Predictive Maintenance of a Fracking Pump with Machine Learning
Disciplines: Data Science and Engineering Analytics
Date 20 Sept 2021 – 8am – 4pm GST
Held in conjunction with SPE Annual Technical Conference & Exhibition
Venue: Hilton Dubai Al Habtoor City
Equipment failures result in costly unplanned downtime of operations and can also lead to serious safety and environmental incidents. Therefore, predicting failures in advance and pinpointing the source unlocks significant value. Application of machine learning to real-time measurements and physics simulations provides a strong predictive maintenance framework for avoiding catastrophic failures and reducing costs.
MathWorks is pleased to present an industry-tested, hands-on workshop on Predictive Maintenance using Machine Learning. In this three-hour, instructor led online workshop, participants will tackle an oilfield equipment failure problem. Participants will build data-driven models in MATLAB using time series data and predict faults.
Disciplines: Mechanical engineering, Physics-based modeling, Data Science
Learn how to use physics-based models to build digital twins and apply machine learning to predict failure.
Mechanical engineers, Maintenance engineers, Asset reliability engineers, Predictive maintenance teams, Data scientists engaged in predictive maintenance
The workshop attendees will need a laptop and an internet connection
Flavio Pol is a Senior Application Engineer at CES, the partner of MathWorks in the Middle East. Flavio has more than 7 years of experience in the field of Artificial Intelligence (AI) and is an active researcher on the topic at the University of Sao Paulo, the largest and most prestigious university in Brazil and Latin America. Flavio has worked extensively with a variety of Machine Learning and Deep Learning applications such as Image Classification using CNN, Text Classification using LSTM, Question Answering Systems using Machine Learning algorithms & Fault Classification for predictive maintenance. Nowadays, Flavio is participating on an proprietary advanced research subject for the national oil company in Brazil.