Ai2Infinity “END GAME”
END GAME unlocks the potential of autonomous manufacturing with its "game-changing” Machine Learning Control (“MLC”) platform that employs Auto Machine Learning (“AML”) at the “edge", insuring seamless, real-time data collection, optimization and closed-loop control. END GAME’s AML engine uses non-linear regression and classification modeling techniques to prescribe optimized values in real time. 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.
END GAME has a native OPC UA/DA driver to provide real-time data collection from OPC Servers. OPC technology is used to collect variable data from PLC’s, DCS, MES, SCADA, historians and multiple other devices. The data is then normalized, validated and archived locally for relational discovery, model building and ongoing dynamic model learning and maintenance. 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 AML predictive/prescriptive analytics tool, designed with the engineer in mind, to discover relationships in the data and to build, validate and score potential models. 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: HMI Interface or MLC
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.
- You can discover what process variables drive your target variables.
- You can determine output suggested control variable values, which can be shared with HMI or posted to a control room screen for manual control and analysis.
- You can optimize your target variables based on multiple input variable conditions, applying prescriptive analytics to directly change process control setpoints by using MLC
As an “edge” solution, END GAME allows production data to remain in the customer’s domain and not be sent offsite to the “cloud”. END GAME can be preloaded on an edge device supplied by Ai2Infinty or loaded on a VM in the customers DMZ. Either way, the customer’s security protocols can be easily integrated during deployment.
