The proposed solution involved the digitization of processes with the centralization and contextualization of the data generated by the equipment, aiming to create a data-driven environment in the industry and increase the efficiency of the process from an authentic Digital Twin.
For this, a 3D model of the Iron Roughneck was processed and inserted into Vidya’s platform. In addition, our engineering team was responsible for processing, contextualizing and linking all the already existing data, just as attributes of each structural component, TAGs, workflows, inspection, with its respective component in the 3D model.
To complete the data collection structure, our team integrated the platform with the client’s PIMS (Plant Information Management System) to process the data already collected by the sensors on board regarding the integrity and efficiency of the Iron Roughneck’s operation. This allowed us to supply our AI algorithms with the available historical data to generate equipment failure predictions.
Then, all this data was arranged in customized dashboards, which together with the combination of all the technologies mentioned above made it possible to:
- Process the constantly generated data from the equipment by sensors, such as efficiency parameters, allowing the tracking and analysis of its time-series data;
- Use the Artificial Intelligence and Machine Learning to generate equipment efficiency forecasts, allowing a much more intelligent and predictive monitoring;
- Trigger automatic visual alerts in the system when an efficiency problem occurs in one of the components, or is predicted by the AI algorithm, enabling faster resolution of the problem, reducing unscheduled downtime in the operation.