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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