Implementing digital twins is becoming a strategic necessity for many industrial companies. Although investment has increased, requirements and expectations have eroded, especially those of digital twin models. In this session, learn the basics of how digital twin models should be built, how to scale and implement these models on assets and systems to create a twin, and discover best practices for how to leverage these twins operationally to predict, diagnose and forecast performance degradation as a part of your reliability work processes.
We will also discuss how these analytics can feed an organization’s reliability work process, including providing central fault and diagnostic models, collaboration across organizational functions, asset strategy optimization, and contributing evidence to root cause failure analysis practices.
In addition, we will discuss several practical implementations of this technology including An upstream oil & gas major implementing their own remote assistance intervention and diagnosis center that enables performance digital twins across 13 of their upstream affiliates around the world. One of the largest petrochemical producers in Russia debottlenecking its gas plants and in turn improving unplanned compressor outages by 45 percent. One of the world’s largest integrated steel producers achieved a reduction in time-based maintenance of over 5,000 hours, resulting in a 10-15 percent maintenance cost reduction across assets where performance twins were implemented.
“R.A.I.” the Reliability.aiTMChatbot
You can ask "R.A.I." anything about maintenance, reliability, and asset management.