TwinPilot is a machine learning model available remotely 24\/7 that supports decision-making and control of machines, installations and industrial processes. We begin by creating a Digital Twin of the system, representing its behavior and the relationships between variables. We then use it to train an ML model, including through reinforcement learning (RL), to prepare it to make decisions under different operating conditions. After deployment, the model keeps learning: it uses new data and experience to improve how it responds to changes in the process. It can consider hundreds of factors simultaneously and update decisions as often as every minute, adjusting setpoints to the current situation, production goals and technical constraints. Remote operation provides round-the-clock access to the model and allows it to evolve as the plant\u2019s needs change. TwinPilot works with existing PLCs, which handle basic control and safety functions, while the model\u2019s decisions are implemented within agreed operating limits.<\/p>","rb_p1_problem":"
TwinLogic translates the results obtained from a trained machine learning model into a mathematical formula or algorithm for deterministic decision-making. We first create a Digital Twin of the system, representing its behavior, the relationships between variables and relevant operating conditions. We use it to train an ML model and then study how the model responds to different inputs, setpoints and scenarios. The findings are used to develop a mathematical formula or algorithm that can run in a PLC or a supervisory system. For the same inputs and the same state, the algorithm produces the same result, making its behavior easier to predict, test and document. The resulting logic operates without continuous model training, within its validated scope of application. If the technology or operating conditions change, we can repeat the research using the Digital Twin and prepare an updated version of the algorithm.<\/p>","rb_p2_problem":"
TwinScope is designed to study the behavior of machines, installations and processes in a virtual environment. The starting point is a Digital Twin of the system, which we use to train a machine learning model. Research using the trained model makes it possible to simulate different scenarios, examine the influence of multiple factors and compare operating alternatives. This allows us to analyze situations that would be costly, time-consuming, difficult to organize or risky to reproduce on a real installation. We draw insights from the simulations and prepare reports that help evaluate planned changes and guide further work. TwinScope supports verification of the effects of new setpoints, algorithm changes or upgrades, as well as recognition of recurring operating profiles. Each report relates its findings to the assumptions made and the scope of the system model, providing a clear basis for engineering decisions.<\/p>","rb_p3_problem":"
PlantAgent is an AI assistant that supports the analysis of information from across the business. It combines context from machine logs, databases, documentation and reports to help production and maintenance teams find the information they need faster and understand how events unfolded. Users can ask questions in natural language, compare data from different periods and prepare summaries without manually reviewing multiple sources. We tailor the scope of analysis, access to information and presentation of answers to the organization. Findings and suggested actions support specialists, who can check them against source data before making decisions.<\/p>","rb_p5_problem":"
PlantGate is a communication gateway connecting industrial automation (OT) with information technology (IT) systems. It makes selected machine and controller data available in a structured format using industrial communication protocols, allowing analytics applications, reporting systems and production management tools to use it. Integration includes agreeing on data sources, names, units, transmission frequency and access rules. The solution organizes information exchange and makes it easier to add devices or data consumers to the installation. Any transmission of commands to the OT layer requires separate definition of permissions and execution conditions; basic control and safety functions remain in the PLCs.<\/p>","rb_p6_problem":"
PlantLink provides secure remote access to PLCs for diagnostics, service and technical support. Connecting to the installation over the internet allows specialists to start investigating a problem without waiting to arrive on site, while reducing travel, accommodation and transit-time costs. Some diagnostic and programming work can be completed entirely remotely; in other cases, remote analysis helps prepare for an on-site visit. The RUNBIT team can be at the remote end of the connection: our automation engineers can connect to the controller, analyze its operation and help the person at the machine carry out agreed tasks. This support makes collaboration with local maintenance teams easier and allows service work to begin sooner. PlantLink includes redundant internet connectivity, so a backup connection reduces the impact of failure of the primary communication link. We tailor user permissions, security measures, access rules and connection failover to the company\u2019s infrastructure. The savings depend on the types of faults, the number of locations served and the work that can be performed remotely.<\/p>","rb_p4_problem":"