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A Data Scientist’s Guide to Modeling Engine Degradation

With the growth of connected “things”, industries are presented with huge opportunities to leverage sensor data to improve their operations, products and services. With the proliferation of these devices, competitive advantages will develop from appropriate leveraging of the deluge of data. From connected appliances to jet engines, industries are already undergoing massive transformations. Critical to success is the ability to not only collect data from sensors, but to also leverage big data technologies and data science expertise to extract actionable insights from the data.

It is critical to be able to model degradation of a machine to prevent catastrophic events and adjust maintenance scheduling. This is true in industries including oil and gas, transportation and even consumer products.

In this latest DSC webinar, the Pivotal Data Science team will present a data-driven approach to detecting and tracking jet-engine degradation using simulated sensor data. In particular we will focus on (1) data integration and cleansing, (2) transformation of time series data from sensors into meaningful features for modeling and (3) the algorithms used to build models to identify engine degradation patterns.

Speakers:
Sarah Aerni, Principal Data Scientist -- Pivotal
April Song, Principal Data Scientist -- Pivotal

Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

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Comment by Hank Roark on March 23, 2016 at 9:02am

I know it was mentioned the dataset is available from NASA, but I'm having problems finding it.  Could a link be posted here?

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