END GAME unlocks the potential of autonomous manufacturing with its "game-changing” prescriptive analytics platform that employs auto machine learning at the “edge", insuring seamless, real-time data collection, optimization and closed-loop control. END GAME’s Auto ML engine uses non-linear regression and classification modeling techniques to prescribe optimized values in real time. END GAME has a native OPC UA/DA driver to provide real-time data collection from OPC Servers, facilitating connectivity to most devices in the industrial and commercial environments. This seamless connectivity also allows optimized setpoints to be sent back to process control systems for closed-loop control.
END GAME provides an easy-to-use prescriptive analytics tool designed with the engineer in mind that uses non-linear modeling capability to enhance operational excellence and six sigma concepts. While END GAME’s technology is founded on some of the most advanced machine learning concepts, the algorithms are embedded in the product and do not require user-knowledge of data science techniques.
END GAME is designed with a simple four-step flow that allows the user to understand and optimize an asset without unnecessary “technology distractions”.
1. Step One: Data ingestion
2. Step Two: Auto Machine Learning
3. Step Three: Result Visualization
4. Step Four: Optional MLC (“Machine Learning Control”)
END GAME’s step-by-step approach gives the user a deep understanding and visualization of the relational data extracted from the process before moving to suggested optimization setpoints and, ultimately, to autonomous control of an asset.
END GAME uses OPC technology to collect variable data from OPC Servers. This allows you to collect data from PLC’s, DCS, SCADA, historians and multiple other devices. The data is then normalized and archived locally for relational discovery, model building and ongoing dynamic model learning and maintenance.
END GAME uses Auto ML (automated machine learning) to discover relationships in the data and to build, validate and score potential models. Modelling can also be done manually
if the user knows the structure of the data required to build the model.
END GAME builds non-linear regression and classification models using a proprietary hybrid algorithm approach. END GAME’s models continuously learn from all new data collected, continuing to optimize their accuracy as new data becomes available. END GAME predicts real values in real-time.
DEEP PROCESS UNDERSTANDING – You can discover what process variables drive your target variables.
DETERMINATION OF SUGGESTED CONTROL VALUES – You can determine output suggested control variable values, which can be shared with MMI (man machine interfaces) or posted to a control room screen for manual control and analysis.
OPTIMIZATION OF TARGET VARIABLES FOR MLC – You can optimize your target variables based on multiple input variable conditions, applying prescriptive analytics to directly change process control setpoints by using MLC (Machine Learning Control).
No. All your data is processed on ENDGAME, stored locally on the device or on a dedicated VM and accessible internally using any web browser.
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