Predictive Maintenance
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This page is part of the Climate Change AI Wiki, which aims provide resources at the intersection of climate change and machine learning. This page is about the applications of machine learning (ML) in the context of predictive maintenance. For an overview of predictive maintenance more generally, please see the Wikipedia page on this topic.
Quickly detecting system faults can help reduce system waste or improve the utilization of low-carbon energy resources. ML can help detect faults in real time from sensor data, or even forecast them ahead of time to enable preemptive maintenance.
Naval Example of Maintenance System
The US Navy operates one of the largest and most complicated fleets on the planet. The lessons learned here[1] are broadly applicable to the data requirements of any predictive maintenance software. Migrating legacy software to be compatible with modern algorithms is a large open area of research in predictive maintenance. Setting an example of the data requirements needed for predictive maintenance is the foundation for any algorithm development.
Background Readings
- Wilson, Bradley, Jessie Riposo, Thomas Goughnour, Rachel M. Burns, Michael J. D. Vermeer, Ajay K. Kochhar, Angelena Bohman, and Mel Eisman, Naval Aviation Maintenance System: Analysis of Alternatives. Santa Monica, CA: RAND Corporation, 2020. https://www.rand.org/pubs/research_reports/RR2974z1.html. Also available in print form.