We are just a few days away from the third edition of the Maintenance Analytics Summit. The inaugural Maintenance Analytics Summit took place in 2018 as a response to the need for a platform where experts can present and discuss innovative case studies on data-driven maintenance in the heavy asset and manufacturing industries.
Machine learning has a proven track record of advancing our lives, whether it is automating tasks and processes, gaining valuable insights out of massive quantities of data and enabling us to take the most effective data-driven actions. But machine learning and IoT also play a crucial role in saving lives from train accidents.
Hamid Al-najjar, Maintenance Engineer at Seco Tools, will be joining us as one of the speakers at the virtual Maintenance Analytics Summit. During his presentation, Hamid will reveal a ten metrics approach that enables industrial companies to manage assets lifecycle and its performance from the inception to disposal with less cost.
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.
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