Predictive Maintenance: Difference between revisions
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''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 [https://en.wikipedia.org/wiki/Predictive_maintenance Wikipedia page] on this topic.'' |
''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 [https://en.wikipedia.org/wiki/Predictive_maintenance Wikipedia page] on this topic.'' |
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Quickly detecting |
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. |
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<u>Naval Example of Maintenance System</u> |
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The US Navy operates one of the largest and most complicated fleets on the planet. The lessons learned here<sup>[1]</sup> 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. Not only is predictive maintenance an opp |
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==Background Readings== |
==Background Readings== |
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# 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. <nowiki>https://www.rand.org/pubs/research_reports/RR2974z1.html</nowiki>. Also available in print form. |
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==Conferences, Journals, and Professional Organizations== |
==Conferences, Journals, and Professional Organizations== |
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==Libraries and Tools== |
==Libraries and Tools== |
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Revision as of 18:03, 27 December 2022
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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. Not only is predictive maintenance an opp
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.