Digital twins are virtual representations of real-life manufacturing assets. But they are not only models of the physical objects; they serve as a bridge and co-creation between data scientists and engineers. However, what digital twins fail to capture is the risk of assets and the environment to prevent failure.
The offshore oil & gas industry is exposed to extreme levels of risk. Companies and equipment are subject to volatile price fluctuations, extreme weather conditions and operational hazards like explosions, spills and workplace injuries. This is why the importance of predictive maintenance of the drilling equipment is much more intensified and puts additional stress on offshore maintenance providers as minor errors in predictions may lead to massive scale consequences.
For the second time, on May 23-24, 2019, Stockholm was host to nearly 200 Predictive Maintenance practitioners and key players, gathered for the Maintenance Analytics Summit, an annual event to share ideas, and discuss ways to harness the full potential of machine data and Advanced Analytics to improve and automate their condition monitoring and maintenance processes.
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