data as well as create (predictive) machine learning models. This offers deeper insight into operational performance, which enables operational experts to create better production dashboards and achieve predictive maintenance efforts on a higher level.
The latest release also includes plug-and-play integration with market-leading cloud-data sources from Amazon and Microsoft. Among the list of new capabilities and enhancements is a new search refinement that helps TrendMiner 2023.R1 users accelerate insights from time-series data.
Production lines in the process manufacturing industry are complex systems with many assets and sensors. Creating a process twin helps engineers identify assets and their parameters quickly. With TrendMiner’s “Digital Twin Manager,” users can build asset hierarchies using a wizard-style drag-and-drop feature inside the new Digital Twin Manager. Not only can they structure tags from enterprise historian or cloud data sources, but they also can attach tags or other information, such as dashboards or knowledgebase links. This includes machine learning models built in TrendMiner’s MLHub, or from a formula or aggregations made with Tag Builder. The new center also hosts previously existing functions to manage asset permissions or specific asset types.
TrendMiner now has three new plug-and-play connectors with market leading cloud-data sources: AWS IoT SiteWise, Amazon Timestream, and Microsoft Azure Data Explorer. In addition to time-series data, IoT SiteWise also supports asset data and contextual data. Both IoT SiteWise and Timestream are compliant with the Health Insurance Portability and Accountability Act (HIPPA) in the U.S.
Operators who are analyzing process events, such as batches, campaigns, defects, downtimes, etc., desire insights with the largest possible dimensionality to understand the behavior of the process or equipment during those events. The new search result refinement feature allows users to identify outliers more easily in process behavior by offering visualizations of the distribution of durations, calculations, and other summary metrics via histograms. Furthermore, the user can interact with the histograms to select a subset of interesting events with respect to one or more summary metrics and subsequently explore the relation with other dimensions as the histograms dynamically update.
