Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA

Critical turbine maintenance: Monitoring and diagnosing planes and power plants in real time

5:10pm5:50pm Wednesday, March 27, 2019
Average rating: ****.
(4.50, 2 ratings)

Who is this presentation for?

  • Data practitioners, managers, and executives working in AI, ML, or data products

Level

Beginner

Prerequisite knowledge

  • Familiarity or experience with ML and AI (useful but not required)

What you'll learn

  • Understand what it takes to release data products in the industrial space
  • Learn how to thoughtfully design ML products for maximizing application reusability and probability of a successful release and use data to identify and solve people challenges

Description

GE produces a third of the world’s power and 60% of its airplane engines—a critical portion of the world’s infrastructure that requires meticulous monitoring of the hundreds of sensors streaming data from each turbine.

The infrastructure and teams involved with processing this data have evolved over the course of decades, incorporating expert knowledge on how sensor variations act as leading indicators for maintenance issues, compliance with regulations and customer agreements, and innovative digital twin models to identify potential issues. These maintenance systems have performed spectacularly, minimizing down time and identifying critical issues early. They have also collected years of hand-labeled data connecting sensor output with downstream impact such as hazardous gas leaks and melting fan blades. With sensor technology steadily increasing the amount of data streaming from these engines, making them harder to analyze, and failure modes becoming increasingly nuanced, GE has embarked on the next chapter of innovation in maintenance, incorporating machine learning.

June Andrews and John Rutherford explain how GE’s monitoring and diagnostics teams released the first real-time ML systems used to determine turbine health into production.

Topics include:

  • Understanding new and complex domains
  • Navigating ML product design to maximize the probability of a successful release
  • Focusing on scalable ML implementations so development efforts in one domain can benefit another
  • Safely releasing and support models in critical environments distributed across continents
  • How to make critical data products “walk-away safe”
  • How to incorporate innovations into a platform for additional turbine applications
Photo of June Andrews

June Andrews

GE

June Andrews is a principal data scientist at GE, where she’s building a machine learning platform used for monitoring the health of airplanes and power plants around the world. Previously, she spearheaded the Data Trustworthiness and Signals Program at Pinterest aimed at creating a healthy data ecosystem for machine learning and led efforts at LinkedIn on growth, engagement, and social network analysis to increase economic opportunity for professionals. June holds degrees in applied mathematics, computer science, and electrical engineering from UC Berkeley and Cornell.

Photo of John Rutherford

John Rutherford

GE

John Rutherford builds machine learning applications and helps develop the Wise data science platform at GE. Previously, he was the data scientist for an energy efficiency startup, where he headed algorithm development and exploratory analyses. He holds physics, mathematics, and astrophysics degrees from Stanford and MIT.