Data Science in Heavy Industry and the Internet of Things

Increasingly cheap and available sensors enable new applications of data science for heavy industries. From locomotives to wind turbines to solar farms, data scientists close to these industries will be the first to attempt to turn the data collected on these machines into consistent sources of economic value. This article discusses an approach to framing industrial analytics problems and goes into detail on one problem in equipment reliability, predictive maintenance. We discuss a host of challenges associated with building and implementing analytics using equipment data, and we give recommendations on how to surmount these challenges through careful data analysis, data collection, and communication. We also discuss training and getting started on industrial analytics problems.

Source: Michael Horrell, Larry Reynolds, and Adam McElhinney, https://hdsr.mitpress.mit.edu/pub/7e1zrs70/release/2
This work is licensed under a Creative Commons Attribution 4.0 License.