Predictive Maintenance of a Fracking Pump with Machine Learning
Disciplines: Data Science and Engineering Analytics
Course Description
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
Learning Level
Intermediate
Course Length
1 Day
Why Attend
Learn how to use physics-based models to build digital twins and apply machine learning to predict failure.
Who Attends
Mechanical engineers, Maintenance engineers, Asset reliability engineers, Predictive maintenance teams, Data scientists engaged in predictive maintenance
Special Requirements
The workshop attendees will need a laptop and an internet connection
CEUs
.8
Cancellation Policy
All cancellations must be received no later than 14 days prior to the course start date. Cancellations made after the 14-day window will not be refunded. Refunds will not be given due to no show situations.
Training sessions attached to SPE conferences and workshops follow the cancellation policies stated on the event information page. Please check that page for specific cancellation information.
SPE reserves the right to cancel or re-schedule courses at will. Notification of changes will be made as quickly as possible; please keep this in mind when arranging travel, as SPE is not responsible for any fees charged for cancelling or changing travel arrangements.
We reserve the right to substitute course instructors as necessary.
Instructor
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.