Many industrial and manufacturing companies have their data stored in file systems and ERPs. But, if data scientists and engineers are to use this data to provide value, it has to be available and accessible in a...
Category - Predictive Maintenance
Predictive maintenance that unites data analysts and domain experts
In predictive maintenance, there is often a gap between "nice to have" and "must-have" solutions. Technical experts and customers disagree on what should be the final outcome of the digitalisation.
Saving lives from train accidents with machine learning and IoT
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...
Asset lifecycle management with a ten metrics approach
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...
Finding black swans in data lakes: Rare events in insurance claims
Considering the current standstill state the world is at the moment caused by the Coronavirus outbreak, there has been much discussion about black swans and whether it can be described as a black swan event.
What are probabilistic digital twins and how are they used in asset management?
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...
Three approaches to predictive maintenance of baggage handling systems
The baggage handling systems at airports are some of the most complex conveyor systems. They are the bloodstream of the airports, which makes them a vital component of airport operations.
Deep learning applications for corrosion detection
Matias Ferrero, RAMS Engineer at Norwegian University of Science and Technology (NTNU) some state-of-the-art deep learning approaches at the Maintenance Analytics Summit 2019.
A (not-so) impossible task: Offshore maintenance analytics
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...
Digital twins: A co-creation between data scientists and engineers
The digital twin bridge the gap in complex assets between OT systems and the IT environment by capturing data to monitor performance, deterioration and failure, location and safety compliance and remote monitoring...
