/predictive-maintenance

🖥 Approaching predictive maintenance and engine failure detection, based on open datasets.

Primary LanguageJupyter NotebookMIT LicenseMIT

RAMP starting kit on the Predictive Maintenance Challenge

Predictive maintenance and understanding root cause of failures can offer millions of dollars in potential savings along with far fewer days of equipment downtime. RUL estimates provide decision makers with information that allows them to change operational characteristics (such as load) which in turn may prolong the life of the component.

Build Status

Go to ramp-worflow for more help on the RAMP ecosystem.

Install ramp-workflow (rampwf), then execute

ramp_test_submission

to test the starting kit submission (submissions/starting_kit) and

ramp_test_submission --submission=starting_kit

to test starting_kit or any other submission in submissions.

Get started on this RAMP with the dedicated notebook.