HOUSTON and HASSELT, Belgium, Feb. 28, 2019 (GLOBE NEWSWIRE) -- TrendMiner NV, a Software AG company, has announced their latest software update: TrendMiner 2019.R1. The release includes a completely new visual representation of time-series data, as well as a number of usability enhancements further improving the views of available data.
New visual representation of time-series data
TrendMiner enables process and asset experts to analyze, monitor and predict operational performance through trend analysis of time-series data. The new 'Stacked Trend View' enables the display of trend data within separate lanes, vertically stacked and aligned. The Stacked Trend View is a great addition to TrendMiner's well known trend view, which gives a clearer view on all trend data. The user friendly interface allows the user to move the trend lines from one lane to another by simply dragging and dropping tags to combine similar measurements (for example to compare performance of similar production lines or various reactors on a production site).
TrendMiner's Recommender Engine, a self-service approach to finding potential root causes for anomalies, can be used to suggest root causes for performance anomalies. The suggested root causes can be added as tags to the appropriate stacked trend lane, including necessary time shifts. Users can now easily visually assess the impact of the early indicators for performance issues.
The Stacked Trend View can also be used to represent golden fingerprints, providing a very practical option to compare the live performance of multiple assets or highlight the relationship of normally disparate elements of a production process. In predictive mode, the most likely evolution of process behavior can now be visually represented in separate lanes, identifying the need of intervention when and where required, ensuring optimum yield and product quality.
Enhancements driven by our users
TrendMiner is designed by engineers for engineers and we highly value user feedback. On each release we make improvements in response to user ideas and suggestions. The key enhancements influenced by our users, released in TrendMiner 2019.R1 include:
The new Stacked Trend View, as well as the various usability enhancements, will serve to further reduce the time and effort it takes to solve equipment and process performance related issues. It is TrendMiner's goal to help enable better decisions, and to improve the overall performance of our customers production facilities.
TrendMiner, a Software AG company and part of the IoT & Analytics division, delivers self-service data analytics to optimize process performance in industries such as chemical, petrochemical, oil & gas, pharmaceutical, metals & mining and other process manufacturing industries. TrendMiner software is based on a high-performance analytics engine, for data captured in time series, that allows users to question data directly without the support of data scientists. This plug and play software adds immediate value on deployment, eliminating the need for infrastructure investment and long implementation projects. Search, diagnostic and predictive capabilities enable users to speed up root cause analysis, define optimal processes and configure early warnings to monitor production. TrendMiner software also helps team members to capture feedback and leverage knowledge across teams and sites. In addition, TrendMiner offers standard integrations with a wide range of historians such as OSIsoft PI, Yokogawa Exaquantum, AspenTech IP.21, Honeywell PHD, GE Proficy Historian and Wonderware InSQL.
Founded in 2008 and now part of Software AG, TrendMiner is a software company with global headquarters in Belgium, and offices in the U.S., Germany, Spain and the Netherlands.
Ripple Effect Communications
TrendMiner 2019 R1 Photo Caption:
TrendMiner’s new ‘Stacked Trend View’ for time-series data makes performance comparison of production lines or assets very easy and gives a clearer view on all trend data.
A photo accompanying this announcement is available at http://www.globenewswire.com/NewsRoom/AttachmentNg/164fcf39-8ea5-4904-899d-c8e7056f6f27