Not-to-Miss at PAW Industry 4.0: GE, Shell, Nanotronics

Senior Engineer

TEMPA - TExt Mining with Predictive Analytics, for Engineering - An approach that allows users to face any unplanned outage in any engineering asset. The asset could be a gas turbine, an aircraft engine, an MRI machine, a locomotive, or a wind turbine. These assets normally provide both descriptive (text) and measured (numerical) output as data - which, when combined properly, have the potential to provide highly actionable insights. TEMPA enables this. This talk would focus on proven methods to automatically extract events from both textual information and operational data, monetize these insights improving profit, as applied in General Electric.

Machine Learning and AI Manager

Data Science in general and Deep Learning in particular continue to reshape the future of the Energy sector across various segments. From exploration, development and production to downstream and new energies business, measurable value of digitalization has been observed in both efficiencies and savings. Deep Learning is one of the key underlying enablers for creating competitive advantage. This presentation provides an overview of some of use case applications and lessons learned from establishing a platform that progress ideas to embedded business enablers.

VP of R&D

Manufacturing has long embraced predictive analytics for process control, but has been slow to adopt active process control in which a black box makes end-to-end decisions for entire process lines. We explore several reasons for this development and compare predict analytics to modern AI based decision making systems, through reinforcement deep learning models. We show that the two are not mutually exclusive and combined can offer a method to overcome the intrinsic limitations of native AI for effective deployment and usage.